Version 0.17.1 (November 21, 2015)#

Note

We are proud to announce that pandas has become a sponsored project of the (NumFOCUS organization). This will help ensure the success of development of pandas as a world-class open-source project.

This is a minor bug-fix release from 0.17.0 and includes a large number of bug fixes along several new features, enhancements, and performance improvements. We recommend that all users upgrade to this version.

Highlights include:

  • Support for Conditional HTML Formatting, see here

  • Releasing the GIL on the csv reader & other ops, see here

  • Fixed regression in DataFrame.drop_duplicates from 0.16.2, causing incorrect results on integer values (GH 11376)

New features#

Conditional HTML formatting#

Warning

This is a new feature and is under active development. We’ll be adding features an possibly making breaking changes in future releases. Feedback is welcome in GH 11610

We’ve added experimental support for conditional HTML formatting: the visual styling of a DataFrame based on the data. The styling is accomplished with HTML and CSS. Accesses the styler class with the pandas.DataFrame.style, attribute, an instance of Styler with your data attached.

Here’s a quick example:

In [1]: np.random.seed(123)

In [2]: df = pd.DataFrame(np.random.randn(10, 5), columns=list("abcde"))

In [3]: html = df.style.background_gradient(cmap="viridis", low=0.5)

We can render the HTML to get the following table.

a b c d e
0 -1.085631 0.997345 0.282978 -1.506295 -0.5786
1 1.651437 -2.426679 -0.428913 1.265936 -0.86674
2 -0.678886 -0.094709 1.49139 -0.638902 -0.443982
3 -0.434351 2.20593 2.186786 1.004054 0.386186
4 0.737369 1.490732 -0.935834 1.175829 -1.253881
5 -0.637752 0.907105 -1.428681 -0.140069 -0.861755
6 -0.255619 -2.798589 -1.771533 -0.699877 0.927462
7 -0.173636 0.002846 0.688223 -0.879536 0.283627
8 -0.805367 -1.727669 -0.3909 0.573806 0.338589
9 -0.01183 2.392365 0.412912 0.978736 2.238143

Styler interacts nicely with the Jupyter Notebook. See the documentation for more.

Enhancements#

  • DatetimeIndex now supports conversion to strings with astype(str) (GH 10442)

  • Support for compression (gzip/bz2) in pandas.DataFrame.to_csv() (GH 7615)

  • pd.read_* functions can now also accept pathlib.Path, or py:py._path.local.LocalPath objects for the filepath_or_buffer argument. (GH 11033) - The DataFrame and Series functions .to_csv(), .to_html() and .to_latex() can now handle paths beginning with tildes (e.g. ~/Documents/) (GH 11438)

  • DataFrame now uses the fields of a namedtuple as columns, if columns are not supplied (GH 11181)

  • DataFrame.itertuples() now returns namedtuple objects, when possible. (GH 11269, GH 11625)

  • Added axvlines_kwds to parallel coordinates plot (GH 10709)

  • Option to .info() and .memory_usage() to provide for deep introspection of memory consumption. Note that this can be expensive to compute and therefore is an optional parameter. (GH 11595)

    In [4]: df = pd.DataFrame({"A": ["foo"] * 1000})  # noqa: F821

In [5]: df["B"] = df["A"].astype("category")

# shows the '+' as we have object dtypes
In [6]: df.info()
<class 'pandas.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype   
---  ------  --------------  -----   
 0   A       1000 non-null   object  
 1   B       1000 non-null   category
dtypes: category(1), object(1)
memory usage: 8.9+ KB

# we have an accurate memory assessment (but can be expensive to compute this)
In [7]: df.info(memory_usage="deep")
<class 'pandas.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype   
---  ------  --------------  -----   
 0   A       1000 non-null   object  
 1   B       1000 non-null   category
dtypes: category(1), object(1)
memory usage: 59.8 KB

  • Index now has a fillna method (GH 10089)

    In [8]: pd.Index([1, np.nan, 3]).fillna(2)
Out[8]: Index([1.0, 2.0, 3.0], dtype='float64')

  • Series of type category now make .str.<...> and .dt.<...> accessor methods / properties available, if the categories are of that type. (GH 10661)

    In [9]: s = pd.Series(list("aabb")).astype("category")

In [10]: s
Out[10]: 
0    a
1    a
2    b
3    b
Length: 4, dtype: category
Categories (2, object): ['a', 'b']

In [11]: s.str.contains("a")
Out[11]: 
0     True
1     True
2    False
3    False
Length: 4, dtype: bool

In [12]: date = pd.Series(pd.date_range("1/1/2015", periods=5)).astype("category")

In [13]: date
Out[13]: 
0   2015-01-01
1   2015-01-02
2   2015-01-03
3   2015-01-04
4   2015-01-05
Length: 5, dtype: category
Categories (5, datetime64[ns]): [2015-01-01, 2015-01-02, 2015-01-03, 2015-01-04, 2015-01-05]

In [14]: date.dt.day
Out[14]: 
0    1
1    2
2    3
3    4
4    5
Length: 5, dtype: int32

  • pivot_table now has a margins_name argument so you can use something other than the default of ‘All’ (GH 3335)

  • Implement export of datetime64[ns, tz] dtypes with a fixed HDF5 store (GH 11411)

  • Pretty printing sets (e.g. in DataFrame cells) now uses set literal syntax ({x, y}) instead of Legacy Python syntax (set([x, y])) (GH 11215)

  • Improve the error message in pandas.io.gbq.to_gbq() when a streaming insert fails (GH 11285) and when the DataFrame does not match the schema of the destination table (GH 11359)

API changes#

  • raise NotImplementedError in Index.shift for non-supported index types (GH 8038)

  • min and max reductions on datetime64 and timedelta64 dtyped series now result in NaT and not nan (GH 11245).

  • Indexing with a null key will raise a TypeError, instead of a ValueError (GH 11356)

  • Series.ptp will now ignore missing values by default (GH 11163)

Deprecations#

  • The pandas.io.ga module which implements google-analytics support is deprecated and will be removed in a future version (GH 11308)

  • Deprecate the engine keyword in .to_csv(), which will be removed in a future version (GH 11274)

Performance improvements#

  • Checking monotonic-ness before sorting on an index (GH 11080)

  • Series.dropna performance improvement when its dtype can’t contain NaN (GH 11159)

  • Release the GIL on most datetime field operations (e.g. DatetimeIndex.year, Series.dt.year), normalization, and conversion to and from Period, DatetimeIndex.to_period and PeriodIndex.to_timestamp (GH 11263)

  • Release the GIL on some rolling algos: rolling_median, rolling_mean, rolling_max, rolling_min, rolling_var, rolling_kurt, rolling_skew (GH 11450)

  • Release the GIL when reading and parsing text files in read_csv, read_table (GH 11272)

  • Improved performance of rolling_median (GH 11450)

  • Improved performance of to_excel (GH 11352)

  • Performance bug in repr of Categorical categories, which was rendering the strings before chopping them for display (GH 11305)

  • Performance improvement in Categorical.remove_unused_categories, (GH 11643).

  • Improved performance of Series constructor with no data and DatetimeIndex (GH 11433)

  • Improved performance of shift, cumprod, and cumsum with groupby (GH 4095)

Bug fixes#

  • SparseArray.__iter__() now does not cause PendingDeprecationWarning in Python 3.5 (GH 11622)

  • Regression from 0.16.2 for output formatting of long floats/nan, restored in (GH 11302)

  • Series.sort_index() now correctly handles the inplace option (GH 11402)

  • Incorrectly distributed .c file in the build on PyPi when reading a csv of floats and passing na_values=<a scalar> would show an exception (GH 11374)

  • Bug in .to_latex() output broken when the index has a name (GH 10660)

  • Bug in HDFStore.append with strings whose encoded length exceeded the max unencoded length (GH 11234)

  • Bug in merging datetime64[ns, tz] dtypes (GH 11405)

  • Bug in HDFStore.select when comparing with a numpy scalar in a where clause (GH 11283)

  • Bug in using DataFrame.ix with a MultiIndex indexer (GH 11372)

  • Bug in date_range with ambiguous endpoints (GH 11626)

  • Prevent adding new attributes to the accessors .str, .dt and .cat. Retrieving such a value was not possible, so error out on setting it. (GH 10673)

  • Bug in tz-conversions with an ambiguous time and .dt accessors (GH 11295)

  • Bug in output formatting when using an index of ambiguous times (GH 11619)

  • Bug in comparisons of Series vs list-likes (GH 11339)

  • Bug in DataFrame.replace with a datetime64[ns, tz] and a non-compat to_replace (GH 11326, GH 11153)

  • Bug in isnull where numpy.datetime64('NaT') in a numpy.array was not determined to be null(GH 11206)

  • Bug in list-like indexing with a mixed-integer Index (GH 11320)

  • Bug in pivot_table with margins=True when indexes are of Categorical dtype (GH 10993)

  • Bug in DataFrame.plot cannot use hex strings colors (GH 10299)

  • Regression in DataFrame.drop_duplicates from 0.16.2, causing incorrect results on integer values (GH 11376)

  • Bug in pd.eval where unary ops in a list error (GH 11235)

  • Bug in squeeze() with zero length arrays (GH 11230, GH 8999)

  • Bug in describe() dropping column names for hierarchical indexes (GH 11517)

  • Bug in DataFrame.pct_change() not propagating axis keyword on .fillna method (GH 11150)

  • Bug in .to_csv() when a mix of integer and string column names are passed as the columns parameter (GH 11637)

  • Bug in indexing with a range, (GH 11652)

  • Bug in inference of numpy scalars and preserving dtype when setting columns (GH 11638)

  • Bug in to_sql using unicode column names giving UnicodeEncodeError with (GH 11431).

  • Fix regression in setting of xticks in plot (GH 11529).

  • Bug in holiday.dates where observance rules could not be applied to holiday and doc enhancement (GH 11477, GH 11533)

  • Fix plotting issues when having plain Axes instances instead of SubplotAxes (GH 11520, GH 11556).

  • Bug in DataFrame.to_latex() produces an extra rule when header=False (GH 7124)

  • Bug in df.groupby(...).apply(func) when a func returns a Series containing a new datetimelike column (GH 11324)

  • Bug in pandas.json when file to load is big (GH 11344)

  • Bugs in to_excel with duplicate columns (GH 11007, GH 10982, GH 10970)

  • Fixed a bug that prevented the construction of an empty series of dtype datetime64[ns, tz] (GH 11245).

  • Bug in read_excel with MultiIndex containing integers (GH 11317)

  • Bug in to_excel with openpyxl 2.2+ and merging (GH 11408)

  • Bug in DataFrame.to_dict() produces a np.datetime64 object instead of Timestamp when only datetime is present in data (GH 11327)

  • Bug in DataFrame.corr() raises exception when computes Kendall correlation for DataFrames with boolean and not boolean columns (GH 11560)

  • Bug in the link-time error caused by C inline functions on FreeBSD 10+ (with clang) (GH 10510)

  • Bug in DataFrame.to_csv in passing through arguments for formatting MultiIndexes, including date_format (GH 7791)

  • Bug in DataFrame.join() with how='right' producing a TypeError (GH 11519)

  • Bug in Series.quantile with empty list results has Index with object dtype (GH 11588)

  • Bug in pd.merge results in empty Int64Index rather than Index(dtype=object) when the merge result is empty (GH 11588)

  • Bug in Categorical.remove_unused_categories when having NaN values (GH 11599)

  • Bug in DataFrame.to_sparse() loses column names for MultiIndexes (GH 11600)

  • Bug in DataFrame.round() with non-unique column index producing a Fatal Python error (GH 11611)

  • Bug in DataFrame.round() with decimals being a non-unique indexed Series producing extra columns (GH 11618)

Contributors#

A total of 63 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

  • Aleksandr Drozd +

  • Alex Chase +

  • Anthonios Partheniou

  • BrenBarn +

  • Brian J. McGuirk +

  • Chris

  • Christian Berendt +

  • Christian Perez +

  • Cody Piersall +

  • Data & Code Expert Experimenting with Code on Data

  • DrIrv +

  • Evan Wright

  • Guillaume Gay

  • Hamed Saljooghinejad +

  • Iblis Lin +

  • Jake VanderPlas

  • Jan Schulz

  • Jean-Mathieu Deschenes +

  • Jeff Reback

  • Jimmy Callin +

  • Joris Van den Bossche

  • K.-Michael Aye

  • Ka Wo Chen

  • Loïc Séguin-C +

  • Luo Yicheng +

  • Magnus Jöud +

  • Manuel Leonhardt +

  • Matthew Gilbert

  • Maximilian Roos

  • Michael +

  • Nicholas Stahl +

  • Nicolas Bonnotte +

  • Pastafarianist +

  • Petra Chong +

  • Phil Schaf +

  • Philipp A +

  • Rob deCarvalho +

  • Roman Khomenko +

  • Rémy Léone +

  • Sebastian Bank +

  • Sinhrks

  • Stephan Hoyer

  • Thierry Moisan

  • Tom Augspurger

  • Tux1 +

  • Varun +

  • Wieland Hoffmann +

  • Winterflower

  • Yoav Ram +

  • Younggun Kim

  • Zeke +

  • ajcr

  • azuranski +

  • behzad nouri

  • cel4

  • emilydolson +

  • hironow +

  • lexual

  • llllllllll +

  • rockg

  • silentquasar +

  • sinhrks

  • taeold +