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What’s New

These are new features and improvements of note in each release.

v0.15.2 (December 12, 2014)

This is a minor release from 0.15.1 and includes a large number of bug fixes along with several new features, enhancements, and performance improvements. A small number of API changes were necessary to fix existing bugs. We recommend that all users upgrade to this version.

API changes

  • Indexing in MultiIndex beyond lex-sort depth is now supported, though a lexically sorted index will have a better performance. (GH2646)

    In [1]: df = pd.DataFrame({'jim':[0, 0, 1, 1],
       ...:                    'joe':['x', 'x', 'z', 'y'],
       ...:                    'jolie':np.random.rand(4)}).set_index(['jim', 'joe'])
       ...: 
    
    In [2]: df
    Out[2]: 
                jolie
    jim joe          
    0   x    0.126970
        x    0.966718
    1   z    0.260476
        y    0.897237
    
    In [3]: df.index.lexsort_depth
    Out[3]: 1
    
    # in prior versions this would raise a KeyError
    # will now show a PerformanceWarning
    In [4]: df.loc[(1, 'z')]
    Out[4]: 
                jolie
    jim joe          
    1   z    0.260476
    
    # lexically sorting
    In [5]: df2 = df.sortlevel()
    
    In [6]: df2
    Out[6]: 
                jolie
    jim joe          
    0   x    0.126970
        x    0.966718
    1   y    0.897237
        z    0.260476
    
    In [7]: df2.index.lexsort_depth
    Out[7]: 2
    
    In [8]: df2.loc[(1,'z')]
    Out[8]: 
                jolie
    jim joe          
    1   z    0.260476
    
  • Bug in unique of Series with category dtype, which returned all categories regardless whether they were “used” or not (see GH8559 for the discussion). Previous behaviour was to return all categories:

    In [3]: cat = pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c'])
    
    In [4]: cat
    Out[4]:
    [a, b, a]
    Categories (3, object): [a < b < c]
    
    In [5]: cat.unique()
    Out[5]: array(['a', 'b', 'c'], dtype=object)
    

    Now, only the categories that do effectively occur in the array are returned:

    In [9]: cat = pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c'])
    
    In [10]: cat.unique()
    Out[10]: array(['a', 'b'], dtype=object)
    
  • Series.all and Series.any now support the level and skipna parameters. Series.all, Series.any, Index.all, and Index.any no longer support the out and keepdims parameters, which existed for compatibility with ndarray. Various index types no longer support the all and any aggregation functions and will now raise TypeError. (GH8302).

  • Allow equality comparisons of Series with a categorical dtype and object dtype; previously these would raise TypeError (GH8938)

  • Bug in NDFrame: conflicting attribute/column names now behave consistently between getting and setting. Previously, when both a column and attribute named y existed, data.y would return the attribute, while data.y = z would update the column (GH8994)

    In [11]: data = pd.DataFrame({'x':[1, 2, 3]})
    
    In [12]: data.y = 2
    
    In [13]: data['y'] = [2, 4, 6]
    
    In [14]: data
    Out[14]: 
       x  y
    0  1  2
    1  2  4
    2  3  6
    
    # this assignment was inconsistent
    In [15]: data.y = 5
    

    Old behavior:

    In [6]: data.y
    Out[6]: 2
    
    In [7]: data['y'].values
    Out[7]: array([5, 5, 5])
    

    New behavior:

    In [16]: data.y
    Out[16]: 5
    
    In [17]: data['y'].values
    Out[17]: array([2, 4, 6], dtype=int64)
    
  • Timestamp('now') is now equivalent to Timestamp.now() in that it returns the local time rather than UTC. Also, Timestamp('today') is now equivalent to Timestamp.today() and both have tz as a possible argument. (GH9000)

  • Fix negative step support for label-based slices (GH8753)

    Old behavior:

    In [1]: s = pd.Series(np.arange(3), ['a', 'b', 'c'])
    Out[1]:
    a    0
    b    1
    c    2
    dtype: int64
    
    In [2]: s.loc['c':'a':-1]
    Out[2]:
    c    2
    dtype: int64
    

    New behavior:

    In [18]: s = pd.Series(np.arange(3), ['a', 'b', 'c'])
    
    In [19]: s.loc['c':'a':-1]
    Out[19]: 
    c    2
    b    1
    a    0
    dtype: int32
    

Enhancements

Categorical enhancements:

  • Added ability to export Categorical data to Stata (GH8633). See here for limitations of categorical variables exported to Stata data files.
  • Added flag order_categoricals to StataReader and read_stata to select whether to order imported categorical data (GH8836). See here for more information on importing categorical variables from Stata data files.
  • Added ability to export Categorical data to to/from HDF5 (GH7621). Queries work the same as if it was an object array. However, the category dtyped data is stored in a more efficient manner. See here for an example and caveats w.r.t. prior versions of pandas.
  • Added support for searchsorted() on Categorical class (GH8420).

Other enhancements:

  • Added the ability to specify the SQL type of columns when writing a DataFrame to a database (GH8778). For example, specifying to use the sqlalchemy String type instead of the default Text type for string columns:

    from sqlalchemy.types import String
    data.to_sql('data_dtype', engine, dtype={'Col_1': String})
    
  • Series.all and Series.any now support the level and skipna parameters (GH8302):

    In [20]: s = pd.Series([False, True, False], index=[0, 0, 1])
    
    In [21]: s.any(level=0)
    Out[21]: 
    0     True
    1    False
    dtype: bool
    
  • Panel now supports the all and any aggregation functions. (GH8302):

    In [22]: p = pd.Panel(np.random.rand(2, 5, 4) > 0.1)
    
    In [23]: p.all()
    Out[23]: 
          0      1
    0  True   True
    1  True  False
    2  True   True
    3  True  False
    
  • Added support for utcfromtimestamp(), fromtimestamp(), and combine() on Timestamp class (GH5351).

  • Added Google Analytics (pandas.io.ga) basic documentation (GH8835). See here.

  • Timedelta arithmetic returns NotImplemented in unknown cases, allowing extensions by custom classes (GH8813).

  • Timedelta now supports arithemtic with numpy.ndarray objects of the appropriate dtype (numpy 1.8 or newer only) (GH8884).

  • Added Timedelta.to_timedelta64() method to the public API (GH8884).

  • Added gbq.generate_bq_schema() function to the gbq module (GH8325).

  • Series now works with map objects the same way as generators (GH8909).

  • Added context manager to HDFStore for automatic closing (GH8791).

  • to_datetime gains an exact keyword to allow for a format to not require an exact match for a provided format string (if its False). exact defaults to True (meaning that exact matching is still the default) (GH8904)

  • Added axvlines boolean option to parallel_coordinates plot function, determines whether vertical lines will be printed, default is True

  • Added ability to read table footers to read_html (GH8552)

  • to_sql now infers datatypes of non-NA values for columns that contain NA values and have dtype object (GH8778).

Performance

  • Reduce memory usage when skiprows is an integer in read_csv (GH8681)
  • Performance boost for to_datetime conversions with a passed format=, and the exact=False (GH8904)

Bug Fixes

  • Bug in concat of Series with category dtype which were coercing to object. (GH8641)
  • Bug in Timestamp-Timestamp not returning a Timedelta type and datelike-datelike ops with timezones (GH8865)
  • Made consistent a timezone mismatch exception (either tz operated with None or incompatible timezone), will now return TypeError rather than ValueError (a couple of edge cases only), (GH8865)
  • Bug in using a pd.Grouper(key=...) with no level/axis or level only (GH8795, GH8866)
  • Report a TypeError when invalid/no paramaters are passed in a groupby (GH8015)
  • Bug in packaging pandas with py2app/cx_Freeze (GH8602, GH8831)
  • Bug in groupby signatures that didn’t include *args or **kwargs (GH8733).
  • io.data.Options now raises RemoteDataError when no expiry dates are available from Yahoo and when it receives no data from Yahoo (GH8761), (GH8783).
  • Unclear error message in csv parsing when passing dtype and names and the parsed data is a different data type (GH8833)
  • Bug in slicing a multi-index with an empty list and at least one boolean indexer (GH8781)
  • io.data.Options now raises RemoteDataError when no expiry dates are available from Yahoo (GH8761).
  • Timedelta kwargs may now be numpy ints and floats (GH8757).
  • Fixed several outstanding bugs for Timedelta arithmetic and comparisons (GH8813, GH5963, GH5436).
  • sql_schema now generates dialect appropriate CREATE TABLE statements (GH8697)
  • slice string method now takes step into account (GH8754)
  • Bug in BlockManager where setting values with different type would break block integrity (GH8850)
  • Bug in DatetimeIndex when using time object as key (GH8667)
  • Bug in merge where how='left' and sort=False would not preserve left frame order (GH7331)
  • Bug in MultiIndex.reindex where reindexing at level would not reorder labels (GH4088)
  • Bug in certain operations with dateutil timezones, manifesting with dateutil 2.3 (GH8639)
  • Regression in DatetimeIndex iteration with a Fixed/Local offset timezone (GH8890)
  • Bug in to_datetime when parsing a nanoseconds using the %f format (GH8989)
  • io.data.Options now raises RemoteDataError when no expiry dates are available from Yahoo and when it receives no data from Yahoo (GH8761), (GH8783).
  • Fix: The font size was only set on x axis if vertical or the y axis if horizontal. (GH8765)
  • Fixed division by 0 when reading big csv files in python 3 (GH8621)
  • Bug in outputing a Multindex with to_html,index=False which would add an extra column (GH8452)
  • Imported categorical variables from Stata files retain the ordinal information in the underlying data (GH8836).
  • Defined .size attribute across NDFrame objects to provide compat with numpy >= 1.9.1; buggy with np.array_split (GH8846)
  • Skip testing of histogram plots for matplotlib <= 1.2 (GH8648).
  • Bug where get_data_google returned object dtypes (GH3995)
  • Bug in DataFrame.stack(..., dropna=False) when the DataFrame’s columns is a MultiIndex whose labels do not reference all its levels. (GH8844)
  • Bug in that Option context applied on __enter__ (GH8514)
  • Bug in resample that causes a ValueError when resampling across multiple days and the last offset is not calculated from the start of the range (GH8683)
  • Bug where DataFrame.plot(kind='scatter') fails when checking if an np.array is in the DataFrame (GH8852)
  • Bug in pd.infer_freq/DataFrame.inferred_freq that prevented proper sub-daily frequency inference when the index contained DST days (GH8772).
  • Bug where index name was still used when plotting a series with use_index=False (GH8558).
  • Bugs when trying to stack multiple columns, when some (or all) of the level names are numbers (GH8584).
  • Bug in MultiIndex where __contains__ returns wrong result if index is not lexically sorted or unique (GH7724)
  • BUG CSV: fix problem with trailing whitespace in skipped rows, (GH8679), (GH8661), (GH8983)
  • Regression in Timestamp does not parse ‘Z’ zone designator for UTC (GH8771)
  • Bug in StataWriter the produces writes strings with 244 characters irrespective of actual size (GH8969)
  • Fixed ValueError raised by cummin/cummax when datetime64 Series contains NaT. (GH8965)
  • Bug in Datareader returns object dtype if there are missing values (GH8980)
  • Bug in plotting if sharex was enabled and index was a timeseries, would show labels on multiple axes (GH3964).
  • Bug where passing a unit to the TimedeltaIndex constructor applied the to nano-second conversion twice. (GH9011).
  • Bug in plotting of a period-like array (GH9012)

v0.15.1 (November 9, 2014)

This is a minor bug-fix release from 0.15.0 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

API changes

  • s.dt.hour and other .dt accessors will now return np.nan for missing values (rather than previously -1), (GH8689)

    In [1]: s = Series(date_range('20130101',periods=5,freq='D'))
    
    In [2]: s.iloc[2] = np.nan
    
    In [3]: s
    Out[3]: 
    0   2013-01-01
    1   2013-01-02
    2          NaT
    3   2013-01-04
    4   2013-01-05
    dtype: datetime64[ns]
    

    previous behavior:

    In [6]: s.dt.hour
    Out[6]:
    0    0
    1    0
    2   -1
    3    0
    4    0
    dtype: int64
    

    current behavior:

    In [4]: s.dt.hour
    Out[4]: 
    0     0
    1     0
    2   NaN
    3     0
    4     0
    dtype: float64
    
  • groupby with as_index=False will not add erroneous extra columns to result (GH8582):

    In [5]: np.random.seed(2718281)
    
    In [6]: df = pd.DataFrame(np.random.randint(0, 100, (10, 2)),
       ...:                   columns=['jim', 'joe'])
       ...: 
    
    In [7]: df.head()
    Out[7]: 
       jim  joe
    0   61   81
    1   96   49
    2   55   65
    3   72   51
    4   77   12
    
    In [8]: ts = pd.Series(5 * np.random.randint(0, 3, 10))
    

    previous behavior:

    In [4]: df.groupby(ts, as_index=False).max()
    Out[4]:
       NaN  jim  joe
    0    0   72   83
    1    5   77   84
    2   10   96   65
    

    current behavior:

    In [9]: df.groupby(ts, as_index=False).max()
    Out[9]: 
       jim  joe
    0   72   83
    1   77   84
    2   96   65
    
  • groupby will not erroneously exclude columns if the column name conflics with the grouper name (GH8112):

    In [10]: df = pd.DataFrame({'jim': range(5), 'joe': range(5, 10)})
    
    In [11]: df
    Out[11]: 
       jim  joe
    0    0    5
    1    1    6
    2    2    7
    3    3    8
    4    4    9
    
    In [12]: gr = df.groupby(df['jim'] < 2)
    

    previous behavior (excludes 1st column from output):

    In [4]: gr.apply(sum)
    Out[4]:
           joe
    jim
    False   24
    True    11
    

    current behavior:

    In [13]: gr.apply(sum)
    Out[13]: 
           jim  joe
    jim            
    False    9   24
    True     1   11
    
  • Support for slicing with monotonic decreasing indexes, even if start or stop is not found in the index (GH7860):

    In [14]: s = pd.Series(['a', 'b', 'c', 'd'], [4, 3, 2, 1])
    
    In [15]: s
    Out[15]: 
    4    a
    3    b
    2    c
    1    d
    dtype: object
    

    previous behavior:

    In [8]: s.loc[3.5:1.5]
    KeyError: 3.5
    

    current behavior:

    In [16]: s.loc[3.5:1.5]
    Out[16]: 
    3    b
    2    c
    dtype: object
    
  • io.data.Options has been fixed for a change in the format of the Yahoo Options page (GH8612), (GH8741)

    Note

    As a result of a change in Yahoo’s option page layout, when an expiry date is given, Options methods now return data for a single expiry date. Previously, methods returned all data for the selected month.

    The month and year parameters have been undeprecated and can be used to get all options data for a given month.

    If an expiry date that is not valid is given, data for the next expiry after the given date is returned.

    Option data frames are now saved on the instance as callsYYMMDD or putsYYMMDD. Previously they were saved as callsMMYY and putsMMYY. The next expiry is saved as calls and puts.

    New features:

    • The expiry parameter can now be a single date or a list-like object containing dates.
    • A new property expiry_dates was added, which returns all available expiry dates.

    Current behavior:

    In [17]: from pandas.io.data import Options
    
    In [18]: aapl = Options('aapl','yahoo')
    
    In [19]: aapl.get_call_data().iloc[0:5,0:1]
    Out[19]: 
                                                 Last
    Strike Expiry     Type Symbol                    
    90     2014-12-12 call AAPL141212C00090000  21.95
    92     2014-12-12 call AAPL141212C00092000  20.22
    93     2014-12-12 call AAPL141212C00093000  19.05
    94     2014-12-12 call AAPL141212C00094000  17.70
    95     2014-12-12 call AAPL141212C00095000  16.65
    
    In [20]: aapl.expiry_dates
    Out[20]: 
    [datetime.date(2014, 12, 12),
     datetime.date(2014, 12, 20),
     datetime.date(2014, 12, 26),
     datetime.date(2015, 1, 2),
     datetime.date(2015, 1, 9),
     datetime.date(2015, 1, 17),
     datetime.date(2015, 1, 23),
     datetime.date(2015, 2, 20),
     datetime.date(2015, 3, 20),
     datetime.date(2015, 4, 17),
     datetime.date(2015, 7, 17),
     datetime.date(2016, 1, 15),
     datetime.date(2017, 1, 20)]
    
    In [21]: aapl.get_near_stock_price(expiry=aapl.expiry_dates[0:3]).iloc[0:5,0:1]
    Out[21]: 
                                                Last
    Strike Expiry     Type Symbol                   
    113    2014-12-20 call AAPL141220C00113000  2.01
           2014-12-26 call AAPL141226C00113000  2.56
    114    2014-12-12 call AAPL141212C00114000  0.50
           2014-12-20 call AAPL141220C00114000  1.51
           2014-12-26 call AAPL141226C00114000  2.02
    

    See the Options documentation in Remote Data

  • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as datetimes. This is activated once pandas is imported. In previous versions, plotting an array of datetime64 values will have resulted in plotted integer values. To keep the previous behaviour, you can do del matplotlib.units.registry[np.datetime64] (GH8614).

Enhancements

  • concat permits a wider variety of iterables of pandas objects to be passed as the first parameter (GH8645):

    In [22]: from collections import deque
    
    In [23]: df1 = pd.DataFrame([1, 2, 3])
    
    In [24]: df2 = pd.DataFrame([4, 5, 6])
    

    previous behavior:

    In [7]: pd.concat(deque((df1, df2)))
    TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque"
    

    current behavior:

    In [25]: pd.concat(deque((df1, df2)))
    Out[25]: 
       0
    0  1
    1  2
    2  3
    0  4
    1  5
    2  6
    
  • Represent MultiIndex labels with a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect as it didn’t show the usage for the memory occupied by the underling data array. (GH8456)

    In [26]: dfi = DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A'])
    

    previous behavior:

    # this was underreported in prior versions
    In [1]: dfi.memory_usage(index=True)
    Out[1]:
    Index    8000 # took about 24008 bytes in < 0.15.1
    A        8000
    dtype: int64
    

    current behavior:

    In [27]: dfi.memory_usage(index=True)
    Out[27]: 
    Index    11020
    A         8000
    dtype: int64
    
  • Added Index properties is_monotonic_increasing and is_monotonic_decreasing (GH8680).

  • Added option to select columns when importing Stata files (GH7935)

  • Qualify memory usage in DataFrame.info() by adding + if it is a lower bound (GH8578)

  • Raise errors in certain aggregation cases where an argument such as numeric_only is not handled (GH8592).

  • Added support for 3-character ISO and non-standard country codes in io.wb.download() (GH8482)

  • World Bank data requests now will warn/raise based on an errors argument, as well as a list of hard-coded country codes and the World Bank’s JSON response. In prior versions, the error messages didn’t look at the World Bank’s JSON response. Problem-inducing input were simply dropped prior to the request. The issue was that many good countries were cropped in the hard-coded approach. All countries will work now, but some bad countries will raise exceptions because some edge cases break the entire response. (GH8482)

  • Added option to Series.str.split() to return a DataFrame rather than a Series (GH8428)

  • Added option to df.info(null_counts=None|True|False) to override the default display options and force showing of the null-counts (GH8701)

Bug Fixes

  • Bug in unpickling of a CustomBusinessDay object (GH8591)
  • Bug in coercing Categorical to a records array, e.g. df.to_records() (GH8626)
  • Bug in Categorical not created properly with Series.to_frame() (GH8626)
  • Bug in coercing in astype of a Categorical of a passed pd.Categorical (this now raises TypeError correctly), (GH8626)
  • Bug in cut/qcut when using Series and retbins=True (GH8589)
  • Bug in writing Categorical columns to an SQL database with to_sql (GH8624).
  • Bug in comparing Categorical of datetime raising when being compared to a scalar datetime (GH8687)
  • Bug in selecting from a Categorical with .iloc (GH8623)
  • Bug in groupby-transform with a Categorical (GH8623)
  • Bug in duplicated/drop_duplicates with a Categorical (GH8623)
  • Bug in Categorical reflected comparison operator raising if the first argument was a numpy array scalar (e.g. np.int64) (GH8658)
  • Bug in Panel indexing with a list-like (GH8710)
  • Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722)
  • Bug in read_csv, dialect parameter would not take a string (:issue: 8703)
  • Bug in slicing a multi-index level with an empty-list (GH8737)
  • Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (GH8608)
  • Bug in setitem with empty indexer and unwanted coercion of dtypes (GH8669)
  • Bug in ix/loc block splitting on setitem (manifests with integer-like dtypes, e.g. datetime64) (GH8607)
  • Bug when doing label based indexing with integers not found in the index for non-unique but monotonic indexes (GH8680).
  • Bug when indexing a Float64Index with np.nan on numpy 1.7 (GH8980).
  • Fix shape attribute for MultiIndex (GH8609)
  • Bug in GroupBy where a name conflict between the grouper and columns would break groupby operations (GH7115, GH8112)
  • Fixed a bug where plotting a column y and specifying a label would mutate the index name of the original DataFrame (GH8494)
  • Fix regression in plotting of a DatetimeIndex directly with matplotlib (GH8614).
  • Bug in date_range where partially-specified dates would incorporate current date (GH6961)
  • Bug in Setting by indexer to a scalar value with a mixed-dtype Panel4d was failing (GH8702)
  • Bug where DataReader‘s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (GH8494)
  • Bug in get_quote_yahoo that wouldn’t allow non-float return values (GH5229).

v0.15.0 (October 18, 2014)

This is a major release from 0.14.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Warning

pandas >= 0.15.0 will no longer support compatibility with NumPy versions < 1.7.0. If you want to use the latest versions of pandas, please upgrade to NumPy >= 1.7.0 (GH7711)

Warning

In 0.15.0 Index has internally been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be a transparent change with only very limited API implications (See the Internal Refactoring)

Warning

The refactorings in Categorical changed the two argument constructor from “codes/labels and levels” to “values and levels (now called ‘categories’)”. This can lead to subtle bugs. If you use Categorical directly, please audit your code before updating to this pandas version and change it to use the from_codes() constructor. See more on Categorical here

New features

Categoricals in Series/DataFrame

Categorical can now be included in Series and DataFrames and gained new methods to manipulate. Thanks to Jan Schulz for much of this API/implementation. (GH3943, GH5313, GH5314, GH7444, GH7839, GH7848, GH7864, GH7914, GH7768, GH8006, GH3678, GH8075, GH8076, GH8143, GH8453, GH8518).

For full docs, see the categorical introduction and the API documentation.

In [1]: df = DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

In [2]: df["grade"] = df["raw_grade"].astype("category")

In [3]: df["grade"]
Out[3]: 
0    a
1    b
2    b
3    a
4    a
5    e
Name: grade, dtype: category
Categories (3, object): [a < b < e]

# Rename the categories
In [4]: df["grade"].cat.categories = ["very good", "good", "very bad"]

# Reorder the categories and simultaneously add the missing categories
In [5]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])

In [6]: df["grade"]
Out[6]: 
0    very good
1         good
2         good
3    very good
4    very good
5     very bad
Name: grade, dtype: category
Categories (5, object): [very bad < bad < medium < good < very good]

In [7]: df.sort("grade")
Out[7]: 
   id raw_grade      grade
5   6         e   very bad
1   2         b       good
2   3         b       good
0   1         a  very good
3   4         a  very good
4   5         a  very good

In [8]: df.groupby("grade").size()
Out[8]: 
grade
very bad      1
bad         NaN
medium      NaN
good          2
very good     3
dtype: float64
  • pandas.core.group_agg and pandas.core.factor_agg were removed. As an alternative, construct a dataframe and use df.groupby(<group>).agg(<func>).
  • Supplying “codes/labels and levels” to the Categorical constructor is not supported anymore. Supplying two arguments to the constructor is now interpreted as “values and levels (now called ‘categories’)”. Please change your code to use the from_codes() constructor.
  • The Categorical.labels attribute was renamed to Categorical.codes and is read only. If you want to manipulate codes, please use one of the API methods on Categoricals.
  • The Categorical.levels attribute is renamed to Categorical.categories.

TimedeltaIndex/Scalar

We introduce a new scalar type Timedelta, which is a subclass of datetime.timedelta, and behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes. This type is very similar to how Timestamp works for datetimes. It is a nice-API box for the type. See the docs. (GH3009, GH4533, GH8209, GH8187, GH8190, GH7869, GH7661, GH8345, GH8471)

Warning

Timedelta scalars (and TimedeltaIndex) component fields are not the same as the component fields on a datetime.timedelta object. For example, .seconds on a datetime.timedelta object returns the total number of seconds combined between hours, minutes and seconds. In contrast, the pandas Timedelta breaks out hours, minutes, microseconds and nanoseconds separately.

# Timedelta accessor
In [9]: tds = Timedelta('31 days 5 min 3 sec')

In [10]: tds.minutes
Out[10]: 5L

In [11]: tds.seconds
Out[11]: 3L

# datetime.timedelta accessor
# this is 5 minutes * 60 + 3 seconds
In [12]: tds.to_pytimedelta().seconds
Out[12]: 303

Warning

Prior to 0.15.0 pd.to_timedelta would return a Series for list-like/Series input, and a np.timedelta64 for scalar input. It will now return a TimedeltaIndex for list-like input, Series for Series input, and Timedelta for scalar input.

The arguments to pd.to_timedelta are now (arg,unit='ns',box=True,coerce=False), previously were (arg,box=True,unit='ns') as these are more logical.

Consruct a scalar

In [13]: Timedelta('1 days 06:05:01.00003')
Out[13]: Timedelta('1 days 06:05:01.000030')

In [14]: Timedelta('15.5us')
Out[14]: Timedelta('0 days 00:00:00.000015')

In [15]: Timedelta('1 hour 15.5us')
Out[15]: Timedelta('0 days 01:00:00.000015')

# negative Timedeltas have this string repr
# to be more consistent with datetime.timedelta conventions
In [16]: Timedelta('-1us')
Out[16]: Timedelta('-1 days +23:59:59.999999')

# a NaT
In [17]: Timedelta('nan')
Out[17]: NaT

Access fields for a Timedelta

In [18]: td = Timedelta('1 hour 3m 15.5us')

In [19]: td.hours
Out[19]: 1L

In [20]: td.minutes
Out[20]: 3L

In [21]: td.microseconds
Out[21]: 15L

In [22]: td.nanoseconds
Out[22]: 500L

Construct a TimedeltaIndex

In [23]: TimedeltaIndex(['1 days','1 days, 00:00:05',
   ....:                 np.timedelta64(2,'D'),timedelta(days=2,seconds=2)])
   ....: 
Out[23]: 
<class 'pandas.tseries.tdi.TimedeltaIndex'>
['1 days 00:00:00', ..., '2 days 00:00:02']
Length: 4, Freq: None

Constructing a TimedeltaIndex with a regular range

In [24]: timedelta_range('1 days',periods=5,freq='D')
Out[24]: 
<class 'pandas.tseries.tdi.TimedeltaIndex'>
['1 days', ..., '5 days']
Length: 5, Freq: <Day>

In [25]: timedelta_range(start='1 days',end='2 days',freq='30T')
Out[25]: 
<class 'pandas.tseries.tdi.TimedeltaIndex'>
['1 days 00:00:00', ..., '2 days 00:00:00']
Length: 49, Freq: <30 * Minutes>

You can now use a TimedeltaIndex as the index of a pandas object

In [26]: s = Series(np.arange(5),
   ....:            index=timedelta_range('1 days',periods=5,freq='s'))
   ....: 

In [27]: s
Out[27]: 
1 days 00:00:00    0
1 days 00:00:01    1
1 days 00:00:02    2
1 days 00:00:03    3
1 days 00:00:04    4
Freq: <Second>, dtype: int32

You can select with partial string selections

In [28]: s['1 day 00:00:02']
Out[28]: 2

In [29]: s['1 day':'1 day 00:00:02']
Out[29]: 
1 days 00:00:00    0
1 days 00:00:01    1
1 days 00:00:02    2
dtype: int32

Finally, the combination of TimedeltaIndex with DatetimeIndex allow certain combination operations that are NaT preserving:

In [30]: tdi = TimedeltaIndex(['1 days',pd.NaT,'2 days'])

In [31]: tdi.tolist()
Out[31]: [Timedelta('1 days 00:00:00'), NaT, Timedelta('2 days 00:00:00')]

In [32]: dti = date_range('20130101',periods=3)

In [33]: dti.tolist()
Out[33]: 
[Timestamp('2013-01-01 00:00:00', offset='D'),
 Timestamp('2013-01-02 00:00:00', offset='D'),
 Timestamp('2013-01-03 00:00:00', offset='D')]

In [34]: (dti + tdi).tolist()
Out[34]: [Timestamp('2013-01-02 00:00:00'), NaT, Timestamp('2013-01-05 00:00:00')]

In [35]: (dti - tdi).tolist()
Out[35]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')]
  • iteration of a Series e.g. list(Series(...)) of timedelta64[ns] would prior to v0.15.0 return np.timedelta64 for each element. These will now be wrapped in Timedelta.

Memory Usage

Implemented methods to find memory usage of a DataFrame. See the FAQ for more. (GH6852).

A new display option display.memory_usage (see Options and Settings) sets the default behavior of the memory_usage argument in the df.info() method. By default display.memory_usage is True.

In [36]: dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
   ....:           'complex128', 'object', 'bool']
   ....: 

In [37]: n = 5000

In [38]: data = dict([ (t, np.random.randint(100, size=n).astype(t))
   ....:                 for t in dtypes])
   ....: 

In [39]: df = DataFrame(data)

In [40]: df['categorical'] = df['object'].astype('category')

In [41]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5000 entries, 0 to 4999
Data columns (total 8 columns):
bool               5000 non-null bool
complex128         5000 non-null complex128
datetime64[ns]     5000 non-null datetime64[ns]
float64            5000 non-null float64
int64              5000 non-null int64
object             5000 non-null object
timedelta64[ns]    5000 non-null timedelta64[ns]
categorical        5000 non-null category
dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1)
memory usage: 303.5+ KB

Additionally memory_usage() is an available method for a dataframe object which returns the memory usage of each column.

In [42]: df.memory_usage(index=True)
Out[42]: 
Index              40000
bool                5000
complex128         80000
datetime64[ns]     40000
float64            40000
int64              40000
object             20000
timedelta64[ns]    40000
categorical         5800
dtype: int64

.dt accessor

Series has gained an accessor to succinctly return datetime like properties for the values of the Series, if its a datetime/period like Series. (GH7207) This will return a Series, indexed like the existing Series. See the docs

# datetime
In [43]: s = Series(date_range('20130101 09:10:12',periods=4))

In [44]: s
Out[44]: 
0   2013-01-01 09:10:12
1   2013-01-02 09:10:12
2   2013-01-03 09:10:12
3   2013-01-04 09:10:12
dtype: datetime64[ns]

In [45]: s.dt.hour
Out[45]: 
0    9
1    9
2    9
3    9
dtype: int64

In [46]: s.dt.second
Out[46]: 
0    12
1    12
2    12
3    12
dtype: int64

In [47]: s.dt.day
Out[47]: 
0    1
1    2
2    3
3    4
dtype: int64

In [48]: s.dt.freq
Out[48]: <Day>

This enables nice expressions like this:

In [49]: s[s.dt.day==2]
Out[49]: 
1   2013-01-02 09:10:12
dtype: datetime64[ns]

You can easily produce tz aware transformations:

In [50]: stz = s.dt.tz_localize('US/Eastern')

In [51]: stz
Out[51]: 
0    2013-01-01 09:10:12-05:00
1    2013-01-02 09:10:12-05:00
2    2013-01-03 09:10:12-05:00
3    2013-01-04 09:10:12-05:00
dtype: object

In [52]: stz.dt.tz
Out[52]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>

You can also chain these types of operations:

In [53]: s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[53]: 
0    2013-01-01 04:10:12-05:00
1    2013-01-02 04:10:12-05:00
2    2013-01-03 04:10:12-05:00
3    2013-01-04 04:10:12-05:00
dtype: object

The .dt accessor works for period and timedelta dtypes.

# period
In [54]: s = Series(period_range('20130101',periods=4,freq='D'))

In [55]: s
Out[55]: 
0    2013-01-01
1    2013-01-02
2    2013-01-03
3    2013-01-04
dtype: object

In [56]: s.dt.year
Out[56]: 
0    2013
1    2013
2    2013
3    2013
dtype: int64

In [57]: s.dt.day
Out[57]: 
0    1
1    2
2    3
3    4
dtype: int64
# timedelta
In [58]: s = Series(timedelta_range('1 day 00:00:05',periods=4,freq='s'))

In [59]: s
Out[59]: 
0   1 days 00:00:05
1   1 days 00:00:06
2   1 days 00:00:07
3   1 days 00:00:08
dtype: timedelta64[ns]

In [60]: s.dt.days
Out[60]: 
0    1
1    1
2    1
3    1
dtype: int64

In [61]: s.dt.seconds
Out[61]: 
0    5
1    6
2    7
3    8
dtype: int64

In [62]: s.dt.components
Out[62]: 
   days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
0     1      0        0        5             0             0            0
1     1      0        0        6             0             0            0
2     1      0        0        7             0             0            0
3     1      0        0        8             0             0            0

Timezone handling improvements

  • tz_localize(None) for tz-aware Timestamp and DatetimeIndex now removes timezone holding local time, previously this resulted in Exception or TypeError (GH7812)

    In [63]: ts = Timestamp('2014-08-01 09:00', tz='US/Eastern')
    
    In [64]: ts
    Out[64]: Timestamp('2014-08-01 09:00:00-0400', tz='US/Eastern')
    
    In [65]: ts.tz_localize(None)
    Out[65]: Timestamp('2014-08-01 09:00:00')
    
    In [66]: didx = DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
    
    In [67]: didx
    Out[67]: 
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2014-08-01 09:00:00-04:00, ..., 2014-08-01 18:00:00-04:00]
    Length: 10, Freq: H, Timezone: US/Eastern
    
    In [68]: didx.tz_localize(None)
    Out[68]: 
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2014-08-01 09:00:00, ..., 2014-08-01 18:00:00]
    Length: 10, Freq: H, Timezone: None
    
  • tz_localize now accepts the ambiguous keyword which allows for passing an array of bools indicating whether the date belongs in DST or not, ‘NaT’ for setting transition times to NaT, ‘infer’ for inferring DST/non-DST, and ‘raise’ (default) for an AmbiguousTimeError to be raised. See the docs for more details (GH7943)

  • DataFrame.tz_localize and DataFrame.tz_convert now accepts an optional level argument for localizing a specific level of a MultiIndex (GH7846)

  • Timestamp.tz_localize and Timestamp.tz_convert now raise TypeError in error cases, rather than Exception (GH8025)

  • a timeseries/index localized to UTC when inserted into a Series/DataFrame will preserve the UTC timezone (rather than being a naive datetime64[ns]) as object dtype (GH8411)

  • Timestamp.__repr__ displays dateutil.tz.tzoffset info (GH7907)

Rolling/Expanding Moments improvements

  • rolling_min(), rolling_max(), rolling_cov(), and rolling_corr() now return objects with all NaN when len(arg) < min_periods <= window rather than raising. (This makes all rolling functions consistent in this behavior). (GH7766)

    Prior to 0.15.0

    In [69]: s = Series([10, 11, 12, 13])
    
    In [15]: rolling_min(s, window=10, min_periods=5)
    ValueError: min_periods (5) must be <= window (4)
    

    New behavior

    In [70]: rolling_min(s, window=10, min_periods=5)
    Out[70]: 
    0   NaN
    1   NaN
    2   NaN
    3   NaN
    dtype: float64
    
  • rolling_max(), rolling_min(), rolling_sum(), rolling_mean(), rolling_median(), rolling_std(), rolling_var(), rolling_skew(), rolling_kurt(), rolling_quantile(), rolling_cov(), rolling_corr(), rolling_corr_pairwise(), rolling_window(), and rolling_apply() with center=True previously would return a result of the same structure as the input arg with NaN in the final (window-1)/2 entries.

    Now the final (window-1)/2 entries of the result are calculated as if the input arg were followed by (window-1)/2 NaN values (or with shrinking windows, in the case of rolling_apply()). (GH7925, GH8269)

    Prior behavior (note final value is NaN):

    In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)
    Out[7]:
    0     1
    1     3
    2     6
    3   NaN
    dtype: float64
    

    New behavior (note final value is 5 = sum([2, 3, NaN])):

    In [71]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)
    Out[71]: 
    0    1
    1    3
    2    6
    3    5
    dtype: float64
    
  • rolling_window() now normalizes the weights properly in rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization for further details. (GH7618)

    In [72]: s = Series([10.5, 8.8, 11.4, 9.7, 9.3])
    

    Behavior prior to 0.15.0:

    In [39]: rolling_window(s, window=3, win_type='triang', center=True)
    Out[39]:
    0         NaN
    1    6.583333
    2    6.883333
    3    6.683333
    4         NaN
    dtype: float64
    

    New behavior

    In [73]: rolling_window(s, window=3, win_type='triang', center=True)
    Out[73]: 
    0       NaN
    1     9.875
    2    10.325
    3    10.025
    4       NaN
    dtype: float64
    
  • Removed center argument from all expanding_ functions (see list), as the results produced when center=True did not make much sense. (GH7925)

  • Added optional ddof argument to expanding_cov() and rolling_cov(). The default value of 1 is backwards-compatible. (GH8279)

  • Documented the ddof argument to expanding_var(), expanding_std(), rolling_var(), and rolling_std(). These functions’ support of a ddof argument (with a default value of 1) was previously undocumented. (GH8064)

  • ewma(), ewmstd(), ewmvol(), ewmvar(), ewmcov(), and ewmcorr() now interpret min_periods in the same manner that the rolling_*() and expanding_*() functions do: a given result entry will be NaN if the (expanding, in this case) window does not contain at least min_periods values. The previous behavior was to set to NaN the min_periods entries starting with the first non- NaN value. (GH7977)

    Prior behavior (note values start at index 2, which is min_periods after index 0 (the index of the first non-empty value)):

    In [74]: s  = Series([1, None, None, None, 2, 3])
    
    In [51]: ewma(s, com=3., min_periods=2)
    Out[51]:
    0         NaN
    1         NaN
    2    1.000000
    3    1.000000
    4    1.571429
    5    2.189189
    dtype: float64
    

    New behavior (note values start at index 4, the location of the 2nd (since min_periods=2) non-empty value):

    In [75]: ewma(s, com=3., min_periods=2)
    Out[75]: 
    0         NaN
    1         NaN
    2         NaN
    3         NaN
    4    1.759644
    5    2.383784
    dtype: float64
    
  • ewmstd(), ewmvol(), ewmvar(), ewmcov(), and ewmcorr() now have an optional adjust argument, just like ewma() does, affecting how the weights are calculated. The default value of adjust is True, which is backwards-compatible. See Exponentially weighted moment functions for details. (GH7911)

  • ewma(), ewmstd(), ewmvol(), ewmvar(), ewmcov(), and ewmcorr() now have an optional ignore_na argument. When ignore_na=False (the default), missing values are taken into account in the weights calculation. When ignore_na=True (which reproduces the pre-0.15.0 behavior), missing values are ignored in the weights calculation. (GH7543)

    In [76]: ewma(Series([None, 1., 8.]), com=2.)
    Out[76]: 
    0    NaN
    1    1.0
    2    5.2
    dtype: float64
    
    In [77]: ewma(Series([1., None, 8.]), com=2., ignore_na=True)  # pre-0.15.0 behavior
    Out[77]: 
    0    1.0
    1    1.0
    2    5.2
    dtype: float64
    
    In [78]: ewma(Series([1., None, 8.]), com=2., ignore_na=False)  # new default
    Out[78]: 
    0    1.000000
    1    1.000000
    2    5.846154
    dtype: float64
    

    Warning

    By default (ignore_na=False) the ewm*() functions’ weights calculation in the presence of missing values is different than in pre-0.15.0 versions. To reproduce the pre-0.15.0 calculation of weights in the presence of missing values one must specify explicitly ignore_na=True.

  • Bug in expanding_cov(), expanding_corr(), rolling_cov(), rolling_cor(), ewmcov(), and ewmcorr() returning results with columns sorted by name and producing an error for non-unique columns; now handles non-unique columns and returns columns in original order (except for the case of two DataFrames with pairwise=False, where behavior is unchanged) (GH7542)

  • Bug in rolling_count() and expanding_*() functions unnecessarily producing error message for zero-length data (GH8056)

  • Bug in rolling_apply() and expanding_apply() interpreting min_periods=0 as min_periods=1 (GH8080)

  • Bug in expanding_std() and expanding_var() for a single value producing a confusing error message (GH7900)

  • Bug in rolling_std() and rolling_var() for a single value producing 0 rather than NaN (GH7900)

  • Bug in ewmstd(), ewmvol(), ewmvar(), and ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now a different factor is used for each entry, based on the actual weights (analogous to the usual N/(N-1) factor). In particular, for a single point a value of NaN is returned when bias=False, whereas previously a value of (approximately) 0 was returned.

    For example, consider the following pre-0.15.0 results for ewmvar(..., bias=False), and the corresponding debiasing factors:

    In [79]: s = Series([1., 2., 0., 4.])
    
    In [89]: ewmvar(s, com=2., bias=False)
    Out[89]:
    0   -2.775558e-16
    1    3.000000e-01
    2    9.556787e-01
    3    3.585799e+00
    dtype: float64
    
    In [90]: ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True)
    Out[90]:
    0    1.25
    1    1.25
    2    1.25
    3    1.25
    dtype: float64
    

    Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors are decreasing (towards 1.25):

    In [80]: ewmvar(s, com=2., bias=False)
    Out[80]: 
    0         NaN
    1    0.500000
    2    1.210526
    3    4.089069
    dtype: float64
    
    In [81]: ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True)
    Out[81]: 
    0         NaN
    1    2.083333
    2    1.583333
    3    1.425439
    dtype: float64
    

    See Exponentially weighted moment functions for details. (GH7912)

Improvements in the sql io module

  • Added support for a chunksize parameter to to_sql function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (GH8062).

  • Added support for a chunksize parameter to read_sql function. Specifying this argument will return an iterator through chunks of the query result (GH2908).

  • Added support for writing datetime.date and datetime.time object columns with to_sql (GH6932).

  • Added support for specifying a schema to read from/write to with read_sql_table and to_sql (GH7441, GH7952). For example:

    df.to_sql('table', engine, schema='other_schema')
    pd.read_sql_table('table', engine, schema='other_schema')
    
  • Added support for writing NaN values with to_sql (GH2754).

  • Added support for writing datetime64 columns with to_sql for all database flavors (GH7103).

Backwards incompatible API changes

Breaking changes

API changes related to Categorical (see here for more details):

  • The Categorical constructor with two arguments changed from “codes/labels and levels” to “values and levels (now called ‘categories’)”. This can lead to subtle bugs. If you use Categorical directly, please audit your code by changing it to use the from_codes() constructor.

    An old function call like (prior to 0.15.0):

    pd.Categorical([0,1,0,2,1], levels=['a', 'b', 'c'])
    

    will have to adapted to the following to keep the same behaviour:

    In [2]: pd.Categorical.from_codes([0,1,0,2,1], categories=['a', 'b', 'c'])
    Out[2]:
    [a, b, a, c, b]
    Categories (3, object): [a, b, c]
    

API changes related to the introduction of the Timedelta scalar (see above for more details):

  • Prior to 0.15.0 to_timedelta() would return a Series for list-like/Series input, and a np.timedelta64 for scalar input. It will now return a TimedeltaIndex for list-like input, Series for Series input, and Timedelta for scalar input.

For API changes related to the rolling and expanding functions, see detailed overview above.

Other notable API changes:

  • Consistency when indexing with .loc and a list-like indexer when no values are found.

    In [82]: df = DataFrame([['a'],['b']],index=[1,2])
    
    In [83]: df
    Out[83]: 
       0
    1  a
    2  b
    

    In prior versions there was a difference in these two constructs:

    • df.loc[[3]] would return a frame reindexed by 3 (with all np.nan values)
    • df.loc[[3],:] would raise KeyError.

    Both will now raise a KeyError. The rule is that at least 1 indexer must be found when using a list-like and .loc (GH7999)

    Furthermore in prior versions these were also different:

    • df.loc[[1,3]] would return a frame reindexed by [1,3]
    • df.loc[[1,3],:] would raise KeyError.

    Both will now return a frame reindex by [1,3]. E.g.

    In [84]: df.loc[[1,3]]
    Out[84]: 
         0
    1    a
    3  NaN
    
    In [85]: df.loc[[1,3],:]
    Out[85]: 
         0
    1    a
    3  NaN
    

    This can also be seen in multi-axis indexing with a Panel.

    In [86]: p = Panel(np.arange(2*3*4).reshape(2,3,4),
       ....:           items=['ItemA','ItemB'],
       ....:           major_axis=[1,2,3],
       ....:           minor_axis=['A','B','C','D'])
       ....: 
    
    In [87]: p
    Out[87]: 
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemB
    Major_axis axis: 1 to 3
    Minor_axis axis: A to D
    

    The following would raise KeyError prior to 0.15.0:

    In [88]: p.loc[['ItemA','ItemD'],:,'D']
    Out[88]: 
       ItemA  ItemD
    1      3    NaN
    2      7    NaN
    3     11    NaN
    

    Furthermore, .loc will raise If no values are found in a multi-index with a list-like indexer:

    In [89]: s = Series(np.arange(3,dtype='int64'),
       ....:            index=MultiIndex.from_product([['A'],['foo','bar','baz']],
       ....:                                          names=['one','two'])
       ....:           ).sortlevel()
       ....: 
    
    In [90]: s
    Out[90]: 
    one  two
    A    bar    1
         baz    2
         foo    0
    dtype: int64
    
    In [91]: try:
       ....:    s.loc[['D']]
       ....: except KeyError as e:
       ....:    print("KeyError: " + str(e))
       ....: 
    KeyError: 'cannot index a multi-index axis with these keys'
    
  • Assigning values to None now considers the dtype when choosing an ‘empty’ value (GH7941).

    Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN:

    In [92]: s = Series([1, 2, 3])
    
    In [93]: s.loc[0] = None
    
    In [94]: s
    Out[94]: 
    0   NaN
    1     2
    2     3
    dtype: float64
    

    NaT is now used similarly for datetime containers.

    For object containers, we now preserve None values (previously these were converted to NaN values).

    In [95]: s = Series(["a", "b", "c"])
    
    In [96]: s.loc[0] = None
    
    In [97]: s
    Out[97]: 
    0    None
    1       b
    2       c
    dtype: object
    

    To insert a NaN, you must explicitly use np.nan. See the docs.

  • In prior versions, updating a pandas object inplace would not reflect in other python references to this object. (GH8511, GH5104)

    In [98]: s = Series([1, 2, 3])
    
    In [99]: s2 = s
    
    In [100]: s += 1.5
    

    Behavior prior to v0.15.0

    # the original object
    In [5]: s
    Out[5]:
    0    2.5
    1    3.5
    2    4.5
    dtype: float64
    
    
    # a reference to the original object
    In [7]: s2
    Out[7]:
    0    1
    1    2
    2    3
    dtype: int64
    

    This is now the correct behavior

    # the original object
    In [101]: s
    Out[101]: 
    0    2.5
    1    3.5
    2    4.5
    dtype: float64
    
    # a reference to the original object
    In [102]: s2
    Out[102]: 
    0    2.5
    1    3.5
    2    4.5
    dtype: float64
    
  • Made both the C-based and Python engines for read_csv and read_table ignore empty lines in input as well as whitespace-filled lines, as long as sep is not whitespace. This is an API change that can be controlled by the keyword parameter skip_blank_lines. See the docs (GH4466)

  • A timeseries/index localized to UTC when inserted into a Series/DataFrame will preserve the UTC timezone and inserted as object dtype rather than being converted to a naive datetime64[ns] (GH8411).

  • Bug in passing a DatetimeIndex with a timezone that was not being retained in DataFrame construction from a dict (GH7822)

    In prior versions this would drop the timezone, now it retains the timezone, but gives a column of object dtype:

    In [103]: i = date_range('1/1/2011', periods=3, freq='10s', tz = 'US/Eastern')
    
    In [104]: i
    Out[104]: 
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2011-01-01 00:00:00-05:00, ..., 2011-01-01 00:00:20-05:00]
    Length: 3, Freq: 10S, Timezone: US/Eastern
    
    In [105]: df = DataFrame( {'a' : i } )
    
    In [106]: df
    Out[106]: 
                               a
    0  2011-01-01 00:00:00-05:00
    1  2011-01-01 00:00:10-05:00
    2  2011-01-01 00:00:20-05:00
    
    In [107]: df.dtypes
    Out[107]: 
    a    object
    dtype: object
    

    Previously this would have yielded a column of datetime64 dtype, but without timezone info.

    The behaviour of assigning a column to an existing dataframe as df[‘a’] = i remains unchanged (this already returned an object column with a timezone).

  • When passing multiple levels to stack(), it will now raise a ValueError when the levels aren’t all level names or all level numbers (GH7660). See Reshaping by stacking and unstacking.

  • Raise a ValueError in df.to_hdf with ‘fixed’ format, if df has non-unique columns as the resulting file will be broken (GH7761)

  • SettingWithCopy raise/warnings (according to the option mode.chained_assignment) will now be issued when setting a value on a sliced mixed-dtype DataFrame using chained-assignment. (GH7845, GH7950)

    In [1]: df = DataFrame(np.arange(0,9), columns=['count'])
    
    In [2]: df['group'] = 'b'
    
    In [3]: df.iloc[0:5]['group'] = 'a'
    /usr/local/bin/ipython:1: SettingWithCopyWarning:
    A value is trying to be set on a copy of a slice from a DataFrame.
    Try using .loc[row_indexer,col_indexer] = value instead
    
    See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
    
  • merge, DataFrame.merge, and ordered_merge now return the same type as the left argument (GH7737).

  • Previously an enlargement with a mixed-dtype frame would act unlike .append which will preserve dtypes (related GH2578, GH8176):

    In [108]: df = DataFrame([[True, 1],[False, 2]],
       .....:                columns=["female","fitness"])
       .....: 
    
    In [109]: df
    Out[109]: 
      female  fitness
    0   True        1
    1  False        2
    
    In [110]: df.dtypes
    Out[110]: 
    female      bool
    fitness    int64
    dtype: object
    
    # dtypes are now preserved
    In [111]: df.loc[2] = df.loc[1]
    
    In [112]: df
    Out[112]: 
      female  fitness
    0   True        1
    1  False        2
    2  False        2
    
    In [113]: df.dtypes
    Out[113]: 
    female      bool
    fitness    int64
    dtype: object
    
  • Series.to_csv() now returns a string when path=None, matching the behaviour of DataFrame.to_csv() (GH8215).

  • read_hdf now raises IOError when a file that doesn’t exist is passed in. Previously, a new, empty file was created, and a KeyError raised (GH7715).

  • DataFrame.info() now ends its output with a newline character (GH8114)

  • Concatenating no objects will now raise a ValueError rather than a bare Exception.

  • Merge errors will now be sub-classes of ValueError rather than raw Exception (GH8501)

  • DataFrame.plot and Series.plot keywords are now have consistent orders (GH8037)

Internal Refactoring

In 0.15.0 Index has internally been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be a transparent change with only very limited API implications (GH5080, GH7439, GH7796, GH8024, GH8367, GH7997, GH8522):

  • you may need to unpickle pandas version < 0.15.0 pickles using pd.read_pickle rather than pickle.load. See pickle docs
  • when plotting with a PeriodIndex, the matplotlib internal axes will now be arrays of Period rather than a PeriodIndex (this is similar to how a DatetimeIndex passes arrays of datetimes now)
  • MultiIndexes will now raise similary to other pandas objects w.r.t. truth testing, see here (GH7897).
  • When plotting a DatetimeIndex directly with matplotlib’s plot function, the axis labels will no longer be formatted as dates but as integers (the internal representation of a datetime64). UPDATE This is fixed in 0.15.1, see here.

Deprecations

  • The attributes Categorical labels and levels attributes are deprecated and renamed to codes and categories.
  • The outtype argument to pd.DataFrame.to_dict has been deprecated in favor of orient. (GH7840)
  • The convert_dummies method has been deprecated in favor of get_dummies (GH8140)
  • The infer_dst argument in tz_localize will be deprecated in favor of ambiguous to allow for more flexibility in dealing with DST transitions. Replace infer_dst=True with ambiguous='infer' for the same behavior (GH7943). See the docs for more details.
  • The top-level pd.value_range has been deprecated and can be replaced by .describe() (GH8481)
  • The Index set operations + and - were deprecated in order to provide these for numeric type operations on certain index types. + can be replaced by .union() or |, and - by .difference(). Further the method name Index.diff() is deprecated and can be replaced by Index.difference() (GH8226)

    # +
    Index(['a','b','c']) + Index(['b','c','d'])
    
    # should be replaced by
    Index(['a','b','c']).union(Index(['b','c','d']))
    
    # -
    Index(['a','b','c']) - Index(['b','c','d'])
    
    # should be replaced by
    Index(['a','b','c']).difference(Index(['b','c','d']))
    
  • The infer_types argument to read_html() now has no effect and is deprecated (GH7762, GH7032).

Removal of prior version deprecations/changes

  • Remove DataFrame.delevel method in favor of DataFrame.reset_index

Enhancements

Enhancements in the importing/exporting of Stata files:

  • Added support for bool, uint8, uint16 and uint32 datatypes in to_stata (GH7097, GH7365)
  • Added conversion option when importing Stata files (GH8527)
  • DataFrame.to_stata and StataWriter check string length for compatibility with limitations imposed in dta files where fixed-width strings must contain 244 or fewer characters. Attempting to write Stata dta files with strings longer than 244 characters raises a ValueError. (GH7858)
  • read_stata and StataReader can import missing data information into a DataFrame by setting the argument convert_missing to True. When using this options, missing values are returned as StataMissingValue objects and columns containing missing values have object data type. (GH8045)

Enhancements in the plotting functions:

  • Added layout keyword to DataFrame.plot. You can pass a tuple of (rows, columns), one of which can be -1 to automatically infer (GH6667, GH8071).
  • Allow to pass multiple axes to DataFrame.plot, hist and boxplot (GH5353, GH6970, GH7069)
  • Added support for c, colormap and colorbar arguments for DataFrame.plot with kind='scatter' (GH7780)
  • Histogram from DataFrame.plot with kind='hist' (GH7809), See the docs.
  • Boxplot from DataFrame.plot with kind='box' (GH7998), See the docs.

Other:

  • read_csv now has a keyword parameter float_precision which specifies which floating-point converter the C engine should use during parsing, see here (GH8002, GH8044)

  • Added searchsorted method to Series objects (GH7447)

  • describe() on mixed-types DataFrames is more flexible. Type-based column filtering is now possible via the include/exclude arguments. See the docs (GH8164).

    In [114]: df = DataFrame({'catA': ['foo', 'foo', 'bar'] * 8,
       .....:                 'catB': ['a', 'b', 'c', 'd'] * 6,
       .....:                 'numC': np.arange(24),
       .....:                 'numD': np.arange(24.) + .5})
       .....: 
    
    In [115]: df.describe(include=["object"])
    Out[115]: 
           catA catB
    count    24   24
    unique    2    4
    top     foo    d
    freq     16    6
    
    In [116]: df.describe(include=["number", "object"], exclude=["float"])
    Out[116]: 
           catA catB       numC
    count    24   24  24.000000
    unique    2    4        NaN
    top     foo    d        NaN
    freq     16    6        NaN
    mean    NaN  NaN  11.500000
    std     NaN  NaN   7.071068
    min     NaN  NaN   0.000000
    25%     NaN  NaN   5.750000
    50%     NaN  NaN  11.500000
    75%     NaN  NaN  17.250000
    max     NaN  NaN  23.000000
    

    Requesting all columns is possible with the shorthand ‘all’

    In [117]: df.describe(include='all')
    Out[117]: 
           catA catB       numC       numD
    count    24   24  24.000000  24.000000
    unique    2    4        NaN        NaN
    top     foo    d        NaN        NaN
    freq     16    6        NaN        NaN
    mean    NaN  NaN  11.500000  12.000000
    std     NaN  NaN   7.071068   7.071068
    min     NaN  NaN   0.000000   0.500000
    25%     NaN  NaN   5.750000   6.250000
    50%     NaN  NaN  11.500000  12.000000
    75%     NaN  NaN  17.250000  17.750000
    max     NaN  NaN  23.000000  23.500000
    

    Without those arguments, ‘describe` will behave as before, including only numerical columns or, if none are, only categorical columns. See also the docs

  • Added split as an option to the orient argument in pd.DataFrame.to_dict. (GH7840)

  • The get_dummies method can now be used on DataFrames. By default only catagorical columns are encoded as 0’s and 1’s, while other columns are left untouched.

    In [118]: df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
       .....:                 'C': [1, 2, 3]})
       .....: 
    
    In [119]: pd.get_dummies(df)
    Out[119]: 
       C  A_a  A_b  B_b  B_c
    0  1    1    0    0    1
    1  2    0    1    0    1
    2  3    1    0    1    0
    
  • PeriodIndex supports resolution as the same as DatetimeIndex (GH7708)

  • pandas.tseries.holiday has added support for additional holidays and ways to observe holidays (GH7070)

  • pandas.tseries.holiday.Holiday now supports a list of offsets in Python3 (GH7070)

  • pandas.tseries.holiday.Holiday now supports a days_of_week parameter (GH7070)

  • GroupBy.nth() now supports selecting multiple nth values (GH7910)

    In [120]: business_dates = date_range(start='4/1/2014', end='6/30/2014', freq='B')
    
    In [121]: df = DataFrame(1, index=business_dates, columns=['a', 'b'])
    
    # get the first, 4th, and last date index for each month
    In [122]: df.groupby((df.index.year, df.index.month)).nth([0, 3, -1])
    Out[122]: 
                a  b
    2014-04-01  1  1
    2014-04-04  1  1
    2014-04-30  1  1
    2014-05-01  1  1
    2014-05-06  1  1
    2014-05-30  1  1
    2014-06-02  1  1
    2014-06-05  1  1
    2014-06-30  1  1
    
  • Period and PeriodIndex supports addition/subtraction with timedelta-likes (GH7966)

    If Period freq is D, H, T, S, L, U, N, Timedelta-like can be added if the result can have same freq. Otherwise, only the same offsets can be added.

    In [123]: idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')
    
    In [124]: idx
    Out[124]: 
    <class 'pandas.tseries.period.PeriodIndex'>
    [2014-07-01 09:00, ..., 2014-07-01 13:00]
    Length: 5, Freq: H
    
    In [125]: idx + pd.offsets.Hour(2)
    Out[125]: 
    <class 'pandas.tseries.period.PeriodIndex'>
    [2014-07-01 11:00, ..., 2014-07-01 15:00]
    Length: 5, Freq: H
    
    In [126]: idx + Timedelta('120m')
    Out[126]: 
    <class 'pandas.tseries.period.PeriodIndex'>
    [2014-07-01 11:00, ..., 2014-07-01 15:00]
    Length: 5, Freq: H
    
    In [127]: idx = pd.period_range('2014-07', periods=5, freq='M')
    
    In [128]: idx
    Out[128]: 
    <class 'pandas.tseries.period.PeriodIndex'>
    [2014-07, ..., 2014-11]
    Length: 5, Freq: M
    
    In [129]: idx + pd.offsets.MonthEnd(3)
    Out[129]: 
    <class 'pandas.tseries.period.PeriodIndex'>
    [2014-10, ..., 2015-02]
    Length: 5, Freq: M
    
  • Added experimental compatibility with openpyxl for versions >= 2.0. The DataFrame.to_excel method engine keyword now recognizes openpyxl1 and openpyxl2 which will explicitly require openpyxl v1 and v2 respectively, failing if the requested version is not available. The openpyxl engine is a now a meta-engine that automatically uses whichever version of openpyxl is installed. (GH7177)

  • DataFrame.fillna can now accept a DataFrame as a fill value (GH8377)

  • Passing multiple levels to stack() will now work when multiple level numbers are passed (GH7660). See Reshaping by stacking and unstacking.

  • set_names(), set_labels(), and set_levels() methods now take an optional level keyword argument to all modification of specific level(s) of a MultiIndex. Additionally set_names() now accepts a scalar string value when operating on an Index or on a specific level of a MultiIndex (GH7792)

    In [130]: idx = MultiIndex.from_product([['a'], range(3), list("pqr")], names=['foo', 'bar', 'baz'])
    
    In [131]: idx.set_names('qux', level=0)
    Out[131]: 
    MultiIndex(levels=[[u'a'], [0, 1, 2], [u'p', u'q', u'r']],
               labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
               names=[u'qux', u'bar', u'baz'])
    
    In [132]: idx.set_names(['qux','baz'], level=[0,1])
    Out[132]: 
    MultiIndex(levels=[[u'a'], [0, 1, 2], [u'p', u'q', u'r']],
               labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
               names=[u'qux', u'baz', u'baz'])
    
    In [133]: idx.set_levels(['a','b','c'], level='bar')
    Out[133]: 
    MultiIndex(levels=[[u'a'], [u'a', u'b', u'c'], [u'p', u'q', u'r']],
               labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
               names=[u'foo', u'bar', u'baz'])
    
    In [134]: idx.set_levels([['a','b','c'],[1,2,3]], level=[1,2])
    Out[134]: 
    MultiIndex(levels=[[u'a'], [u'a', u'b', u'c'], [1, 2, 3]],
               labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
               names=[u'foo', u'bar', u'baz'])
    
  • Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890)

    In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']])
    
    In [2]: idx.values
    Out[2]: array([(0, 'a'), (0, 'b'), (0, 'c'), (1, 'a'), (1, 'b'), (1, 'c')], dtype=object)
    
    In [3]: idx.isin(['a', 'c', 'e'], level=1)
    Out[3]: array([ True, False,  True,  True, False,  True], dtype=bool)
    
  • Index now supports duplicated and drop_duplicates. (GH4060)

    In [135]: idx = Index([1, 2, 3, 4, 1, 2])
    
    In [136]: idx
    Out[136]: Int64Index([1, 2, 3, 4, 1, 2], dtype='int64')
    
    In [137]: idx.duplicated()
    Out[137]: Index([False, False, False, False, True, True], dtype='object')
    
    In [138]: idx.drop_duplicates()
    Out[138]: Int64Index([1, 2, 3, 4], dtype='int64')
    
  • add copy=True argument to pd.concat to enable pass thru of complete blocks (GH8252)

  • Added support for numpy 1.8+ data types (bool_, int_, float_, string_) for conversion to R dataframe (GH8400)

Performance

  • Performance improvements in DatetimeIndex.__iter__ to allow faster iteration (GH7683)
  • Performance improvements in Period creation (and PeriodIndex setitem) (GH5155)
  • Improvements in Series.transform for significant performance gains (revised) (GH6496)
  • Performance improvements in StataReader when reading large files (GH8040, GH8073)
  • Performance improvements in StataWriter when writing large files (GH8079)
  • Performance and memory usage improvements in multi-key groupby (GH8128)
  • Performance improvements in groupby .agg and .apply where builtins max/min were not mapped to numpy/cythonized versions (GH7722)
  • Performance improvement in writing to sql (to_sql) of up to 50% (GH8208).
  • Performance benchmarking of groupby for large value of ngroups (GH6787)
  • Performance improvement in CustomBusinessDay, CustomBusinessMonth (GH8236)
  • Performance improvement for MultiIndex.values for multi-level indexes containing datetimes (GH8543)

Bug Fixes

  • Bug in pivot_table, when using margins and a dict aggfunc (GH8349)
  • Bug in read_csv where squeeze=True would return a view (GH8217)
  • Bug in checking of table name in read_sql in certain cases (GH7826).
  • Bug in DataFrame.groupby where Grouper does not recognize level when frequency is specified (GH7885)
  • Bug in multiindexes dtypes getting mixed up when DataFrame is saved to SQL table (GH8021)
  • Bug in Series 0-division with a float and integer operand dtypes (GH7785)
  • Bug in Series.astype("unicode") not calling unicode on the values correctly (GH7758)
  • Bug in DataFrame.as_matrix() with mixed datetime64[ns] and timedelta64[ns] dtypes (GH7778)
  • Bug in HDFStore.select_column() not preserving UTC timezone info when selecting a DatetimeIndex (GH7777)
  • Bug in to_datetime when format='%Y%m%d' and coerce=True are specified, where previously an object array was returned (rather than a coerced time-series with NaT), (GH7930)
  • Bug in DatetimeIndex and PeriodIndex in-place addition and subtraction cause different result from normal one (GH6527)
  • Bug in adding and subtracting PeriodIndex with PeriodIndex raise TypeError (GH7741)
  • Bug in combine_first with PeriodIndex data raises TypeError (GH3367)
  • Bug in multi-index slicing with missing indexers (GH7866)
  • Bug in multi-index slicing with various edge cases (GH8132)
  • Regression in multi-index indexing with a non-scalar type object (GH7914)
  • Bug in Timestamp comparisons with == and int64 dtype (GH8058)
  • Bug in pickles contains DateOffset may raise AttributeError when normalize attribute is reffered internally (GH7748)
  • Bug in Panel when using major_xs and copy=False is passed (deprecation warning fails because of missing warnings) (GH8152).
  • Bug in pickle deserialization that failed for pre-0.14.1 containers with dup items trying to avoid ambiguity when matching block and manager items, when there’s only one block there’s no ambiguity (GH7794)
  • Bug in putting a PeriodIndex into a Series would convert to int64 dtype, rather than object of Periods (GH7932)
  • Bug in HDFStore iteration when passing a where (GH8014)
  • Bug in DataFrameGroupby.transform when transforming with a passed non-sorted key (GH8046, GH8430)
  • Bug in repeated timeseries line and area plot may result in ValueError or incorrect kind (GH7733)
  • Bug in inference in a MultiIndex with datetime.date inputs (GH7888)
  • Bug in get where an IndexError would not cause the default value to be returned (GH7725)
  • Bug in offsets.apply, rollforward and rollback may reset nanosecond (GH7697)
  • Bug in offsets.apply, rollforward and rollback may raise AttributeError if Timestamp has dateutil tzinfo (GH7697)
  • Bug in sorting a multi-index frame with a Float64Index (GH8017)
  • Bug in inconsistent panel setitem with a rhs of a DataFrame for alignment (GH7763)
  • Bug in is_superperiod and is_subperiod cannot handle higher frequencies than S (GH7760, GH7772, GH7803)
  • Bug in 32-bit platforms with Series.shift (GH8129)
  • Bug in PeriodIndex.unique returns int64 np.ndarray (GH7540)
  • Bug in groupby.apply with a non-affecting mutation in the function (GH8467)
  • Bug in DataFrame.reset_index which has MultiIndex contains PeriodIndex or DatetimeIndex with tz raises ValueError (GH7746, GH7793)
  • Bug in DataFrame.plot with subplots=True may draw unnecessary minor xticks and yticks (GH7801)
  • Bug in StataReader which did not read variable labels in 117 files due to difference between Stata documentation and implementation (GH7816)
  • Bug in StataReader where strings were always converted to 244 characters-fixed width irrespective of underlying string size (GH7858)
  • Bug in DataFrame.plot and Series.plot may ignore rot and fontsize keywords (GH7844)
  • Bug in DatetimeIndex.value_counts doesn’t preserve tz (GH7735)
  • Bug in PeriodIndex.value_counts results in Int64Index (GH7735)
  • Bug in DataFrame.join when doing left join on index and there are multiple matches (GH5391)
  • Bug in GroupBy.transform() where int groups with a transform that didn’t preserve the index were incorrectly truncated (GH7972).
  • Bug in groupby where callable objects without name attributes would take the wrong path, and produce a DataFrame instead of a Series (GH7929)
  • Bug in groupby error message when a DataFrame grouping column is duplicated (GH7511)
  • Bug in read_html where the infer_types argument forced coercion of date-likes incorrectly (GH7762, GH7032).
  • Bug in Series.str.cat with an index which was filtered as to not include the first item (GH7857)
  • Bug in Timestamp cannot parse nanosecond from string (GH7878)
  • Bug in Timestamp with string offset and tz results incorrect (GH7833)
  • Bug in tslib.tz_convert and tslib.tz_convert_single may return different results (GH7798)
  • Bug in DatetimeIndex.intersection of non-overlapping timestamps with tz raises IndexError (GH7880)
  • Bug in alignment with TimeOps and non-unique indexes (GH8363)
  • Bug in GroupBy.filter() where fast path vs. slow path made the filter return a non scalar value that appeared valid but wasn’t (GH7870).
  • Bug in date_range()/DatetimeIndex() when the timezone was inferred from input dates yet incorrect times were returned when crossing DST boundaries (GH7835, GH7901).
  • Bug in to_excel() where a negative sign was being prepended to positive infinity and was absent for negative infinity (GH7949)
  • Bug in area plot draws legend with incorrect alpha when stacked=True (GH8027)
  • Period and PeriodIndex addition/subtraction with np.timedelta64 results in incorrect internal representations (GH7740)
  • Bug in Holiday with no offset or observance (GH7987)
  • Bug in DataFrame.to_latex formatting when columns or index is a MultiIndex (GH7982).
  • Bug in DateOffset around Daylight Savings Time produces unexpected results (GH5175).
  • Bug in DataFrame.shift where empty columns would throw ZeroDivisionError on numpy 1.7 (GH8019)
  • Bug in installation where html_encoding/*.html wasn’t installed and therefore some tests were not running correctly (GH7927).
  • Bug in read_html where bytes objects were not tested for in _read (GH7927).
  • Bug in DataFrame.stack() when one of the column levels was a datelike (GH8039)
  • Bug in broadcasting numpy scalars with DataFrame (GH8116)
  • Bug in pivot_table performed with nameless index and columns raises KeyError (GH8103)
  • Bug in DataFrame.plot(kind='scatter') draws points and errorbars with different colors when the color is specified by c keyword (GH8081)
  • Bug in Float64Index where iat and at were not testing and were failing (GH8092).
  • Bug in DataFrame.boxplot() where y-limits were not set correctly when producing multiple axes (GH7528, GH5517).
  • Bug in read_csv where line comments were not handled correctly given a custom line terminator or delim_whitespace=True (GH8122).
  • Bug in read_html where empty tables caused a StopIteration (GH7575)
  • Bug in casting when setting a column in a same-dtype block (GH7704)
  • Bug in accessing groups from a GroupBy when the original grouper was a tuple (GH8121).
  • Bug in .at that would accept integer indexers on a non-integer index and do fallback (GH7814)
  • Bug with kde plot and NaNs (GH8182)
  • Bug in GroupBy.count with float32 data type were nan values were not excluded (GH8169).
  • Bug with stacked barplots and NaNs (GH8175).
  • Bug in resample with non evenly divisible offsets (e.g. ‘7s’) (GH8371)
  • Bug in interpolation methods with the limit keyword when no values needed interpolating (GH7173).
  • Bug where col_space was ignored in DataFrame.to_string() when header=False (GH8230).
  • Bug with DatetimeIndex.asof incorrectly matching partial strings and returning the wrong date (GH8245).
  • Bug in plotting methods modifying the global matplotlib rcParams (GH8242).
  • Bug in DataFrame.__setitem__ that caused errors when setting a dataframe column to a sparse array (GH8131)
  • Bug where Dataframe.boxplot() failed when entire column was empty (GH8181).
  • Bug with messed variables in radviz visualization (GH8199).
  • Bug in interpolation methods with the limit keyword when no values needed interpolating (GH7173).
  • Bug where col_space was ignored in DataFrame.to_string() when header=False (GH8230).
  • Bug in to_clipboard that would clip long column data (GH8305)
  • Bug in DataFrame terminal display: Setting max_column/max_rows to zero did not trigger auto-resizing of dfs to fit terminal width/height (GH7180).
  • Bug in OLS where running with “cluster” and “nw_lags” parameters did not work correctly, but also did not throw an error (GH5884).
  • Bug in DataFrame.dropna that interpreted non-existent columns in the subset argument as the ‘last column’ (GH8303)
  • Bug in Index.intersection on non-monotonic non-unique indexes (GH8362).
  • Bug in masked series assignment where mismatching types would break alignment (GH8387)
  • Bug in NDFrame.equals gives false negatives with dtype=object (GH8437)
  • Bug in assignment with indexer where type diversity would break alignment (GH8258)
  • Bug in NDFrame.loc indexing when row/column names were lost when target was a list/ndarray (GH6552)
  • Regression in NDFrame.loc indexing when rows/columns were converted to Float64Index if target was an empty list/ndarray (GH7774)
  • Bug in Series that allows it to be indexed by a DataFrame which has unexpected results. Such indexing is no longer permitted (GH8444)
  • Bug in item assignment of a DataFrame with multi-index columns where right-hand-side columns were not aligned (GH7655)
  • Suppress FutureWarning generated by NumPy when comparing object arrays containing NaN for equality (GH7065)
  • Bug in DataFrame.eval() where the dtype of the not operator (~) was not correctly inferred as bool.

v0.14.1 (July 11, 2014)

This is a minor release from 0.14.0 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

API changes

  • Openpyxl now raises a ValueError on construction of the openpyxl writer instead of warning on pandas import (GH7284).

  • For StringMethods.extract, when no match is found, the result - only containing NaN values - now also has dtype=object instead of float (GH7242)

  • Period objects no longer raise a TypeError when compared using == with another object that isn’t a Period. Instead when comparing a Period with another object using == if the other object isn’t a Period False is returned. (GH7376)

  • Previously, the behaviour on resetting the time or not in offsets.apply, rollforward and rollback operations differed between offsets. With the support of the normalize keyword for all offsets(see below) with a default value of False (preserve time), the behaviour changed for certain offsets (BusinessMonthBegin, MonthEnd, BusinessMonthEnd, CustomBusinessMonthEnd, BusinessYearBegin, LastWeekOfMonth, FY5253Quarter, LastWeekOfMonth, Easter):

    In [6]: from pandas.tseries import offsets
    
    In [7]: d = pd.Timestamp('2014-01-01 09:00')
    
    # old behaviour < 0.14.1
    In [8]: d + offsets.MonthEnd()
    Out[8]: Timestamp('2014-01-31 00:00:00')
    

    Starting from 0.14.1 all offsets preserve time by default. The old behaviour can be obtained with normalize=True

    # new behaviour
    In [1]: d + offsets.MonthEnd()
    Out[1]: Timestamp('2014-01-31 09:00:00')
    
    In [2]: d + offsets.MonthEnd(normalize=True)
    Out[2]: Timestamp('2014-01-31 00:00:00')
    

    Note that for the other offsets the default behaviour did not change.

  • Add back #N/A N/A as a default NA value in text parsing, (regresion from 0.12) (GH5521)

  • Raise a TypeError on inplace-setting with a .where and a non np.nan value as this is inconsistent with a set-item expression like df[mask] = None (GH7656)

Enhancements

  • Add dropna argument to value_counts and nunique (GH5569).

  • Add select_dtypes() method to allow selection of columns based on dtype (GH7316). See the docs.

  • All offsets suppports the normalize keyword to specify whether offsets.apply, rollforward and rollback resets the time (hour, minute, etc) or not (default False, preserves time) (GH7156):

    In [3]: import pandas.tseries.offsets as offsets
    
    In [4]: day = offsets.Day()
    
    In [5]: day.apply(Timestamp('2014-01-01 09:00'))
    Out[5]: Timestamp('2014-01-02 09:00:00')
    
    In [6]: day = offsets.Day(normalize=True)
    
    In [7]: day.apply(Timestamp('2014-01-01 09:00'))
    Out[7]: Timestamp('2014-01-02 00:00:00')
    
  • PeriodIndex is represented as the same format as DatetimeIndex (GH7601)

  • StringMethods now work on empty Series (GH7242)

  • The file parsers read_csv and read_table now ignore line comments provided by the parameter comment, which accepts only a single character for the C reader. In particular, they allow for comments before file data begins (GH2685)

  • Add NotImplementedError for simultaneous use of chunksize and nrows for read_csv() (GH6774).

  • Tests for basic reading of public S3 buckets now exist (GH7281).

  • read_html now sports an encoding argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323).

  • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809)

  • Support for dateutil timezones, which can now be used in the same way as pytz timezones across pandas. (GH4688)

    In [8]: rng = date_range('3/6/2012 00:00', periods=10, freq='D',
       ...:                  tz='dateutil/Europe/London')
       ...: 
    
    In [9]: rng.tz
    Out[9]: tzfile('Europe/London')
    

    See the docs.

  • Implemented sem (standard error of the mean) operation for Series, DataFrame, Panel, and Groupby (GH6897)

  • Add nlargest and nsmallest to the Series groupby whitelist, which means you can now use these methods on a SeriesGroupBy object (GH7053).

  • All offsets apply, rollforward and rollback can now handle np.datetime64, previously results in ApplyTypeError (GH7452)

  • Period and PeriodIndex can contain NaT in its values (GH7485)

  • Support pickling Series, DataFrame and Panel objects with non-unique labels along item axis (index, columns and items respectively) (GH7370).

  • Improved inference of datetime/timedelta with mixed null objects. Regression from 0.13.1 in interpretation of an object Index with all null elements (GH7431)

Performance

  • Improvements in dtype inference for numeric operations involving yielding performance gains for dtypes: int64, timedelta64, datetime64 (GH7223)
  • Improvements in Series.transform for significant performance gains (GH6496)
  • Improvements in DataFrame.transform with ufuncs and built-in grouper functions for signifcant performance gains (GH7383)
  • Regression in groupby aggregation of datetime64 dtypes (GH7555)
  • Improvements in MultiIndex.from_product for large iterables (GH7627)

Experimental

  • pandas.io.data.Options has a new method, get_all_data method, and now consistently returns a multi-indexed DataFrame, see the docs. (GH5602)
  • io.gbq.read_gbq and io.gbq.to_gbq were refactored to remove the dependency on the Google bq.py command line client. This submodule now uses httplib2 and the Google apiclient and oauth2client API client libraries which should be more stable and, therefore, reliable than bq.py. See the docs. (GH6937).

Bug Fixes

  • Bug in DataFrame.where with a symmetric shaped frame and a passed other of a DataFrame (GH7506)
  • Bug in Panel indexing with a multi-index axis (GH7516)
  • Regression in datetimelike slice indexing with a duplicated index and non-exact end-points (GH7523)
  • Bug in setitem with list-of-lists and single vs mixed types (GH7551:)
  • Bug in timeops with non-aligned Series (GH7500)
  • Bug in timedelta inference when assigning an incomplete Series (GH7592)
  • Bug in groupby .nth with a Series and integer-like column name (GH7559)
  • Bug in Series.get with a boolean accessor (GH7407)
  • Bug in value_counts where NaT did not qualify as missing (NaN) (GH7423)
  • Bug in to_timedelta that accepted invalid units and misinterpreted ‘m/h’ (GH7611, GH6423)
  • Bug in line plot doesn’t set correct xlim if secondary_y=True (GH7459)
  • Bug in grouped hist and scatter plots use old figsize default (GH7394)
  • Bug in plotting subplots with DataFrame.plot, hist clears passed ax even if the number of subplots is one (GH7391).
  • Bug in plotting subplots with DataFrame.boxplot with by kw raises ValueError if the number of subplots exceeds 1 (GH7391).
  • Bug in subplots displays ticklabels and labels in different rule (GH5897)
  • Bug in Panel.apply with a multi-index as an axis (GH7469)
  • Bug in DatetimeIndex.insert doesn’t preserve name and tz (GH7299)
  • Bug in DatetimeIndex.asobject doesn’t preserve name (GH7299)
  • Bug in multi-index slicing with datetimelike ranges (strings and Timestamps), (GH7429)
  • Bug in Index.min and max doesn’t handle nan and NaT properly (GH7261)
  • Bug in PeriodIndex.min/max results in int (GH7609)
  • Bug in resample where fill_method was ignored if you passed how (GH2073)
  • Bug in TimeGrouper doesn’t exclude column specified by key (GH7227)
  • Bug in DataFrame and Series bar and barh plot raises TypeError when bottom and left keyword is specified (GH7226)
  • Bug in DataFrame.hist raises TypeError when it contains non numeric column (GH7277)
  • Bug in Index.delete does not preserve name and freq attributes (GH7302)
  • Bug in DataFrame.query()/eval where local string variables with the @ sign were being treated as temporaries attempting to be deleted (GH7300).
  • Bug in Float64Index which didn’t allow duplicates (GH7149).
  • Bug in DataFrame.replace() where truthy values were being replaced (GH7140).
  • Bug in StringMethods.extract() where a single match group Series would use the matcher’s name instead of the group name (GH7313).
  • Bug in isnull() when mode.use_inf_as_null == True where isnull wouldn’t test True when it encountered an inf/-inf (GH7315).
  • Bug in inferred_freq results in None for eastern hemisphere timezones (GH7310)
  • Bug in Easter returns incorrect date when offset is negative (GH7195)
  • Bug in broadcasting with .div, integer dtypes and divide-by-zero (GH7325)
  • Bug in CustomBusinessDay.apply raiases NameError when np.datetime64 object is passed (GH7196)
  • Bug in MultiIndex.append, concat and pivot_table don’t preserve timezone (GH6606)
  • Bug in .loc with a list of indexers on a single-multi index level (that is not nested) (GH7349)
  • Bug in Series.map when mapping a dict with tuple keys of different lengths (GH7333)
  • Bug all StringMethods now work on empty Series (GH7242)
  • Fix delegation of read_sql to read_sql_query when query does not contain ‘select’ (GH7324).
  • Bug where a string column name assignment to a DataFrame with a Float64Index raised a TypeError during a call to np.isnan (GH7366).
  • Bug where NDFrame.replace() didn’t correctly replace objects with Period values (GH7379).
  • Bug in .ix getitem should always return a Series (GH7150)
  • Bug in multi-index slicing with incomplete indexers (GH7399)
  • Bug in multi-index slicing with a step in a sliced level (GH7400)
  • Bug where negative indexers in DatetimeIndex were not correctly sliced (GH7408)
  • Bug where NaT wasn’t repr’d correctly in a MultiIndex (GH7406, GH7409).
  • Bug where bool objects were converted to nan in convert_objects (GH7416).
  • Bug in quantile ignoring the axis keyword argument (:issue`7306`)
  • Bug where nanops._maybe_null_out doesn’t work with complex numbers (GH7353)
  • Bug in several nanops functions when axis==0 for 1-dimensional nan arrays (GH7354)
  • Bug where nanops.nanmedian doesn’t work when axis==None (GH7352)
  • Bug where nanops._has_infs doesn’t work with many dtypes (GH7357)
  • Bug in StataReader.data where reading a 0-observation dta failed (GH7369)
  • Bug in StataReader when reading Stata 13 (117) files containing fixed width strings (GH7360)
  • Bug in StataWriter where encoding was ignored (GH7286)
  • Bug in DatetimeIndex comparison doesn’t handle NaT properly (GH7529)
  • Bug in passing input with tzinfo to some offsets apply, rollforward or rollback resets tzinfo or raises ValueError (GH7465)
  • Bug in DatetimeIndex.to_period, PeriodIndex.asobject, PeriodIndex.to_timestamp doesn’t preserve name (GH7485)
  • Bug in DatetimeIndex.to_period and PeriodIndex.to_timestanp handle NaT incorrectly (GH7228)
  • Bug in offsets.apply, rollforward and rollback may return normal datetime (GH7502)
  • Bug in resample raises ValueError when target contains NaT (GH7227)
  • Bug in Timestamp.tz_localize resets nanosecond info (GH7534)
  • Bug in DatetimeIndex.asobject raises ValueError when it contains NaT (GH7539)
  • Bug in Timestamp.__new__ doesn’t preserve nanosecond properly (GH7610)
  • Bug in Index.astype(float) where it would return an object dtype Index (GH7464).
  • Bug in DataFrame.reset_index loses tz (GH3950)
  • Bug in DatetimeIndex.freqstr raises AttributeError when freq is None (GH7606)
  • Bug in GroupBy.size created by TimeGrouper raises AttributeError (GH7453)
  • Bug in single column bar plot is misaligned (GH7498).
  • Bug in area plot with tz-aware time series raises ValueError (GH7471)
  • Bug in non-monotonic Index.union may preserve name incorrectly (GH7458)
  • Bug in DatetimeIndex.intersection doesn’t preserve timezone (GH4690)
  • Bug in rolling_var where a window larger than the array would raise an error(GH7297)
  • Bug with last plotted timeseries dictating xlim (GH2960)
  • Bug with secondary_y axis not being considered for timeseries xlim (GH3490)
  • Bug in Float64Index assignment with a non scalar indexer (GH7586)
  • Bug in pandas.core.strings.str_contains does not properly match in a case insensitive fashion when regex=False and case=False (GH7505)
  • Bug in expanding_cov, expanding_corr, rolling_cov, and rolling_corr for two arguments with mismatched index (GH7512)
  • Bug in to_sql taking the boolean column as text column (GH7678)
  • Bug in grouped hist doesn’t handle rot kw and sharex kw properly (GH7234)
  • Bug in .loc performing fallback integer indexing with object dtype indices (GH7496)
  • Bug (regression) in PeriodIndex constructor when passed Series objects (GH7701).

v0.14.0 (May 31 , 2014)

This is a major release from 0.13.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Warning

In 0.14.0 all NDFrame based containers have undergone significant internal refactoring. Before that each block of homogeneous data had its own labels and extra care was necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745)

API changes

  • read_excel uses 0 as the default sheet (GH6573)

  • iloc will now accept out-of-bounds indexers for slices, e.g. a value that exceeds the length of the object being indexed. These will be excluded. This will make pandas conform more with python/numpy indexing of out-of-bounds values. A single indexer that is out-of-bounds and drops the dimensions of the object will still raise IndexError (GH6296, GH6299). This could result in an empty axis (e.g. an empty DataFrame being returned)

    In [1]: dfl = DataFrame(np.random.randn(5,2),columns=list('AB'))
    
    In [2]: dfl
    Out[2]: 
              A         B
    0  1.583584 -0.438313
    1 -0.402537 -0.780572
    2 -0.141685  0.542241
    3  0.370966 -0.251642
    4  0.787484  1.666563
    
    In [3]: dfl.iloc[:,2:3]
    Out[3]: 
    Empty DataFrame
    Columns: []
    Index: [0, 1, 2, 3, 4]
    
    In [4]: dfl.iloc[:,1:3]
    Out[4]: 
              B
    0 -0.438313
    1 -0.780572
    2  0.542241
    3 -0.251642
    4  1.666563
    
    In [5]: dfl.iloc[4:6]
    Out[5]: 
              A         B
    4  0.787484  1.666563
    

    These are out-of-bounds selections

    dfl.iloc[[4,5,6]]
    IndexError: positional indexers are out-of-bounds
    
    dfl.iloc[:,4]
    IndexError: single positional indexer is out-of-bounds
    
  • Slicing with negative start, stop & step values handles corner cases better (GH6531):

    • df.iloc[:-len(df)] is now empty
    • df.iloc[len(df)::-1] now enumerates all elements in reverse
  • The DataFrame.interpolate() keyword downcast default has been changed from infer to None. This is to preseve the original dtype unless explicitly requested otherwise (GH6290).

  • When converting a dataframe to HTML it used to return Empty DataFrame. This special case has been removed, instead a header with the column names is returned (GH6062).

  • Series and Index now internall share more common operations, e.g. factorize(),nunique(),value_counts() are now supported on Index types as well. The Series.weekday property from is removed from Series for API consistency. Using a DatetimeIndex/PeriodIndex method on a Series will now raise a TypeError. (GH4551, GH4056, GH5519, GH6380, GH7206).

  • Add is_month_start, is_month_end, is_quarter_start, is_quarter_end, is_year_start, is_year_end accessors for DateTimeIndex / Timestamp which return a boolean array of whether the timestamp(s) are at the start/end of the month/quarter/year defined by the frequency of the DateTimeIndex / Timestamp (GH4565, GH6998)

  • Local variable usage has changed in pandas.eval()/DataFrame.eval()/DataFrame.query() (GH5987). For the DataFrame methods, two things have changed

    • Column names are now given precedence over locals
    • Local variables must be referred to explicitly. This means that even if you have a local variable that is not a column you must still refer to it with the '@' prefix.
    • You can have an expression like df.query('@a < a') with no complaints from pandas about ambiguity of the name a.
    • The top-level pandas.eval() function does not allow you use the '@' prefix and provides you with an error message telling you so.
    • NameResolutionError was removed because it isn’t necessary anymore.
  • Define and document the order of column vs index names in query/eval (GH6676)

  • concat will now concatenate mixed Series and DataFrames using the Series name or numbering columns as needed (GH2385). See the docs

  • Slicing and advanced/boolean indexing operations on Index classes as well as Index.delete() and Index.drop() methods will no longer change the type of the resulting index (GH6440, GH7040)

    In [6]: i = pd.Index([1, 2, 3, 'a' , 'b', 'c'])
    
    In [7]: i[[0,1,2]]
    Out[7]: Index([1, 2, 3], dtype='object')
    
    In [8]: i.drop(['a', 'b', 'c'])
    Out[8]: Int64Index([1, 2, 3], dtype='int64')
    

    Previously, the above operation would return Int64Index. If you’d like to do this manually, use Index.astype()

    In [9]: i[[0,1,2]].astype(np.int_)
    Out[9]: Int64Index([1, 2, 3], dtype='int32')
    
  • set_index no longer converts MultiIndexes to an Index of tuples. For example, the old behavior returned an Index in this case (GH6459):

    # Old behavior, casted MultiIndex to an Index
    In [10]: tuple_ind
    Out[10]: Index([(u'a', u'c'), (u'a', u'd'), (u'b', u'c'), (u'b', u'd')], dtype='object')
    
    In [11]: df_multi.set_index(tuple_ind)
    Out[11]: 
                   0         1
    (a, c)  0.471435 -1.190976
    (a, d)  1.432707 -0.312652
    (b, c) -0.720589  0.887163
    (b, d)  0.859588 -0.636524
    
    # New behavior
    In [12]: mi
    Out[12]: 
    MultiIndex(levels=[[u'a', u'b'], [u'c', u'd']],
               labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
    
    In [13]: df_multi.set_index(mi)
    Out[13]: 
                0         1
    a c  0.471435 -1.190976
      d  1.432707 -0.312652
    b c -0.720589  0.887163
      d  0.859588 -0.636524
    

    This also applies when passing multiple indices to set_index:

    # Old output, 2-level MultiIndex of tuples
    In [14]: df_multi.set_index([df_multi.index, df_multi.index])
    Out[14]: 
                          0         1
    (a, c) (a, c)  0.471435 -1.190976
    (a, d) (a, d)  1.432707 -0.312652
    (b, c) (b, c) -0.720589  0.887163
    (b, d) (b, d)  0.859588 -0.636524
    
    # New output, 4-level MultiIndex
    In [15]: df_multi.set_index([df_multi.index, df_multi.index])
    Out[15]: 
                    0         1
    a c a c  0.471435 -1.190976
      d a d  1.432707 -0.312652
    b c b c -0.720589  0.887163
      d b d  0.859588 -0.636524
    
  • pairwise keyword was added to the statistical moment functions rolling_cov, rolling_corr, ewmcov, ewmcorr, expanding_cov, expanding_corr to allow the calculation of moving window covariance and correlation matrices (GH4950). See Computing rolling pairwise covariances and correlations in the docs.

    In [16]: df = DataFrame(np.random.randn(10,4),columns=list('ABCD'))
    
    In [17]: covs = rolling_cov(df[['A','B','C']], df[['B','C','D']], 5, pairwise=True)
    
    In [18]: covs[df.index[-1]]
    Out[18]: 
              B         C         D
    A  0.128104  0.183628 -0.047358
    B  0.856265  0.058945  0.145447
    C  0.058945  0.335350  0.390637
    
  • Series.iteritems() is now lazy (returns an iterator rather than a list). This was the documented behavior prior to 0.14. (GH6760)

  • Added nunique and value_counts functions to Index for counting unique elements. (GH6734)

  • stack and unstack now raise a ValueError when the level keyword refers to a non-unique item in the Index (previously raised a KeyError). (GH6738)

  • drop unused order argument from Series.sort; args now are in the same order as Series.order; add na_position arg to conform to Series.order (GH6847)

  • default sorting algorithm for Series.order is now quicksort, to conform with Series.sort (and numpy defaults)

  • add inplace keyword to Series.order/sort to make them inverses (GH6859)

  • DataFrame.sort now places NaNs at the beginning or end of the sort according to the na_position parameter. (GH3917)

  • accept TextFileReader in concat, which was affecting a common user idiom (GH6583), this was a regression from 0.13.1

  • Added factorize functions to Index and Series to get indexer and unique values (GH7090)

  • describe on a DataFrame with a mix of Timestamp and string like objects returns a different Index (GH7088). Previously the index was unintentionally sorted.

  • Arithmetic operations with only bool dtypes now give a warning indicating that they are evaluated in Python space for +, -, and * operations and raise for all others (GH7011, GH6762, GH7015, GH7210)

    x = pd.Series(np.random.rand(10) > 0.5)
    y = True
    x + y  # warning generated: should do x | y instead
    x / y  # this raises because it doesn't make sense
    
    NotImplementedError: operator '/' not implemented for bool dtypes
    
  • In HDFStore, select_as_multiple will always raise a KeyError, when a key or the selector is not found (GH6177)

  • df['col'] = value and df.loc[:,'col'] = value are now completely equivalent; previously the .loc would not necessarily coerce the dtype of the resultant series (GH6149)

  • dtypes and ftypes now return a series with dtype=object on empty containers (GH5740)

  • df.to_csv will now return a string of the CSV data if neither a target path nor a buffer is provided (GH6061)

  • pd.infer_freq() will now raise a TypeError if given an invalid Series/Index type (GH6407, GH6463)

  • A tuple passed to DataFame.sort_index will be interpreted as the levels of the index, rather than requiring a list of tuple (GH4370)

  • all offset operations now return Timestamp types (rather than datetime), Business/Week frequencies were incorrect (GH4069)

  • to_excel now converts np.inf into a string representation, customizable by the inf_rep keyword argument (Excel has no native inf representation) (GH6782)

  • Replace pandas.compat.scipy.scoreatpercentile with numpy.percentile (GH6810)

  • .quantile on a datetime[ns] series now returns Timestamp instead of np.datetime64 objects (GH6810)

  • change AssertionError to TypeError for invalid types passed to concat (GH6583)

  • Raise a TypeError when DataFrame is passed an iterator as the data argument (GH5357)

Display Changes

  • The default way of printing large DataFrames has changed. DataFrames exceeding max_rows and/or max_columns are now displayed in a centrally truncated view, consistent with the printing of a pandas.Series (GH5603).

    In previous versions, a DataFrame was truncated once the dimension constraints were reached and an ellipse (...) signaled that part of the data was cut off.

    The previous look of truncate.

    In the current version, large DataFrames are centrally truncated, showing a preview of head and tail in both dimensions.

    The new look.
  • allow option 'truncate' for display.show_dimensions to only show the dimensions if the frame is truncated (GH6547).

    The default for display.show_dimensions will now be truncate. This is consistent with how Series display length.

    In [19]: dfd = pd.DataFrame(np.arange(25).reshape(-1,5), index=[0,1,2,3,4], columns=[0,1,2,3,4])
    
    # show dimensions since this is truncated
    In [20]: with pd.option_context('display.max_rows', 2, 'display.max_columns', 2,
       ....:                        'display.show_dimensions', 'truncate'):
       ....:    print(dfd)
       ....: 
         0 ...   4
    0    0 ...   4
    ..  .. ...  ..
    4   20 ...  24
    
    [5 rows x 5 columns]
    
    # will not show dimensions since it is not truncated
    In [21]: with pd.option_context('display.max_rows', 10, 'display.max_columns', 40,
       ....:                        'display.show_dimensions', 'truncate'):
       ....:    print(dfd)
       ....: 
        0   1   2   3   4
    0   0   1   2   3   4
    1   5   6   7   8   9
    2  10  11  12  13  14
    3  15  16  17  18  19
    4  20  21  22  23  24
    
  • Regression in the display of a MultiIndexed Series with display.max_rows is less than the length of the series (GH7101)

  • Fixed a bug in the HTML repr of a truncated Series or DataFrame not showing the class name with the large_repr set to ‘info’ (GH7105)

  • The verbose keyword in DataFrame.info(), which controls whether to shorten the info representation, is now None by default. This will follow the global setting in display.max_info_columns. The global setting can be overriden with verbose=True or verbose=False.

  • Fixed a bug with the info repr not honoring the display.max_info_columns setting (GH6939)

  • Offset/freq info now in Timestamp __repr__ (GH4553)

Text Parsing API Changes

read_csv()/read_table() will now be noiser w.r.t invalid options rather than falling back to the PythonParser.

  • Raise ValueError when sep specified with delim_whitespace=True in read_csv()/read_table() (GH6607)
  • Raise ValueError when engine='c' specified with unsupported options in read_csv()/read_table() (GH6607)
  • Raise ValueError when fallback to python parser causes options to be ignored (GH6607)
  • Produce ParserWarning on fallback to python parser when no options are ignored (GH6607)
  • Translate sep='\s+' to delim_whitespace=True in read_csv()/read_table() if no other C-unsupported options specified (GH6607)

Groupby API Changes

More consistent behaviour for some groupby methods:

  • groupby head and tail now act more like filter rather than an aggregation:

    In [22]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
    
    In [23]: g = df.groupby('A')
    
    In [24]: g.head(1)  # filters DataFrame
    Out[24]: 
       A  B
    0  1  2
    2  5  6
    
    In [25]: g.apply(lambda x: x.head(1))  # used to simply fall-through
    Out[25]: 
         A  B
    A        
    1 0  1  2
    5 2  5  6
    
  • groupby head and tail respect column selection:

    In [26]: g[['B']].head(1)
    Out[26]: 
       B
    0  2
    2  6
    
  • groupby nth now reduces by default; filtering can be achieved by passing as_index=False. With an optional dropna argument to ignore NaN. See the docs.

    Reducing

    In [27]: df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
    
    In [28]: g = df.groupby('A')
    
    In [29]: g.nth(0)
    Out[29]: 
        B
    A    
    1 NaN
    5   6
    
    # this is equivalent to g.first()
    In [30]: g.nth(0, dropna='any')
    Out[30]: 
       B
    A   
    1  4
    5  6
    
    # this is equivalent to g.last()
    In [31]: g.nth(-1, dropna='any')
    Out[31]: 
       B
    A   
    1  4
    5  6
    

    Filtering

    In [32]: gf = df.groupby('A',as_index=False)
    
    In [33]: gf.nth(0)
    Out[33]: 
       A   B
    0  1 NaN
    2  5   6
    
    In [34]: gf.nth(0, dropna='any')
    Out[34]: 
       B
    A   
    1  4
    5  6
    
  • groupby will now not return the grouped column for non-cython functions (GH5610, GH5614, GH6732), as its already the index

    In [35]: df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
    
    In [36]: g = df.groupby('A')
    
    In [37]: g.count()
    Out[37]: 
       B
    A   
    1  1
    5  2
    
    In [38]: g.describe()
    Out[38]: 
                    B
    A                
    1 count  1.000000
      mean   4.000000
      std         NaN
      min    4.000000
      25%    4.000000
      50%    4.000000
      75%    4.000000
    ...           ...
    5 mean   7.000000
      std    1.414214
      min    6.000000
      25%    6.500000
      50%    7.000000
      75%    7.500000
      max    8.000000
    
    [16 rows x 1 columns]
    
  • passing as_index will leave the grouped column in-place (this is not change in 0.14.0)

    In [39]: df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
    
    In [40]: g = df.groupby('A',as_index=False)
    
    In [41]: g.count()
    Out[41]: 
       A  B
    0  1  1
    1  5  2
    
    In [42]: g.describe()
    Out[42]: 
             A         B
    0 count  2  1.000000
      mean   1  4.000000
      std    0       NaN
      min    1  4.000000
      25%    1  4.000000
      50%    1  4.000000
      75%    1  4.000000
    ...     ..       ...
    1 mean   5  7.000000
      std    0  1.414214
      min    5  6.000000
      25%    5  6.500000
      50%    5  7.000000
      75%    5  7.500000
      max    5  8.000000
    
    [16 rows x 2 columns]
    
  • Allow specification of a more complex groupby via pd.Grouper, such as grouping by a Time and a string field simultaneously. See the docs. (GH3794)

  • Better propagation/preservation of Series names when performing groupby operations:

    • SeriesGroupBy.agg will ensure that the name attribute of the original series is propagated to the result (GH6265).
    • If the function provided to GroupBy.apply returns a named series, the name of the series will be kept as the name of the column index of the DataFrame returned by GroupBy.apply (GH6124). This facilitates DataFrame.stack operations where the name of the column index is used as the name of the inserted column containing the pivoted data.

SQL

The SQL reading and writing functions now support more database flavors through SQLAlchemy (GH2717, GH4163, GH5950, GH6292). All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects).

The functionality of providing DBAPI connection objects will only be supported for sqlite3 in the future. The 'mysql' flavor is deprecated.

The new functions read_sql_query() and read_sql_table() are introduced. The function read_sql() is kept as a convenience wrapper around the other two and will delegate to specific function depending on the provided input (database table name or sql query).

In practice, you have to provide a SQLAlchemy engine to the sql functions. To connect with SQLAlchemy you use the create_engine() function to create an engine object from database URI. You only need to create the engine once per database you are connecting to. For an in-memory sqlite database:

In [43]: from sqlalchemy import create_engine

# Create your connection.
In [44]: engine = create_engine('sqlite:///:memory:')

This engine can then be used to write or read data to/from this database:

In [45]: df = pd.DataFrame({'A': [1,2,3], 'B': ['a', 'b', 'c']})

In [46]: df.to_sql('db_table', engine, index=False)

You can read data from a database by specifying the table name:

In [47]: pd.read_sql_table('db_table', engine)
Out[47]: 
   A  B
0  1  a
1  2  b
2  3  c

or by specifying a sql query:

In [48]: pd.read_sql_query('SELECT * FROM db_table', engine)
Out[48]: 
   A  B
0  1  a
1  2  b
2  3  c

Some other enhancements to the sql functions include:

  • support for writing the index. This can be controlled with the index keyword (default is True).
  • specify the column label to use when writing the index with index_label.
  • specify string columns to parse as datetimes withh the parse_dates keyword in read_sql_query() and read_sql_table().

Warning

Some of the existing functions or function aliases have been deprecated and will be removed in future versions. This includes: tquery, uquery, read_frame, frame_query, write_frame.

Warning

The support for the ‘mysql’ flavor when using DBAPI connection objects has been deprecated. MySQL will be further supported with SQLAlchemy engines (GH6900).

MultiIndexing Using Slicers

In 0.14.0 we added a new way to slice multi-indexed objects. You can slice a multi-index by providing multiple indexers.

You can provide any of the selectors as if you are indexing by label, see Selection by Label, including slices, lists of labels, labels, and boolean indexers.

You can use slice(None) to select all the contents of that level. You do not need to specify all the deeper levels, they will be implied as slice(None).

As usual, both sides of the slicers are included as this is label indexing.

See the docs See also issues (GH6134, GH4036, GH3057, GH2598, GH5641, GH7106)

Warning

You should specify all axes in the .loc specifier, meaning the indexer for the index and for the columns. Their are some ambiguous cases where the passed indexer could be mis-interpreted as indexing both axes, rather than into say the MuliIndex for the rows.

You should do this:

df.loc[(slice('A1','A3'),.....),:]

rather than this:

df.loc[(slice('A1','A3'),.....)]

Warning

You will need to make sure that the selection axes are fully lexsorted!

In [49]: def mklbl(prefix,n):
   ....:     return ["%s%s" % (prefix,i)  for i in range(n)]
   ....: 

In [50]: index = MultiIndex.from_product([mklbl('A',4),
   ....:                                  mklbl('B',2),
   ....:                                  mklbl('C',4),
   ....:                                  mklbl('D',2)])
   ....: 

In [51]: columns = MultiIndex.from_tuples([('a','foo'),('a','bar'),
   ....:                                   ('b','foo'),('b','bah')],
   ....:                                    names=['lvl0', 'lvl1'])
   ....: 

In [52]: df = DataFrame(np.arange(len(index)*len(columns)).reshape((len(index),len(columns))),
   ....:                index=index,
   ....:                columns=columns).sortlevel().sortlevel(axis=1)
   ....: 

In [53]: df
Out[53]: 
lvl0           a         b     
lvl1         bar  foo  bah  foo
A0 B0 C0 D0    1    0    3    2
         D1    5    4    7    6
      C1 D0    9    8   11   10
         D1   13   12   15   14
      C2 D0   17   16   19   18
         D1   21   20   23   22
      C3 D0   25   24   27   26
...          ...  ...  ...  ...
A3 B1 C0 D1  229  228  231  230
      C1 D0  233  232  235  234
         D1  237  236  239  238
      C2 D0  241  240  243  242
         D1  245  244  247  246
      C3 D0  249  248  251  250
         D1  253  252  255  254

[64 rows x 4 columns]

Basic multi-index slicing using slices, lists, and labels.

In [54]: df.loc[(slice('A1','A3'),slice(None), ['C1','C3']),:]
Out[54]: 
lvl0           a         b     
lvl1         bar  foo  bah  foo
A1 B0 C1 D0   73   72   75   74
         D1   77   76   79   78
      C3 D0   89   88   91   90
         D1   93   92   95   94
   B1 C1 D0  105  104  107  106
         D1  109  108  111  110
      C3 D0  121  120  123  122
...          ...  ...  ...  ...
A3 B0 C1 D1  205  204  207  206
      C3 D0  217  216  219  218
         D1  221  220  223  222
   B1 C1 D0  233  232  235  234
         D1  237  236  239  238
      C3 D0  249  248  251  250
         D1  253  252  255  254

[24 rows x 4 columns]

You can use a pd.IndexSlice to shortcut the creation of these slices

In [55]: idx = pd.IndexSlice

In [56]: df.loc[idx[:,:,['C1','C3']],idx[:,'foo']]
Out[56]: 
lvl0           a    b
lvl1         foo  foo
A0 B0 C1 D0    8   10
         D1   12   14
      C3 D0   24   26
         D1   28   30
   B1 C1 D0   40   42
         D1   44   46
      C3 D0   56   58
...          ...  ...
A3 B0 C1 D1  204  206
      C3 D0  216  218
         D1  220  222
   B1 C1 D0  232  234
         D1  236  238
      C3 D0  248  250
         D1  252  254

[32 rows x 2 columns]

It is possible to perform quite complicated selections using this method on multiple axes at the same time.

In [57]: df.loc['A1',(slice(None),'foo')]
Out[57]: 
lvl0        a    b
lvl1      foo  foo
B0 C0 D0   64   66
      D1   68   70
   C1 D0   72   74
      D1   76   78
   C2 D0   80   82
      D1   84   86
   C3 D0   88   90
...       ...  ...
B1 C0 D1  100  102
   C1 D0  104  106
      D1  108  110
   C2 D0  112  114
      D1  116  118
   C3 D0  120  122
      D1  124  126

[16 rows x 2 columns]

In [58]: df.loc[idx[:,:,['C1','C3']],idx[:,'foo']]
Out[58]: 
lvl0           a    b
lvl1         foo  foo
A0 B0 C1 D0    8   10
         D1   12   14
      C3 D0   24   26
         D1   28   30
   B1 C1 D0   40   42
         D1   44   46
      C3 D0   56   58
...          ...  ...
A3 B0 C1 D1  204  206
      C3 D0  216  218
         D1  220  222
   B1 C1 D0  232  234
         D1  236  238
      C3 D0  248  250
         D1  252  254

[32 rows x 2 columns]

Using a boolean indexer you can provide selection related to the values.

In [59]: mask = df[('a','foo')]>200

In [60]: df.loc[idx[mask,:,['C1','C3']],idx[:,'foo']]
Out[60]: 
lvl0           a    b
lvl1         foo  foo
A3 B0 C1 D1  204  206
      C3 D0  216  218
         D1  220  222
   B1 C1 D0  232  234
         D1  236  238
      C3 D0  248  250
         D1  252  254

You can also specify the axis argument to .loc to interpret the passed slicers on a single axis.

In [61]: df.loc(axis=0)[:,:,['C1','C3']]
Out[61]: 
lvl0           a         b     
lvl1         bar  foo  bah  foo
A0 B0 C1 D0    9    8   11   10
         D1   13   12   15   14
      C3 D0   25   24   27   26
         D1   29   28   31   30
   B1 C1 D0   41   40   43   42
         D1   45   44   47   46
      C3 D0   57   56   59   58
...          ...  ...  ...  ...
A3 B0 C1 D1  205  204  207  206
      C3 D0  217  216  219  218
         D1  221  220  223  222
   B1 C1 D0  233  232  235  234
         D1  237  236  239  238
      C3 D0  249  248  251  250
         D1  253  252  255  254

[32 rows x 4 columns]

Furthermore you can set the values using these methods

In [62]: df2 = df.copy()

In [63]: df2.loc(axis=0)[:,:,['C1','C3']] = -10

In [64]: df2
Out[64]: 
lvl0           a         b     
lvl1         bar  foo  bah  foo
A0 B0 C0 D0    1    0    3    2
         D1    5    4    7    6
      C1 D0  -10  -10  -10  -10
         D1  -10  -10  -10  -10
      C2 D0   17   16   19   18
         D1   21   20   23   22
      C3 D0  -10  -10  -10  -10
...          ...  ...  ...  ...
A3 B1 C0 D1  229  228  231  230
      C1 D0  -10  -10  -10  -10
         D1  -10  -10  -10  -10
      C2 D0  241  240  243  242
         D1  245  244  247  246
      C3 D0  -10  -10  -10  -10
         D1  -10  -10  -10  -10

[64 rows x 4 columns]

You can use a right-hand-side of an alignable object as well.

In [65]: df2 = df.copy()

In [66]: df2.loc[idx[:,:,['C1','C3']],:] = df2*1000

In [67]: df2
Out[67]: 
lvl0              a               b        
lvl1            bar     foo     bah     foo
A0 B0 C0 D0       1       0       3       2
         D1       5       4       7       6
      C1 D0    9000    8000   11000   10000
         D1   13000   12000   15000   14000
      C2 D0      17      16      19      18
         D1      21      20      23      22
      C3 D0   25000   24000   27000   26000
...             ...     ...     ...     ...
A3 B1 C0 D1     229     228     231     230
      C1 D0  233000  232000  235000  234000
         D1  237000  236000  239000  238000
      C2 D0     241     240     243     242
         D1     245     244     247     246
      C3 D0  249000  248000  251000  250000
         D1  253000  252000  255000  254000

[64 rows x 4 columns]

Plotting

  • Hexagonal bin plots from DataFrame.plot with kind='hexbin' (GH5478), See the docs.

  • DataFrame.plot and Series.plot now supports area plot with specifying kind='area' (GH6656), See the docs

  • Pie plots from Series.plot and DataFrame.plot with kind='pie' (GH6976), See the docs.

  • Plotting with Error Bars is now supported in the .plot method of DataFrame and Series objects (GH3796, GH6834), See the docs.

  • DataFrame.plot and Series.plot now support a table keyword for plotting matplotlib.Table, See the docs. The table keyword can receive the following values.

    • False: Do nothing (default).
    • True: Draw a table using the DataFrame or Series called plot method. Data will be transposed to meet matplotlib’s default layout.
    • DataFrame or Series: Draw matplotlib.table using the passed data. The data will be drawn as displayed in print method (not transposed automatically). Also, helper function pandas.tools.plotting.table is added to create a table from DataFrame and Series, and add it to an matplotlib.Axes.
  • plot(legend='reverse') will now reverse the order of legend labels for most plot kinds. (GH6014)

  • Line plot and area plot can be stacked by stacked=True (GH6656)

  • Following keywords are now acceptable for DataFrame.plot() with kind='bar' and kind='barh':

    • width: Specify the bar width. In previous versions, static value 0.5 was passed to matplotlib and it cannot be overwritten. (GH6604)
    • align: Specify the bar alignment. Default is center (different from matplotlib). In previous versions, pandas passes align=’edge’ to matplotlib and adjust the location to center by itself, and it results align keyword is not applied as expected. (GH4525)
    • position: Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1(right/top-end). Default is 0.5 (center). (GH6604)

    Because of the default align value changes, coordinates of bar plots are now located on integer values (0.0, 1.0, 2.0 ...). This is intended to make bar plot be located on the same coodinates as line plot. However, bar plot may differs unexpectedly when you manually adjust the bar location or drawing area, such as using set_xlim, set_ylim, etc. In this cases, please modify your script to meet with new coordinates.

  • The parallel_coordinates() function now takes argument color instead of colors. A FutureWarning is raised to alert that the old colors argument will not be supported in a future release. (GH6956)

  • The parallel_coordinates() and andrews_curves() functions now take positional argument frame instead of data. A FutureWarning is raised if the old data argument is used by name. (GH6956)

  • DataFrame.boxplot() now supports layout keyword (GH6769)

  • DataFrame.boxplot() has a new keyword argument, return_type. It accepts 'dict', 'axes', or 'both', in which case a namedtuple with the matplotlib axes and a dict of matplotlib Lines is returned.

Prior Version Deprecations/Changes

There are prior version deprecations that are taking effect as of 0.14.0.

Deprecations

  • The pivot_table()/DataFrame.pivot_table() and crosstab() functions now take arguments index and columns instead of rows and cols. A FutureWarning is raised to alert that the old rows and cols arguments will not be supported in a future release (GH5505)

  • The DataFrame.drop_duplicates() and DataFrame.duplicated() methods now take argument subset instead of cols to better align with DataFrame.dropna(). A FutureWarning is raised to alert that the old cols arguments will not be supported in a future release (GH6680)

  • The DataFrame.to_csv() and DataFrame.to_excel() functions now takes argument columns instead of cols. A FutureWarning is raised to alert that the old cols arguments will not be supported in a future release (GH6645)

  • Indexers will warn FutureWarning when used with a scalar indexer and a non-floating point Index (GH4892, GH6960)

    # non-floating point indexes can only be indexed by integers / labels
    In [1]: Series(1,np.arange(5))[3.0]
            pandas/core/index.py:469: FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point
    Out[1]: 1
    
    In [2]: Series(1,np.arange(5)).iloc[3.0]
            pandas/core/index.py:469: FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point
    Out[2]: 1
    
    In [3]: Series(1,np.arange(5)).iloc[3.0:4]
            pandas/core/index.py:527: FutureWarning: slice indexers when using iloc should be integers and not floating point
    Out[3]:
            3    1
            dtype: int64
    
    # these are Float64Indexes, so integer or floating point is acceptable
    In [4]: Series(1,np.arange(5.))[3]
    Out[4]: 1
    
    In [5]: Series(1,np.arange(5.))[3.0]
    Out[6]: 1
    
  • Numpy 1.9 compat w.r.t. deprecation warnings (GH6960)

  • Panel.shift() now has a function signature that matches DataFrame.shift(). The old positional argument lags has been changed to a keyword argument periods with a default value of 1. A FutureWarning is raised if the old argument lags is used by name. (GH6910)

  • The order keyword argument of factorize() will be removed. (GH6926).

  • Remove the copy keyword from DataFrame.xs(), Panel.major_xs(), Panel.minor_xs(). A view will be returned if possible, otherwise a copy will be made. Previously the user could think that copy=False would ALWAYS return a view. (GH6894)

  • The parallel_coordinates() function now takes argument color instead of colors. A FutureWarning is raised to alert that the old colors argument will not be supported in a future release. (GH6956)

  • The parallel_coordinates() and andrews_curves() functions now take positional argument frame instead of data. A FutureWarning is raised if the old data argument is used by name. (GH6956)

  • The support for the ‘mysql’ flavor when using DBAPI connection objects has been deprecated. MySQL will be further supported with SQLAlchemy engines (GH6900).

  • The following io.sql functions have been deprecated: tquery, uquery, read_frame, frame_query, write_frame.

  • The percentile_width keyword argument in describe() has been deprecated. Use the percentiles keyword instead, which takes a list of percentiles to display. The default output is unchanged.

  • The default return type of boxplot() will change from a dict to a matpltolib Axes in a future release. You can use the future behavior now by passing return_type='axes' to boxplot.

Known Issues

  • OpenPyXL 2.0.0 breaks backwards compatibility (GH7169)

Enhancements

  • DataFrame and Series will create a MultiIndex object if passed a tuples dict, See the docs (GH3323)

    In [68]: Series({('a', 'b'): 1, ('a', 'a'): 0,
       ....:         ('a', 'c'): 2, ('b', 'a'): 3, ('b', 'b'): 4})
       ....: 
    Out[68]: 
    a  a    0
       b    1
       c    2
    b  a    3
       b    4
    dtype: int64
    
    In [69]: DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
       ....:            ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},
       ....:            ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},
       ....:            ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},
       ....:            ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
       ....: 
    Out[69]: 
          a           b    
          a   b   c   a   b
    A B   4   1   5   8  10
      C   3   2   6   7 NaN
      D NaN NaN NaN NaN   9
    
  • Added the sym_diff method to Index (GH5543)

  • DataFrame.to_latex now takes a longtable keyword, which if True will return a table in a longtable environment. (GH6617)

  • Add option to turn off escaping in DataFrame.to_latex (GH6472)

  • pd.read_clipboard will, if the keyword sep is unspecified, try to detect data copied from a spreadsheet and parse accordingly. (GH6223)

  • Joining a singly-indexed DataFrame with a multi-indexed DataFrame (GH3662)

    See the docs. Joining multi-index DataFrames on both the left and right is not yet supported ATM.

    In [70]: household = DataFrame(dict(household_id = [1,2,3],
       ....:                            male = [0,1,0],
       ....:                            wealth = [196087.3,316478.7,294750]),
       ....:                       columns = ['household_id','male','wealth']
       ....:                      ).set_index('household_id')
       ....: 
    
    In [71]: household
    Out[71]: 
                  male    wealth
    household_id                
    1                0  196087.3
    2                1  316478.7
    3                0  294750.0
    
    In [72]: portfolio = DataFrame(dict(household_id = [1,2,2,3,3,3,4],
       ....:                            asset_id = ["nl0000301109","nl0000289783","gb00b03mlx29",
       ....:                                        "gb00b03mlx29","lu0197800237","nl0000289965",np.nan],
       ....:                            name = ["ABN Amro","Robeco","Royal Dutch Shell","Royal Dutch Shell",
       ....:                                    "AAB Eastern Europe Equity Fund","Postbank BioTech Fonds",np.nan],
       ....:                            share = [1.0,0.4,0.6,0.15,0.6,0.25,1.0]),
       ....:                       columns = ['household_id','asset_id','name','share']
       ....:                      ).set_index(['household_id','asset_id'])
       ....: 
    
    In [73]: portfolio
    Out[73]: 
                                                         name  share
    household_id asset_id                                           
    1            nl0000301109                        ABN Amro   1.00
    2            nl0000289783                          Robeco   0.40
                 gb00b03mlx29               Royal Dutch Shell   0.60
    3            gb00b03mlx29               Royal Dutch Shell   0.15
                 lu0197800237  AAB Eastern Europe Equity Fund   0.60
                 nl0000289965          Postbank BioTech Fonds   0.25
    4            NaN                                      NaN   1.00
    
    In [74]: household.join(portfolio, how='inner')
    Out[74]: 
                               male    wealth                            name  \
    household_id asset_id                                                       
    1            nl0000301109     0  196087.3                        ABN Amro   
    2            nl0000289783     1  316478.7                          Robeco   
                 gb00b03mlx29     1  316478.7               Royal Dutch Shell   
    3            gb00b03mlx29     0  294750.0               Royal Dutch Shell   
                 lu0197800237     0  294750.0  AAB Eastern Europe Equity Fund   
                 nl0000289965     0  294750.0          Postbank BioTech Fonds   
    
                               share  
    household_id asset_id             
    1            nl0000301109   1.00  
    2            nl0000289783   0.40  
                 gb00b03mlx29   0.60  
    3            gb00b03mlx29   0.15  
                 lu0197800237   0.60  
                 nl0000289965   0.25  
    
  • quotechar, doublequote, and escapechar can now be specified when using DataFrame.to_csv (GH5414, GH4528)

  • Partially sort by only the specified levels of a MultiIndex with the sort_remaining boolean kwarg. (GH3984)

  • Added to_julian_date to TimeStamp and DatetimeIndex. The Julian Date is used primarily in astronomy and represents the number of days from noon, January 1, 4713 BC. Because nanoseconds are used to define the time in pandas the actual range of dates that you can use is 1678 AD to 2262 AD. (GH4041)

  • DataFrame.to_stata will now check data for compatibility with Stata data types and will upcast when needed. When it is not possible to losslessly upcast, a warning is issued (GH6327)

  • DataFrame.to_stata and StataWriter will accept keyword arguments time_stamp and data_label which allow the time stamp and dataset label to be set when creating a file. (GH6545)

  • pandas.io.gbq now handles reading unicode strings properly. (GH5940)

  • Holidays Calendars are now available and can be used with the CustomBusinessDay offset (GH6719)

  • Float64Index is now backed by a float64 dtype ndarray instead of an object dtype array (GH6471).

  • Implemented Panel.pct_change (GH6904)

  • Added how option to rolling-moment functions to dictate how to handle resampling; rolling_max() defaults to max, rolling_min() defaults to min, and all others default to mean (GH6297)

  • CustomBuisnessMonthBegin and CustomBusinessMonthEnd are now available (GH6866)

  • Series.quantile() and DataFrame.quantile() now accept an array of quantiles.

  • describe() now accepts an array of percentiles to include in the summary statistics (GH4196)

  • pivot_table can now accept Grouper by index and columns keywords (GH6913)

    In [75]: import datetime
    
    In [76]: df = DataFrame({
       ....:   'Branch' : 'A A A A A B'.split(),
       ....:   'Buyer': 'Carl Mark Carl Carl Joe Joe'.split(),
       ....:   'Quantity': [1, 3, 5, 1, 8, 1],
       ....:   'Date' : [datetime.datetime(2013,11,1,13,0), datetime.datetime(2013,9,1,13,5),
       ....:             datetime.datetime(2013,10,1,20,0), datetime.datetime(2013,10,2,10,0),
       ....:             datetime.datetime(2013,11,1,20,0), datetime.datetime(2013,10,2,10,0)],
       ....:   'PayDay' : [datetime.datetime(2013,10,4,0,0), datetime.datetime(2013,10,15,13,5),
       ....:               datetime.datetime(2013,9,5,20,0), datetime.datetime(2013,11,2,10,0),
       ....:               datetime.datetime(2013,10,7,20,0), datetime.datetime(2013,9,5,10,0)]})
       ....: 
    
    In [77]: df
    Out[77]: 
      Branch Buyer                Date              PayDay  Quantity
    0      A  Carl 2013-11-01 13:00:00 2013-10-04 00:00:00         1
    1      A  Mark 2013-09-01 13:05:00 2013-10-15 13:05:00         3
    2      A  Carl 2013-10-01 20:00:00 2013-09-05 20:00:00         5
    3      A  Carl 2013-10-02 10:00:00 2013-11-02 10:00:00         1
    4      A   Joe 2013-11-01 20:00:00 2013-10-07 20:00:00         8
    5      B   Joe 2013-10-02 10:00:00 2013-09-05 10:00:00         1
    
    In [78]: pivot_table(df, index=Grouper(freq='M', key='Date'),
       ....:             columns=Grouper(freq='M', key='PayDay'),
       ....:             values='Quantity', aggfunc=np.sum)
       ....: 
    Out[78]: 
    PayDay      2013-09-30  2013-10-31  2013-11-30
    Date                                          
    2013-09-30         NaN           3         NaN
    2013-10-31           6         NaN           1
    2013-11-30         NaN           9         NaN
    
  • Arrays of strings can be wrapped to a specified width (str.wrap) (GH6999)

  • Add nsmallest() and Series.nlargest() methods to Series, See the docs (GH3960)

  • PeriodIndex fully supports partial string indexing like DatetimeIndex (GH7043)

    In [79]: prng = period_range('2013-01-01 09:00', periods=100, freq='H')
    
    In [80]: ps = Series(np.random.randn(len(prng)), index=prng)
    
    In [81]: ps
    Out[81]: 
    2013-01-01 09:00    0.755414
    2013-01-01 10:00    0.215269
    2013-01-01 11:00    0.841009
    2013-01-01 12:00   -1.445810
    2013-01-01 13:00   -1.401973
    ...
    2013-01-05 07:00    0.702562
    2013-01-05 08:00   -0.850346
    2013-01-05 09:00    1.176812
    2013-01-05 10:00   -0.524336
    2013-01-05 11:00    0.700908
    2013-01-05 12:00    0.984188
    Freq: H, Length: 100
    
    In [82]: ps['2013-01-02']
    Out[82]: 
    2013-01-02 00:00   -0.208499
    2013-01-02 01:00    1.033801
    2013-01-02 02:00   -2.400454
    2013-01-02 03:00    2.030604
    2013-01-02 04:00   -1.142631
    ...
    2013-01-02 18:00   -3.563517
    2013-01-02 19:00    1.321106
    2013-01-02 20:00    0.152631
    2013-01-02 21:00    0.164530
    2013-01-02 22:00   -0.430096
    2013-01-02 23:00    0.767369
    Freq: H, Length: 24
    
  • read_excel can now read milliseconds in Excel dates and times with xlrd >= 0.9.3. (GH5945)

  • pd.stats.moments.rolling_var now uses Welford’s method for increased numerical stability (GH6817)

  • pd.expanding_apply and pd.rolling_apply now take args and kwargs that are passed on to the func (GH6289)

  • DataFrame.rank() now has a percentage rank option (GH5971)

  • Series.rank() now has a percentage rank option (GH5971)

  • Series.rank() and DataFrame.rank() now accept method='dense' for ranks without gaps (GH6514)

  • Support passing encoding with xlwt (GH3710)

  • Refactor Block classes removing Block.items attributes to avoid duplication in item handling (GH6745, GH6988).

  • Testing statements updated to use specialized asserts (GH6175)

Performance

  • Performance improvement when converting DatetimeIndex to floating ordinals using DatetimeConverter (GH6636)
  • Performance improvement for DataFrame.shift (GH5609)
  • Performance improvement in indexing into a multi-indexed Series (GH5567)
  • Performance improvements in single-dtyped indexing (GH6484)
  • Improve performance of DataFrame construction with certain offsets, by removing faulty caching (e.g. MonthEnd,BusinessMonthEnd), (GH6479)
  • Improve performance of CustomBusinessDay (GH6584)
  • improve performance of slice indexing on Series with string keys (GH6341, GH6372)
  • Performance improvement for DataFrame.from_records when reading a specified number of rows from an iterable (GH6700)
  • Performance improvements in timedelta conversions for integer dtypes (GH6754)
  • Improved performance of compatible pickles (GH6899)
  • Improve performance in certain reindexing operations by optimizing take_2d (GH6749)
  • GroupBy.count() is now implemented in Cython and is much faster for large numbers of groups (GH7016).

Experimental

There are no experimental changes in 0.14.0

Bug Fixes

  • Bug in Series ValueError when index doesn’t match data (GH6532)
  • Prevent segfault due to MultiIndex not being supported in HDFStore table format (GH1848)
  • Bug in pd.DataFrame.sort_index where mergesort wasn’t stable when ascending=False (GH6399)
  • Bug in pd.tseries.frequencies.to_offset when argument has leading zeroes (GH6391)
  • Bug in version string gen. for dev versions with shallow clones / install from tarball (GH6127)
  • Inconsistent tz parsing Timestamp / to_datetime for current year (GH5958)
  • Indexing bugs with reordered indexes (GH6252, GH6254)
  • Bug in .xs with a Series multiindex (GH6258, GH5684)
  • Bug in conversion of a string types to a DatetimeIndex with a specified frequency (GH6273, GH6274)
  • Bug in eval where type-promotion failed for large expressions (GH6205)
  • Bug in interpolate with inplace=True (GH6281)
  • HDFStore.remove now handles start and stop (GH6177)
  • HDFStore.select_as_multiple handles start and stop the same way as select (GH6177)
  • HDFStore.select_as_coordinates and select_column works with a where clause that results in filters (GH6177)
  • Regression in join of non_unique_indexes (GH6329)
  • Issue with groupby agg with a single function and a a mixed-type frame (GH6337)
  • Bug in DataFrame.replace() when passing a non- bool to_replace argument (GH6332)
  • Raise when trying to align on different levels of a multi-index assignment (GH3738)
  • Bug in setting complex dtypes via boolean indexing (GH6345)
  • Bug in TimeGrouper/resample when presented with a non-monotonic DatetimeIndex that would return invalid results. (GH4161)
  • Bug in index name propogation in TimeGrouper/resample (GH4161)
  • TimeGrouper has a more compatible API to the rest of the groupers (e.g. groups was missing) (GH3881)
  • Bug in multiple grouping with a TimeGrouper depending on target column order (GH6764)
  • Bug in pd.eval when parsing strings with possible tokens like '&' (GH6351)
  • Bug correctly handle placements of -inf in Panels when dividing by integer 0 (GH6178)
  • DataFrame.shift with axis=1 was raising (GH6371)
  • Disabled clipboard tests until release time (run locally with nosetests -A disabled) (GH6048).
  • Bug in DataFrame.replace() when passing a nested dict that contained keys not in the values to be replaced (GH6342)
  • str.match ignored the na flag (GH6609).
  • Bug in take with duplicate columns that were not consolidated (GH6240)
  • Bug in interpolate changing dtypes (GH6290)
  • Bug in Series.get, was using a buggy access method (GH6383)
  • Bug in hdfstore queries of the form where=[('date', '>=', datetime(2013,1,1)), ('date', '<=', datetime(2014,1,1))] (GH6313)
  • Bug in DataFrame.dropna with duplicate indices (GH6355)
  • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394)
  • Float64Index with nans not comparing correctly (GH6401)
  • eval/query expressions with strings containing the @ character will now work (GH6366).
  • Bug in Series.reindex when specifying a method with some nan values was inconsistent (noted on a resample) (GH6418)
  • Bug in DataFrame.replace() where nested dicts were erroneously depending on the order of dictionary keys and values (GH5338).
  • Perf issue in concatting with empty objects (GH3259)
  • Clarify sorting of sym_diff on Index objects with NaN values (GH6444)
  • Regression in MultiIndex.from_product with a DatetimeIndex as input (GH6439)
  • Bug in str.extract when passed a non-default index (GH6348)
  • Bug in str.split when passed pat=None and n=1 (GH6466)
  • Bug in io.data.DataReader when passed "F-F_Momentum_Factor" and data_source="famafrench" (GH6460)
  • Bug in sum of a timedelta64[ns] series (GH6462)
  • Bug in resample with a timezone and certain offsets (GH6397)
  • Bug in iat/iloc with duplicate indices on a Series (GH6493)
  • Bug in read_html where nan’s were incorrectly being used to indicate missing values in text. Should use the empty string for consistency with the rest of pandas (GH5129).
  • Bug in read_html tests where redirected invalid URLs would make one test fail (GH6445).
  • Bug in multi-axis indexing using .loc on non-unique indices (GH6504)
  • Bug that caused _ref_locs corruption when slice indexing across columns axis of a DataFrame (GH6525)
  • Regression from 0.13 in the treatment of numpy datetime64 non-ns dtypes in Series creation (GH6529)
  • .names attribute of MultiIndexes passed to set_index are now preserved (GH6459).
  • Bug in setitem with a duplicate index and an alignable rhs (GH6541)
  • Bug in setitem with .loc on mixed integer Indexes (GH6546)
  • Bug in pd.read_stata which would use the wrong data types and missing values (GH6327)
  • Bug in DataFrame.to_stata that lead to data loss in certain cases, and could be exported using the wrong data types and missing values (GH6335)
  • StataWriter replaces missing values in string columns by empty string (GH6802)
  • Inconsistent types in Timestamp addition/subtraction (GH6543)
  • Bug in preserving frequency across Timestamp addition/subtraction (GH4547)
  • Bug in empty list lookup caused IndexError exceptions (GH6536, GH6551)
  • Series.quantile raising on an object dtype (GH6555)
  • Bug in .xs with a nan in level when dropped (GH6574)
  • Bug in fillna with method='bfill/ffill' and datetime64[ns] dtype (GH6587)
  • Bug in sql writing with mixed dtypes possibly leading to data loss (GH6509)
  • Bug in Series.pop (GH6600)
  • Bug in iloc indexing when positional indexer matched Int64Index of the corresponding axis and no reordering happened (GH6612)
  • Bug in fillna with limit and value specified
  • Bug in DataFrame.to_stata when columns have non-string names (GH4558)
  • Bug in compat with np.compress, surfaced in (GH6658)
  • Bug in binary operations with a rhs of a Series not aligning (GH6681)
  • Bug in DataFrame.to_stata which incorrectly handles nan values and ignores with_index keyword argument (GH6685)
  • Bug in resample with extra bins when using an evenly divisible frequency (GH4076)
  • Bug in consistency of groupby aggregation when passing a custom function (GH6715)
  • Bug in resample when how=None resample freq is the same as the axis frequency (GH5955)
  • Bug in downcasting inference with empty arrays (GH6733)
  • Bug in obj.blocks on sparse containers dropping all but the last items of same for dtype (GH6748)
  • Bug in unpickling NaT (NaTType) (GH4606)
  • Bug in DataFrame.replace() where regex metacharacters were being treated as regexs even when regex=False (GH6777).
  • Bug in timedelta ops on 32-bit platforms (GH6808)
  • Bug in setting a tz-aware index directly via .index (GH6785)
  • Bug in expressions.py where numexpr would try to evaluate arithmetic ops (GH6762).
  • Bug in Makefile where it didn’t remove Cython generated C files with make clean (GH6768)
  • Bug with numpy < 1.7.2 when reading long strings from HDFStore (GH6166)
  • Bug in DataFrame._reduce where non bool-like (0/1) integers were being coverted into bools. (GH6806)
  • Regression from 0.13 with fillna and a Series on datetime-like (GH6344)
  • Bug in adding np.timedelta64 to DatetimeIndex with timezone outputs incorrect results (GH6818)
  • Bug in DataFrame.replace() where changing a dtype through replacement would only replace the first occurrence of a value (GH6689)
  • Better error message when passing a frequency of ‘MS’ in Period construction (GH5332)
  • Bug in Series.__unicode__ when max_rows=None and the Series has more than 1000 rows. (GH6863)
  • Bug in groupby.get_group where a datetlike wasn’t always accepted (GH5267)
  • Bug in groupBy.get_group created by TimeGrouper raises AttributeError (GH6914)
  • Bug in DatetimeIndex.tz_localize and DatetimeIndex.tz_convert converting NaT incorrectly (GH5546)
  • Bug in arithmetic operations affecting NaT (GH6873)
  • Bug in Series.str.extract where the resulting Series from a single group match wasn’t renamed to the group name
  • Bug in DataFrame.to_csv where setting index=False ignored the header kwarg (GH6186)
  • Bug in DataFrame.plot and Series.plot, where the legend behave inconsistently when plotting to the same axes repeatedly (GH6678)
  • Internal tests for patching __finalize__ / bug in merge not finalizing (GH6923, GH6927)
  • accept TextFileReader in concat, which was affecting a common user idiom (GH6583)
  • Bug in C parser with leading whitespace (GH3374)
  • Bug in C parser with delim_whitespace=True and \r-delimited lines
  • Bug in python parser with explicit multi-index in row following column header (GH6893)
  • Bug in Series.rank and DataFrame.rank that caused small floats (<1e-13) to all receive the same rank (GH6886)
  • Bug in DataFrame.apply with functions that used *args`` or **kwargs and returned an empty result (GH6952)
  • Bug in sum/mean on 32-bit platforms on overflows (GH6915)
  • Moved Panel.shift to NDFrame.slice_shift and fixed to respect multiple dtypes. (GH6959)
  • Bug in enabling subplots=True in DataFrame.plot only has single column raises TypeError, and Series.plot raises AttributeError (GH6951)
  • Bug in DataFrame.plot draws unnecessary axes when enabling subplots and kind=scatter (GH6951)
  • Bug in read_csv from a filesystem with non-utf-8 encoding (GH6807)
  • Bug in iloc when setting / aligning (GH6766)
  • Bug causing UnicodeEncodeError when get_dummies called with unicode values and a prefix (GH6885)
  • Bug in timeseries-with-frequency plot cursor display (GH5453)
  • Bug surfaced in groupby.plot when using a Float64Index (GH7025)
  • Stopped tests from failing if options data isn’t able to be downloaded from Yahoo (GH7034)
  • Bug in parallel_coordinates and radviz where reordering of class column caused possible color/class mismatch (GH6956)
  • Bug in radviz and andrews_curves where multiple values of ‘color’ were being passed to plotting method (GH6956)
  • Bug in Float64Index.isin() where containing nan s would make indices claim that they contained all the things (GH7066).
  • Bug in DataFrame.boxplot where it failed to use the axis passed as the ax argument (GH3578)
  • Bug in the XlsxWriter and XlwtWriter implementations that resulted in datetime columns being formatted without the time (GH7075) were being passed to plotting method
  • read_fwf() treats None in colspec like regular python slices. It now reads from the beginning or until the end of the line when colspec contains a None (previously raised a TypeError)
  • Bug in cache coherence with chained indexing and slicing; add _is_view property to NDFrame to correctly predict views; mark is_copy on xs only if its an actual copy (and not a view) (GH7084)
  • Bug in DatetimeIndex creation from string ndarray with dayfirst=True (GH5917)
  • Bug in MultiIndex.from_arrays created from DatetimeIndex doesn’t preserve freq and tz (GH7090)
  • Bug in unstack raises ValueError when MultiIndex contains PeriodIndex (GH4342)
  • Bug in boxplot and hist draws unnecessary axes (GH6769)
  • Regression in groupby.nth() for out-of-bounds indexers (GH6621)
  • Bug in quantile with datetime values (GH6965)
  • Bug in Dataframe.set_index, reindex and pivot don’t preserve DatetimeIndex and PeriodIndex attributes (GH3950, GH5878, GH6631)
  • Bug in MultiIndex.get_level_values doesn’t preserve DatetimeIndex and PeriodIndex attributes (GH7092)
  • Bug in Groupby doesn’t preserve tz (GH3950)
  • Bug in PeriodIndex partial string slicing (GH6716)
  • Bug in the HTML repr of a truncated Series or DataFrame not showing the class name with the large_repr set to ‘info’ (GH7105)
  • Bug in DatetimeIndex specifying freq raises ValueError when passed value is too short (GH7098)
  • Fixed a bug with the info repr not honoring the display.max_info_columns setting (GH6939)
  • Bug PeriodIndex string slicing with out of bounds values (GH5407)
  • Fixed a memory error in the hashtable implementation/factorizer on resizing of large tables (GH7157)
  • Bug in isnull when applied to 0-dimensional object arrays (GH7176)
  • Bug in query/eval where global constants were not looked up correctly (GH7178)
  • Bug in recognizing out-of-bounds positional list indexers with iloc and a multi-axis tuple indexer (GH7189)
  • Bug in setitem with a single value, multi-index and integer indices (GH7190, GH7218)
  • Bug in expressions evaluation with reversed ops, showing in series-dataframe ops (GH7198, GH7192)
  • Bug in multi-axis indexing with > 2 ndim and a multi-index (GH7199)
  • Fix a bug where invalid eval/query operations would blow the stack (GH5198)

v0.13.1 (February 3, 2014)

This is a minor release from 0.13.0 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Highlights include:

  • Added infer_datetime_format keyword to read_csv/to_datetime to allow speedups for homogeneously formatted datetimes.
  • Will intelligently limit display precision for datetime/timedelta formats.
  • Enhanced Panel apply() method.
  • Suggested tutorials in new Tutorials section.
  • Our pandas ecosystem is growing, We now feature related projects in a new Pandas Ecosystem section.
  • Much work has been taking place on improving the docs, and a new Contributing section has been added.
  • Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI.

Warning

0.13.1 fixes a bug that was caused by a combination of having numpy < 1.8, and doing chained assignment on a string-like array. Please review the docs, chained indexing can have unexpected results and should generally be avoided.

This would previously segfault:

In [1]: df = DataFrame(dict(A = np.array(['foo','bar','bah','foo','bar'])))

In [2]: df['A'].iloc[0] = np.nan

In [3]: df
Out[3]: 
     A
0  NaN
1  bar
2  bah
3  foo
4  bar

The recommended way to do this type of assignment is:

In [4]: df = DataFrame(dict(A = np.array(['foo','bar','bah','foo','bar'])))

In [5]: df.ix[0,'A'] = np.nan

In [6]: df
Out[6]: 
     A
0  NaN
1  bar
2  bah
3  foo
4  bar

Output Formatting Enhancements

  • df.info() view now display dtype info per column (GH5682)

  • df.info() now honors the option max_info_rows, to disable null counts for large frames (GH5974)

    In [7]: max_info_rows = pd.get_option('max_info_rows')
    
    In [8]: df = DataFrame(dict(A = np.random.randn(10),
       ...:                     B = np.random.randn(10),
       ...:                     C = date_range('20130101',periods=10)))
       ...: 
    
    In [9]: df.iloc[3:6,[0,2]] = np.nan
    
    # set to not display the null counts
    In [10]: pd.set_option('max_info_rows',0)
    
    In [11]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 10 entries, 0 to 9
    Data columns (total 3 columns):
    A    float64
    B    float64
    C    datetime64[ns]
    dtypes: datetime64[ns](1), float64(2)
    memory usage: 320.0 bytes
    
    # this is the default (same as in 0.13.0)
    In [12]: pd.set_option('max_info_rows',max_info_rows)
    
    In [13]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 10 entries, 0 to 9
    Data columns (total 3 columns):
    A    7 non-null float64
    B    10 non-null float64
    C    7 non-null datetime64[ns]
    dtypes: datetime64[ns](1), float64(2)
    memory usage: 320.0 bytes
    
  • Add show_dimensions display option for the new DataFrame repr to control whether the dimensions print.

    In [14]: df = DataFrame([[1, 2], [3, 4]])
    
    In [15]: pd.set_option('show_dimensions', False)
    
    In [16]: df
    Out[16]: 
       0  1
    0  1  2
    1  3  4
    
    In [17]: pd.set_option('show_dimensions', True)
    
    In [18]: df
    Out[18]: 
       0  1
    0  1  2
    1  3  4
    
    [2 rows x 2 columns]
    
  • The ArrayFormatter for datetime and timedelta64 now intelligently limit precision based on the values in the array (GH3401)

    Previously output might look like:

      age                 today               diff
    0 2001-01-01 00:00:00 2013-04-19 00:00:00 4491 days, 00:00:00
    1 2004-06-01 00:00:00 2013-04-19 00:00:00 3244 days, 00:00:00
    

    Now the output looks like:

    In [19]: df = DataFrame([ Timestamp('20010101'),
       ....:                  Timestamp('20040601') ], columns=['age'])
       ....: 
    
    In [20]: df['today'] = Timestamp('20130419')
    
    In [21]: df['diff'] = df['today']-df['age']
    
    In [22]: df
    Out[22]: 
             age      today      diff
    0 2001-01-01 2013-04-19 4491 days
    1 2004-06-01 2013-04-19 3244 days
    
    [2 rows x 3 columns]
    

API changes

  • Add -NaN and -nan to the default set of NA values (GH5952). See NA Values.

  • Added Series.str.get_dummies vectorized string method (GH6021), to extract dummy/indicator variables for separated string columns:

    In [23]: s = Series(['a', 'a|b', np.nan, 'a|c'])
    
    In [24]: s.str.get_dummies(sep='|')
    Out[24]: 
       a  b  c
    0  1  0  0
    1  1  1  0
    2  0  0  0
    3  1  0  1
    
    [4 rows x 3 columns]
    
  • Added the NDFrame.equals() method to compare if two NDFrames are equal have equal axes, dtypes, and values. Added the array_equivalent function to compare if two ndarrays are equal. NaNs in identical locations are treated as equal. (GH5283) See also the docs for a motivating example.

    In [25]: df = DataFrame({'col':['foo', 0, np.nan]})
    
    In [26]: df2 = DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0])
    
    In [27]: df.equals(df2)
    Out[27]: False
    
    In [28]: df.equals(df2.sort())
    Out[28]: True
    
    In [29]: import pandas.core.common as com
    
    In [30]: com.array_equivalent(np.array([0, np.nan]), np.array([0, np.nan]))
    Out[30]: True
    
    In [31]: np.array_equal(np.array([0, np.nan]), np.array([0, np.nan]))
    Out[31]: False
    
  • DataFrame.apply will use the reduce argument to determine whether a Series or a DataFrame should be returned when the DataFrame is empty (GH6007).

    Previously, calling DataFrame.apply an empty DataFrame would return either a DataFrame if there were no columns, or the function being applied would be called with an empty Series to guess whether a Series or DataFrame should be returned:

    In [32]: def applied_func(col):
       ....:    print("Apply function being called with: ", col)
       ....:    return col.sum()
       ....: 
    
    In [33]: empty = DataFrame(columns=['a', 'b'])
    
    In [34]: empty.apply(applied_func)
    ('Apply function being called with: ', Series([], dtype: float64))
    Out[34]: 
    a   NaN
    b   NaN
    dtype: float64
    

    Now, when apply is called on an empty DataFrame: if the reduce argument is True a Series will returned, if it is False a DataFrame will be returned, and if it is None (the default) the function being applied will be called with an empty series to try and guess the return type.

    In [35]: empty.apply(applied_func, reduce=True)
    Out[35]: 
    a   NaN
    b   NaN
    dtype: float64
    
    In [36]: empty.apply(applied_func, reduce=False)
    Out[36]: 
    Empty DataFrame
    Columns: [a, b]
    Index: []
    
    [0 rows x 2 columns]
    

Prior Version Deprecations/Changes

There are no announced changes in 0.13 or prior that are taking effect as of 0.13.1

Deprecations

There are no deprecations of prior behavior in 0.13.1

Enhancements

  • pd.read_csv and pd.to_datetime learned a new infer_datetime_format keyword which greatly improves parsing perf in many cases. Thanks to @lexual for suggesting and @danbirken for rapidly implementing. (GH5490, GH6021)

    If parse_dates is enabled and this flag is set, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x.

    # Try to infer the format for the index column
    df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
                     infer_datetime_format=True)
    
  • date_format and datetime_format keywords can now be specified when writing to excel files (GH4133)

  • MultiIndex.from_product convenience function for creating a MultiIndex from the cartesian product of a set of iterables (GH6055):

    In [37]: shades = ['light', 'dark']
    
    In [38]: colors = ['red', 'green', 'blue']
    
    In [39]: MultiIndex.from_product([shades, colors], names=['shade', 'color'])
    Out[39]: 
    MultiIndex(levels=[[u'dark', u'light'], [u'blue', u'green', u'red']],
               labels=[[1, 1, 1, 0, 0, 0], [2, 1, 0, 2, 1, 0]],
               names=[u'shade', u'color'])
    
  • Panel apply() will work on non-ufuncs. See the docs.

    In [40]: import pandas.util.testing as tm
    
    In [41]: panel = tm.makePanel(5)
    
    In [42]: panel
    Out[42]: 
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemC
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: A to D
    
    In [43]: panel['ItemA']
    Out[43]: 
                       A         B         C         D
    2000-01-03  0.952478 -1.239072 -1.409432 -0.014752
    2000-01-04  0.988138  0.139683  1.422986  1.272395
    2000-01-05 -0.072608 -0.223019 -2.147855 -1.449567
    2000-01-06 -0.550603  2.123692 -1.347533 -1.195524
    2000-01-07 -0.938153  0.122273  0.363565 -0.591863
    
    [5 rows x 4 columns]
    

    Specifying an apply that operates on a Series (to return a single element)

    In [44]: panel.apply(lambda x: x.dtype, axis='items')
    Out[44]: 
                      A        B        C        D
    2000-01-03  float64  float64  float64  float64
    2000-01-04  float64  float64  float64  float64
    2000-01-05  float64  float64  float64  float64
    2000-01-06  float64  float64  float64  float64
    2000-01-07  float64  float64  float64  float64
    
    [5 rows x 4 columns]
    

    A similar reduction type operation

    In [45]: panel.apply(lambda x: x.sum(), axis='major_axis')
    Out[45]: 
          ItemA     ItemB     ItemC
    A  0.379252 -3.696907  3.709335
    B  0.923558  0.504242  4.656781
    C -3.118269 -1.545718  3.188329
    D -1.979310 -0.758060 -1.436483
    
    [4 rows x 3 columns]
    

    This is equivalent to

    In [46]: panel.sum('major_axis')
    Out[46]: 
          ItemA     ItemB     ItemC
    A  0.379252 -3.696907  3.709335
    B  0.923558  0.504242  4.656781
    C -3.118269 -1.545718  3.188329
    D -1.979310 -0.758060 -1.436483
    
    [4 rows x 3 columns]
    

    A transformation operation that returns a Panel, but is computing the z-score across the major_axis

    In [47]: result = panel.apply(
       ....:            lambda x: (x-x.mean())/x.std(),
       ....:            axis='major_axis')
       ....: 
    
    In [48]: result
    Out[48]: 
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemC
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: A to D
    
    In [49]: result['ItemA']
    Out[49]: 
                       A         B         C         D
    2000-01-03  1.004994 -1.166509 -0.535027  0.350970
    2000-01-04  1.045875 -0.036892  1.393532  1.536326
    2000-01-05 -0.170198 -0.334055 -1.037810 -0.970374
    2000-01-06 -0.718186  1.588611 -0.492880 -0.736422
    2000-01-07 -1.162486 -0.051156  0.672185 -0.180500
    
    [5 rows x 4 columns]
    
  • Panel apply() operating on cross-sectional slabs. (GH1148)

    In [50]: f = lambda x: ((x.T-x.mean(1))/x.std(1)).T
    
    In [51]: result = panel.apply(f, axis = ['items','major_axis'])
    
    In [52]: result
    Out[52]: 
    <class 'pandas.core.panel.Panel'>
    Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
    Items axis: A to D
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: ItemA to ItemC
    
    In [53]: result.loc[:,:,'ItemA']
    Out[53]: 
                       A         B         C         D
    2000-01-03  0.116579 -0.667845 -1.151538 -0.157547
    2000-01-04  0.650448 -1.114910  0.841527  0.760706
    2000-01-05 -0.987433 -0.438897 -1.154468 -0.015033
    2000-01-06  0.494000  1.060450 -0.775993 -1.140165
    2000-01-07 -0.363770  0.013169  0.392036 -1.123913
    
    [5 rows x 4 columns]
    

    This is equivalent to the following

    In [54]: result = Panel(dict([ (ax,f(panel.loc[:,:,ax]))
       ....:                         for ax in panel.minor_axis ]))
       ....: 
    
    In [55]: result
    Out[55]: 
    <class 'pandas.core.panel.Panel'>
    Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
    Items axis: A to D
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: ItemA to ItemC
    
    In [56]: result.loc[:,:,'ItemA']
    Out[56]: 
                       A         B         C         D
    2000-01-03  0.116579 -0.667845 -1.151538 -0.157547
    2000-01-04  0.650448 -1.114910  0.841527  0.760706
    2000-01-05 -0.987433 -0.438897 -1.154468 -0.015033
    2000-01-06  0.494000  1.060450 -0.775993 -1.140165
    2000-01-07 -0.363770  0.013169  0.392036 -1.123913
    
    [5 rows x 4 columns]
    

Performance

Performance improvements for 0.13.1

  • Series datetime/timedelta binary operations (GH5801)
  • DataFrame count/dropna for axis=1
  • Series.str.contains now has a regex=False keyword which can be faster for plain (non-regex) string patterns. (GH5879)
  • Series.str.extract (GH5944)
  • dtypes/ftypes methods (GH5968)
  • indexing with object dtypes (GH5968)
  • DataFrame.apply (GH6013)
  • Regression in JSON IO (GH5765)
  • Index construction from Series (GH6150)

Experimental

There are no experimental changes in 0.13.1

Bug Fixes

See V0.13.1 Bug Fixes for an extensive list of bugs that have been fixed in 0.13.1.

See the full release notes or issue tracker on GitHub for a complete list of all API changes, Enhancements and Bug Fixes.

v0.13.0 (January 3, 2014)

This is a major release from 0.12.0 and includes a number of API changes, several new features and enhancements along with a large number of bug fixes.

Highlights include:

  • support for a new index type Float64Index, and other Indexing enhancements
  • HDFStore has a new string based syntax for query specification
  • support for new methods of interpolation
  • updated timedelta operations
  • a new string manipulation method extract
  • Nanosecond support for Offsets
  • isin for DataFrames

Several experimental features are added, including:

  • new eval/query methods for expression evaluation
  • support for msgpack serialization
  • an i/o interface to Google’s BigQuery

Their are several new or updated docs sections including:

Warning

In 0.13.0 Series has internally been refactored to no longer sub-class ndarray but instead subclass NDFrame, similar to the rest of the pandas containers. This should be a transparent change with only very limited API implications. See Internal Refactoring

API changes

  • read_excel now supports an integer in its sheetname argument giving the index of the sheet to read in (GH4301).

  • Text parser now treats anything that reads like inf (“inf”, “Inf”, “-Inf”, “iNf”, etc.) as infinity. (GH4220, GH4219), affecting read_table, read_csv, etc.

  • pandas now is Python 2/3 compatible without the need for 2to3 thanks to @jtratner. As a result, pandas now uses iterators more extensively. This also led to the introduction of substantive parts of the Benjamin Peterson’s six library into compat. (GH4384, GH4375, GH4372)

  • pandas.util.compat and pandas.util.py3compat have been merged into pandas.compat. pandas.compat now includes many functions allowing 2/3 compatibility. It contains both list and iterator versions of range, filter, map and zip, plus other necessary elements for Python 3 compatibility. lmap, lzip, lrange and lfilter all produce lists instead of iterators, for compatibility with numpy, subscripting and pandas constructors.(GH4384, GH4375, GH4372)

  • Series.get with negative indexers now returns the same as [] (GH4390)

  • Changes to how Index and MultiIndex handle metadata (levels, labels, and names) (GH4039):

    # previously, you would have set levels or labels directly
    index.levels = [[1, 2, 3, 4], [1, 2, 4, 4]]
    
    # now, you use the set_levels or set_labels methods
    index = index.set_levels([[1, 2, 3, 4], [1, 2, 4, 4]])
    
    # similarly, for names, you can rename the object
    # but setting names is not deprecated
    index = index.set_names(["bob", "cranberry"])
    
    # and all methods take an inplace kwarg - but return None
    index.set_names(["bob", "cranberry"], inplace=True)
    
  • All division with NDFrame objects is now truedivision, regardless of the future import. This means that operating on pandas objects will by default use floating point division, and return a floating point dtype. You can use // and floordiv to do integer division.

    Integer division

    In [3]: arr = np.array([1, 2, 3, 4])
    
    In [4]: arr2 = np.array([5, 3, 2, 1])
    
    In [5]: arr / arr2
    Out[5]: array([0, 0, 1, 4])
    
    In [6]: Series(arr) // Series(arr2)
    Out[6]:
    0    0
    1    0
    2    1
    3    4
    dtype: int64
    

    True Division

    In [7]: pd.Series(arr) / pd.Series(arr2) # no future import required
    Out[7]:
    0    0.200000
    1    0.666667
    2    1.500000
    3    4.000000
    dtype: float64
    
  • Infer and downcast dtype if downcast='infer' is passed to fillna/ffill/bfill (GH4604)

  • __nonzero__ for all NDFrame objects, will now raise a ValueError, this reverts back to (GH1073, GH4633) behavior. See gotchas for a more detailed discussion.

    This prevents doing boolean comparison on entire pandas objects, which is inherently ambiguous. These all will raise a ValueError.

    if df:
       ....
    df1 and df2
    s1 and s2
    

    Added the .bool() method to NDFrame objects to facilitate evaluating of single-element boolean Series:

    In [1]: Series([True]).bool()
    Out[1]: True
    
    In [2]: Series([False]).bool()
    Out[2]: False
    
    In [3]: DataFrame([[True]]).bool()
    Out[3]: True
    
    In [4]: DataFrame([[False]]).bool()
    Out[4]: False
    
  • All non-Index NDFrames (Series, DataFrame, Panel, Panel4D, SparsePanel, etc.), now support the entire set of arithmetic operators and arithmetic flex methods (add, sub, mul, etc.). SparsePanel does not support pow or mod with non-scalars. (GH3765)

  • Series and DataFrame now have a mode() method to calculate the statistical mode(s) by axis/Series. (GH5367)

  • Chained assignment will now by default warn if the user is assigning to a copy. This can be changed with the option mode.chained_assignment, allowed options are raise/warn/None. See the docs.

    In [5]: dfc = DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})
    
    In [6]: pd.set_option('chained_assignment','warn')
    

    The following warning / exception will show if this is attempted.

    In [7]: dfc.loc[0]['A'] = 1111
    
    Traceback (most recent call last)
       ...
    SettingWithCopyWarning:
       A value is trying to be set on a copy of a slice from a DataFrame.
       Try using .loc[row_index,col_indexer] = value instead

    Here is the correct method of assignment.

    In [8]: dfc.loc[0,'A'] = 11
    
    In [9]: dfc
    Out[9]: 
         A  B
    0   11  1
    1  bbb  2
    2  ccc  3
    
    [3 rows x 2 columns]
    
  • Panel.reindex has the following call signature Panel.reindex(items=None, major_axis=None, minor_axis=None, **kwargs)

    to conform with other NDFrame objects. See Internal Refactoring for more information.

  • Series.argmin and Series.argmax are now aliased to Series.idxmin and Series.idxmax. These return the index of the

    min or max element respectively. Prior to 0.13.0 these would return the position of the min / max element. (GH6214)

Prior Version Deprecations/Changes

These were announced changes in 0.12 or prior that are taking effect as of 0.13.0

  • Remove deprecated Factor (GH3650)
  • Remove deprecated set_printoptions/reset_printoptions (GH3046)
  • Remove deprecated _verbose_info (GH3215)
  • Remove deprecated read_clipboard/to_clipboard/ExcelFile/ExcelWriter from pandas.io.parsers (GH3717) These are available as functions in the main pandas namespace (e.g. pd.read_clipboard)
  • default for tupleize_cols is now False for both to_csv and read_csv. Fair warning in 0.12 (GH3604)
  • default for display.max_seq_len is now 100 rather then None. This activates truncated display (”...”) of long sequences in various places. (GH3391)

Deprecations

Deprecated in 0.13.0

  • deprecated iterkv, which will be removed in a future release (this was an alias of iteritems used to bypass 2to3‘s changes). (GH4384, GH4375, GH4372)
  • deprecated the string method match, whose role is now performed more idiomatically by extract. In a future release, the default behavior of match will change to become analogous to contains, which returns a boolean indexer. (Their distinction is strictness: match relies on re.match while contains relies on re.search.) In this release, the deprecated behavior is the default, but the new behavior is available through the keyword argument as_indexer=True.

Indexing API Changes

Prior to 0.13, it was impossible to use a label indexer (.loc/.ix) to set a value that was not contained in the index of a particular axis. (GH2578). See the docs

In the Series case this is effectively an appending operation

In [10]: s = Series([1,2,3])

In [11]: s
Out[11]: 
0    1
1    2
2    3
dtype: int64

In [12]: s[5] = 5.

In [13]: s
Out[13]: 
0    1
1    2
2    3
5    5
dtype: float64
In [14]: dfi = DataFrame(np.arange(6).reshape(3,2),
   ....:                 columns=['A','B'])
   ....: 

In [15]: dfi
Out[15]: 
   A  B
0  0  1
1  2  3
2  4  5

[3 rows x 2 columns]

This would previously KeyError

In [16]: dfi.loc[:,'C'] = dfi.loc[:,'A']

In [17]: dfi
Out[17]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4

[3 rows x 3 columns]

This is like an append operation.

In [18]: dfi.loc[3] = 5

In [19]: dfi
Out[19]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4
3  5  5  5

[4 rows x 3 columns]

A Panel setting operation on an arbitrary axis aligns the input to the Panel

In [20]: p = pd.Panel(np.arange(16).reshape(2,4,2),
   ....:             items=['Item1','Item2'],
   ....:             major_axis=pd.date_range('2001/1/12',periods=4),
   ....:             minor_axis=['A','B'],dtype='float64')
   ....: 

In [21]: p
Out[21]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to B

In [22]: p.loc[:,:,'C'] = Series([30,32],index=p.items)

In [23]: p
Out[23]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to C

In [24]: p.loc[:,:,'C']
Out[24]: 
            Item1  Item2
2001-01-12     30     32
2001-01-13     30     32
2001-01-14     30     32
2001-01-15     30     32

[4 rows x 2 columns]

Float64Index API Change

  • Added a new index type, Float64Index. This will be automatically created when passing floating values in index creation. This enables a pure label-based slicing paradigm that makes [],ix,loc for scalar indexing and slicing work exactly the same. See the docs, (GH263)

    Construction is by default for floating type values.

    In [25]: index = Index([1.5, 2, 3, 4.5, 5])
    
    In [26]: index
    Out[26]: Float64Index([1.5, 2.0, 3.0, 4.5, 5.0], dtype='float64')
    
    In [27]: s = Series(range(5),index=index)
    
    In [28]: s
    Out[28]: 
    1.5    0
    2.0    1
    3.0    2
    4.5    3
    5.0    4
    dtype: int64
    

    Scalar selection for [],.ix,.loc will always be label based. An integer will match an equal float index (e.g. 3 is equivalent to 3.0)

    In [29]: s[3]
    Out[29]: 2
    
    In [30]: s.ix[3]
    Out[30]: 2
    
    In [31]: s.loc[3]
    Out[31]: 2
    

    The only positional indexing is via iloc

    In [32]: s.iloc[3]
    Out[32]: 3
    

    A scalar index that is not found will raise KeyError

    Slicing is ALWAYS on the values of the index, for [],ix,loc and ALWAYS positional with iloc

    In [33]: s[2:4]
    Out[33]: 
    2    1
    3    2
    dtype: int64
    
    In [34]: s.ix[2:4]
    Out[34]: 
    2    1
    3    2
    dtype: int64
    
    In [35]: s.loc[2:4]
    Out[35]: 
    2    1
    3    2
    dtype: int64
    
    In [36]: s.iloc[2:4]
    Out[36]: 
    3.0    2
    4.5    3
    dtype: int64
    

    In float indexes, slicing using floats are allowed

    In [37]: s[2.1:4.6]
    Out[37]: 
    3.0    2
    4.5    3
    dtype: int64
    
    In [38]: s.loc[2.1:4.6]
    Out[38]: 
    3.0    2
    4.5    3
    dtype: int64
    
  • Indexing on other index types are preserved (and positional fallback for [],ix), with the exception, that floating point slicing on indexes on non Float64Index will now raise a TypeError.

    In [1]: Series(range(5))[3.5]
    TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index)
    
    In [1]: Series(range(5))[3.5:4.5]
    TypeError: the slice start [3.5] is not a proper indexer for this index type (Int64Index)
    

    Using a scalar float indexer will be deprecated in a future version, but is allowed for now.

    In [3]: Series(range(5))[3.0]
    Out[3]: 3
    

HDFStore API Changes

  • Query Format Changes. A much more string-like query format is now supported. See the docs.

    In [39]: path = 'test.h5'
    
    In [40]: dfq = DataFrame(randn(10,4),
       ....:          columns=list('ABCD'),
       ....:          index=date_range('20130101',periods=10))
       ....: 
    
    In [41]: dfq.to_hdf(path,'dfq',format='table',data_columns=True)
    

    Use boolean expressions, with in-line function evaluation.

    In [42]: read_hdf(path,'dfq',
       ....:     where="index>Timestamp('20130104') & columns=['A', 'B']")
       ....: 
    Out[42]: 
                       A         B
    2013-01-05 -1.392054  1.153922
    2013-01-06 -0.881047  0.295080
    2013-01-07 -1.407085  0.126781
    2013-01-08 -0.838843  0.553921
    2013-01-09  1.529401  0.205455
    2013-01-10  0.299071  1.076541
    
    [6 rows x 2 columns]
    

    Use an inline column reference

    In [43]: read_hdf(path,'dfq',
       ....:     where="A>0 or C>0")
       ....: 
    Out[43]: 
                       A         B         C         D
    2013-01-01  1.126386  0.247112  0.121172  0.298984
    2013-01-03  0.581073  2.763844  0.399325  0.668488
    2013-01-04 -0.275774  0.500483  0.863065 -1.051628
    2013-01-05 -1.392054  1.153922  1.181944  0.391371
    2013-01-06 -0.881047  0.295080  1.863801 -1.712274
    2013-01-07 -1.407085  0.126781  0.003760 -1.268994
    2013-01-09  1.529401  0.205455  0.313013  0.866521
    2013-01-10  0.299071  1.076541  0.363177  1.893680
    
    [8 rows x 4 columns]
    
  • the format keyword now replaces the table keyword; allowed values are fixed(f) or table(t) the same defaults as prior < 0.13.0 remain, e.g. put implies fixed format and append implies table format. This default format can be set as an option by setting io.hdf.default_format.

    In [44]: path = 'test.h5'
    
    In [45]: df = DataFrame(randn(10,2))
    
    In [46]: df.to_hdf(path,'df_table',format='table')
    
    In [47]: df.to_hdf(path,'df_table2',append=True)
    
    In [48]: df.to_hdf(path,'df_fixed')
    
    In [49]: with get_store(path) as store:
       ....:    print(store)
       ....: 
    <class 'pandas.io.pytables.HDFStore'>
    File path: test.h5
    /df_fixed             frame        (shape->[10,2])                                       
    /df_table             frame_table  (typ->appendable,nrows->10,ncols->2,indexers->[index])
    /df_table2            frame_table  (typ->appendable,nrows->10,ncols->2,indexers->[index])
    
  • Significant table writing performance improvements

  • handle a passed Series in table format (GH4330)

  • can now serialize a timedelta64[ns] dtype in a table (GH3577), See the docs.

  • added an is_open property to indicate if the underlying file handle is_open; a closed store will now report ‘CLOSED’ when viewing the store (rather than raising an error) (GH4409)

  • a close of a HDFStore now will close that instance of the HDFStore but will only close the actual file if the ref count (by PyTables) w.r.t. all of the open handles are 0. Essentially you have a local instance of HDFStore referenced by a variable. Once you close it, it will report closed. Other references (to the same file) will continue to operate until they themselves are closed. Performing an action on a closed file will raise ClosedFileError

    In [50]: path = 'test.h5'
    
    In [51]: df = DataFrame(randn(10,2))
    
    In [52]: store1 = HDFStore(path)
    
    In [53]: store2 = HDFStore(path)
    
    In [54]: store1.append('df',df)
    
    In [55]: store2.append('df2',df)
    
    In [56]: store1
    Out[56]: 
    <class 'pandas.io.pytables.HDFStore'>
    File path: test.h5
    /df            frame_table  (typ->appendable,nrows->10,ncols->2,indexers->[index])
    
    In [57]: store2
    Out[57]: 
    <class 'pandas.io.pytables.HDFStore'>
    File path: test.h5
    /df             frame_table  (typ->appendable,nrows->10,ncols->2,indexers->[index])
    /df2            frame_table  (typ->appendable,nrows->10,ncols->2,indexers->[index])
    
    In [58]: store1.close()
    
    In [59]: store2
    Out[59]: 
    <class 'pandas.io.pytables.HDFStore'>
    File path: test.h5
    /df             frame_table  (typ->appendable,nrows->10,ncols->2,indexers->[index])
    /df2            frame_table  (typ->appendable,nrows->10,ncols->2,indexers->[index])
    
    In [60]: store2.close()
    
    In [61]: store2
    Out[61]: 
    <class 'pandas.io.pytables.HDFStore'>
    File path: test.h5
    File is CLOSED
    
  • removed the _quiet attribute, replace by a DuplicateWarning if retrieving duplicate rows from a table (GH4367)

  • removed the warn argument from open. Instead a PossibleDataLossError exception will be raised if you try to use mode='w' with an OPEN file handle (GH4367)

  • allow a passed locations array or mask as a where condition (GH4467). See the docs for an example.

  • add the keyword dropna=True to append to change whether ALL nan rows are not written to the store (default is True, ALL nan rows are NOT written), also settable via the option io.hdf.dropna_table (GH4625)

  • pass thru store creation arguments; can be used to support in-memory stores

DataFrame repr Changes

The HTML and plain text representations of DataFrame now show a truncated view of the table once it exceeds a certain size, rather than switching to the short info view (GH4886, GH5550). This makes the representation more consistent as small DataFrames get larger.

Truncated HTML representation of a DataFrame

To get the info view, call DataFrame.info(). If you prefer the info view as the repr for large DataFrames, you can set this by running set_option('display.large_repr', 'info').

Enhancements

  • df.to_clipboard() learned a new excel keyword that let’s you paste df data directly into excel (enabled by default). (GH5070).

  • read_html now raises a URLError instead of catching and raising a ValueError (GH4303, GH4305)

  • Added a test for read_clipboard() and to_clipboard() (GH4282)

  • Clipboard functionality now works with PySide (GH4282)

  • Added a more informative error message when plot arguments contain overlapping color and style arguments (GH4402)

  • to_dict now takes records as a possible outtype. Returns an array of column-keyed dictionaries. (GH4936)

  • NaN handing in get_dummies (GH4446) with dummy_na

    # previously, nan was erroneously counted as 2 here
    # now it is not counted at all
    In [62]: get_dummies([1, 2, np.nan])
    Out[62]: 
       1  2
    0  1  0
    1  0  1
    2  0  0
    
    [3 rows x 2 columns]
    
    # unless requested
    In [63]: get_dummies([1, 2, np.nan], dummy_na=True)
    Out[63]: 
        1    2   NaN
    0    1    0    0
    1    0    1    0
    2    0    0    1
    
    [3 rows x 3 columns]
    
  • timedelta64[ns] operations. See the docs.

    Warning

    Most of these operations require numpy >= 1.7

    Using the new top-level to_timedelta, you can convert a scalar or array from the standard timedelta format (produced by to_csv) into a timedelta type (np.timedelta64 in nanoseconds).

    In [64]: to_timedelta('1 days 06:05:01.00003')
    Out[64]: Timedelta('1 days 06:05:01.000030')
    
    In [65]: to_timedelta('15.5us')
    Out[65]: Timedelta('0 days 00:00:00.000015')
    
    In [66]: to_timedelta(['1 days 06:05:01.00003','15.5us','nan'])
    Out[66]: 
    <class 'pandas.tseries.tdi.TimedeltaIndex'>
    ['1 days 06:05:01.000030', ..., NaT]
    Length: 3, Freq: None
    
    In [67]: to_timedelta(np.arange(5),unit='s')
    Out[67]: 
    <class 'pandas.tseries.tdi.TimedeltaIndex'>
    ['00:00:00', ..., '00:00:04']
    Length: 5, Freq: None
    
    In [68]: to_timedelta(np.arange(5),unit='d')
    Out[68]: 
    <class 'pandas.tseries.tdi.TimedeltaIndex'>
    ['0 days', ..., '4 days']
    Length: 5, Freq: None
    

    A Series of dtype timedelta64[ns] can now be divided by another timedelta64[ns] object, or astyped to yield a float64 dtyped Series. This is frequency conversion. See the docs for the docs.

    In [69]: from datetime import timedelta
    
    In [70]: td = Series(date_range('20130101',periods=4))-Series(date_range('20121201',periods=4))
    
    In [71]: td[2] += np.timedelta64(timedelta(minutes=5,seconds=3))
    
    In [72]: td[3] = np.nan
    
    In [73]: td
    Out[73]: 
    0   31 days 00:00:00
    1   31 days 00:00:00
    2   31 days 00:05:03
    3                NaT
    dtype: timedelta64[ns]
    
    # to days
    In [74]: td / np.timedelta64(1,'D')
    Out[74]: 
    0    31.000000
    1    31.000000
    2    31.003507
    3          NaN
    dtype: float64
    
    In [75]: td.astype('timedelta64[D]')
    Out[75]: 
    0    31
    1    31
    2    31
    3   NaN
    dtype: float64
    
    # to seconds
    In [76]: td / np.timedelta64(1,'s')
    Out[76]: 
    0    2678400
    1    2678400
    2    2678703
    3        NaN
    dtype: float64
    
    In [77]: td.astype('timedelta64[s]')
    Out[77]: 
    0    2678400
    1    2678400
    2    2678703
    3        NaN
    dtype: float64
    

    Dividing or multiplying a timedelta64[ns] Series by an integer or integer Series

    In [78]: td * -1
    Out[78]: 
    0   -31 days +00:00:00
    1   -31 days +00:00:00
    2   -32 days +23:54:57
    3                  NaT
    dtype: timedelta64[ns]
    
    In [79]: td * Series([1,2,3,4])
    Out[79]: 
    0   31 days 00:00:00
    1   62 days 00:00:00
    2   93 days 00:15:09
    3                NaT
    dtype: timedelta64[ns]
    

    Absolute DateOffset objects can act equivalently to timedeltas

    In [80]: from pandas import offsets
    
    In [81]: td + offsets.Minute(5) + offsets.Milli(5)
    Out[81]: 
    0   31 days 00:05:00.005000
    1   31 days 00:05:00.005000
    2   31 days 00:10:03.005000
    3                       NaT
    dtype: timedelta64[ns]
    

    Fillna is now supported for timedeltas

    In [82]: td.fillna(0)
    Out[82]: 
    0   31 days 00:00:00
    1   31 days 00:00:00
    2   31 days 00:05:03
    3    0 days 00:00:00
    dtype: timedelta64[ns]
    
    In [83]: td.fillna(timedelta(days=1,seconds=5))
    Out[83]: 
    0   31 days 00:00:00
    1   31 days 00:00:00
    2   31 days 00:05:03
    3    1 days 00:00:05
    dtype: timedelta64[ns]
    

    You can do numeric reduction operations on timedeltas.

    In [84]: td.mean()
    Out[84]: Timedelta('31 days 00:01:41')
    
    In [85]: td.quantile(.1)
    Out[85]: Timedelta('31 days 00:00:00')
    
  • plot(kind='kde') now accepts the optional parameters bw_method and ind, passed to scipy.stats.gaussian_kde() (for scipy >= 0.11.0) to set the bandwidth, and to gkde.evaluate() to specify the indices at which it is evaluated, respectively. See scipy docs. (GH4298)

  • DataFrame constructor now accepts a numpy masked record array (GH3478)

  • The new vectorized string method extract return regular expression matches more conveniently.

    In [86]: Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
    Out[86]: 
    0      1
    1      2
    2    NaN
    dtype: object
    

    Elements that do not match return NaN. Extracting a regular expression with more than one group returns a DataFrame with one column per group.

    In [87]: Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
    Out[87]: 
         0    1
    0    a    1
    1    b    2
    2  NaN  NaN
    
    [3 rows x 2 columns]
    

    Elements that do not match return a row of NaN. Thus, a Series of messy strings can be converted into a like-indexed Series or DataFrame of cleaned-up or more useful strings, without necessitating get() to access tuples or re.match objects.

    Named groups like

    In [88]: Series(['a1', 'b2', 'c3']).str.extract(
       ....:         '(?P<letter>[ab])(?P<digit>\d)')
       ....: 
    Out[88]: 
      letter digit
    0      a     1
    1      b     2
    2    NaN   NaN
    
    [3 rows x 2 columns]
    

    and optional groups can also be used.

    In [89]: Series(['a1', 'b2', '3']).str.extract(
       ....:         '(?P<letter>[ab])?(?P<digit>\d)')
       ....: 
    Out[89]: 
      letter digit
    0      a     1
    1      b     2
    2    NaN     3
    
    [3 rows x 2 columns]
    
  • read_stata now accepts Stata 13 format (GH4291)

  • read_fwf now infers the column specifications from the first 100 rows of the file if the data has correctly separated and properly aligned columns using the delimiter provided to the function (GH4488).

  • support for nanosecond times as an offset

    Warning

    These operations require numpy >= 1.7

    Period conversions in the range of seconds and below were reworked and extended up to nanoseconds. Periods in the nanosecond range are now available.

    In [90]: date_range('2013-01-01', periods=5, freq='5N')
    Out[90]: 
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2013-01-01, ..., 2013-01-01]
    Length: 5, Freq: 5N, Timezone: None
    

    or with frequency as offset

    In [91]: date_range('2013-01-01', periods=5, freq=pd.offsets.Nano(5))
    Out[91]: 
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2013-01-01, ..., 2013-01-01]
    Length: 5, Freq: 5N, Timezone: None
    

    Timestamps can be modified in the nanosecond range

    In [92]: t = Timestamp('20130101 09:01:02')
    
    In [93]: t + pd.datetools.Nano(123)
    Out[93]: Timestamp('2013-01-01 09:01:02.000000123')
    
  • A new method, isin for DataFrames, which plays nicely with boolean indexing. The argument to isin, what we’re comparing the DataFrame to, can be a DataFrame, Series, dict, or array of values. See the docs for more.

    To get the rows where any of the conditions are met:

    In [94]: dfi = DataFrame({'A': [1, 2, 3, 4], 'B': ['a', 'b', 'f', 'n']})
    
    In [95]: dfi
    Out[95]: 
       A  B
    0  1  a
    1  2  b
    2  3  f
    3  4  n
    
    [4 rows x 2 columns]
    
    In [96]: other = DataFrame({'A': [1, 3, 3, 7], 'B': ['e', 'f', 'f', 'e']})
    
    In [97]: mask = dfi.isin(other)
    
    In [98]: mask
    Out[98]: 
           A      B
    0   True  False
    1  False  False
    2   True   True
    3  False  False
    
    [4 rows x 2 columns]
    
    In [99]: dfi[mask.any(1)]
    Out[99]: 
       A  B
    0  1  a
    2  3  f
    
    [2 rows x 2 columns]
    
  • Series now supports a to_frame method to convert it to a single-column DataFrame (GH5164)

  • All R datasets listed here http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html can now be loaded into Pandas objects

    import pandas.rpy.common as com
    com.load_data('Titanic')
    
  • tz_localize can infer a fall daylight savings transition based on the structure of the unlocalized data (GH4230), see the docs

  • DatetimeIndex is now in the API documentation, see the docs

  • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067)

  • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget.

  • Python csv parser now supports usecols (GH4335)

  • Frequencies gained several new offsets:

    • LastWeekOfMonth (GH4637)
    • FY5253, and FY5253Quarter (GH4511)
  • DataFrame has a new interpolate method, similar to Series (GH4434, GH1892)

    In [100]: df = DataFrame({'A': [1, 2.1, np.nan, 4.7, 5.6, 6.8],
       .....:                 'B': [.25, np.nan, np.nan, 4, 12.2, 14.4]})
       .....: 
    
    In [101]: df.interpolate()
    Out[101]: 
         A      B
    0  1.0   0.25
    1  2.1   1.50
    2  3.4   2.75
    3  4.7   4.00
    4  5.6  12.20
    5  6.8  14.40
    
    [6 rows x 2 columns]
    

    Additionally, the method argument to interpolate has been expanded to include 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh', 'piecewise_polynomial', 'pchip', `polynomial`, 'spline' The new methods require scipy. Consult the Scipy reference guide and documentation for more information about when the various methods are appropriate. See the docs.

    Interpolate now also accepts a limit keyword argument. This works similar to fillna‘s limit:

    In [102]: ser = Series([1, 3, np.nan, np.nan, np.nan, 11])
    
    In [103]: ser.interpolate(limit=2)
    Out[103]: 
    0     1
    1     3
    2     5
    3     7
    4   NaN
    5    11
    dtype: float64
    
  • Added wide_to_long panel data convenience function. See the docs.

    In [104]: np.random.seed(123)
    
    In [105]: df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
       .....:                    "A1980" : {0 : "d", 1 : "e", 2 : "f"},
       .....:                    "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
       .....:                    "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
       .....:                    "X"     : dict(zip(range(3), np.random.randn(3)))
       .....:                   })
       .....: 
    
    In [106]: df["id"] = df.index
    
    In [107]: df
    Out[107]: 
      A1970 A1980  B1970  B1980         X  id
    0     a     d    2.5    3.2 -1.085631   0
    1     b     e    1.2    1.3  0.997345   1
    2     c     f    0.7    0.1  0.282978   2
    
    [3 rows x 6 columns]
    
    In [108]: wide_to_long(df, ["A", "B"], i="id", j="year")
    Out[108]: 
                    X  A    B
    id year                  
    0  1970 -1.085631  a  2.5
    1  1970  0.997345  b  1.2
    2  1970  0.282978  c  0.7
    0  1980 -1.085631  d  3.2
    1  1980  0.997345  e  1.3
    2  1980  0.282978  f  0.1
    
    [6 rows x 3 columns]
    
  • to_csv now takes a date_format keyword argument that specifies how output datetime objects should be formatted. Datetimes encountered in the index, columns, and values will all have this formatting applied. (GH4313)
  • DataFrame.plot will scatter plot x versus y by passing kind='scatter' (GH2215)
  • Added support for Google Analytics v3 API segment IDs that also supports v2 IDs. (GH5271)

Experimental

  • The new eval() function implements expression evaluation using numexpr behind the scenes. This results in large speedups for complicated expressions involving large DataFrames/Series. For example,

    In [109]: nrows, ncols = 20000, 100
    
    In [110]: df1, df2, df3, df4 = [DataFrame(randn(nrows, ncols))
       .....:                       for _ in range(4)]
       .....: 
    
    # eval with NumExpr backend
    In [111]: %timeit pd.eval('df1 + df2 + df3 + df4')
    100 loops, best of 3: 15.5 ms per loop
    
    # pure Python evaluation
    In [112]: %timeit df1 + df2 + df3 + df4
    10 loops, best of 3: 24.9 ms per loop
    

    For more details, see the the docs

  • Similar to pandas.eval, DataFrame has a new DataFrame.eval method that evaluates an expression in the context of the DataFrame. For example,

    In [113]: df = DataFrame(randn(10, 2), columns=['a', 'b'])
    
    In [114]: df.eval('a + b')
    Out[114]: 
    0   -0.685204
    1    1.589745
    2    0.325441
    3   -1.784153
    4   -0.432893
    5    0.171850
    6    1.895919
    7    3.065587
    8   -0.092759
    9    1.391365
    dtype: float64
    
  • query() method has been added that allows you to select elements of a DataFrame using a natural query syntax nearly identical to Python syntax. For example,

    In [115]: n = 20
    
    In [116]: df = DataFrame(np.random.randint(n, size=(n, 3)), columns=['a', 'b', 'c'])
    
    In [117]: df.query('a < b < c')
    Out[117]: 
        a   b   c
    11  1   5   8
    15  8  16  19
    
    [2 rows x 3 columns]
    

    selects all the rows of df where a < b < c evaluates to True. For more details see the the docs.

  • pd.read_msgpack() and pd.to_msgpack() are now a supported method of serialization of arbitrary pandas (and python objects) in a lightweight portable binary format. See the docs

    Warning

    Since this is an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release.

    In [118]: df = DataFrame(np.random.rand(5,2),columns=list('AB'))
    
    In [119]: df.to_msgpack('foo.msg')
    
    In [120]: pd.read_msgpack('foo.msg')
    Out[120]: 
              A         B
    0  0.251082  0.017357
    1  0.347915  0.929879
    2  0.546233  0.203368
    3  0.064942  0.031722
    4  0.355309  0.524575
    
    [5 rows x 2 columns]
    
    In [121]: s = Series(np.random.rand(5),index=date_range('20130101',periods=5))
    
    In [122]: pd.to_msgpack('foo.msg', df, s)
    
    In [123]: pd.read_msgpack('foo.msg')
    Out[123]: 
    [          A         B
     0  0.251082  0.017357
     1  0.347915  0.929879
     2  0.546233  0.203368
     3  0.064942  0.031722
     4  0.355309  0.524575
     
     [5 rows x 2 columns], 2013-01-01    0.022321
     2013-01-02    0.227025
     2013-01-03    0.383282
     2013-01-04    0.193225
     2013-01-05    0.110977
     Freq: D, dtype: float64]
    

    You can pass iterator=True to iterator over the unpacked results

    In [124]: for o in pd.read_msgpack('foo.msg',iterator=True):
       .....:    print o
       .....: 
              A         B
    0  0.251082  0.017357
    1  0.347915  0.929879
    2  0.546233  0.203368
    3  0.064942  0.031722
    4  0.355309  0.524575
    
    [5 rows x 2 columns]
    2013-01-01    0.022321
    2013-01-02    0.227025
    2013-01-03    0.383282
    2013-01-04    0.193225
    2013-01-05    0.110977
    Freq: D, dtype: float64
    
  • pandas.io.gbq provides a simple way to extract from, and load data into, Google’s BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large datasets. See the docs

    from pandas.io import gbq
    
    # A query to select the average monthly temperatures in the
    # in the year 2000 across the USA. The dataset,
    # publicata:samples.gsod, is available on all BigQuery accounts,
    # and is based on NOAA gsod data.
    
    query = """SELECT station_number as STATION,
    month as MONTH, AVG(mean_temp) as MEAN_TEMP
    FROM publicdata:samples.gsod
    WHERE YEAR = 2000
    GROUP BY STATION, MONTH
    ORDER BY STATION, MONTH ASC"""
    
    # Fetch the result set for this query
    
    # Your Google BigQuery Project ID
    # To find this, see your dashboard:
    # https://code.google.com/apis/console/b/0/?noredirect
    projectid = xxxxxxxxx;
    
    df = gbq.read_gbq(query, project_id = projectid)
    
    # Use pandas to process and reshape the dataset
    
    df2 = df.pivot(index='STATION', columns='MONTH', values='MEAN_TEMP')
    df3 = pandas.concat([df2.min(), df2.mean(), df2.max()],
                        axis=1,keys=["Min Tem", "Mean Temp", "Max Temp"])
    

    The resulting DataFrame is:

    > df3
                Min Tem  Mean Temp    Max Temp
     MONTH
     1     -53.336667  39.827892   89.770968
     2     -49.837500  43.685219   93.437932
     3     -77.926087  48.708355   96.099998
     4     -82.892858  55.070087   97.317240
     5     -92.378261  61.428117  102.042856
     6     -77.703334  65.858888  102.900000
     7     -87.821428  68.169663  106.510714
     8     -89.431999  68.614215  105.500000
     9     -86.611112  63.436935  107.142856
     10    -78.209677  56.880838   92.103333
     11    -50.125000  48.861228   94.996428
     12    -50.332258  42.286879   94.396774

    Warning

    To use this module, you will need a BigQuery account. See <https://cloud.google.com/products/big-query> for details.

    As of 10/10/13, there is a bug in Google’s API preventing result sets from being larger than 100,000 rows. A patch is scheduled for the week of 10/14/13.

Internal Refactoring

In 0.13.0 there is a major refactor primarily to subclass Series from NDFrame, which is the base class currently for DataFrame and Panel, to unify methods and behaviors. Series formerly subclassed directly from ndarray. (GH4080, GH3862, GH816)

Warning

There are two potential incompatibilities from < 0.13.0

  • Using certain numpy functions would previously return a Series if passed a Series as an argument. This seems only to affect np.ones_like, np.empty_like, np.diff and np.where. These now return ndarrays.

    In [125]: s = Series([1,2,3,4])
    

    Numpy Usage

    In [126]: np.ones_like(s)
    Out[126]: array([1, 1, 1, 1], dtype=int64)
    
    In [127]: np.diff(s)
    Out[127]: array([1, 1, 1], dtype=int64)
    
    In [128]: np.where(s>1,s,np.nan)
    Out[128]: array([ nan,   2.,   3.,   4.])
    

    Pandonic Usage

    In [129]: Series(1,index=s.index)
    Out[129]: 
    0    1
    1    1
    2    1
    3    1
    dtype: int64
    
    In [130]: s.diff()
    Out[130]: 
    0   NaN
    1     1
    2     1
    3     1
    dtype: float64
    
    In [131]: s.where(s>1)
    Out[131]: 
    0   NaN
    1     2
    2     3
    3     4
    dtype: float64
    
  • Passing a Series directly to a cython function expecting an ndarray type will no long work directly, you must pass Series.values, See Enhancing Performance

  • Series(0.5) would previously return the scalar 0.5, instead this will return a 1-element Series

  • This change breaks rpy2<=2.3.8. an Issue has been opened against rpy2 and a workaround is detailed in GH5698. Thanks @JanSchulz.

  • Pickle compatibility is preserved for pickles created prior to 0.13. These must be unpickled with pd.read_pickle, see Pickling.

  • Refactor of series.py/frame.py/panel.py to move common code to generic.py

    • added _setup_axes to created generic NDFrame structures
    • moved methods
      • from_axes,_wrap_array,axes,ix,loc,iloc,shape,empty,swapaxes,transpose,pop
      • __iter__,keys,__contains__,__len__,__neg__,__invert__
      • convert_objects,as_blocks,as_matrix,values
      • __getstate__,__setstate__ (compat remains in frame/panel)
      • __getattr__,__setattr__
      • _indexed_same,reindex_like,align,where,mask
      • fillna,replace (Series replace is now consistent with DataFrame)
      • filter (also added axis argument to selectively filter on a different axis)
      • reindex,reindex_axis,take
      • truncate (moved to become part of NDFrame)
  • These are API changes which make Panel more consistent with DataFrame

    • swapaxes on a Panel with the same axes specified now return a copy
    • support attribute access for setting
    • filter supports the same API as the original DataFrame filter
  • Reindex called with no arguments will now return a copy of the input object

  • TimeSeries is now an alias for Series. the property is_time_series can be used to distinguish (if desired)

  • Refactor of Sparse objects to use BlockManager

    • Created a new block type in internals, SparseBlock, which can hold multi-dtypes and is non-consolidatable. SparseSeries and SparseDataFrame now inherit more methods from there hierarchy (Series/DataFrame), and no longer inherit from SparseArray (which instead is the object of the SparseBlock)
    • Sparse suite now supports integration with non-sparse data. Non-float sparse data is supportable (partially implemented)
    • Operations on sparse structures within DataFrames should preserve sparseness, merging type operations will convert to dense (and back to sparse), so might be somewhat inefficient
    • enable setitem on SparseSeries for boolean/integer/slices
    • SparsePanels implementation is unchanged (e.g. not using BlockManager, needs work)
  • added ftypes method to Series/DataFrame, similar to dtypes, but indicates if the underlying is sparse/dense (as well as the dtype)

  • All NDFrame objects can now use __finalize__() to specify various values to propagate to new objects from an existing one (e.g. name in Series will follow more automatically now)

  • Internal type checking is now done via a suite of generated classes, allowing isinstance(value, klass) without having to directly import the klass, courtesy of @jtratner

  • Bug in Series update where the parent frame is not updating its cache based on changes (GH4080) or types (GH3217), fillna (GH3386)

  • Indexing with dtype conversions fixed (GH4463, GH4204)

  • Refactor Series.reindex to core/generic.py (GH4604, GH4618), allow method= in reindexing on a Series to work

  • Series.copy no longer accepts the order parameter and is now consistent with NDFrame copy

  • Refactor rename methods to core/generic.py; fixes Series.rename for (GH4605), and adds rename with the same signature for Panel

  • Refactor clip methods to core/generic.py (GH4798)

  • Refactor of _get_numeric_data/_get_bool_data to core/generic.py, allowing Series/Panel functionality

  • Series (for index) / Panel (for items) now allow attribute access to its elements (GH1903)

    In [132]: s = Series([1,2,3],index=list('abc'))
    
    In [133]: s.b
    Out[133]: 2
    
    In [134]: s.a = 5
    
    In [135]: s
    Out[135]: 
    a    5
    b    2
    c    3
    dtype: int64
    

Bug Fixes

See V0.13.0 Bug Fixes for an extensive list of bugs that have been fixed in 0.13.0.

See the full release notes or issue tracker on GitHub for a complete list of all API changes, Enhancements and Bug Fixes.

v0.12.0 (July 24, 2013)

This is a major release from 0.11.0 and includes several new features and enhancements along with a large number of bug fixes.

Highlights include a consistent I/O API naming scheme, routines to read html, write multi-indexes to csv files, read & write STATA data files, read & write JSON format files, Python 3 support for HDFStore, filtering of groupby expressions via filter, and a revamped replace routine that accepts regular expressions.

API changes

  • The I/O API is now much more consistent with a set of top level reader functions accessed like pd.read_csv() that generally return a pandas object.

    • read_csv
    • read_excel
    • read_hdf
    • read_sql
    • read_json
    • read_html
    • read_stata
    • read_clipboard

    The corresponding writer functions are object methods that are accessed like df.to_csv()

    • to_csv
    • to_excel
    • to_hdf
    • to_sql
    • to_json
    • to_html
    • to_stata
    • to_clipboard
  • Fix modulo and integer division on Series,DataFrames to act similary to float dtypes to return np.nan or np.inf as appropriate (GH3590). This correct a numpy bug that treats integer and float dtypes differently.

    In [1]: p = DataFrame({ 'first' : [4,5,8], 'second' : [0,0,3] })
    
    In [2]: p % 0
    Out[2]: 
       first  second
    0    NaN     NaN
    1    NaN     NaN
    2    NaN     NaN
    
    [3 rows x 2 columns]
    
    In [3]: p % p
    Out[3]: 
       first  second
    0      0     NaN
    1      0     NaN
    2      0       0
    
    [3 rows x 2 columns]
    
    In [4]: p / p
    Out[4]: 
       first    second
    0      1       inf
    1      1       inf
    2      1  1.000000
    
    [3 rows x 2 columns]
    
    In [5]: p / 0
    Out[5]: 
       first  second
    0    inf     inf
    1    inf     inf
    2    inf     inf
    
    [3 rows x 2 columns]
    
  • Add squeeze keyword to groupby to allow reduction from DataFrame -> Series if groups are unique. This is a Regression from 0.10.1. We are reverting back to the prior behavior. This means groupby will return the same shaped objects whether the groups are unique or not. Revert this issue (GH2893) with (GH3596).

    In [6]: df2 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19},
       ...:                  {"val1":1, "val2": 27}, {"val1":1, "val2": 12}])
       ...: 
    
    In [7]: def func(dataf):
       ...:     return dataf["val2"]  - dataf["val2"].mean()
       ...: 
    
    # squeezing the result frame to a series (because we have unique groups)
    In [8]: df2.groupby("val1", squeeze=True).apply(func)
    Out[8]: 
    0    0.5
    1   -0.5
    2    7.5
    3   -7.5
    Name: 1, dtype: float64
    
    # no squeezing (the default, and behavior in 0.10.1)
    In [9]: df2.groupby("val1").apply(func)
    Out[9]: 
    val2    0    1    2    3
    val1                    
    1     0.5 -0.5  7.5 -7.5
    
    [1 rows x 4 columns]
    
  • Raise on iloc when boolean indexing with a label based indexer mask e.g. a boolean Series, even with integer labels, will raise. Since iloc is purely positional based, the labels on the Series are not alignable (GH3631)

    This case is rarely used, and there are plently of alternatives. This preserves the iloc API to be purely positional based.

    In [10]: df = DataFrame(lrange(5), list('ABCDE'), columns=['a'])
    
    In [11]: mask = (df.a%2 == 0)
    
    In [12]: mask
    Out[12]: 
    A     True
    B    False
    C     True
    D    False
    E     True
    Name: a, dtype: bool
    
    # this is what you should use
    In [13]: df.loc[mask]
    Out[13]: 
       a
    A  0
    C  2
    E  4
    
    [3 rows x 1 columns]
    
    # this will work as well
    In [14]: df.iloc[mask.values]
    Out[14]: 
       a
    A  0
    C  2
    E  4
    
    [3 rows x 1 columns]
    

    df.iloc[mask] will raise a ValueError

  • The raise_on_error argument to plotting functions is removed. Instead, plotting functions raise a TypeError when the dtype of the object is object to remind you to avoid object arrays whenever possible and thus you should cast to an appropriate numeric dtype if you need to plot something.

  • Add colormap keyword to DataFrame plotting methods. Accepts either a matplotlib colormap object (ie, matplotlib.cm.jet) or a string name of such an object (ie, ‘jet’). The colormap is sampled to select the color for each column. Please see Colormaps for more information. (GH3860)

  • DataFrame.interpolate() is now deprecated. Please use DataFrame.fillna() and DataFrame.replace() instead. (GH3582, GH3675, GH3676)

  • the method and axis arguments of DataFrame.replace() are deprecated

  • DataFrame.replace ‘s infer_types parameter is removed and now performs conversion by default. (GH3907)

  • Add the keyword allow_duplicates to DataFrame.insert to allow a duplicate column to be inserted if True, default is False (same as prior to 0.12) (GH3679)

  • Implement __nonzero__ for NDFrame objects (GH3691, GH3696)

  • IO api

    • added top-level function read_excel to replace the following, The original API is deprecated and will be removed in a future version

      from pandas.io.parsers import ExcelFile
      xls = ExcelFile('path_to_file.xls')
      xls.parse('Sheet1', index_col=None, na_values=['NA'])
      

      With

      import pandas as pd
      pd.read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])
      
    • added top-level function read_sql that is equivalent to the following

      from pandas.io.sql import read_frame
      read_frame(....)
      
  • DataFrame.to_html and DataFrame.to_latex now accept a path for their first argument (GH3702)

  • Do not allow astypes on datetime64[ns] except to object, and timedelta64[ns] to object/int (GH3425)

  • The behavior of datetime64 dtypes has changed with respect to certain so-called reduction operations (GH3726). The following operations now raise a TypeError when perfomed on a Series and return an empty Series when performed on a DataFrame similar to performing these operations on, for example, a DataFrame of slice objects:

    • sum, prod, mean, std, var, skew, kurt, corr, and cov
  • read_html now defaults to None when reading, and falls back on bs4 + html5lib when lxml fails to parse. a list of parsers to try until success is also valid

  • The internal pandas class hierarchy has changed (slightly). The previous PandasObject now is called PandasContainer and a new PandasObject has become the baseclass for PandasContainer as well as Index, Categorical, GroupBy, SparseList, and SparseArray (+ their base classes). Currently, PandasObject provides string methods (from StringMixin). (GH4090, GH4092)

  • New StringMixin that, given a __unicode__ method, gets python 2 and python 3 compatible string methods (__str__, __bytes__, and __repr__). Plus string safety throughout. Now employed in many places throughout the pandas library. (GH4090, GH4092)

I/O Enhancements

  • pd.read_html() can now parse HTML strings, files or urls and return DataFrames, courtesy of @cpcloud. (GH3477, GH3605, GH3606, GH3616). It works with a single parser backend: BeautifulSoup4 + html5lib See the docs

    You can use pd.read_html() to read the output from DataFrame.to_html() like so

    In [15]: df = DataFrame({'a': range(3), 'b': list('abc')})
    
    In [16]: print(df)
       a  b
    0  0  a
    1  1  b
    2  2  c
    
    [3 rows x 2 columns]
    
    In [17]: html = df.to_html()
    
    In [18]: alist = pd.read_html(html, infer_types=True, index_col=0)
    
    In [19]: print(df == alist[0])
          a     b
    0  True  True
    1  True  True
    2  True  True
    
    [3 rows x 2 columns]
    

    Note that alist here is a Python list so pd.read_html() and DataFrame.to_html() are not inverses.

    • pd.read_html() no longer performs hard conversion of date strings (GH3656).

    Warning

    You may have to install an older version of BeautifulSoup4, See the installation docs

  • Added module for reading and writing Stata files: pandas.io.stata (GH1512) accessable via read_stata top-level function for reading, and to_stata DataFrame method for writing, See the docs

  • Added module for reading and writing json format files: pandas.io.json accessable via read_json top-level function for reading, and to_json DataFrame method for writing, See the docs various issues (GH1226, GH3804, GH3876, GH3867, GH1305)

  • MultiIndex column support for reading and writing csv format files

    • The header option in read_csv now accepts a list of the rows from which to read the index.

    • The option, tupleize_cols can now be specified in both to_csv and read_csv, to provide compatiblity for the pre 0.12 behavior of writing and reading MultIndex columns via a list of tuples. The default in 0.12 is to write lists of tuples and not interpret list of tuples as a MultiIndex column.

      Note: The default behavior in 0.12 remains unchanged from prior versions, but starting with 0.13, the default to write and read MultiIndex columns will be in the new format. (GH3571, GH1651, GH3141)

    • If an index_col is not specified (e.g. you don’t have an index, or wrote it with df.to_csv(..., index=False), then any names on the columns index will be lost.

      In [20]: from pandas.util.testing import makeCustomDataframe as mkdf
      
      In [21]: df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
      
      In [22]: df.to_csv('mi.csv',tupleize_cols=False)
      
      In [23]: print(open('mi.csv').read())
      C0,,C_l0_g0,C_l0_g1,C_l0_g2
      C1,,C_l1_g0,C_l1_g1,C_l1_g2
      C2,,C_l2_g0,C_l2_g1,C_l2_g2
      C3,,C_l3_g0,C_l3_g1,C_l3_g2
      R0,R1,,,
      R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2
      R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2
      R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2
      R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2
      R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2
      
      
      In [24]: pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1],tupleize_cols=False)
      Out[24]: 
      C0              C_l0_g0 C_l0_g1 C_l0_g2
      C1              C_l1_g0 C_l1_g1 C_l1_g2
      C2              C_l2_g0 C_l2_g1 C_l2_g2
      C3              C_l3_g0 C_l3_g1 C_l3_g2
      R0      R1                             
      R_l0_g0 R_l1_g0    R0C0    R0C1    R0C2
      R_l0_g1 R_l1_g1    R1C0    R1C1    R1C2
      R_l0_g2 R_l1_g2    R2C0    R2C1    R2C2
      R_l0_g3 R_l1_g3    R3C0    R3C1    R3C2
      R_l0_g4 R_l1_g4    R4C0    R4C1    R4C2
      
      [5 rows x 3 columns]
      
  • Support for HDFStore (via PyTables 3.0.0) on Python3

  • Iterator support via read_hdf that automatically opens and closes the store when iteration is finished. This is only for tables

    In [25]: path = 'store_iterator.h5'
    
    In [26]: DataFrame(randn(10,2)).to_hdf(path,'df',table=True)
    
    In [27]: for df in read_hdf(path,'df', chunksize=3):
       ....:      print(df)
       ....: 
              0         1
    0  1.392665 -0.123497
    1 -0.402761 -0.246604
    2 -0.288433 -0.763434
    
    [3 rows x 2 columns]
              0         1
    3  2.069526 -1.203569
    4  0.591830  0.841159
    5 -0.501083 -0.816561
    
    [3 rows x 2 columns]
              0         1
    6 -0.207082 -0.664112
    7  0.580411 -0.965628
    8 -0.038605 -0.460478
    
    [3 rows x 2 columns]
              0         1
    9 -0.310458  0.866493
    
    [1 rows x 2 columns]
    
  • read_csv will now throw a more informative error message when a file contains no columns, e.g., all newline characters

Other Enhancements

  • DataFrame.replace() now allows regular expressions on contained Series with object dtype. See the examples section in the regular docs Replacing via String Expression

    For example you can do

    In [28]: df = DataFrame({'a': list('ab..'), 'b': [1, 2, 3, 4]})
    
    In [29]: df.replace(regex=r'\s*\.\s*', value=np.nan)
    Out[29]: 
         a  b
    0    a  1
    1    b  2
    2  NaN  3
    3  NaN  4
    
    [4 rows x 2 columns]
    

    to replace all occurrences of the string '.' with zero or more instances of surrounding whitespace with NaN.

    Regular string replacement still works as expected. For example, you can do

    In [30]: df.replace('.', np.nan)
    Out[30]: 
         a  b
    0    a  1
    1    b  2
    2  NaN  3
    3  NaN  4
    
    [4 rows x 2 columns]
    

    to replace all occurrences of the string '.' with NaN.

  • pd.melt() now accepts the optional parameters var_name and value_name to specify custom column names of the returned DataFrame.

  • pd.set_option() now allows N option, value pairs (GH3667).

    Let’s say that we had an option 'a.b' and another option 'b.c'. We can set them at the same time:

    In [31]: pd.get_option('a.b')
    Out[31]: 2
    
    In [32]: pd.get_option('b.c')
    Out[32]: 3
    
    In [33]: pd.set_option('a.b', 1, 'b.c', 4)
    
    In [34]: pd.get_option('a.b')
    Out[34]: 1
    
    In [35]: pd.get_option('b.c')
    Out[35]: 4
    
  • The filter method for group objects returns a subset of the original object. Suppose we want to take only elements that belong to groups with a group sum greater than 2.

    In [36]: sf = Series([1, 1, 2, 3, 3, 3])
    
    In [37]: sf.groupby(sf).filter(lambda x: x.sum() > 2)
    Out[37]: 
    3    3
    4    3
    5    3
    dtype: int64
    

    The argument of filter must a function that, applied to the group as a whole, returns True or False.

    Another useful operation is filtering out elements that belong to groups with only a couple members.

    In [38]: dff = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
    
    In [39]: dff.groupby('B').filter(lambda x: len(x) > 2)
    Out[39]: 
       A  B
    2  2  b
    3  3  b
    4  4  b
    5  5  b
    
    [4 rows x 2 columns]
    

    Alternatively, instead of dropping the offending groups, we can return a like-indexed objects where the groups that do not pass the filter are filled with NaNs.

    In [40]: dff.groupby('B').filter(lambda x: len(x) > 2, dropna=False)
    Out[40]: 
        A    B
    0 NaN  NaN
    1 NaN  NaN
    2   2    b
    3   3    b
    4   4    b
    5   5    b
    6 NaN  NaN
    7 NaN  NaN
    
    [8 rows x 2 columns]
    
  • Series and DataFrame hist methods now take a figsize argument (GH3834)

  • DatetimeIndexes no longer try to convert mixed-integer indexes during join operations (GH3877)

  • Timestamp.min and Timestamp.max now represent valid Timestamp instances instead of the default datetime.min and datetime.max (respectively), thanks @SleepingPills

  • read_html now raises when no tables are found and BeautifulSoup==4.2.0 is detected (GH4214)

Experimental Features

  • Added experimental CustomBusinessDay class to support DateOffsets with custom holiday calendars and custom weekmasks. (GH2301)

    Note

    This uses the numpy.busdaycalendar API introduced in Numpy 1.7 and therefore requires Numpy 1.7.0 or newer.

    In [41]: from pandas.tseries.offsets import CustomBusinessDay
    
    In [42]: from datetime import datetime
    
    # As an interesting example, let's look at Egypt where
    # a Friday-Saturday weekend is observed.
    In [43]: weekmask_egypt = 'Sun Mon Tue Wed Thu'
    
    # They also observe International Workers' Day so let's
    # add that for a couple of years
    In [44]: holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]
    
    In [45]: bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)
    
    In [46]: dt = datetime(2013, 4, 30)
    
    In [47]: print(dt + 2 * bday_egypt)
    2013-05-05 00:00:00
    
    In [48]: dts = date_range(dt, periods=5, freq=bday_egypt)
    
    In [49]: print(Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split())))
    2013-04-30    Tue
    2013-05-02    Thu
    2013-05-05    Sun
    2013-05-06    Mon
    2013-05-07    Tue
    Freq: C, dtype: object
    

Bug Fixes

  • Plotting functions now raise a TypeError before trying to plot anything if the associated objects have have a dtype of object (GH1818, GH3572, GH3911, GH3912), but they will try to convert object arrays to numeric arrays if possible so that you can still plot, for example, an object array with floats. This happens before any drawing takes place which elimnates any spurious plots from showing up.

  • fillna methods now raise a TypeError if the value parameter is a list or tuple.

  • Series.str now supports iteration (GH3638). You can iterate over the individual elements of each string in the Series. Each iteration yields yields a Series with either a single character at each index of the original Series or NaN. For example,

    In [50]: strs = 'go', 'bow', 'joe', 'slow'
    
    In [51]: ds = Series(strs)
    
    In [52]: for s in ds.str:
       ....:     print(s)
       ....: 
    0    g
    1    b
    2    j
    3    s
    dtype: object
    0    o
    1    o
    2    o
    3    l
    dtype: object
    0    NaN
    1      w
    2      e
    3      o
    dtype: object
    0    NaN
    1    NaN
    2    NaN
    3      w
    dtype: object
    
    In [53]: s
    Out[53]: 
    0    NaN
    1    NaN
    2    NaN
    3      w
    dtype: object
    
    In [54]: s.dropna().values.item() == 'w'
    Out[54]: True
    

    The last element yielded by the iterator will be a Series containing the last element of the longest string in the Series with all other elements being NaN. Here since 'slow' is the longest string and there are no other strings with the same length 'w' is the only non-null string in the yielded Series.

  • HDFStore

    • will retain index attributes (freq,tz,name) on recreation (GH3499)
    • will warn with a AttributeConflictWarning if you are attempting to append an index with a different frequency than the existing, or attempting to append an index with a different name than the existing
    • support datelike columns with a timezone as data_columns (GH2852)
  • Non-unique index support clarified (GH3468).

    • Fix assigning a new index to a duplicate index in a DataFrame would fail (GH3468)
    • Fix construction of a DataFrame with a duplicate index
    • ref_locs support to allow duplicative indices across dtypes, allows iget support to always find the index (even across dtypes) (GH2194)
    • applymap on a DataFrame with a non-unique index now works (removed warning) (GH2786), and fix (GH3230)
    • Fix to_csv to handle non-unique columns (GH3495)
    • Duplicate indexes with getitem will return items in the correct order (GH3455, GH3457) and handle missing elements like unique indices (GH3561)
    • Duplicate indexes with and empty DataFrame.from_records will return a correct frame (GH3562)
    • Concat to produce a non-unique columns when duplicates are across dtypes is fixed (GH3602)
    • Allow insert/delete to non-unique columns (GH3679)
    • Non-unique indexing with a slice via loc and friends fixed (GH3659)
    • Allow insert/delete to non-unique columns (GH3679)
    • Extend reindex to correctly deal with non-unique indices (GH3679)
    • DataFrame.itertuples() now works with frames with duplicate column names (GH3873)
    • Bug in non-unique indexing via iloc (GH4017); added takeable argument to reindex for location-based taking
    • Allow non-unique indexing in series via .ix/.loc and __getitem__ (GH4246)
    • Fixed non-unique indexing memory allocation issue with .ix/.loc (GH4280)
  • DataFrame.from_records did not accept empty recarrays (GH3682)

  • read_html now correctly skips tests (GH3741)

  • Fixed a bug where DataFrame.replace with a compiled regular expression in the to_replace argument wasn’t working (GH3907)

  • Improved network test decorator to catch IOError (and therefore URLError as well). Added with_connectivity_check decorator to allow explicitly checking a website as a proxy for seeing if there is network connectivity. Plus, new optional_args decorator factory for decorators. (GH3910, GH3914)

  • Fixed testing issue where too many sockets where open thus leading to a connection reset issue (GH3982, GH3985, GH4028, GH4054)

  • Fixed failing tests in test_yahoo, test_google where symbols were not retrieved but were being accessed (GH3982, GH3985, GH4028, GH4054)

  • Series.hist will now take the figure from the current environment if one is not passed

  • Fixed bug where a 1xN DataFrame would barf on a 1xN mask (GH4071)

  • Fixed running of tox under python3 where the pickle import was getting rewritten in an incompatible way (GH4062, GH4063)

  • Fixed bug where sharex and sharey were not being passed to grouped_hist (GH4089)

  • Fixed bug in DataFrame.replace where a nested dict wasn’t being iterated over when regex=False (GH4115)

  • Fixed bug in the parsing of microseconds when using the format argument in to_datetime (GH4152)

  • Fixed bug in PandasAutoDateLocator where invert_xaxis triggered incorrectly MilliSecondLocator (GH3990)

  • Fixed bug in plotting that wasn’t raising on invalid colormap for matplotlib 1.1.1 (GH4215)

  • Fixed the legend displaying in DataFrame.plot(kind='kde') (GH4216)

  • Fixed bug where Index slices weren’t carrying the name attribute (GH4226)

  • Fixed bug in initializing DatetimeIndex with an array of strings in a certain time zone (GH4229)

  • Fixed bug where html5lib wasn’t being properly skipped (GH4265)

  • Fixed bug where get_data_famafrench wasn’t using the correct file edges (GH4281)

See the full release notes or issue tracker on GitHub for a complete list.

v0.11.0 (April 22, 2013)

This is a major release from 0.10.1 and includes many new features and enhancements along with a large number of bug fixes. The methods of Selecting Data have had quite a number of additions, and Dtype support is now full-fledged. There are also a number of important API changes that long-time pandas users should pay close attention to.

There is a new section in the documentation, 10 Minutes to Pandas, primarily geared to new users.

There is a new section in the documentation, Cookbook, a collection of useful recipes in pandas (and that we want contributions!).

There are several libraries that are now Recommended Dependencies

Selection Choices

Starting in 0.11.0, object selection has had a number of user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing.

  • .loc is strictly label based, will raise KeyError when the items are not found, allowed inputs are:

    • A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index. This use is not an integer position along the index)
    • A list or array of labels ['a', 'b', 'c']
    • A slice object with labels 'a':'f', (note that contrary to usual python slices, both the start and the stop are included!)
    • A boolean array

    See more at Selection by Label

  • .iloc is strictly integer position based (from 0 to length-1 of the axis), will raise IndexError when the requested indicies are out of bounds. Allowed inputs are:

    • An integer e.g. 5
    • A list or array of integers [4, 3, 0]
    • A slice object with ints 1:7
    • A boolean array

    See more at Selection by Position

  • .ix supports mixed integer and label based access. It is primarily label based, but will fallback to integer positional access. .ix is the most general and will support any of the inputs to .loc and .iloc, as well as support for floating point label schemes. .ix is especially useful when dealing with mixed positional and label based hierarchial indexes.

    As using integer slices with .ix have different behavior depending on whether the slice is interpreted as position based or label based, it’s usually better to be explicit and use .iloc or .loc.

    See more at Advanced Indexing and Advanced Hierarchical.

Selection Deprecations

Starting in version 0.11.0, these methods may be deprecated in future versions.

  • irow
  • icol
  • iget_value

See the section Selection by Position for substitutes.

Dtypes

Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the dtype keyword, a passed ndarray, or a passed Series, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will NOT be combined. The following example will give you a taste.

In [1]: df1 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')

In [2]: df1
Out[2]: 
          A
0  0.245972
1  0.319442
2  1.378512
3  0.292502
4  0.329791
5  1.392047
6  0.769914
7 -2.472300

[8 rows x 1 columns]

In [3]: df1.dtypes
Out[3]: 
A    float32
dtype: object

In [4]: df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'),
   ...:                       B = Series(randn(8)),
   ...:                       C = Series(randn(8),dtype='uint8') ))
   ...: 

In [5]: df2
Out[5]: 
          A         B    C
0 -0.611328 -0.270630  255
1  1.044922 -1.685677    0
2  1.503906 -0.440747    0
3 -1.328125 -0.115070    1
4  1.024414 -0.632102    0
5  0.660156 -0.585977    0
6  1.236328 -1.444787    0
7 -2.169922 -0.201135    0

[8 rows x 3 columns]

In [6]: df2.dtypes
Out[6]: 
A    float16
B    float64
C      uint8
dtype: object

# here you get some upcasting
In [7]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2

In [8]: df3
Out[8]: 
          A         B    C
0 -0.365356 -0.270630  255
1  1.364364 -1.685677    0
2  2.882418 -0.440747    0
3 -1.035623 -0.115070    1
4  1.354205 -0.632102    0
5  2.052203 -0.585977    0
6  2.006243 -1.444787    0
7 -4.642221 -0.201135    0

[8 rows x 3 columns]

In [9]: df3.dtypes
Out[9]: 
A    float32
B    float64
C    float64
dtype: object

Dtype Conversion

This is lower-common-denomicator upcasting, meaning you get the dtype which can accomodate all of the types

In [10]: df3.values.dtype
Out[10]: dtype('float64')

Conversion

In [11]: df3.astype('float32').dtypes
Out[11]: 
A    float32
B    float32
C    float32
dtype: object

Mixed Conversion

In [12]: df3['D'] = '1.'

In [13]: df3['E'] = '1'

In [14]: df3.convert_objects(convert_numeric=True).dtypes
Out[14]: 
A    float32
B    float64
C    float64
D    float64
E      int64
dtype: object

# same, but specific dtype conversion
In [15]: df3['D'] = df3['D'].astype('float16')

In [16]: df3['E'] = df3['E'].astype('int32')

In [17]: df3.dtypes
Out[17]: 
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

Forcing Date coercion (and setting NaT when not datelike)

In [18]: from datetime import datetime

In [19]: s = Series([datetime(2001,1,1,0,0), 'foo', 1.0, 1,
   ....:             Timestamp('20010104'), '20010105'],dtype='O')
   ....: 

In [20]: s.convert_objects(convert_dates='coerce')
Out[20]: 
0   2001-01-01
1          NaT
2          NaT
3          NaT
4   2001-01-04
5   2001-01-05
dtype: datetime64[ns]

Dtype Gotchas

Platform Gotchas

Starting in 0.11.0, construction of DataFrame/Series will use default dtypes of int64 and float64, regardless of platform. This is not an apparent change from earlier versions of pandas. If you specify dtypes, they WILL be respected, however (GH2837)

The following will all result in int64 dtypes

In [21]: DataFrame([1,2],columns=['a']).dtypes
Out[21]: 
a    int64
dtype: object

In [22]: DataFrame({'a' : [1,2] }).dtypes
Out[22]: 
a    int64
dtype: object

In [23]: DataFrame({'a' : 1 }, index=range(2)).dtypes
Out[23]: 
a    int64
dtype: object

Keep in mind that DataFrame(np.array([1,2])) WILL result in int32 on 32-bit platforms!

Upcasting Gotchas

Performing indexing operations on integer type data can easily upcast the data. The dtype of the input data will be preserved in cases where nans are not introduced.

In [24]: dfi = df3.astype('int32')

In [25]: dfi['D'] = dfi['D'].astype('int64')

In [26]: dfi
Out[26]: 
   A  B    C  D  E
0  0  0  255  1  1
1  1 -1    0  1  1
2  2  0    0  1  1
3 -1  0    1  1  1
4  1  0    0  1  1
5  2  0    0  1  1
6  2 -1    0  1  1
7 -4  0    0  1  1

[8 rows x 5 columns]

In [27]: dfi.dtypes
Out[27]: 
A    int32
B    int32
C    int32
D    int64
E    int32
dtype: object

In [28]: casted = dfi[dfi>0]

In [29]: casted
Out[29]: 
    A   B    C  D  E
0 NaN NaN  255  1  1
1   1 NaN  NaN  1  1
2   2 NaN  NaN  1  1
3 NaN NaN    1  1  1
4   1 NaN  NaN  1  1
5   2 NaN  NaN  1  1
6   2 NaN  NaN  1  1
7 NaN NaN  NaN  1  1

[8 rows x 5 columns]

In [30]: casted.dtypes
Out[30]: 
A    float64
B    float64
C    float64
D      int64
E      int32
dtype: object

While float dtypes are unchanged.

In [31]: df4 = df3.copy()

In [32]: df4['A'] = df4['A'].astype('float32')

In [33]: df4.dtypes
Out[33]: 
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

In [34]: casted = df4[df4>0]

In [35]: casted
Out[35]: 
          A   B    C  D  E
0       NaN NaN  255  1  1
1  1.364364 NaN  NaN  1  1
2  2.882418 NaN  NaN  1  1
3       NaN NaN    1  1  1
4  1.354205 NaN  NaN  1  1
5  2.052203 NaN  NaN  1  1
6  2.006243 NaN  NaN  1  1
7       NaN NaN  NaN  1  1

[8 rows x 5 columns]

In [36]: casted.dtypes
Out[36]: 
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

Datetimes Conversion

Datetime64[ns] columns in a DataFrame (or a Series) allow the use of np.nan to indicate a nan value, in addition to the traditional NaT, or not-a-time. This allows convenient nan setting in a generic way. Furthermore datetime64[ns] columns are created by default, when passed datetimelike objects (this change was introduced in 0.10.1) (GH2809, GH2810)

In [37]: df = DataFrame(randn(6,2),date_range('20010102',periods=6),columns=['A','B'])

In [38]: df['timestamp'] = Timestamp('20010103')

In [39]: df
Out[39]: 
                   A         B  timestamp
2001-01-02 -1.448835  0.153437 2001-01-03
2001-01-03 -1.123570 -0.791498 2001-01-03
2001-01-04  0.105400  1.262401 2001-01-03
2001-01-05 -0.721844 -0.647645 2001-01-03
2001-01-06 -0.830631  0.761823 2001-01-03
2001-01-07  0.597819  1.045558 2001-01-03

[6 rows x 3 columns]

# datetime64[ns] out of the box
In [40]: df.get_dtype_counts()
Out[40]: 
datetime64[ns]    1
float64           2
dtype: int64

# use the traditional nan, which is mapped to NaT internally
In [41]: df.ix[2:4,['A','timestamp']] = np.nan

In [42]: df
Out[42]: 
                   A         B  timestamp
2001-01-02 -1.448835  0.153437 2001-01-03
2001-01-03 -1.123570 -0.791498 2001-01-03
2001-01-04       NaN  1.262401        NaT
2001-01-05       NaN -0.647645        NaT
2001-01-06 -0.830631  0.761823 2001-01-03
2001-01-07  0.597819  1.045558 2001-01-03

[6 rows x 3 columns]

Astype conversion on datetime64[ns] to object, implicity converts NaT to np.nan

In [43]: import datetime

In [44]: s = Series([datetime.datetime(2001, 1, 2, 0, 0) for i in range(3)])

In [45]: s.dtype
Out[45]: dtype('<M8[ns]')

In [46]: s[1] = np.nan

In [47]: s
Out[47]: 
0   2001-01-02
1          NaT
2   2001-01-02
dtype: datetime64[ns]

In [48]: s.dtype
Out[48]: dtype('<M8[ns]')

In [49]: s = s.astype('O')

In [50]: s
Out[50]: 
0    2001-01-02 00:00:00
1                    NaN
2    2001-01-02 00:00:00
dtype: object

In [51]: s.dtype
Out[51]: dtype('O')

API changes

  • Added to_series() method to indicies, to facilitate the creation of indexers (GH3275)
  • HDFStore
    • added the method select_column to select a single column from a table as a Series.
    • deprecated the unique method, can be replicated by select_column(key,column).unique()
    • min_itemsize parameter to append will now automatically create data_columns for passed keys

Enhancements

  • Improved performance of df.to_csv() by up to 10x in some cases. (GH3059)

  • Numexpr is now a Recommended Dependencies, to accelerate certain types of numerical and boolean operations

  • Bottleneck is now a Recommended Dependencies, to accelerate certain types of nan operations

  • HDFStore

    • support read_hdf/to_hdf API similar to read_csv/to_csv

      In [52]: df = DataFrame(dict(A=lrange(5), B=lrange(5)))
      
      In [53]: df.to_hdf('store.h5','table',append=True)
      
      In [54]: read_hdf('store.h5', 'table', where = ['index>2'])
      Out[54]: 
         A  B
      3  3  3
      4  4  4
      
      [2 rows x 2 columns]
      
    • provide dotted attribute access to get from stores, e.g. store.df == store['df']

    • new keywords iterator=boolean, and chunksize=number_in_a_chunk are provided to support iteration on select and select_as_multiple (GH3076)

  • You can now select timestamps from an unordered timeseries similarly to an ordered timeseries (GH2437)

  • You can now select with a string from a DataFrame with a datelike index, in a similar way to a Series (GH3070)

    In [55]: idx = date_range("2001-10-1", periods=5, freq='M')
    
    In [56]: ts = Series(np.random.rand(len(idx)),index=idx)
    
    In [57]: ts['2001']
    Out[57]: 
    2001-10-31    0.483450
    2001-11-30    0.407530
    2001-12-31    0.965096
    Freq: M, dtype: float64
    
    In [58]: df = DataFrame(dict(A = ts))
    
    In [59]: df['2001']
    Out[59]: 
                       A
    2001-10-31  0.483450
    2001-11-30  0.407530
    2001-12-31  0.965096
    
    [3 rows x 1 columns]
    
  • Squeeze to possibly remove length 1 dimensions from an object.

    In [60]: p = Panel(randn(3,4,4),items=['ItemA','ItemB','ItemC'],
       ....:                    major_axis=date_range('20010102',periods=4),
       ....:                    minor_axis=['A','B','C','D'])
       ....: 
    
    In [61]: p
    Out[61]: 
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemC
    Major_axis axis: 2001-01-02 00:00:00 to 2001-01-05 00:00:00
    Minor_axis axis: A to D
    
    In [62]: p.reindex(items=['ItemA']).squeeze()
    Out[62]: 
                       A         B         C         D
    2001-01-02  0.396537  0.534880 -0.488797 -1.539385
    2001-01-03 -0.829037  0.306681 -0.331032  1.544977
    2001-01-04 -0.621754  1.026208 -0.413106 -1.490869
    2001-01-05 -1.253235 -0.538879 -1.487449 -1.426475
    
    [4 rows x 4 columns]
    
    In [63]: p.reindex(items=['ItemA'],minor=['B']).squeeze()
    Out[63]: 
    2001-01-02    0.534880
    2001-01-03    0.306681
    2001-01-04    1.026208
    2001-01-05   -0.538879
    Freq: D, Name: B, dtype: float64
    
  • In pd.io.data.Options,

    • Fix bug when trying to fetch data for the current month when already past expiry.
    • Now using lxml to scrape html instead of BeautifulSoup (lxml was faster).
    • New instance variables for calls and puts are automatically created when a method that creates them is called. This works for current month where the instance variables are simply calls and puts. Also works for future expiry months and save the instance variable as callsMMYY or putsMMYY, where MMYY are, respectively, the month and year of the option’s expiry.
    • Options.get_near_stock_price now allows the user to specify the month for which to get relevant options data.
    • Options.get_forward_data now has optional kwargs near and above_below. This allows the user to specify if they would like to only return forward looking data for options near the current stock price. This just obtains the data from Options.get_near_stock_price instead of Options.get_xxx_data() (GH2758).
  • Cursor coordinate information is now displayed in time-series plots.

  • added option display.max_seq_items to control the number of elements printed per sequence pprinting it. (GH2979)

  • added option display.chop_threshold to control display of small numerical values. (GH2739)

  • added option display.max_info_rows to prevent verbose_info from being calculated for frames above 1M rows (configurable). (GH2807, GH2918)

  • value_counts() now accepts a “normalize” argument, for normalized histograms. (GH2710).

  • DataFrame.from_records now accepts not only dicts but any instance of the collections.Mapping ABC.

  • added option display.mpl_style providing a sleeker visual style for plots. Based on https://gist.github.com/huyng/816622 (GH3075).

  • Treat boolean values as integers (values 1 and 0) for numeric operations. (GH2641)

  • to_html() now accepts an optional “escape” argument to control reserved HTML character escaping (enabled by default) and escapes &, in addition to < and >. (GH2919)

See the full release notes or issue tracker on GitHub for a complete list.

v0.10.1 (January 22, 2013)

This is a minor release from 0.10.0 and includes new features, enhancements, and bug fixes. In particular, there is substantial new HDFStore functionality contributed by Jeff Reback.

An undesired API breakage with functions taking the inplace option has been reverted and deprecation warnings added.

API changes

  • Functions taking an inplace option return the calling object as before. A deprecation message has been added
  • Groupby aggregations Max/Min no longer exclude non-numeric data (GH2700)
  • Resampling an empty DataFrame now returns an empty DataFrame instead of raising an exception (GH2640)
  • The file reader will now raise an exception when NA values are found in an explicitly specified integer column instead of converting the column to float (GH2631)
  • DatetimeIndex.unique now returns a DatetimeIndex with the same name and
  • timezone instead of an array (GH2563)

New features

  • MySQL support for database (contribution from Dan Allan)

HDFStore

You may need to upgrade your existing data files. Please visit the compatibility section in the main docs.

You can designate (and index) certain columns that you want to be able to perform queries on a table, by passing a list to data_columns

In [1]: store = HDFStore('store.h5')

In [2]: df = DataFrame(randn(8, 3), index=date_range('1/1/2000', periods=8),
   ...:            columns=['A', 'B', 'C'])
   ...: 

In [3]: df['string'] = 'foo'

In [4]: df.ix[4:6,'string'] = np.nan

In [5]: df.ix[7:9,'string'] = 'bar'

In [6]: df['string2'] = 'cool'

In [7]: df
Out[7]: 
                   A         B         C string string2
2000-01-01 -1.601262 -0.256718  0.239369    foo    cool
2000-01-02  0.174122 -1.131794 -1.948006    foo    cool
2000-01-03  0.980347 -0.674429 -0.361633    foo    cool
2000-01-04 -0.761218  1.768215  0.152288    foo    cool
2000-01-05 -0.862613 -0.210968 -0.859278    NaN    cool
2000-01-06  1.498195  0.462413 -0.647604    NaN    cool
2000-01-07  1.511487 -0.727189 -0.342928    foo    cool
2000-01-08 -0.007364  1.427674  0.104020    bar    cool

[8 rows x 5 columns]

# on-disk operations
In [8]: store.append('df', df, data_columns = ['B','C','string','string2'])

In [9]: store.select('df',[ 'B > 0', 'string == foo' ])
Out[9]: 
                   A         B         C string string2
2000-01-04 -0.761218  1.768215  0.152288    foo    cool

[1 rows x 5 columns]

# this is in-memory version of this type of selection
In [10]: df[(df.B > 0) & (df.string == 'foo')]
Out[10]: 
                   A         B         C string string2
2000-01-04 -0.761218  1.768215  0.152288    foo    cool

[1 rows x 5 columns]

Retrieving unique values in an indexable or data column.

# note that this is deprecated as of 0.14.0
# can be replicated by: store.select_column('df','index').unique()
store.unique('df','index')
store.unique('df','string')

You can now store datetime64 in data columns

In [11]: df_mixed               = df.copy()

In [12]: df_mixed['datetime64'] = Timestamp('20010102')

In [13]: df_mixed.ix[3:4,['A','B']] = np.nan

In [14]: store.append('df_mixed', df_mixed)

In [15]: df_mixed1 = store.select('df_mixed')

In [16]: df_mixed1
Out[16]: 
                   A         B         C string string2 datetime64
2000-01-01 -1.601262 -0.256718  0.239369    foo    cool 2001-01-02
2000-01-02  0.174122 -1.131794 -1.948006    foo    cool 2001-01-02
2000-01-03  0.980347 -0.674429 -0.361633    foo    cool 2001-01-02
2000-01-04       NaN       NaN  0.152288    foo    cool 2001-01-02
2000-01-05 -0.862613 -0.210968 -0.859278    NaN    cool 2001-01-02
2000-01-06  1.498195  0.462413 -0.647604    NaN    cool 2001-01-02
2000-01-07  1.511487 -0.727189 -0.342928    foo    cool 2001-01-02
2000-01-08 -0.007364  1.427674  0.104020    bar    cool 2001-01-02

[8 rows x 6 columns]

In [17]: df_mixed1.get_dtype_counts()
Out[17]: 
datetime64[ns]    1
float64           3
object            2
dtype: int64

You can pass columns keyword to select to filter a list of the return columns, this is equivalent to passing a Term('columns',list_of_columns_to_filter)

In [18]: store.select('df',columns = ['A','B'])
Out[18]: 
                   A         B
2000-01-01 -1.601262 -0.256718
2000-01-02  0.174122 -1.131794
2000-01-03  0.980347 -0.674429
2000-01-04 -0.761218  1.768215
2000-01-05 -0.862613 -0.210968
2000-01-06  1.498195  0.462413
2000-01-07  1.511487 -0.727189
2000-01-08 -0.007364  1.427674

[8 rows x 2 columns]

HDFStore now serializes multi-index dataframes when appending tables.

In [19]: index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
   ....:                            ['one', 'two', 'three']],
   ....:                    labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
   ....:                            [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
   ....:                    names=['foo', 'bar'])
   ....: 

In [20]: df = DataFrame(np.random.randn(10, 3), index=index,
   ....:                columns=['A', 'B', 'C'])
   ....: 

In [21]: df
Out[21]: 
                  A         B         C
foo bar                                
foo one    2.052171 -1.230963 -0.019240
    two   -1.713238  0.838912 -0.637855
    three  0.215109 -1.515362  1.586924
bar one   -0.447974 -1.573998  0.630925
    two   -0.071659 -1.277640 -0.102206
baz two    0.870302  1.275280 -1.199212
    three  1.060780  1.673018  1.249874
qux one    1.458210 -0.710542  0.825392
    two    1.557329  1.993441 -0.616293
    three  0.150468  0.132104  0.580923

[10 rows x 3 columns]

In [22]: store.append('mi',df)

In [23]: store.select('mi')
Out[23]: 
                  A         B         C
foo bar                                
foo one    2.052171 -1.230963 -0.019240
    two   -1.713238  0.838912 -0.637855
    three  0.215109 -1.515362  1.586924
bar one   -0.447974 -1.573998  0.630925
    two   -0.071659 -1.277640 -0.102206
baz two    0.870302  1.275280 -1.199212
    three  1.060780  1.673018  1.249874
qux one    1.458210 -0.710542  0.825392
    two    1.557329  1.993441 -0.616293
    three  0.150468  0.132104  0.580923

[10 rows x 3 columns]

# the levels are automatically included as data columns
In [24]: store.select('mi', Term('foo=bar'))
Out[24]: 
                A         B         C
foo bar                              
bar one -0.447974 -1.573998  0.630925
    two -0.071659 -1.277640 -0.102206

[2 rows x 3 columns]

Multi-table creation via append_to_multiple and selection via select_as_multiple can create/select from multiple tables and return a combined result, by using where on a selector table.

In [25]: df_mt = DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8),
   ....:                                columns=['A', 'B', 'C', 'D', 'E', 'F'])
   ....: 

In [26]: df_mt['foo'] = 'bar'

# you can also create the tables individually
In [27]: store.append_to_multiple({ 'df1_mt' : ['A','B'], 'df2_mt' : None }, df_mt, selector = 'df1_mt')

In [28]: store
Out[28]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5
/df                  frame_table  (typ->appendable,nrows->8,ncols->5,indexers->[index],dc->[B,C,string,string2])
/df1_mt              frame_table  (typ->appendable,nrows->8,ncols->2,indexers->[index],dc->[A,B])               
/df2_mt              frame_table  (typ->appendable,nrows->8,ncols->5,indexers->[index])                         
/df_mixed            frame_table  (typ->appendable,nrows->8,ncols->6,indexers->[index])                         
/mi                  frame_table  (typ->appendable_multi,nrows->10,ncols->5,indexers->[index],dc->[bar,foo])    

# indiviual tables were created
In [29]: store.select('df1_mt')
Out[29]: 
                   A         B
2000-01-01 -0.128750  1.445964
2000-01-02 -0.688741  0.228006
2000-01-03  0.932498 -2.200069
2000-01-04  1.298390  1.662964
2000-01-05 -0.462446 -0.112019
2000-01-06 -1.626124  0.982041
2000-01-07  0.942864  2.502156
2000-01-08  0.268766 -1.225092

[8 rows x 2 columns]

In [30]: store.select('df2_mt')
Out[30]: 
                   C         D         E         F  foo
2000-01-01 -0.431163  0.016640  0.904578 -1.645852  bar
2000-01-02  0.800353 -0.451572  0.831767  0.228760  bar
2000-01-03  1.239198  0.185437 -0.540770 -0.370038  bar
2000-01-04 -0.040863  0.290110 -0.096145  1.717830  bar
2000-01-05 -0.134024 -0.205969  1.348944 -1.198246  bar
2000-01-06  0.059493 -0.460111 -1.565401 -0.025706  bar
2000-01-07 -0.302741  0.261551 -0.066342  0.897097  bar
2000-01-08  0.582752 -1.490764 -0.639757 -0.952750  bar

[8 rows x 5 columns]

# as a multiple
In [31]: store.select_as_multiple(['df1_mt','df2_mt'], where = [ 'A>0','B>0' ], selector = 'df1_mt')
Out[31]: 
                   A         B         C         D         E         F  foo
2000-01-04  1.298390  1.662964 -0.040863  0.290110 -0.096145  1.717830  bar
2000-01-07  0.942864  2.502156 -0.302741  0.261551 -0.066342  0.897097  bar

[2 rows x 7 columns]

Enhancements

  • HDFStore now can read native PyTables table format tables
  • You can pass nan_rep = 'my_nan_rep' to append, to change the default nan representation on disk (which converts to/from np.nan), this defaults to nan.
  • You can pass index to append. This defaults to True. This will automagically create indicies on the indexables and data columns of the table
  • You can pass chunksize=an integer to append, to change the writing chunksize (default is 50000). This will signficantly lower your memory usage on writing.
  • You can pass expectedrows=an integer to the first append, to set the TOTAL number of expectedrows that PyTables will expected. This will optimize read/write performance.
  • Select now supports passing start and stop to provide selection space limiting in selection.
  • Greatly improved ISO8601 (e.g., yyyy-mm-dd) date parsing for file parsers (GH2698)
  • Allow DataFrame.merge to handle combinatorial sizes too large for 64-bit integer (GH2690)
  • Series now has unary negation (-series) and inversion (~series) operators (GH2686)
  • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327)
  • Series arithmetic operators can now handle constant and ndarray input (GH2574)
  • ExcelFile now takes a kind argument to specify the file type (GH2613)
  • A faster implementation for Series.str methods (GH2602)

Bug Fixes

  • HDFStore tables can now store float32 types correctly (cannot be mixed with float64 however)
  • Fixed Google Analytics prefix when specifying request segment (GH2713).
  • Function to reset Google Analytics token store so users can recover from improperly setup client secrets (GH2687).
  • Fixed groupby bug resulting in segfault when passing in MultiIndex (GH2706)
  • Fixed bug where passing a Series with datetime64 values into to_datetime results in bogus output values (GH2699)
  • Fixed bug in pattern in HDFStore expressions when pattern is not a valid regex (GH2694)
  • Fixed performance issues while aggregating boolean data (GH2692)
  • When given a boolean mask key and a Series of new values, Series __setitem__ will now align the incoming values with the original Series (GH2686)
  • Fixed MemoryError caused by performing counting sort on sorting MultiIndex levels with a very large number of combinatorial values (GH2684)
  • Fixed bug that causes plotting to fail when the index is a DatetimeIndex with a fixed-offset timezone (GH2683)
  • Corrected businessday subtraction logic when the offset is more than 5 bdays and the starting date is on a weekend (GH2680)
  • Fixed C file parser behavior when the file has more columns than data (GH2668)
  • Fixed file reader bug that misaligned columns with data in the presence of an implicit column and a specified usecols value
  • DataFrames with numerical or datetime indices are now sorted prior to plotting (GH2609)
  • Fixed DataFrame.from_records error when passed columns, index, but empty records (GH2633)
  • Several bug fixed for Series operations when dtype is datetime64 (GH2689, GH2629, GH2626)

See the full release notes or issue tracker on GitHub for a complete list.

v0.10.0 (December 17, 2012)

This is a major release from 0.9.1 and includes many new features and enhancements along with a large number of bug fixes. There are also a number of important API changes that long-time pandas users should pay close attention to.

File parsing new features

The delimited file parsing engine (the guts of read_csv and read_table) has been rewritten from the ground up and now uses a fraction the amount of memory while parsing, while being 40% or more faster in most use cases (in some cases much faster).

There are also many new features:

  • Much-improved Unicode handling via the encoding option.
  • Column filtering (usecols)
  • Dtype specification (dtype argument)
  • Ability to specify strings to be recognized as True/False
  • Ability to yield NumPy record arrays (as_recarray)
  • High performance delim_whitespace option
  • Decimal format (e.g. European format) specification
  • Easier CSV dialect options: escapechar, lineterminator, quotechar, etc.
  • More robust handling of many exceptional kinds of files observed in the wild

API changes

Deprecated DataFrame BINOP TimeSeries special case behavior

The default behavior of binary operations between a DataFrame and a Series has always been to align on the DataFrame’s columns and broadcast down the rows, except in the special case that the DataFrame contains time series. Since there are now method for each binary operator enabling you to specify how you want to broadcast, we are phasing out this special case (Zen of Python: Special cases aren’t special enough to break the rules). Here’s what I’m talking about:

In [1]: import pandas as pd

In [2]: df = pd.DataFrame(np.random.randn(6, 4),
   ...:                   index=pd.date_range('1/1/2000', periods=6))
   ...: 

In [3]: df
Out[3]: 
                   0         1         2         3
2000-01-01 -0.892402  0.505987 -0.681624  0.850162
2000-01-02  0.586586  1.175843 -0.160391  0.481679
2000-01-03  0.408279  1.641246  0.383888 -1.495227
2000-01-04  1.166096 -0.802272 -0.275253  0.517938
2000-01-05 -0.750872  1.216537 -0.910343 -0.606534
2000-01-06 -0.410659  0.264024 -0.069315 -1.814768

[6 rows x 4 columns]

# deprecated now
In [4]: df - df[0]
Out[4]: 
            0         1         2         3
2000-01-01  0  1.398389  0.210778  1.742564
2000-01-02  0  0.589256 -0.746978 -0.104908
2000-01-03  0  1.232968 -0.024391 -1.903505
2000-01-04  0 -1.968368 -1.441350 -0.648158
2000-01-05  0  1.967410 -0.159471  0.144338
2000-01-06  0  0.674682  0.341344 -1.404109

[6 rows x 4 columns]

# Change your code to
In [5]: df.sub(df[0], axis=0) # align on axis 0 (rows)
Out[5]: 
            0         1         2         3
2000-01-01  0  1.398389  0.210778  1.742564
2000-01-02  0  0.589256 -0.746978 -0.104908
2000-01-03  0  1.232968 -0.024391 -1.903505
2000-01-04  0 -1.968368 -1.441350 -0.648158
2000-01-05  0  1.967410 -0.159471  0.144338
2000-01-06  0  0.674682  0.341344 -1.404109

[6 rows x 4 columns]

You will get a deprecation warning in the 0.10.x series, and the deprecated functionality will be removed in 0.11 or later.

Altered resample default behavior

The default time series resample binning behavior of daily D and higher frequencies has been changed to closed='left', label='left'. Lower nfrequencies are unaffected. The prior defaults were causing a great deal of confusion for users, especially resampling data to daily frequency (which labeled the aggregated group with the end of the interval: the next day).

Note:

In [6]: dates = pd.date_range('1/1/2000', '1/5/2000', freq='4h')

In [7]: series = Series(np.arange(len(dates)), index=dates)

In [8]: series
Out[8]: 
2000-01-01 00:00:00    0
2000-01-01 04:00:00    1
2000-01-01 08:00:00    2
2000-01-01 12:00:00    3
2000-01-01 16:00:00    4
...
2000-01-04 04:00:00    19
2000-01-04 08:00:00    20
2000-01-04 12:00:00    21
2000-01-04 16:00:00    22
2000-01-04 20:00:00    23
2000-01-05 00:00:00    24
Freq: 4H, Length: 25

In [9]: series.resample('D', how='sum')
Out[9]: 
2000-01-01     15
2000-01-02     51
2000-01-03     87
2000-01-04    123
2000-01-05     24
Freq: D, dtype: int32

# old behavior
In [10]: series.resample('D', how='sum', closed='right', label='right')
Out[10]: 
2000-01-01      0
2000-01-02     21
2000-01-03     57
2000-01-04     93
2000-01-05    129
Freq: D, dtype: int32
  • Infinity and negative infinity are no longer treated as NA by isnull and notnull. That they every were was a relic of early pandas. This behavior can be re-enabled globally by the mode.use_inf_as_null option:
In [11]: s = pd.Series([1.5, np.inf, 3.4, -np.inf])

In [12]: pd.isnull(s)
Out[12]: 
0    False
1    False
2    False
3    False
dtype: bool

In [13]: s.fillna(0)
Out[13]: 
0    1.500000
1         inf
2    3.400000
3        -inf
dtype: float64

In [14]: pd.set_option('use_inf_as_null', True)

In [15]: pd.isnull(s)
Out[15]: 
0    False
1     True
2    False
3     True
dtype: bool

In [16]: s.fillna(0)
Out[16]: 
0    1.5
1    0.0
2    3.4
3    0.0
dtype: float64

In [17]: pd.reset_option('use_inf_as_null')
  • Methods with the inplace option now all return None instead of the calling object. E.g. code written like df = df.fillna(0, inplace=True) may stop working. To fix, simply delete the unnecessary variable assignment.
  • pandas.merge no longer sorts the group keys (sort=False) by default. This was done for performance reasons: the group-key sorting is often one of the more expensive parts of the computation and is often unnecessary.
  • The default column names for a file with no header have been changed to the integers 0 through N - 1. This is to create consistency with the DataFrame constructor with no columns specified. The v0.9.0 behavior (names X0, X1, ...) can be reproduced by specifying prefix='X':
In [18]: data= 'a,b,c\n1,Yes,2\n3,No,4'

In [19]: print(data)
a,b,c
1,Yes,2
3,No,4

In [20]: pd.read_csv(StringIO(data), header=None)
Out[20]: 
   0    1  2
0  a    b  c
1  1  Yes  2
2  3   No  4

[3 rows x 3 columns]

In [21]: pd.read_csv(StringIO(data), header=None, prefix='X')
Out[21]: 
  X0   X1 X2
0  a    b  c
1  1  Yes  2
2  3   No  4

[3 rows x 3 columns]
  • Values like 'Yes' and 'No' are not interpreted as boolean by default, though this can be controlled by new true_values and false_values arguments:
In [22]: print(data)
a,b,c
1,Yes,2
3,No,4

In [23]: pd.read_csv(StringIO(data))
Out[23]: 
   a    b  c
0  1  Yes  2
1  3   No  4

[2 rows x 3 columns]

In [24]: pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No'])
Out[24]: 
   a      b  c
0  1   True  2
1  3  False  4

[2 rows x 3 columns]
  • The file parsers will not recognize non-string values arising from a converter function as NA if passed in the na_values argument. It’s better to do post-processing using the replace function instead.
  • Calling fillna on Series or DataFrame with no arguments is no longer valid code. You must either specify a fill value or an interpolation method:
In [25]: s = Series([np.nan, 1., 2., np.nan, 4])

In [26]: s
Out[26]: 
0   NaN
1     1
2     2
3   NaN
4     4
dtype: float64

In [27]: s.fillna(0)
Out[27]: 
0    0
1    1
2    2
3    0
4    4
dtype: float64

In [28]: s.fillna(method='pad')
Out[28]: 
0   NaN
1     1
2     2
3     2
4     4
dtype: float64

Convenience methods ffill and bfill have been added:

In [29]: s.ffill()
Out[29]: 
0   NaN
1     1
2     2
3     2
4     4
dtype: float64
  • Series.apply will now operate on a returned value from the applied function, that is itself a series, and possibly upcast the result to a DataFrame

    In [30]: def f(x):
       ....:     return Series([ x, x**2 ], index = ['x', 'x^2'])
       ....: 
    
    In [31]: s = Series(np.random.rand(5))
    
    In [32]: s
    Out[32]: 
    0    0.013135
    1    0.909855
    2    0.098093
    3    0.023540
    4    0.141354
    dtype: float64
    
    In [33]: s.apply(f)
    Out[33]: 
              x       x^2
    0  0.013135  0.000173
    1  0.909855  0.827836
    2  0.098093  0.009622
    3  0.023540  0.000554
    4  0.141354  0.019981
    
    [5 rows x 2 columns]
    
  • New API functions for working with pandas options (GH2097):

    • get_option / set_option - get/set the value of an option. Partial names are accepted. - reset_option - reset one or more options to their default value. Partial names are accepted. - describe_option - print a description of one or more options. When called with no arguments. print all registered options.

    Note: set_printoptions/ reset_printoptions are now deprecated (but functioning), the print options now live under “display.XYZ”. For example:

    In [34]: get_option("display.max_rows")
    Out[34]: 15
    
  • to_string() methods now always return unicode strings (GH2224).

New features

Wide DataFrame Printing

Instead of printing the summary information, pandas now splits the string representation across multiple rows by default:

In [35]: wide_frame = DataFrame(randn(5, 16))

In [36]: wide_frame
Out[36]: 
         0         1         2         3         4         5         6   \
0  2.520045  1.570114 -0.360875 -0.880096  0.235532  0.207232 -1.983857   
1  0.422194  0.288403 -0.487393 -0.777639  0.055865  1.383381  0.085638   
2  0.585174 -0.568825 -0.719412  1.191340 -0.456362  0.089931  0.776079   
3  1.218080 -0.564705 -0.581790  0.286071  0.048725  1.002440  1.276582   
4 -0.376280  0.511936 -0.116412 -0.625256 -0.550627  1.261433 -0.552429   

         7         8         9         10        11        12        13  \
0 -1.702547 -1.621234 -0.906840  1.014601 -0.475108 -0.358944  1.262942   
1  0.246392  0.965887  0.246354 -0.727728 -0.094414 -0.276854  0.158399   
2  0.752889 -1.195795 -1.425911 -0.548829  0.774225  0.740501  1.510263   
3  0.054399  0.241963 -0.471786  0.314510 -0.059986 -2.069319 -1.115104   
4  1.695803 -1.025917 -0.910942  0.426805 -0.131749  0.432600  0.044671   

         14        15  
0 -0.412451 -0.462580  
1 -0.277255  1.331263  
2 -1.642511  0.432560  
3 -0.369325 -1.502617  
4 -0.341265  1.844536  

[5 rows x 16 columns]

The old behavior of printing out summary information can be achieved via the ‘expand_frame_repr’ print option:

In [37]: pd.set_option('expand_frame_repr', False)

In [38]: wide_frame
Out[38]: 
         0         1         2         3         4         5         6         7         8         9         10        11        12        13        14        15
0  2.520045  1.570114 -0.360875 -0.880096  0.235532  0.207232 -1.983857 -1.702547 -1.621234 -0.906840  1.014601 -0.475108 -0.358944  1.262942 -0.412451 -0.462580
1  0.422194  0.288403 -0.487393 -0.777639  0.055865  1.383381  0.085638  0.246392  0.965887  0.246354 -0.727728 -0.094414 -0.276854  0.158399 -0.277255  1.331263
2  0.585174 -0.568825 -0.719412  1.191340 -0.456362  0.089931  0.776079  0.752889 -1.195795 -1.425911 -0.548829  0.774225  0.740501  1.510263 -1.642511  0.432560
3  1.218080 -0.564705 -0.581790  0.286071  0.048725  1.002440  1.276582  0.054399  0.241963 -0.471786  0.314510 -0.059986 -2.069319 -1.115104 -0.369325 -1.502617
4 -0.376280  0.511936 -0.116412 -0.625256 -0.550627  1.261433 -0.552429  1.695803 -1.025917 -0.910942  0.426805 -0.131749  0.432600  0.044671 -0.341265  1.844536

[5 rows x 16 columns]

The width of each line can be changed via ‘line_width’ (80 by default):

In [39]: pd.set_option('line_width', 40)
line_width has been deprecated, use display.width instead (currently both are
identical)


In [40]: wide_frame
Out[40]: 
         0         1         2   \
0  2.520045  1.570114 -0.360875   
1  0.422194  0.288403 -0.487393   
2  0.585174 -0.568825 -0.719412   
3  1.218080 -0.564705 -0.581790   
4 -0.376280  0.511936 -0.116412   

         3         4         5   \
0 -0.880096  0.235532  0.207232   
1 -0.777639  0.055865  1.383381   
2  1.191340 -0.456362  0.089931   
3  0.286071  0.048725  1.002440   
4 -0.625256 -0.550627  1.261433   

         6         7         8   \
0 -1.983857 -1.702547 -1.621234   
1  0.085638  0.246392  0.965887   
2  0.776079  0.752889 -1.195795   
3  1.276582  0.054399  0.241963   
4 -0.552429  1.695803 -1.025917   

         9         10        11  \
0 -0.906840  1.014601 -0.475108   
1  0.246354 -0.727728 -0.094414   
2 -1.425911 -0.548829  0.774225   
3 -0.471786  0.314510 -0.059986   
4 -0.910942  0.426805 -0.131749   

         12        13        14  \
0 -0.358944  1.262942 -0.412451   
1 -0.276854  0.158399 -0.277255   
2  0.740501  1.510263 -1.642511   
3 -2.069319 -1.115104 -0.369325   
4  0.432600  0.044671 -0.341265   

         15  
0 -0.462580  
1  1.331263  
2  0.432560  
3 -1.502617  
4  1.844536  

[5 rows x 16 columns]

Updated PyTables Support

Docs for PyTables Table format & several enhancements to the api. Here is a taste of what to expect.

In [41]: store = HDFStore('store.h5')

In [42]: df = DataFrame(randn(8, 3), index=date_range('1/1/2000', periods=8),
   ....:            columns=['A', 'B', 'C'])
   ....: 

In [43]: df
Out[43]: 
                   A         B         C
2000-01-01 -2.036047  0.000830 -0.955697
2000-01-02 -0.898872 -0.725411  0.059904
2000-01-03 -0.449644  1.082900 -1.221265
2000-01-04  0.361078  1.330704  0.855932
2000-01-05 -1.216718  1.488887  0.018993
2000-01-06 -0.877046  0.045976  0.437274
2000-01-07 -0.567182 -0.888657 -0.556383
2000-01-08  0.655457  1.117949 -2.782376

[8 rows x 3 columns]

# appending data frames
In [44]: df1 = df[0:4]

In [45]: df2 = df[4:]

In [46]: store.append('df', df1)

In [47]: store.append('df', df2)

In [48]: store
Out[48]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5
/df            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])

# selecting the entire store
In [49]: store.select('df')
Out[49]: 
                   A         B         C
2000-01-01 -2.036047  0.000830 -0.955697
2000-01-02 -0.898872 -0.725411  0.059904
2000-01-03 -0.449644  1.082900 -1.221265
2000-01-04  0.361078  1.330704  0.855932
2000-01-05 -1.216718  1.488887  0.018993
2000-01-06 -0.877046  0.045976  0.437274
2000-01-07 -0.567182 -0.888657 -0.556383
2000-01-08  0.655457  1.117949 -2.782376

[8 rows x 3 columns]
In [50]: wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
   ....:        major_axis=date_range('1/1/2000', periods=5),
   ....:        minor_axis=['A', 'B', 'C', 'D'])
   ....: 

In [51]: wp
Out[51]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D

# storing a panel
In [52]: store.append('wp',wp)

# selecting via A QUERY
In [53]: store.select('wp',
   ....:   [ Term('major_axis>20000102'), Term('minor_axis', '=', ['A','B']) ])
   ....: 
Out[53]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to B

# removing data from tables
In [54]: store.remove('wp', Term('major_axis>20000103'))
Out[54]: 8

In [55]: store.select('wp')
Out[55]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-03 00:00:00
Minor_axis axis: A to D

# deleting a store
In [56]: del store['df']

In [57]: store
Out[57]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5
/wp            wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])

Enhancements

  • added ability to hierarchical keys

    In [58]: store.put('foo/bar/bah', df)
    
    In [59]: store.append('food/orange', df)
    
    In [60]: store.append('food/apple',  df)
    
    In [61]: store
    Out[61]: 
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /foo/bar/bah            frame        (shape->[8,3])                                                        
    /food/apple             frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])                 
    /food/orange            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])                 
    /wp                     wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])
    
    # remove all nodes under this level
    In [62]: store.remove('food')
    
    In [63]: store
    Out[63]: 
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /foo/bar/bah            frame        (shape->[8,3])                                                        
    /wp                     wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])
    
  • added mixed-dtype support!

    In [64]: df['string'] = 'string'
    
    In [65]: df['int']    = 1
    
    In [66]: store.append('df',df)
    
    In [67]: df1 = store.select('df')
    
    In [68]: df1
    Out[68]: 
                       A         B         C  string  int
    2000-01-01 -2.036047  0.000830 -0.955697  string    1
    2000-01-02 -0.898872 -0.725411  0.059904  string    1
    2000-01-03 -0.449644  1.082900 -1.221265  string    1
    2000-01-04  0.361078  1.330704  0.855932  string    1
    2000-01-05 -1.216718  1.488887  0.018993  string    1
    2000-01-06 -0.877046  0.045976  0.437274  string    1
    2000-01-07 -0.567182 -0.888657 -0.556383  string    1
    2000-01-08  0.655457  1.117949 -2.782376  string    1
    
    [8 rows x 5 columns]
    
    In [69]: df1.get_dtype_counts()
    Out[69]: 
    float64    3
    int64      1
    object     1
    dtype: int64
    
  • performance improvments on table writing

  • support for arbitrarily indexed dimensions

  • SparseSeries now has a density property (GH2384)

  • enable Series.str.strip/lstrip/rstrip methods to take an input argument to strip arbitrary characters (GH2411)

  • implement value_vars in melt to limit values to certain columns and add melt to pandas namespace (GH2412)

Bug Fixes

  • added Term method of specifying where conditions (GH1996).
  • del store['df'] now call store.remove('df') for store deletion
  • deleting of consecutive rows is much faster than before
  • min_itemsize parameter can be specified in table creation to force a minimum size for indexing columns (the previous implementation would set the column size based on the first append)
  • indexing support via create_table_index (requires PyTables >= 2.3) (GH698).
  • appending on a store would fail if the table was not first created via put
  • fixed issue with missing attributes after loading a pickled dataframe (GH2431)
  • minor change to select and remove: require a table ONLY if where is also provided (and not None)

Compatibility

0.10 of HDFStore is backwards compatible for reading tables created in a prior version of pandas, however, query terms using the prior (undocumented) methodology are unsupported. You must read in the entire file and write it out using the new format to take advantage of the updates.

N Dimensional Panels (Experimental)

Adding experimental support for Panel4D and factory functions to create n-dimensional named panels. Docs for NDim. Here is a taste of what to expect.

In [70]: p4d = Panel4D(randn(2, 2, 5, 4),
   ....:       labels=['Label1','Label2'],
   ....:       items=['Item1', 'Item2'],
   ....:       major_axis=date_range('1/1/2000', periods=5),
   ....:       minor_axis=['A', 'B', 'C', 'D'])
   ....: 

In [71]: p4d
Out[71]: 
<class 'pandas.core.panelnd.Panel4D'>
Dimensions: 2 (labels) x 2 (items) x 5 (major_axis) x 4 (minor_axis)
Labels axis: Label1 to Label2
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D

See the full release notes or issue tracker on GitHub for a complete list.

v0.9.1 (November 14, 2012)

This is a bugfix release from 0.9.0 and includes several new features and enhancements along with a large number of bug fixes. The new features include by-column sort order for DataFrame and Series, improved NA handling for the rank method, masking functions for DataFrame, and intraday time-series filtering for DataFrame.

New features

  • Series.sort, DataFrame.sort, and DataFrame.sort_index can now be specified in a per-column manner to support multiple sort orders (GH928)

    In [1]: df = DataFrame(np.random.randint(0, 2, (6, 3)), columns=['A', 'B', 'C'])
    
    In [2]: df.sort(['A', 'B'], ascending=[1, 0])
    Out[2]: 
       A  B  C
    2  0  1  1
    3  0  1  1
    4  0  0  1
    0  1  1  0
    1  1  0  1
    5  1  0  1
    
    [6 rows x 3 columns]
    
  • DataFrame.rank now supports additional argument values for the na_option parameter so missing values can be assigned either the largest or the smallest rank (GH1508, GH2159)

    In [3]: df = DataFrame(np.random.randn(6, 3), columns=['A', 'B', 'C'])
    
    In [4]: df.ix[2:4] = np.nan
    
    In [5]: df.rank()
    Out[5]: 
        A   B   C
    0   3   2   1
    1   2   1   3
    2 NaN NaN NaN
    3 NaN NaN NaN
    4 NaN NaN NaN
    5   1   3   2
    
    [6 rows x 3 columns]
    
    In [6]: df.rank(na_option='top')
    Out[6]: 
       A  B  C
    0  6  5  4
    1  5  4  6
    2  2  2  2
    3  2  2  2
    4  2  2  2
    5  4  6  5
    
    [6 rows x 3 columns]
    
    In [7]: df.rank(na_option='bottom')
    Out[7]: 
       A  B  C
    0  3  2  1
    1  2  1  3
    2  5  5  5
    3  5  5  5
    4  5  5  5
    5  1  3  2
    
    [6 rows x 3 columns]
    
  • DataFrame has new where and mask methods to select values according to a given boolean mask (GH2109, GH2151)

    DataFrame currently supports slicing via a boolean vector the same length as the DataFrame (inside the []). The returned DataFrame has the same number of columns as the original, but is sliced on its index.

    In [8]: df = DataFrame(np.random.randn(5, 3), columns = ['A','B','C'])
    
    In [9]: df
    Out[9]: 
              A         B         C
    0  0.706220 -1.130744 -0.690308
    1 -0.885387  0.246004  1.986687
    2  0.212595 -1.189832 -0.344258
    3  0.816335 -1.514102  1.298184
    4  0.089527  0.576687 -0.737750
    
    [5 rows x 3 columns]
    
    In [10]: df[df['A'] > 0]
    Out[10]: 
              A         B         C
    0  0.706220 -1.130744 -0.690308
    2  0.212595 -1.189832 -0.344258
    3  0.816335 -1.514102  1.298184
    4  0.089527  0.576687 -0.737750
    
    [4 rows x 3 columns]
    

    If a DataFrame is sliced with a DataFrame based boolean condition (with the same size as the original DataFrame), then a DataFrame the same size (index and columns) as the original is returned, with elements that do not meet the boolean condition as NaN. This is accomplished via the new method DataFrame.where. In addition, where takes an optional other argument for replacement.

    In [11]: df[df>0]
    Out[11]: 
              A         B         C
    0  0.706220       NaN       NaN
    1       NaN  0.246004  1.986687
    2  0.212595       NaN       NaN
    3  0.816335       NaN  1.298184
    4  0.089527  0.576687       NaN
    
    [5 rows x 3 columns]
    
    In [12]: df.where(df>0)
    Out[12]: 
              A         B         C
    0  0.706220       NaN       NaN
    1       NaN  0.246004  1.986687
    2  0.212595       NaN       NaN
    3  0.816335       NaN  1.298184
    4  0.089527  0.576687       NaN
    
    [5 rows x 3 columns]
    
    In [13]: df.where(df>0,-df)
    Out[13]: 
              A         B         C
    0  0.706220  1.130744  0.690308
    1  0.885387  0.246004  1.986687
    2  0.212595  1.189832  0.344258
    3  0.816335  1.514102  1.298184
    4  0.089527  0.576687  0.737750
    
    [5 rows x 3 columns]
    

    Furthermore, where now aligns the input boolean condition (ndarray or DataFrame), such that partial selection with setting is possible. This is analagous to partial setting via .ix (but on the contents rather than the axis labels)

    In [14]: df2 = df.copy()
    
    In [15]: df2[ df2[1:4] > 0 ] = 3
    
    In [16]: df2
    Out[16]: 
              A         B         C
    0  0.706220 -1.130744 -0.690308
    1 -0.885387  3.000000  3.000000
    2  3.000000 -1.189832 -0.344258
    3  3.000000 -1.514102  3.000000
    4  0.089527  0.576687 -0.737750
    
    [5 rows x 3 columns]
    

    DataFrame.mask is the inverse boolean operation of where.

    In [17]: df.mask(df<=0)
    Out[17]: 
              A         B         C
    0  0.706220       NaN       NaN
    1       NaN  0.246004  1.986687
    2  0.212595       NaN       NaN
    3  0.816335       NaN  1.298184
    4  0.089527  0.576687       NaN
    
    [5 rows x 3 columns]
    
  • Enable referencing of Excel columns by their column names (GH1936)

    In [18]: xl = ExcelFile('data/test.xls')
    
    In [19]: xl.parse('Sheet1', index_col=0, parse_dates=True,
       ....:          parse_cols='A:D')
       ....: 
    Out[19]: 
                       A         B         C
    2000-01-03  0.980269  3.685731 -0.364217
    2000-01-04  1.047916 -0.041232 -0.161812
    2000-01-05  0.498581  0.731168 -0.537677
    2000-01-06  1.120202  1.567621  0.003641
    2000-01-07 -0.487094  0.571455 -1.611639
    2000-01-10  0.836649  0.246462  0.588543
    2000-01-11 -0.157161  1.340307  1.195778
    
    [7 rows x 3 columns]
    
  • Added option to disable pandas-style tick locators and formatters using series.plot(x_compat=True) or pandas.plot_params[‘x_compat’] = True (GH2205)

  • Existing TimeSeries methods at_time and between_time were added to DataFrame (GH2149)

  • DataFrame.dot can now accept ndarrays (GH2042)

  • DataFrame.drop now supports non-unique indexes (GH2101)

  • Panel.shift now supports negative periods (GH2164)

  • DataFrame now support unary ~ operator (GH2110)

API changes

  • Upsampling data with a PeriodIndex will result in a higher frequency TimeSeries that spans the original time window

    In [20]: prng = period_range('2012Q1', periods=2, freq='Q')
    
    In [21]: s = Series(np.random.randn(len(prng)), prng)
    
    In [22]: s.resample('M')
    Out[22]: 
    2012-01    0.194513
    2012-02         NaN
    2012-03         NaN
    2012-04   -0.854246
    2012-05         NaN
    2012-06         NaN
    Freq: M, dtype: float64
    
  • Period.end_time now returns the last nanosecond in the time interval (GH2124, GH2125, GH1764)

    In [23]: p = Period('2012')
    
    In [24]: p.end_time
    Out[24]: Timestamp('2012-12-31 23:59:59.999999999')
    
  • File parsers no longer coerce to float or bool for columns that have custom converters specified (GH2184)

    In [25]: data = 'A,B,C\n00001,001,5\n00002,002,6'
    
    In [26]: read_csv(StringIO(data), converters={'A' : lambda x: x.strip()})
    Out[26]: 
           A  B  C
    0  00001  1  5
    1  00002  2  6
    
    [2 rows x 3 columns]
    

See the full release notes or issue tracker on GitHub for a complete list.

v0.9.0 (October 7, 2012)

This is a major release from 0.8.1 and includes several new features and enhancements along with a large number of bug fixes. New features include vectorized unicode encoding/decoding for Series.str, to_latex method to DataFrame, more flexible parsing of boolean values, and enabling the download of options data from Yahoo! Finance.

New features

  • Add encode and decode for unicode handling to vectorized string processing methods in Series.str (GH1706)
  • Add DataFrame.to_latex method (GH1735)
  • Add convenient expanding window equivalents of all rolling_* ops (GH1785)
  • Add Options class to pandas.io.data for fetching options data from Yahoo! Finance (GH1748, GH1739)
  • More flexible parsing of boolean values (Yes, No, TRUE, FALSE, etc) (GH1691, GH1295)
  • Add level parameter to Series.reset_index
  • TimeSeries.between_time can now select times across midnight (GH1871)
  • Series constructor can now handle generator as input (GH1679)
  • DataFrame.dropna can now take multiple axes (tuple/list) as input (GH924)
  • Enable skip_footer parameter in ExcelFile.parse (GH1843)

API changes

  • The default column names when header=None and no columns names passed to functions like read_csv has changed to be more Pythonic and amenable to attribute access:
In [1]: data = '0,0,1\n1,1,0\n0,1,0'

In [2]: df = read_csv(StringIO(data), header=None)

In [3]: df
Out[3]: 
   0  1  2
0  0  0  1
1  1  1  0
2  0  1  0

[3 rows x 3 columns]
  • Creating a Series from another Series, passing an index, will cause reindexing to happen inside rather than treating the Series like an ndarray. Technically improper usages like Series(df[col1], index=df[col2]) that worked before “by accident” (this was never intended) will lead to all NA Series in some cases. To be perfectly clear:
In [4]: s1 = Series([1, 2, 3])

In [5]: s1
Out[5]: 
0    1
1    2
2    3
dtype: int64

In [6]: s2 = Series(s1, index=['foo', 'bar', 'baz'])

In [7]: s2
Out[7]: 
foo   NaN
bar   NaN
baz   NaN
dtype: float64
  • Deprecated day_of_year API removed from PeriodIndex, use dayofyear (GH1723)
  • Don’t modify NumPy suppress printoption to True at import time
  • The internal HDF5 data arrangement for DataFrames has been transposed. Legacy files will still be readable by HDFStore (GH1834, GH1824)
  • Legacy cruft removed: pandas.stats.misc.quantileTS
  • Use ISO8601 format for Period repr: monthly, daily, and on down (GH1776)
  • Empty DataFrame columns are now created as object dtype. This will prevent a class of TypeErrors that was occurring in code where the dtype of a column would depend on the presence of data or not (e.g. a SQL query having results) (GH1783)
  • Setting parts of DataFrame/Panel using ix now aligns input Series/DataFrame (GH1630)
  • first and last methods in GroupBy no longer drop non-numeric columns (GH1809)
  • Resolved inconsistencies in specifying custom NA values in text parser. na_values of type dict no longer override default NAs unless keep_default_na is set to false explicitly (GH1657)
  • DataFrame.dot will not do data alignment, and also work with Series (GH1915)

See the full release notes or issue tracker on GitHub for a complete list.

v0.8.1 (July 22, 2012)

This release includes a few new features, performance enhancements, and over 30 bug fixes from 0.8.0. New features include notably NA friendly string processing functionality and a series of new plot types and options.

New features

Performance improvements

  • Improved implementation of rolling min and max (thanks to Bottleneck !)
  • Add accelerated 'median' GroupBy option (GH1358)
  • Significantly improve the performance of parsing ISO8601-format date strings with DatetimeIndex or to_datetime (GH1571)
  • Improve the performance of GroupBy on single-key aggregations and use with Categorical types
  • Significant datetime parsing performance improvments

v0.8.0 (June 29, 2012)

This is a major release from 0.7.3 and includes extensive work on the time series handling and processing infrastructure as well as a great deal of new functionality throughout the library. It includes over 700 commits from more than 20 distinct authors. Most pandas 0.7.3 and earlier users should not experience any issues upgrading, but due to the migration to the NumPy datetime64 dtype, there may be a number of bugs and incompatibilities lurking. Lingering incompatibilities will be fixed ASAP in a 0.8.1 release if necessary. See the full release notes or issue tracker on GitHub for a complete list.

Support for non-unique indexes

All objects can now work with non-unique indexes. Data alignment / join operations work according to SQL join semantics (including, if application, index duplication in many-to-many joins)

NumPy datetime64 dtype and 1.6 dependency

Time series data are now represented using NumPy’s datetime64 dtype; thus, pandas 0.8.0 now requires at least NumPy 1.6. It has been tested and verified to work with the development version (1.7+) of NumPy as well which includes some significant user-facing API changes. NumPy 1.6 also has a number of bugs having to do with nanosecond resolution data, so I recommend that you steer clear of NumPy 1.6’s datetime64 API functions (though limited as they are) and only interact with this data using the interface that pandas provides.

See the end of the 0.8.0 section for a “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0.

Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as they arise. There will be no more further development in 0.7.x beyond bug fixes.

Time series changes and improvements

Note

With this release, legacy scikits.timeseries users should be able to port their code to use pandas.

Note

See documentation for overview of pandas timeseries API.

  • New datetime64 representation speeds up join operations and data alignment, reduces memory usage, and improve serialization / deserialization performance significantly over datetime.datetime
  • High performance and flexible resample method for converting from high-to-low and low-to-high frequency. Supports interpolation, user-defined aggregation functions, and control over how the intervals and result labeling are defined. A suite of high performance Cython/C-based resampling functions (including Open-High-Low-Close) have also been implemented.
  • Revamp of frequency aliases and support for frequency shortcuts like ‘15min’, or ‘1h30min’
  • New DatetimeIndex class supports both fixed frequency and irregular time series. Replaces now deprecated DateRange class
  • New PeriodIndex and Period classes for representing time spans and performing calendar logic, including the 12 fiscal quarterly frequencies <timeseries.quarterly>. This is a partial port of, and a substantial enhancement to, elements of the scikits.timeseries codebase. Support for conversion between PeriodIndex and DatetimeIndex
  • New Timestamp data type subclasses datetime.datetime, providing the same interface while enabling working with nanosecond-resolution data. Also provides easy time zone conversions.
  • Enhanced support for time zones. Add tz_convert and tz_lcoalize methods to TimeSeries and DataFrame. All timestamps are stored as UTC; Timestamps from DatetimeIndex objects with time zone set will be localized to localtime. Time zone conversions are therefore essentially free. User needs to know very little about pytz library now; only time zone names as as strings are required. Time zone-aware timestamps are equal if and only if their UTC timestamps match. Operations between time zone-aware time series with different time zones will result in a UTC-indexed time series.
  • Time series string indexing conveniences / shortcuts: slice years, year and month, and index values with strings
  • Enhanced time series plotting; adaptation of scikits.timeseries matplotlib-based plotting code
  • New date_range, bdate_range, and period_range factory functions
  • Robust frequency inference function infer_freq and inferred_freq property of DatetimeIndex, with option to infer frequency on construction of DatetimeIndex
  • to_datetime function efficiently parses array of strings to DatetimeIndex. DatetimeIndex will parse array or list of strings to datetime64
  • Optimized support for datetime64-dtype data in Series and DataFrame columns
  • New NaT (Not-a-Time) type to represent NA in timestamp arrays
  • Optimize Series.asof for looking up “as of” values for arrays of timestamps
  • Milli, Micro, Nano date offset objects
  • Can index time series with datetime.time objects to select all data at particular time of day (TimeSeries.at_time) or between two times (TimeSeries.between_time)
  • Add tshift method for leading/lagging using the frequency (if any) of the index, as opposed to a naive lead/lag using shift

Other new features

  • New cut and qcut functions (like R’s cut function) for computing a categorical variable from a continuous variable by binning values either into value-based (cut) or quantile-based (qcut) bins
  • Rename Factor to Categorical and add a number of usability features
  • Add limit argument to fillna/reindex
  • More flexible multiple function application in GroupBy, and can pass list (name, function) tuples to get result in particular order with given names
  • Add flexible replace method for efficiently substituting values
  • Enhanced read_csv/read_table for reading time series data and converting multiple columns to dates
  • Add comments option to parser functions: read_csv, etc.
  • Add :ref`dayfirst <io.dayfirst>` option to parser functions for parsing international DD/MM/YYYY dates
  • Allow the user to specify the CSV reader dialect to control quoting etc.
  • Handling thousands separators in read_csv to improve integer parsing.
  • Enable unstacking of multiple levels in one shot. Alleviate pivot_table bugs (empty columns being introduced)
  • Move to klib-based hash tables for indexing; better performance and less memory usage than Python’s dict
  • Add first, last, min, max, and prod optimized GroupBy functions
  • New ordered_merge function
  • Add flexible comparison instance methods eq, ne, lt, gt, etc. to DataFrame, Series
  • Improve scatter_matrix plotting function and add histogram or kernel density estimates to diagonal
  • Add ‘kde’ plot option for density plots
  • Support for converting DataFrame to R data.frame through rpy2
  • Improved support for complex numbers in Series and DataFrame
  • Add pct_change method to all data structures
  • Add max_colwidth configuration option for DataFrame console output
  • Interpolate Series values using index values
  • Can select multiple columns from GroupBy
  • Add update methods to Series/DataFrame for updating values in place
  • Add any and all method to DataFrame

New plotting methods

Series.plot now supports a secondary_y option:

In [1]: plt.figure()
Out[1]: <matplotlib.figure.Figure at 0xacd3590c>

In [2]: fx['FR'].plot(style='g')
Out[2]: <matplotlib.axes._subplots.AxesSubplot at 0xacd3552c>

In [3]: fx['IT'].plot(style='k--', secondary_y=True)
Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0xacc8dd8c>
../_static/whatsnew_secondary_y.png

Vytautas Jancauskas, the 2012 GSOC participant, has added many new plot types. For example, 'kde' is a new option:

In [4]: s = Series(np.concatenate((np.random.randn(1000),
   ...:                            np.random.randn(1000) * 0.5 + 3)))
   ...: 

In [5]: plt.figure()
Out[5]: <matplotlib.figure.Figure at 0xa3769bcc>

In [6]: s.hist(normed=True, alpha=0.2)
Out[6]: <matplotlib.axes._subplots.AxesSubplot at 0xacf344ec>

In [7]: s.plot(kind='kde')
Out[7]: <matplotlib.axes._subplots.AxesSubplot at 0xacf344ec>
../_static/whatsnew_kde.png

See the plotting page for much more.

Other API changes

  • Deprecation of offset, time_rule, and timeRule arguments names in time series functions. Warnings will be printed until pandas 0.9 or 1.0.

Potential porting issues for pandas <= 0.7.3 users

The major change that may affect you in pandas 0.8.0 is that time series indexes use NumPy’s datetime64 data type instead of dtype=object arrays of Python’s built-in datetime.datetime objects. DateRange has been replaced by DatetimeIndex but otherwise behaved identically. But, if you have code that converts DateRange or Index objects that used to contain datetime.datetime values to plain NumPy arrays, you may have bugs lurking with code using scalar values because you are handing control over to NumPy:

In [8]: import datetime

In [9]: rng = date_range('1/1/2000', periods=10)

In [10]: rng[5]
Out[10]: Timestamp('2000-01-06 00:00:00', offset='D')

In [11]: isinstance(rng[5], datetime.datetime)
Out[11]: True

In [12]: rng_asarray = np.asarray(rng)

In [13]: scalar_val = rng_asarray[5]

In [14]: type(scalar_val)
Out[14]: numpy.datetime64

pandas’s Timestamp object is a subclass of datetime.datetime that has nanosecond support (the nanosecond field store the nanosecond value between 0 and 999). It should substitute directly into any code that used datetime.datetime values before. Thus, I recommend not casting DatetimeIndex to regular NumPy arrays.

If you have code that requires an array of datetime.datetime objects, you have a couple of options. First, the asobject property of DatetimeIndex produces an array of Timestamp objects:

In [15]: stamp_array = rng.asobject

In [16]: stamp_array
Out[16]: Index([2000-01-01 00:00:00, 2000-01-02 00:00:00, 2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00, 2000-01-06 00:00:00, 2000-01-07 00:00:00, 2000-01-08 00:00:00, 2000-01-09 00:00:00, 2000-01-10 00:00:00], dtype='object')

In [17]: stamp_array[5]
Out[17]: Timestamp('2000-01-06 00:00:00', offset='D')

To get an array of proper datetime.datetime objects, use the to_pydatetime method:

In [18]: dt_array = rng.to_pydatetime()

In [19]: dt_array
Out[19]: 
array([datetime.datetime(2000, 1, 1, 0, 0),
       datetime.datetime(2000, 1, 2, 0, 0),
       datetime.datetime(2000, 1, 3, 0, 0),
       datetime.datetime(2000, 1, 4, 0, 0),
       datetime.datetime(2000, 1, 5, 0, 0),
       datetime.datetime(2000, 1, 6, 0, 0),
       datetime.datetime(2000, 1, 7, 0, 0),
       datetime.datetime(2000, 1, 8, 0, 0),
       datetime.datetime(2000, 1, 9, 0, 0),
       datetime.datetime(2000, 1, 10, 0, 0)], dtype=object)

In [20]: dt_array[5]
Out[20]: datetime.datetime(2000, 1, 6, 0, 0)

matplotlib knows how to handle datetime.datetime but not Timestamp objects. While I recommend that you plot time series using TimeSeries.plot, you can either use to_pydatetime or register a converter for the Timestamp type. See matplotlib documentation for more on this.

Warning

There are bugs in the user-facing API with the nanosecond datetime64 unit in NumPy 1.6. In particular, the string version of the array shows garbage values, and conversion to dtype=object is similarly broken.

In [21]: rng = date_range('1/1/2000', periods=10)

In [22]: rng
Out[22]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01, ..., 2000-01-10]
Length: 10, Freq: D, Timezone: None

In [23]: np.asarray(rng)
Out[23]: 
array(['2000-01-01T01:00:00.000000000+0100',
       '2000-01-02T01:00:00.000000000+0100',
       '2000-01-03T01:00:00.000000000+0100',
       '2000-01-04T01:00:00.000000000+0100',
       '2000-01-05T01:00:00.000000000+0100',
       '2000-01-06T01:00:00.000000000+0100',
       '2000-01-07T01:00:00.000000000+0100',
       '2000-01-08T01:00:00.000000000+0100',
       '2000-01-09T01:00:00.000000000+0100',
       '2000-01-10T01:00:00.000000000+0100'], dtype='datetime64[ns]')

In [24]: converted = np.asarray(rng, dtype=object)

In [25]: converted[5]
Out[25]: 947116800000000000L

Trust me: don’t panic. If you are using NumPy 1.6 and restrict your interaction with datetime64 values to pandas’s API you will be just fine. There is nothing wrong with the data-type (a 64-bit integer internally); all of the important data processing happens in pandas and is heavily tested. I strongly recommend that you do not work directly with datetime64 arrays in NumPy 1.6 and only use the pandas API.

Support for non-unique indexes: In the latter case, you may have code inside a try:... catch: block that failed due to the index not being unique. In many cases it will no longer fail (some method like append still check for uniqueness unless disabled). However, all is not lost: you can inspect index.is_unique and raise an exception explicitly if it is False or go to a different code branch.

v.0.7.3 (April 12, 2012)

This is a minor release from 0.7.2 and fixes many minor bugs and adds a number of nice new features. There are also a couple of API changes to note; these should not affect very many users, and we are inclined to call them “bug fixes” even though they do constitute a change in behavior. See the full release notes or issue tracker on GitHub for a complete list.

New features

from pandas.tools.plotting import scatter_matrix
scatter_matrix(df, alpha=0.2)
_images/scatter_matrix_kde.png
  • Add stacked argument to Series and DataFrame’s plot method for stacked bar plots.
df.plot(kind='bar', stacked=True)
_images/bar_plot_stacked_ex.png
df.plot(kind='barh', stacked=True)
_images/barh_plot_stacked_ex.png
  • Add log x and y scaling options to DataFrame.plot and Series.plot
  • Add kurt methods to Series and DataFrame for computing kurtosis

NA Boolean Comparison API Change

Reverted some changes to how NA values (represented typically as NaN or None) are handled in non-numeric Series:

In [1]: series = Series(['Steve', np.nan, 'Joe'])

In [2]: series == 'Steve'
Out[2]: 
0     True
1    False
2    False
dtype: bool

In [3]: series != 'Steve'
Out[3]: 
0    False
1     True
2     True
dtype: bool

In comparisons, NA / NaN will always come through as False except with != which is True. Be very careful with boolean arithmetic, especially negation, in the presence of NA data. You may wish to add an explicit NA filter into boolean array operations if you are worried about this:

In [4]: mask = series == 'Steve'

In [5]: series[mask & series.notnull()]
Out[5]: 
0    Steve
dtype: object

While propagating NA in comparisons may seem like the right behavior to some users (and you could argue on purely technical grounds that this is the right thing to do), the evaluation was made that propagating NA everywhere, including in numerical arrays, would cause a large amount of problems for users. Thus, a “practicality beats purity” approach was taken. This issue may be revisited at some point in the future.

Other API Changes

When calling apply on a grouped Series, the return value will also be a Series, to be more consistent with the groupby behavior with DataFrame:

In [1]: df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
   ...:                     'foo', 'bar', 'foo', 'foo'],
   ...:                 'B' : ['one', 'one', 'two', 'three',
   ...:                        'two', 'two', 'one', 'three'],
   ...:                 'C' : np.random.randn(8), 'D' : np.random.randn(8)})
   ...: 

In [2]: df
Out[2]: 
     A      B         C         D
0  foo    one  0.144909  1.387310
1  bar    one -1.033812  0.063490
2  foo    two  0.197333  1.437656
3  bar  three -0.059730 -0.814844
4  foo    two  0.087205 -0.482060
5  bar    two -1.607906  1.521442
6  foo    one -1.275249  0.882182
7  foo  three -0.054460 -0.108020

[8 rows x 4 columns]

In [3]: grouped = df.groupby('A')['C']

In [4]: grouped.describe()
Out[4]: 
A         
bar  count    3.000000
     mean    -0.900483
     std      0.782652
     min     -1.607906
     25%     -1.320859
...
foo  std    0.619410
     min   -1.275249
     25%   -0.054460
     50%    0.087205
     75%    0.144909
     max    0.197333
Length: 16, dtype: float64

In [5]: grouped.apply(lambda x: x.order()[-2:]) # top 2 values
Out[5]: 
A     
bar  1   -1.033812
     3   -0.059730
foo  0    0.144909
     2    0.197333
dtype: float64

v.0.7.2 (March 16, 2012)

This release targets bugs in 0.7.1, and adds a few minor features.

New features

  • Add additional tie-breaking methods in DataFrame.rank (GH874)
  • Add ascending parameter to rank in Series, DataFrame (GH875)
  • Add coerce_float option to DataFrame.from_records (GH893)
  • Add sort_columns parameter to allow unsorted plots (GH918)
  • Enable column access via attributes on GroupBy (GH882)
  • Can pass dict of values to DataFrame.fillna (GH661)
  • Can select multiple hierarchical groups by passing list of values in .ix (GH134)
  • Add axis option to DataFrame.fillna (GH174)
  • Add level keyword to drop for dropping values from a level (GH159)

Performance improvements

  • Use khash for Series.value_counts, add raw function to algorithms.py (GH861)
  • Intercept __builtin__.sum in groupby (GH885)

v.0.7.1 (February 29, 2012)

This release includes a few new features and addresses over a dozen bugs in 0.7.0.

New features

  • Add to_clipboard function to pandas namespace for writing objects to the system clipboard (GH774)
  • Add itertuples method to DataFrame for iterating through the rows of a dataframe as tuples (GH818)
  • Add ability to pass fill_value and method to DataFrame and Series align method (GH806, GH807)
  • Add fill_value option to reindex, align methods (GH784)
  • Enable concat to produce DataFrame from Series (GH787)
  • Add between method to Series (GH802)
  • Add HTML representation hook to DataFrame for the IPython HTML notebook (GH773)
  • Support for reading Excel 2007 XML documents using openpyxl

Performance improvements

  • Improve performance and memory usage of fillna on DataFrame
  • Can concatenate a list of Series along axis=1 to obtain a DataFrame (GH787)

v.0.7.0 (February 9, 2012)

New features

  • New unified merge function for efficiently performing full gamut of database / relational-algebra operations. Refactored existing join methods to use the new infrastructure, resulting in substantial performance gains (GH220, GH249, GH267)
  • New unified concatenation function for concatenating Series, DataFrame or Panel objects along an axis. Can form union or intersection of the other axes. Improves performance of Series.append and DataFrame.append (GH468, GH479, GH273)
  • Can pass multiple DataFrames to DataFrame.append to concatenate (stack) and multiple Series to Series.append too
  • Can pass list of dicts (e.g., a list of JSON objects) to DataFrame constructor (GH526)
  • You can now set multiple columns in a DataFrame via __getitem__, useful for transformation (GH342)
  • Handle differently-indexed output values in DataFrame.apply (GH498)
In [1]: df = DataFrame(randn(10, 4))

In [2]: df.apply(lambda x: x.describe())
Out[2]: 
               0          1          2          3
count  10.000000  10.000000  10.000000  10.000000
mean    0.119046   0.455043  -0.093701  -0.330828
std     0.814006   0.972606   0.948124   0.814913
min    -0.964456  -0.790943  -1.921164  -1.578003
25%    -0.512550  -0.462622  -0.683389  -0.934434
50%     0.013691   0.415879  -0.061961  -0.343709
75%     0.616168   1.351857   0.671847   0.150746
max     1.507974   1.755240   1.183075   1.051356

[8 rows x 4 columns]
  • Add reorder_levels method to Series and DataFrame (GH534)
  • Add dict-like get function to DataFrame and Panel (GH521)
  • Add DataFrame.iterrows method for efficiently iterating through the rows of a DataFrame
  • Add DataFrame.to_panel with code adapted from LongPanel.to_long
  • Add reindex_axis method added to DataFrame
  • Add level option to binary arithmetic functions on DataFrame and Series
  • Add level option to the reindex and align methods on Series and DataFrame for broadcasting values across a level (GH542, GH552, others)
  • Add attribute-based item access to Panel and add IPython completion (GH563)
  • Add logy option to Series.plot for log-scaling on the Y axis
  • Add index and header options to DataFrame.to_string
  • Can pass multiple DataFrames to DataFrame.join to join on index (GH115)
  • Can pass multiple Panels to Panel.join (GH115)
  • Added justify argument to DataFrame.to_string to allow different alignment of column headers
  • Add sort option to GroupBy to allow disabling sorting of the group keys for potential speedups (GH595)
  • Can pass MaskedArray to Series constructor (GH563)
  • Add Panel item access via attributes and IPython completion (GH554)
  • Implement DataFrame.lookup, fancy-indexing analogue for retrieving values given a sequence of row and column labels (GH338)
  • Can pass a list of functions to aggregate with groupby on a DataFrame, yielding an aggregated result with hierarchical columns (GH166)
  • Can call cummin and cummax on Series and DataFrame to get cumulative minimum and maximum, respectively (GH647)
  • value_range added as utility function to get min and max of a dataframe (GH288)
  • Added encoding argument to read_csv, read_table, to_csv and from_csv for non-ascii text (GH717)
  • Added abs method to pandas objects
  • Added crosstab function for easily computing frequency tables
  • Added isin method to index objects
  • Added level argument to xs method of DataFrame.

API Changes to integer indexing

One of the potentially riskiest API changes in 0.7.0, but also one of the most important, was a complete review of how integer indexes are handled with regard to label-based indexing. Here is an example:

In [3]: s = Series(randn(10), index=range(0, 20, 2))

In [4]: s
Out[4]: 
0    -0.392051
2    -0.189537
4     0.886170
6    -1.125894
8     0.319635
10    0.998222
12    0.091743
14   -2.032047
16   -0.448560
18    0.730510
dtype: float64

In [5]: s[0]
Out[5]: -0.39205110783730307

In [6]: s[2]
Out[6]: -0.18953739573269113

In [7]: s[4]
Out[7]: 0.88617008348573789

This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based lookup. This now raises a KeyError:

In [2]: s[1]
KeyError: 1

This change also has the same impact on DataFrame:

In [3]: df = DataFrame(randn(8, 4), index=range(0, 16, 2))

In [4]: df
    0        1       2       3
0   0.88427  0.3363 -0.1787  0.03162
2   0.14451 -0.1415  0.2504  0.58374
4  -1.44779 -0.9186 -1.4996  0.27163
6  -0.26598 -2.4184 -0.2658  0.11503
8  -0.58776  0.3144 -0.8566  0.61941
10  0.10940 -0.7175 -1.0108  0.47990
12 -1.16919 -0.3087 -0.6049 -0.43544
14 -0.07337  0.3410  0.0424 -0.16037

In [5]: df.ix[3]
KeyError: 3

In order to support purely integer-based indexing, the following methods have been added:

Method Description
Series.iget_value(i) Retrieve value stored at location i
Series.iget(i) Alias for iget_value
DataFrame.irow(i) Retrieve the i-th row
DataFrame.icol(j) Retrieve the j-th column
DataFrame.iget_value(i, j) Retrieve the value at row i and column j

API tweaks regarding label-based slicing

Label-based slicing using ix now requires that the index be sorted (monotonic) unless both the start and endpoint are contained in the index:

In [8]: s = Series(randn(6), index=list('gmkaec'))

In [9]: s
Out[9]: 
g    1.269713
m    1.209524
k    2.160843
a    0.533532
e   -2.371548
c    0.562726
dtype: float64

Then this is OK:

In [10]: s.ix['k':'e']
Out[10]: 
k    2.160843
a    0.533532
e   -2.371548
dtype: float64

But this is not:

In [12]: s.ix['b':'h']
KeyError 'b'

If the index had been sorted, the “range selection” would have been possible:

In [11]: s2 = s.sort_index()

In [12]: s2
Out[12]: 
a    0.533532
c    0.562726
e   -2.371548
g    1.269713
k    2.160843
m    1.209524
dtype: float64

In [13]: s2.ix['b':'h']
Out[13]: 
c    0.562726
e   -2.371548
g    1.269713
dtype: float64

Changes to Series [] operator

As as notational convenience, you can pass a sequence of labels or a label slice to a Series when getting and setting values via [] (i.e. the __getitem__ and __setitem__ methods). The behavior will be the same as passing similar input to ix except in the case of integer indexing:

In [14]: s = Series(randn(6), index=list('acegkm'))

In [15]: s
Out[15]: 
a    2.031757
c    0.851077
e    0.660056
g   -1.662471
k    0.571380
m    0.945588
dtype: float64

In [16]: s[['m', 'a', 'c', 'e']]
Out[16]: 
m    0.945588
a    2.031757
c    0.851077
e    0.660056
dtype: float64

In [17]: s['b':'l']
Out[17]: 
c    0.851077
e    0.660056
g   -1.662471
k    0.571380
dtype: float64

In [18]: s['c':'k']
Out[18]: 
c    0.851077
e    0.660056
g   -1.662471
k    0.571380
dtype: float64

In the case of integer indexes, the behavior will be exactly as before (shadowing ndarray):

In [19]: s = Series(randn(6), index=range(0, 12, 2))

In [20]: s[[4, 0, 2]]
Out[20]: 
4   -1.263534
0   -0.414691
2    2.108285
dtype: float64

In [21]: s[1:5]
Out[21]: 
2    2.108285
4   -1.263534
6    2.617801
8    1.967592
dtype: float64

If you wish to do indexing with sequences and slicing on an integer index with label semantics, use ix.

Other API Changes

  • The deprecated LongPanel class has been completely removed
  • If Series.sort is called on a column of a DataFrame, an exception will now be raised. Before it was possible to accidentally mutate a DataFrame’s column by doing df[col].sort() instead of the side-effect free method df[col].order() (GH316)
  • Miscellaneous renames and deprecations which will (harmlessly) raise FutureWarning
  • drop added as an optional parameter to DataFrame.reset_index (GH699)

Performance improvements

  • Cythonized GroupBy aggregations no longer presort the data, thus achieving a significant speedup (GH93). GroupBy aggregations with Python functions significantly sped up by clever manipulation of the ndarray data type in Cython (GH496).
  • Better error message in DataFrame constructor when passed column labels don’t match data (GH497)
  • Substantially improve performance of multi-GroupBy aggregation when a Python function is passed, reuse ndarray object in Cython (GH496)
  • Can store objects indexed by tuples and floats in HDFStore (GH492)
  • Don’t print length by default in Series.to_string, add length option (GH489)
  • Improve Cython code for multi-groupby to aggregate without having to sort the data (GH93)
  • Improve MultiIndex reindexing speed by storing tuples in the MultiIndex, test for backwards unpickling compatibility
  • Improve column reindexing performance by using specialized Cython take function
  • Further performance tweaking of Series.__getitem__ for standard use cases
  • Avoid Index dict creation in some cases (i.e. when getting slices, etc.), regression from prior versions
  • Friendlier error message in setup.py if NumPy not installed
  • Use common set of NA-handling operations (sum, mean, etc.) in Panel class also (GH536)
  • Default name assignment when calling reset_index on DataFrame with a regular (non-hierarchical) index (GH476)
  • Use Cythonized groupers when possible in Series/DataFrame stat ops with level parameter passed (GH545)
  • Ported skiplist data structure to C to speed up rolling_median by about 5-10x in most typical use cases (GH374)

v.0.6.1 (December 13, 2011)

New features

Performance improvements

  • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425)
  • Optimize scalar value lookups in the general case by 25% or more in Series and DataFrame
  • Fix performance regression in cross-sectional count in DataFrame, affecting DataFrame.dropna speed
  • Column deletion in DataFrame copies no data (computes views on blocks) (GH #158)

v.0.6.0 (November 25, 2011)

New Features

  • Added melt function to pandas.core.reshape
  • Added level parameter to group by level in Series and DataFrame descriptive statistics (GH313)
  • Added head and tail methods to Series, analogous to to DataFrame (GH296)
  • Added Series.isin function which checks if each value is contained in a passed sequence (GH289)
  • Added float_format option to Series.to_string
  • Added skip_footer (GH291) and converters (GH343) options to read_csv and read_table
  • Added drop_duplicates and duplicated functions for removing duplicate DataFrame rows and checking for duplicate rows, respectively (GH319)
  • Implemented operators ‘&’, ‘|’, ‘^’, ‘-‘ on DataFrame (GH347)
  • Added Series.mad, mean absolute deviation
  • Added QuarterEnd DateOffset (GH321)
  • Added dot to DataFrame (GH65)
  • Added orient option to Panel.from_dict (GH359, GH301)
  • Added orient option to DataFrame.from_dict
  • Added passing list of tuples or list of lists to DataFrame.from_records (GH357)
  • Added multiple levels to groupby (GH103)
  • Allow multiple columns in by argument of DataFrame.sort_index (GH92, GH362)
  • Added fast get_value and put_value methods to DataFrame (GH360)
  • Added cov instance methods to Series and DataFrame (GH194, GH362)
  • Added kind='bar' option to DataFrame.plot (GH348)
  • Added idxmin and idxmax to Series and DataFrame (GH286)
  • Added read_clipboard function to parse DataFrame from clipboard (GH300)
  • Added nunique function to Series for counting unique elements (GH297)
  • Made DataFrame constructor use Series name if no columns passed (GH373)
  • Support regular expressions in read_table/read_csv (GH364)
  • Added DataFrame.to_html for writing DataFrame to HTML (GH387)
  • Added support for MaskedArray data in DataFrame, masked values converted to NaN (GH396)
  • Added DataFrame.boxplot function (GH368)
  • Can pass extra args, kwds to DataFrame.apply (GH376)
  • Implement DataFrame.join with vector on argument (GH312)
  • Added legend boolean flag to DataFrame.plot (GH324)
  • Can pass multiple levels to stack and unstack (GH370)
  • Can pass multiple values columns to pivot_table (GH381)
  • Use Series name in GroupBy for result index (GH363)
  • Added raw option to DataFrame.apply for performance if only need ndarray (GH309)
  • Added proper, tested weighted least squares to standard and panel OLS (GH303)

Performance Enhancements

  • VBENCH Cythonized cache_readonly, resulting in substantial micro-performance enhancements throughout the codebase (GH361)
  • VBENCH Special Cython matrix iterator for applying arbitrary reduction operations with 3-5x better performance than np.apply_along_axis (GH309)
  • VBENCH Improved performance of MultiIndex.from_tuples
  • VBENCH Special Cython matrix iterator for applying arbitrary reduction operations
  • VBENCH + DOCUMENT Add raw option to DataFrame.apply for getting better performance when
  • VBENCH Faster cythonized count by level in Series and DataFrame (GH341)
  • VBENCH? Significant GroupBy performance enhancement with multiple keys with many “empty” combinations
  • VBENCH New Cython vectorized function map_infer speeds up Series.apply and Series.map significantly when passed elementwise Python function, motivated by (GH355)
  • VBENCH Significantly improved performance of Series.order, which also makes np.unique called on a Series faster (GH327)
  • VBENCH Vastly improved performance of GroupBy on axes with a MultiIndex (GH299)

v.0.5.0 (October 24, 2011)

New Features

  • Added DataFrame.align method with standard join options
  • Added parse_dates option to read_csv and read_table methods to optionally try to parse dates in the index columns
  • Added nrows, chunksize, and