Version 0.18.1 (May 3, 2016)#

This is a minor bug-fix release from 0.18.0 and includes a large number of bug fixes along with several new features, enhancements, and performance improvements. We recommend that all users upgrade to this version.

Highlights include:

  • .groupby(...) has been enhanced to provide convenient syntax when working with .rolling(..), .expanding(..) and .resample(..) per group, see here

  • pd.to_datetime() has gained the ability to assemble dates from a DataFrame, see here

  • Method chaining improvements, see here.

  • Custom business hour offset, see here.

  • Many bug fixes in the handling of sparse, see here

  • Expanded the Tutorials section with a feature on modern pandas, courtesy of @TomAugsburger. (GH 13045).

New features#

Custom business hour#

The CustomBusinessHour is a mixture of BusinessHour and CustomBusinessDay which allows you to specify arbitrary holidays. For details, see Custom Business Hour (GH 11514)

In [1]: from pandas.tseries.offsets import CustomBusinessHour

In [2]: from pandas.tseries.holiday import USFederalHolidayCalendar

In [3]: bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar())

Friday before MLK Day

In [4]: import datetime

In [5]: dt = datetime.datetime(2014, 1, 17, 15)

In [6]: dt + bhour_us
Out[6]: Timestamp('2014-01-17 16:00:00')

Tuesday after MLK Day (Monday is skipped because it’s a holiday)

In [7]: dt + bhour_us * 2
Out[7]: Timestamp('2014-01-20 09:00:00')

Method .groupby(..) syntax with window and resample operations#

.groupby(...) has been enhanced to provide convenient syntax when working with .rolling(..), .expanding(..) and .resample(..) per group, see (GH 12486, GH 12738).

You can now use .rolling(..) and .expanding(..) as methods on groupbys. These return another deferred object (similar to what .rolling() and .expanding() do on ungrouped pandas objects). You can then operate on these RollingGroupby objects in a similar manner.

Previously you would have to do this to get a rolling window mean per-group:

In [8]: df = pd.DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})

In [9]: df
Out[9]: 
    A   B
0   1   0
1   1   1
2   1   2
3   1   3
4   1   4
.. ..  ..
35  3  35
36  3  36
37  3  37
38  3  38
39  3  39

[40 rows x 2 columns]

In [1]: df.groupby("A").apply(lambda x: x.rolling(4).B.mean())
Out[1]:
A
1  0      NaN
   1      NaN
   2      NaN
   3      1.5
   4      2.5
   5      3.5
   6      4.5
   7      5.5
   8      6.5
   9      7.5
   10     8.5
   11     9.5
   12    10.5
   13    11.5
   14    12.5
   15    13.5
   16    14.5
   17    15.5
   18    16.5
   19    17.5
2  20     NaN
   21     NaN
   22     NaN
   23    21.5
   24    22.5
   25    23.5
   26    24.5
   27    25.5
   28    26.5
   29    27.5
   30    28.5
   31    29.5
3  32     NaN
   33     NaN
   34     NaN
   35    33.5
   36    34.5
   37    35.5
   38    36.5
   39    37.5
Name: B, dtype: float64

Now you can do:

In [10]: df.groupby("A").rolling(4).B.mean()
Out[10]: 
A    
1  0      NaN
   1      NaN
   2      NaN
   3      1.5
   4      2.5
         ... 
3  35    33.5
   36    34.5
   37    35.5
   38    36.5
   39    37.5
Name: B, Length: 40, dtype: float64

For .resample(..) type of operations, previously you would have to:

In [11]: df = pd.DataFrame(
   ....:     {
   ....:         "date": pd.date_range(start="2016-01-01", periods=4, freq="W"),
   ....:         "group": [1, 1, 2, 2],
   ....:         "val": [5, 6, 7, 8],
   ....:     }
   ....: ).set_index("date")
   ....: 

In [12]: df
Out[12]: 
            group  val
date                  
2016-01-03      1    5
2016-01-10      1    6
2016-01-17      2    7
2016-01-24      2    8

[4 rows x 2 columns]

In[1]: df.groupby("group").apply(lambda x: x.resample("1D").ffill())
Out[1]:
                  group  val
group date
1     2016-01-03      1    5
      2016-01-04      1    5
      2016-01-05      1    5
      2016-01-06      1    5
      2016-01-07      1    5
      2016-01-08      1    5
      2016-01-09      1    5
      2016-01-10      1    6
2     2016-01-17      2    7
      2016-01-18      2    7
      2016-01-19      2    7
      2016-01-20      2    7
      2016-01-21      2    7
      2016-01-22      2    7
      2016-01-23      2    7
      2016-01-24      2    8

Now you can do:

In[1]: df.groupby("group").resample("1D").ffill()
Out[1]:
                  group  val
group date
1     2016-01-03      1    5
      2016-01-04      1    5
      2016-01-05      1    5
      2016-01-06      1    5
      2016-01-07      1    5
      2016-01-08      1    5
      2016-01-09      1    5
      2016-01-10      1    6
2     2016-01-17      2    7
      2016-01-18      2    7
      2016-01-19      2    7
      2016-01-20      2    7
      2016-01-21      2    7
      2016-01-22      2    7
      2016-01-23      2    7
      2016-01-24      2    8

Method chaining improvements#

The following methods / indexers now accept a callable. It is intended to make these more useful in method chains, see the documentation. (GH 11485, GH 12533)

  • .where() and .mask()

  • .loc[], iloc[] and .ix[]

  • [] indexing

Methods .where() and .mask()#

These can accept a callable for the condition and other arguments.

In [13]: df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})

In [14]: df.where(lambda x: x > 4, lambda x: x + 10)
Out[14]: 
    A   B  C
0  11  14  7
1  12   5  8
2  13   6  9

[3 rows x 3 columns]

Methods .loc[], .iloc[], .ix[]#

These can accept a callable, and a tuple of callable as a slicer. The callable can return a valid boolean indexer or anything which is valid for these indexer’s input.

# callable returns bool indexer
In [15]: df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10]
Out[15]: 
   B  C
1  5  8
2  6  9

[2 rows x 2 columns]

# callable returns list of labels
In [16]: df.loc[lambda x: [1, 2], lambda x: ["A", "B"]]
Out[16]: 
   A  B
1  2  5
2  3  6

[2 rows x 2 columns]

Indexing with []#

Finally, you can use a callable in [] indexing of Series, DataFrame and Panel. The callable must return a valid input for [] indexing depending on its class and index type.

In [17]: df[lambda x: "A"]
Out[17]: 
0    1
1    2
2    3
Name: A, Length: 3, dtype: int64

Using these methods / indexers, you can chain data selection operations without using temporary variable.

In [18]: bb = pd.read_csv("data/baseball.csv", index_col="id")

In [19]: (bb.groupby(["year", "team"]).sum(numeric_only=True).loc[lambda df: df.r > 100])
Out[19]: 
           stint    g    ab    r    h  X2b  ...     so   ibb   hbp    sh    sf  gidp
year team                                   ...                                     
2007 CIN       6  379   745  101  203   35  ...  127.0  14.0   1.0   1.0  15.0  18.0
     DET       5  301  1062  162  283   54  ...  176.0   3.0  10.0   4.0   8.0  28.0
     HOU       4  311   926  109  218   47  ...  212.0   3.0   9.0  16.0   6.0  17.0
     LAN      11  413  1021  153  293   61  ...  141.0   8.0   9.0   3.0   8.0  29.0
     NYN      13  622  1854  240  509  101  ...  310.0  24.0  23.0  18.0  15.0  48.0
     SFN       5  482  1305  198  337   67  ...  188.0  51.0   8.0  16.0   6.0  41.0
     TEX       2  198   729  115  200   40  ...  140.0   4.0   5.0   2.0   8.0  16.0
     TOR       4  459  1408  187  378   96  ...  265.0  16.0  12.0   4.0  16.0  38.0

[8 rows x 18 columns]

Partial string indexing on DatetimeIndex when part of a MultiIndex#

Partial string indexing now matches on DateTimeIndex when part of a MultiIndex (GH 10331)

In [20]: dft2 = pd.DataFrame(
   ....:     np.random.randn(20, 1),
   ....:     columns=["A"],
   ....:     index=pd.MultiIndex.from_product(
   ....:         [pd.date_range("20130101", periods=10, freq="12H"), ["a", "b"]]
   ....:     ),
   ....: )
   ....:

In [21]: dft2
Out[21]:
                              A
2013-01-01 00:00:00 a  0.469112
                    b -0.282863
2013-01-01 12:00:00 a -1.509059
                    b -1.135632
2013-01-02 00:00:00 a  1.212112
...                         ...
2013-01-04 12:00:00 b  0.271860
2013-01-05 00:00:00 a -0.424972
                    b  0.567020
2013-01-05 12:00:00 a  0.276232
                    b -1.087401

[20 rows x 1 columns]

In [22]: dft2.loc["2013-01-05"]
Out[22]:
                              A
2013-01-05 00:00:00 a -0.424972
                    b  0.567020
2013-01-05 12:00:00 a  0.276232
                    b -1.087401

[4 rows x 1 columns]

On other levels

In [26]: idx = pd.IndexSlice

In [27]: dft2 = dft2.swaplevel(0, 1).sort_index()

In [28]: dft2
Out[28]:
                              A
a 2013-01-01 00:00:00  0.469112
  2013-01-01 12:00:00 -1.509059
  2013-01-02 00:00:00  1.212112
  2013-01-02 12:00:00  0.119209
  2013-01-03 00:00:00 -0.861849
...                         ...
b 2013-01-03 12:00:00  1.071804
  2013-01-04 00:00:00 -0.706771
  2013-01-04 12:00:00  0.271860
  2013-01-05 00:00:00  0.567020
  2013-01-05 12:00:00 -1.087401

[20 rows x 1 columns]

In [29]: dft2.loc[idx[:, "2013-01-05"], :]
Out[29]:
                              A
a 2013-01-05 00:00:00 -0.424972
  2013-01-05 12:00:00  0.276232
b 2013-01-05 00:00:00  0.567020
  2013-01-05 12:00:00 -1.087401

[4 rows x 1 columns]

Assembling datetimes#

pd.to_datetime() has gained the ability to assemble datetimes from a passed in DataFrame or a dict. (GH 8158).

In [20]: df = pd.DataFrame(
   ....:     {"year": [2015, 2016], "month": [2, 3], "day": [4, 5], "hour": [2, 3]}
   ....: )
   ....: 

In [21]: df
Out[21]: 
   year  month  day  hour
0  2015      2    4     2
1  2016      3    5     3

[2 rows x 4 columns]

Assembling using the passed frame.

In [22]: pd.to_datetime(df)
Out[22]: 
0   2015-02-04 02:00:00
1   2016-03-05 03:00:00
Length: 2, dtype: datetime64[ns]

You can pass only the columns that you need to assemble.

In [23]: pd.to_datetime(df[["year", "month", "day"]])
Out[23]: 
0   2015-02-04
1   2016-03-05
Length: 2, dtype: datetime64[ns]

Other enhancements#

  • pd.read_csv() now supports delim_whitespace=True for the Python engine (GH 12958)

  • pd.read_csv() now supports opening ZIP files that contains a single CSV, via extension inference or explicit compression='zip' (GH 12175)

  • pd.read_csv() now supports opening files using xz compression, via extension inference or explicit compression='xz' is specified; xz compressions is also supported by DataFrame.to_csv in the same way (GH 11852)

  • pd.read_msgpack() now always gives writeable ndarrays even when compression is used (GH 12359).

  • pd.read_msgpack() now supports serializing and de-serializing categoricals with msgpack (GH 12573)

  • .to_json() now supports NDFrames that contain categorical and sparse data (GH 10778)

  • interpolate() now supports method='akima' (GH 7588).

  • pd.read_excel() now accepts path objects (e.g. pathlib.Path, py.path.local) for the file path, in line with other read_* functions (GH 12655)

  • Added .weekday_name property as a component to DatetimeIndex and the .dt accessor. (GH 11128)

  • Index.take now handles allow_fill and fill_value consistently (GH 12631)

    In [24]: idx = pd.Index([1.0, 2.0, 3.0, 4.0], dtype="float")

# default, allow_fill=True, fill_value=None
In [25]: idx.take([2, -1])
Out[25]: Index([3.0, 4.0], dtype='float64')

In [26]: idx.take([2, -1], fill_value=True)
Out[26]: Index([3.0, nan], dtype='float64')

  • Index now supports .str.get_dummies() which returns MultiIndex, see Creating Indicator Variables (GH 10008, GH 10103)

    In [27]: idx = pd.Index(["a|b", "a|c", "b|c"])

In [28]: idx.str.get_dummies("|")
Out[28]: 
MultiIndex([(1, 1, 0),
            (1, 0, 1),
            (0, 1, 1)],
           names=['a', 'b', 'c'])

  • pd.crosstab() has gained a normalize argument for normalizing frequency tables (GH 12569). Examples in the updated docs here.

  • .resample(..).interpolate() is now supported (GH 12925)

  • .isin() now accepts passed sets (GH 12988)

Sparse changes#

These changes conform sparse handling to return the correct types and work to make a smoother experience with indexing.

SparseArray.take now returns a scalar for scalar input, SparseArray for others. Furthermore, it handles a negative indexer with the same rule as Index (GH 10560, GH 12796)

s = pd.SparseArray([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
s.take(0)
s.take([1, 2, 3])

  • Bug in SparseSeries[] indexing with Ellipsis raises KeyError (GH 9467)

  • Bug in SparseArray[] indexing with tuples are not handled properly (GH 12966)

  • Bug in SparseSeries.loc[] with list-like input raises TypeError (GH 10560)

  • Bug in SparseSeries.iloc[] with scalar input may raise IndexError (GH 10560)

  • Bug in SparseSeries.loc[], .iloc[] with slice returns SparseArray, rather than SparseSeries (GH 10560)

  • Bug in SparseDataFrame.loc[], .iloc[] may results in dense Series, rather than SparseSeries (GH 12787)

  • Bug in SparseArray addition ignores fill_value of right hand side (GH 12910)

  • Bug in SparseArray mod raises AttributeError (GH 12910)

  • Bug in SparseArray pow calculates 1 ** np.nan as np.nan which must be 1 (GH 12910)

  • Bug in SparseArray comparison output may incorrect result or raise ValueError (GH 12971)

  • Bug in SparseSeries.__repr__ raises TypeError when it is longer than max_rows (GH 10560)

  • Bug in SparseSeries.shape ignores fill_value (GH 10452)

  • Bug in SparseSeries and SparseArray may have different dtype from its dense values (GH 12908)

  • Bug in SparseSeries.reindex incorrectly handle fill_value (GH 12797)

  • Bug in SparseArray.to_frame() results in DataFrame, rather than SparseDataFrame (GH 9850)

  • Bug in SparseSeries.value_counts() does not count fill_value (GH 6749)

  • Bug in SparseArray.to_dense() does not preserve dtype (GH 10648)

  • Bug in SparseArray.to_dense() incorrectly handle fill_value (GH 12797)

  • Bug in pd.concat() of SparseSeries results in dense (GH 10536)

  • Bug in pd.concat() of SparseDataFrame incorrectly handle fill_value (GH 9765)

  • Bug in pd.concat() of SparseDataFrame may raise AttributeError (GH 12174)

  • Bug in SparseArray.shift() may raise NameError or TypeError (GH 12908)

API changes#

Method .groupby(..).nth() changes#

The index in .groupby(..).nth() output is now more consistent when the as_index argument is passed (GH 11039):

In [29]: df = pd.DataFrame({"A": ["a", "b", "a"], "B": [1, 2, 3]})

In [30]: df
Out[30]: 
   A  B
0  a  1
1  b  2
2  a  3

[3 rows x 2 columns]

Previous behavior:

In [3]: df.groupby('A', as_index=True)['B'].nth(0)
Out[3]:
0    1
1    2
Name: B, dtype: int64

In [4]: df.groupby('A', as_index=False)['B'].nth(0)
Out[4]:
0    1
1    2
Name: B, dtype: int64

New behavior:

In [31]: df.groupby("A", as_index=True)["B"].nth(0)
Out[31]: 
0    1
1    2
Name: B, Length: 2, dtype: int64

In [32]: df.groupby("A", as_index=False)["B"].nth(0)
Out[32]: 
0    1
1    2
Name: B, Length: 2, dtype: int64

Furthermore, previously, a .groupby would always sort, regardless if sort=False was passed with .nth().

In [33]: np.random.seed(1234)

In [34]: df = pd.DataFrame(np.random.randn(100, 2), columns=["a", "b"])

In [35]: df["c"] = np.random.randint(0, 4, 100)

Previous behavior:

In [4]: df.groupby('c', sort=True).nth(1)
Out[4]:
          a         b
c
0 -0.334077  0.002118
1  0.036142 -2.074978
2 -0.720589  0.887163
3  0.859588 -0.636524

In [5]: df.groupby('c', sort=False).nth(1)
Out[5]:
          a         b
c
0 -0.334077  0.002118
1  0.036142 -2.074978
2 -0.720589  0.887163
3  0.859588 -0.636524

New behavior:

In [36]: df.groupby("c", sort=True).nth(1)
Out[36]: 
           a         b  c
2  -0.720589  0.887163  2
3   0.859588 -0.636524  3
7  -0.334077  0.002118  0
21  0.036142 -2.074978  1

[4 rows x 3 columns]

In [37]: df.groupby("c", sort=False).nth(1)
Out[37]: 
           a         b  c
2  -0.720589  0.887163  2
3   0.859588 -0.636524  3
7  -0.334077  0.002118  0
21  0.036142 -2.074978  1

[4 rows x 3 columns]

NumPy function compatibility#

Compatibility between pandas array-like methods (e.g. sum and take) and their numpy counterparts has been greatly increased by augmenting the signatures of the pandas methods so as to accept arguments that can be passed in from numpy, even if they are not necessarily used in the pandas implementation (GH 12644, GH 12638, GH 12687)

  • .searchsorted() for Index and TimedeltaIndex now accept a sorter argument to maintain compatibility with numpy’s searchsorted function (GH 12238)

  • Bug in numpy compatibility of np.round() on a Series (GH 12600)

An example of this signature augmentation is illustrated below:

sp = pd.SparseDataFrame([1, 2, 3])
sp

Previous behaviour:

In [2]: np.cumsum(sp, axis=0)
...
TypeError: cumsum() takes at most 2 arguments (4 given)

New behaviour:

np.cumsum(sp, axis=0)

Using .apply on GroupBy resampling#

Using apply on resampling groupby operations (using a pd.TimeGrouper) now has the same output types as similar apply calls on other groupby operations. (GH 11742).

In [38]: df = pd.DataFrame(
   ....:     {"date": pd.to_datetime(["10/10/2000", "11/10/2000"]), "value": [10, 13]}
   ....: )
   ....: 

In [39]: df
Out[39]: 
        date  value
0 2000-10-10     10
1 2000-11-10     13

[2 rows x 2 columns]

Previous behavior:

In [1]: df.groupby(pd.TimeGrouper(key='date',
   ...:                           freq='M')).apply(lambda x: x.value.sum())
Out[1]:
...
TypeError: cannot concatenate a non-NDFrame object

# Output is a Series
In [2]: df.groupby(pd.TimeGrouper(key='date',
   ...:                           freq='M')).apply(lambda x: x[['value']].sum())
Out[2]:
date
2000-10-31  value    10
2000-11-30  value    13
dtype: int64

New behavior:

# Output is a Series
In [55]: df.groupby(pd.TimeGrouper(key='date',
    ...:                           freq='M')).apply(lambda x: x.value.sum())
Out[55]:
date
2000-10-31    10
2000-11-30    13
Freq: M, dtype: int64

# Output is a DataFrame
In [56]: df.groupby(pd.TimeGrouper(key='date',
    ...:                           freq='M')).apply(lambda x: x[['value']].sum())
Out[56]:
            value
date
2000-10-31     10
2000-11-30     13

Changes in read_csv exceptions#

In order to standardize the read_csv API for both the c and python engines, both will now raise an EmptyDataError, a subclass of ValueError, in response to empty columns or header (GH 12493, GH 12506)

Previous behaviour:

In [1]: import io

In [2]: df = pd.read_csv(io.StringIO(''), engine='c')
...
ValueError: No columns to parse from file

In [3]: df = pd.read_csv(io.StringIO(''), engine='python')
...
StopIteration

New behaviour:

In [1]: df = pd.read_csv(io.StringIO(''), engine='c')
...
pandas.io.common.EmptyDataError: No columns to parse from file

In [2]: df = pd.read_csv(io.StringIO(''), engine='python')
...
pandas.io.common.EmptyDataError: No columns to parse from file

In addition to this error change, several others have been made as well:

  • CParserError now sub-classes ValueError instead of just a Exception (GH 12551)

  • A CParserError is now raised instead of a generic Exception in read_csv when the c engine cannot parse a column (GH 12506)

  • A ValueError is now raised instead of a generic Exception in read_csv when the c engine encounters a NaN value in an integer column (GH 12506)

  • A ValueError is now raised instead of a generic Exception in read_csv when true_values is specified, and the c engine encounters an element in a column containing unencodable bytes (GH 12506)

  • pandas.parser.OverflowError exception has been removed and has been replaced with Python’s built-in OverflowError exception (GH 12506)

  • pd.read_csv() no longer allows a combination of strings and integers for the usecols parameter (GH 12678)

Method to_datetime error changes#

Bugs in pd.to_datetime() when passing a unit with convertible entries and errors='coerce' or non-convertible with errors='ignore'. Furthermore, an OutOfBoundsDateime exception will be raised when an out-of-range value is encountered for that unit when errors='raise'. (GH 11758, GH 13052, GH 13059)

Previous behaviour:

In [27]: pd.to_datetime(1420043460, unit='s', errors='coerce')
Out[27]: NaT

In [28]: pd.to_datetime(11111111, unit='D', errors='ignore')
OverflowError: Python int too large to convert to C long

In [29]: pd.to_datetime(11111111, unit='D', errors='raise')
OverflowError: Python int too large to convert to C long

New behaviour:

In [2]: pd.to_datetime(1420043460, unit='s', errors='coerce')
Out[2]: Timestamp('2014-12-31 16:31:00')

In [3]: pd.to_datetime(11111111, unit='D', errors='ignore')
Out[3]: 11111111

In [4]: pd.to_datetime(11111111, unit='D', errors='raise')
OutOfBoundsDatetime: cannot convert input with unit 'D'

Other API changes#

  • .swaplevel() for Series, DataFrame, Panel, and MultiIndex now features defaults for its first two parameters i and j that swap the two innermost levels of the index. (GH 12934)

  • .searchsorted() for Index and TimedeltaIndex now accept a sorter argument to maintain compatibility with numpy’s searchsorted function (GH 12238)

  • Period and PeriodIndex now raises IncompatibleFrequency error which inherits ValueError rather than raw ValueError (GH 12615)

  • Series.apply for category dtype now applies the passed function to each of the .categories (and not the .codes), and returns a category dtype if possible (GH 12473)

  • read_csv will now raise a TypeError if parse_dates is neither a boolean, list, or dictionary (matches the doc-string) (GH 5636)

  • The default for .query()/.eval() is now engine=None, which will use numexpr if it’s installed; otherwise it will fallback to the python engine. This mimics the pre-0.18.1 behavior if numexpr is installed (and which, previously, if numexpr was not installed, .query()/.eval() would raise). (GH 12749)

  • pd.show_versions() now includes pandas_datareader version (GH 12740)

  • Provide a proper __name__ and __qualname__ attributes for generic functions (GH 12021)

  • pd.concat(ignore_index=True) now uses RangeIndex as default (GH 12695)

  • pd.merge() and DataFrame.join() will show a UserWarning when merging/joining a single- with a multi-leveled dataframe (GH 9455, GH 12219)

  • Compat with scipy > 0.17 for deprecated piecewise_polynomial interpolation method; support for the replacement from_derivatives method (GH 12887)

Deprecations#

  • The method name Index.sym_diff() is deprecated and can be replaced by Index.symmetric_difference() (GH 12591)

  • The method name Categorical.sort() is deprecated in favor of Categorical.sort_values() (GH 12882)

Performance improvements#

  • Improved speed of SAS reader (GH 12656, GH 12961)

  • Performance improvements in .groupby(..).cumcount() (GH 11039)

  • Improved memory usage in pd.read_csv() when using skiprows=an_integer (GH 13005)

  • Improved performance of DataFrame.to_sql when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (GH 12876)

  • Improved performance of Period construction and time series plotting (GH 12903, GH 11831).

  • Improved performance of .str.encode() and .str.decode() methods (GH 13008)

  • Improved performance of to_numeric if input is numeric dtype (GH 12777)

  • Improved performance of sparse arithmetic with IntIndex (GH 13036)

Bug fixes#

  • usecols parameter in pd.read_csv is now respected even when the lines of a CSV file are not even (GH 12203)

  • Bug in groupby.transform(..) when axis=1 is specified with a non-monotonic ordered index (GH 12713)

  • Bug in Period and PeriodIndex creation raises KeyError if freq="Minute" is specified. Note that “Minute” freq is deprecated in v0.17.0, and recommended to use freq="T" instead (GH 11854)

  • Bug in .resample(...).count() with a PeriodIndex always raising a TypeError (GH 12774)

  • Bug in .resample(...) with a PeriodIndex casting to a DatetimeIndex when empty (GH 12868)

  • Bug in .resample(...) with a PeriodIndex when resampling to an existing frequency (GH 12770)

  • Bug in printing data which contains Period with different freq raises ValueError (GH 12615)

  • Bug in Series construction with Categorical and dtype='category' is specified (GH 12574)

  • Bugs in concatenation with a coercible dtype was too aggressive, resulting in different dtypes in output formatting when an object was longer than display.max_rows (GH 12411, GH 12045, GH 11594, GH 10571, GH 12211)

  • Bug in float_format option with option not being validated as a callable. (GH 12706)

  • Bug in GroupBy.filter when dropna=False and no groups fulfilled the criteria (GH 12768)

  • Bug in __name__ of .cum* functions (GH 12021)

  • Bug in .astype() of a Float64Inde/Int64Index to an Int64Index (GH 12881)

  • Bug in round tripping an integer based index in .to_json()/.read_json() when orient='index' (the default) (GH 12866)

  • Bug in plotting Categorical dtypes cause error when attempting stacked bar plot (GH 13019)

  • Compat with >= numpy 1.11 for NaT comparisons (GH 12969)

  • Bug in .drop() with a non-unique MultiIndex. (GH 12701)

  • Bug in .concat of datetime tz-aware and naive DataFrames (GH 12467)

  • Bug in correctly raising a ValueError in .resample(..).fillna(..) when passing a non-string (GH 12952)

  • Bug fixes in various encoding and header processing issues in pd.read_sas() (GH 12659, GH 12654, GH 12647, GH 12809)

  • Bug in pd.crosstab() where would silently ignore aggfunc if values=None (GH 12569).

  • Potential segfault in DataFrame.to_json when serialising datetime.time (GH 11473).

  • Potential segfault in DataFrame.to_json when attempting to serialise 0d array (GH 11299).

  • Segfault in to_json when attempting to serialise a DataFrame or Series with non-ndarray values; now supports serialization of category, sparse, and datetime64[ns, tz] dtypes (GH 10778).

  • Bug in DataFrame.to_json with unsupported dtype not passed to default handler (GH 12554).

  • Bug in .align not returning the sub-class (GH 12983)

  • Bug in aligning a Series with a DataFrame (GH 13037)

  • Bug in ABCPanel in which Panel4D was not being considered as a valid instance of this generic type (GH 12810)

  • Bug in consistency of .name on .groupby(..).apply(..) cases (GH 12363)

  • Bug in Timestamp.__repr__ that caused pprint to fail in nested structures (GH 12622)

  • Bug in Timedelta.min and Timedelta.max, the properties now report the true minimum/maximum timedeltas as recognized by pandas. See the documentation. (GH 12727)

  • Bug in .quantile() with interpolation may coerce to float unexpectedly (GH 12772)

  • Bug in .quantile() with empty Series may return scalar rather than empty Series (GH 12772)

  • Bug in .loc with out-of-bounds in a large indexer would raise IndexError rather than KeyError (GH 12527)

  • Bug in resampling when using a TimedeltaIndex and .asfreq(), would previously not include the final fencepost (GH 12926)

  • Bug in equality testing with a Categorical in a DataFrame (GH 12564)

  • Bug in GroupBy.first(), .last() returns incorrect row when TimeGrouper is used (GH 7453)

  • Bug in pd.read_csv() with the c engine when specifying skiprows with newlines in quoted items (GH 10911, GH 12775)

  • Bug in DataFrame timezone lost when assigning tz-aware datetime Series with alignment (GH 12981)

  • Bug in .value_counts() when normalize=True and dropna=True where nulls still contributed to the normalized count (GH 12558)

  • Bug in Series.value_counts() loses name if its dtype is category (GH 12835)

  • Bug in Series.value_counts() loses timezone info (GH 12835)

  • Bug in Series.value_counts(normalize=True) with Categorical raises UnboundLocalError (GH 12835)

  • Bug in Panel.fillna() ignoring inplace=True (GH 12633)

  • Bug in pd.read_csv() when specifying names, usecols, and parse_dates simultaneously with the c engine (GH 9755)

  • Bug in pd.read_csv() when specifying delim_whitespace=True and lineterminator simultaneously with the c engine (GH 12912)

  • Bug in Series.rename, DataFrame.rename and DataFrame.rename_axis not treating Series as mappings to relabel (GH 12623).

  • Clean in .rolling.min and .rolling.max to enhance dtype handling (GH 12373)

  • Bug in groupby where complex types are coerced to float (GH 12902)

  • Bug in Series.map raises TypeError if its dtype is category or tz-aware datetime (GH 12473)

  • Bugs on 32bit platforms for some test comparisons (GH 12972)

  • Bug in index coercion when falling back from RangeIndex construction (GH 12893)

  • Better error message in window functions when invalid argument (e.g. a float window) is passed (GH 12669)

  • Bug in slicing subclassed DataFrame defined to return subclassed Series may return normal Series (GH 11559)

  • Bug in .str accessor methods may raise ValueError if input has name and the result is DataFrame or MultiIndex (GH 12617)

  • Bug in DataFrame.last_valid_index() and DataFrame.first_valid_index() on empty frames (GH 12800)

  • Bug in CategoricalIndex.get_loc returns different result from regular Index (GH 12531)

  • Bug in PeriodIndex.resample where name not propagated (GH 12769)

  • Bug in date_range closed keyword and timezones (GH 12684).

  • Bug in pd.concat raises AttributeError when input data contains tz-aware datetime and timedelta (GH 12620)

  • Bug in pd.concat did not handle empty Series properly (GH 11082)

  • Bug in .plot.bar alignment when width is specified with int (GH 12979)

  • Bug in fill_value is ignored if the argument to a binary operator is a constant (GH 12723)

  • Bug in pd.read_html() when using bs4 flavor and parsing table with a header and only one column (GH 9178)

  • Bug in .pivot_table when margins=True and dropna=True where nulls still contributed to margin count (GH 12577)

  • Bug in .pivot_table when dropna=False where table index/column names disappear (GH 12133)

  • Bug in pd.crosstab() when margins=True and dropna=False which raised (GH 12642)

  • Bug in Series.name when name attribute can be a hashable type (GH 12610)

  • Bug in .describe() resets categorical columns information (GH 11558)

  • Bug where loffset argument was not applied when calling resample().count() on a timeseries (GH 12725)

  • pd.read_excel() now accepts column names associated with keyword argument names (GH 12870)

  • Bug in pd.to_numeric() with Index returns np.ndarray, rather than Index (GH 12777)

  • Bug in pd.to_numeric() with datetime-like may raise TypeError (GH 12777)

  • Bug in pd.to_numeric() with scalar raises ValueError (GH 12777)

Contributors#

A total of 60 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

  • Andrew Fiore-Gartland +

  • Bastiaan +

  • Benoît Vinot +

  • Brandon Rhodes +

  • DaCoEx +

  • Drew Fustin +

  • Ernesto Freitas +

  • Filip Ter +

  • Gregory Livschitz +

  • Gábor Lipták

  • Hassan Kibirige +

  • Iblis Lin

  • Israel Saeta Pérez +

  • Jason Wolosonovich +

  • Jeff Reback

  • Joe Jevnik

  • Joris Van den Bossche

  • Joshua Storck +

  • Ka Wo Chen

  • Kerby Shedden

  • Kieran O’Mahony

  • Leif Walsh +

  • Mahmoud Lababidi +

  • Maoyuan Liu +

  • Mark Roth +

  • Matt Wittmann

  • MaxU +

  • Maximilian Roos

  • Michael Droettboom +

  • Nick Eubank

  • Nicolas Bonnotte

  • OXPHOS +

  • Pauli Virtanen +

  • Peter Waller +

  • Pietro Battiston

  • Prabhjot Singh +

  • Robin Wilson

  • Roger Thomas +

  • Sebastian Bank

  • Stephen Hoover

  • Tim Hopper +

  • Tom Augspurger

  • WANG Aiyong

  • Wes Turner

  • Winand +

  • Xbar +

  • Yan Facai +

  • adneu +

  • ajenkins-cargometrics +

  • behzad nouri

  • chinskiy +

  • gfyoung

  • jeps-journal +

  • jonaslb +

  • kotrfa +

  • nileracecrew +

  • onesandzeroes

  • rs2 +

  • sinhrks

  • tsdlovell +