Table Of Contents

Search

Enter search terms or a module, class or function name.

What’s New

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

v0.24.0

New features

Other Enhancements

Backwards incompatible API changes

Tick DateOffset Normalize Restrictions

Creating a Tick object (:class:Day, :class:Hour, :class:Minute, :class:Second, :class:Milli, :class:Micro, :class:Nano) with normalize=True is no longer supported. This prevents unexpected behavior where addition could fail to be monotone or associative. (GH21427)

In [1]: ts = pd.Timestamp('2018-06-11 18:01:14')

In [2]: ts
Out[2]: Timestamp('2018-06-11 18:01:14')

In [3]: tic = pd.offsets.Hour(n=2, normalize=True)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-e29d1e1495b1> in <module>()
----> 1 tic = pd.offsets.Hour(n=2, normalize=True)

~/build/pandas-dev/pandas/pandas/tseries/offsets.py in __init__(self, n, normalize)
   2220         self.n = self._validate_n(n)
   2221         if normalize:
-> 2222             raise ValueError("Tick offset with `normalize=True` are not "
   2223                              "allowed.")  # GH#21427
   2224         self.normalize = normalize

ValueError: Tick offset with `normalize=True` are not allowed.

In [4]: tic
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-cd137cbd8d43> in <module>()
----> 1 tic

NameError: name 'tic' is not defined

Previous Behavior:

In [4]: ts + tic
Out [4]: Timestamp('2018-06-11 00:00:00')

In [5]: ts + tic + tic + tic == ts + (tic + tic + tic)
Out [5]: False

Current Behavior:

In [5]: tic = pd.offsets.Hour(n=2)

In [6]: ts + tic + tic + tic == ts + (tic + tic + tic)
Out[6]: True
  • For DatetimeIndex and TimedeltaIndex with non-None freq attribute, addition or subtraction of integer-dtyped array or Index will return an object of the same class (GH19959)

Deprecations

  • DataFrame.to_stata(), read_stata(), StataReader and StataWriter have deprecated the encoding argument. The encoding of a Stata dta file is determined by the file type and cannot be changed (GH21244).

Removal of prior version deprecations/changes

  • The LongPanel and WidePanel classes have been removed (GH10892)

Performance Improvements

Documentation Changes

  • Added sphinx spelling extension, updated documentation on how to use the spell check (GH21079)

Bug Fixes

Categorical

Datetimelike

  • Fixed bug where two DateOffset objects with different normalize attributes could evaluate as equal (GH21404)
  • Bug in Index with datetime64[ns, tz] dtype that did not localize integer data correctly (GH20964)

Timedelta

Timezones

Offsets

Numeric

Strings

Indexing

MultiIndex

I/O

Plotting

Groupby/Resample/Rolling

Sparse

Reshaping

ExtensionArray

Other

  • meth:~pandas.io.formats.style.Styler.background_gradient now takes a text_color_threshold parameter to automatically lighten the text color based on the luminance of the background color. This improves readability with dark background colors without the need to limit the background colormap range. (GH21258)

v0.23.1

This is a minor bug-fix release in the 0.23.x series and includes some small regression fixes and bug fixes. We recommend that all users upgrade to this version.

Fixed Regressions

Comparing Series with datetime.date

We’ve reverted a 0.23.0 change to comparing a Series holding datetimes and a datetime.date object (GH21152). In pandas 0.22 and earlier, comparing a Series holding datetimes and datetime.date objects would coerce the datetime.date to a datetime before comapring. This was inconsistent with Python, NumPy, and DatetimeIndex, which never consider a datetime and datetime.date equal.

In 0.23.0, we unified operations between DatetimeIndex and Series, and in the process changed comparisons between a Series of datetimes and datetime.date without warning.

We’ve temporarily restored the 0.22.0 behavior, so datetimes and dates may again compare equal, but restore the 0.23.0 behavior in a future release.

To summarize, here’s the behavior in 0.22.0, 0.23.0, 0.23.1:

# 0.22.0... Silently coerce the datetime.date
>>> Series(pd.date_range('2017', periods=2)) == datetime.date(2017, 1, 1)
0     True
1    False
dtype: bool

# 0.23.0... Do not coerce the datetime.date
>>> Series(pd.date_range('2017', periods=2)) == datetime.date(2017, 1, 1)
0    False
1    False
dtype: bool

# 0.23.1... Coerce the datetime.date with a warning
>>> Series(pd.date_range('2017', periods=2)) == datetime.date(2017, 1, 1)
/bin/python:1: FutureWarning: Comparing Series of datetimes with 'datetime.date'.  Currently, the
'datetime.date' is coerced to a datetime. In the future pandas will
not coerce, and the values not compare equal to the 'datetime.date'.
To retain the current behavior, convert the 'datetime.date' to a
datetime with 'pd.Timestamp'.
  #!/bin/python3
0     True
1    False
dtype: bool

In addition, ordering comparisons will raise a TypeError in the future.

Other Fixes

  • Reverted the ability of to_sql() to perform multivalue inserts as this caused regression in certain cases (GH21103). In the future this will be made configurable.
  • Fixed regression in the DatetimeIndex.date and DatetimeIndex.time attributes in case of timezone-aware data: DatetimeIndex.time returned a tz-aware time instead of tz-naive (GH21267) and DatetimeIndex.date returned incorrect date when the input date has a non-UTC timezone (GH21230).
  • Fixed regression in pandas.io.json.json_normalize() when called with None values in nested levels in JSON, and to not drop keys with value as None (GH21158, GH21356).
  • Bug in to_csv() causes encoding error when compression and encoding are specified (GH21241, GH21118)
  • Bug preventing pandas from being importable with -OO optimization (GH21071)
  • Bug in Categorical.fillna() incorrectly raising a TypeError when value the individual categories are iterable and value is an iterable (GH21097, GH19788)
  • Fixed regression in constructors coercing NA values like None to strings when passing dtype=str (GH21083)
  • Regression in pivot_table() where an ordered Categorical with missing values for the pivot’s index would give a mis-aligned result (GH21133)
  • Fixed regression in merging on boolean index/columns (GH21119).

Performance Improvements

  • Improved performance of CategoricalIndex.is_monotonic_increasing(), CategoricalIndex.is_monotonic_decreasing() and CategoricalIndex.is_monotonic() (GH21025)
  • Improved performance of CategoricalIndex.is_unique() (GH21107)

Bug Fixes

Groupby/Resample/Rolling

Data-type specific

Sparse

  • Bug in SparseArray.shape which previously only returned the shape SparseArray.sp_values (GH21126)

Indexing

  • Bug in Series.reset_index() where appropriate error was not raised with an invalid level name (GH20925)
  • Bug in interval_range() when start/periods or end/periods are specified with float start or end (GH21161)
  • Bug in MultiIndex.set_names() where error raised for a MultiIndex with nlevels == 1 (GH21149)
  • Bug in IntervalIndex constructors where creating an IntervalIndex from categorical data was not fully supported (GH21243, GH21253)
  • Bug in MultiIndex.sort_index() which was not guaranteed to sort correctly with level=1; this was also causing data misalignment in particular DataFrame.stack() operations (GH20994, GH20945, GH21052)

Plotting

  • New keywords (sharex, sharey) to turn on/off sharing of x/y-axis by subplots generated with pandas.DataFrame().groupby().boxplot() (GH20968)

I/O

  • Bug in IO methods specifying compression='zip' which produced uncompressed zip archives (GH17778, GH21144)
  • Bug in DataFrame.to_stata() which prevented exporting DataFrames to buffers and most file-like objects (GH21041)
  • Bug in read_stata() and StataReader which did not correctly decode utf-8 strings on Python 3 from Stata 14 files (dta version 118) (GH21244)
  • Bug in IO JSON read_json() reading empty JSON schema with orient='table' back to DataFrame caused an error (GH21287)

Reshaping

  • Bug in concat() where error was raised in concatenating Series with numpy scalar and tuple names (GH21015)
  • Bug in concat() warning message providing the wrong guidance for future behavior (GH21101)

Other

  • Tab completion on Index in IPython no longer outputs deprecation warnings (GH21125)
  • Bug preventing pandas being used on Windows without C++ redistributable installed (GH21106)

v0.23.0 (May 15, 2018)

This is a major release from 0.22.0 and includes a number of API changes, deprecations, 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:

Check the API Changes and deprecations before updating.

Warning

Starting January 1, 2019, pandas feature releases will support Python 3 only. See Plan for dropping Python 2.7 for more.

What’s new in v0.23.0

New features

JSON read/write round-trippable with orient='table'

A DataFrame can now be written to and subsequently read back via JSON while preserving metadata through usage of the orient='table' argument (see GH18912 and GH9146). Previously, none of the available orient values guaranteed the preservation of dtypes and index names, amongst other metadata.

In [1]: df = pd.DataFrame({'foo': [1, 2, 3, 4],
   ...:                    'bar': ['a', 'b', 'c', 'd'],
   ...:                    'baz': pd.date_range('2018-01-01', freq='d', periods=4),
   ...:                    'qux': pd.Categorical(['a', 'b', 'c', 'c'])
   ...:                    }, index=pd.Index(range(4), name='idx'))
   ...: 

In [2]: df
Out[2]: 
     foo bar        baz qux
idx                        
0      1   a 2018-01-01   a
1      2   b 2018-01-02   b
2      3   c 2018-01-03   c
3      4   d 2018-01-04   c

In [3]: df.dtypes
Out[3]: 
foo             int64
bar            object
baz    datetime64[ns]
qux          category
dtype: object

In [4]: df.to_json('test.json', orient='table')

In [5]: new_df = pd.read_json('test.json', orient='table')

In [6]: new_df
Out[6]: 
     foo bar        baz qux
idx                        
0      1   a 2018-01-01   a
1      2   b 2018-01-02   b
2      3   c 2018-01-03   c
3      4   d 2018-01-04   c

In [7]: new_df.dtypes
Out[7]: 
foo             int64
bar            object
baz    datetime64[ns]
qux          category
dtype: object

Please note that the string index is not supported with the round trip format, as it is used by default in write_json to indicate a missing index name.

In [8]: df.index.name = 'index'

In [9]: df.to_json('test.json', orient='table')

In [10]: new_df = pd.read_json('test.json', orient='table')

In [11]: new_df
Out[11]: 
   foo bar        baz qux
0    1   a 2018-01-01   a
1    2   b 2018-01-02   b
2    3   c 2018-01-03   c
3    4   d 2018-01-04   c

In [12]: new_df.dtypes
Out[12]: 
foo             int64
bar            object
baz    datetime64[ns]
qux          category
dtype: object

.assign() accepts dependent arguments

The DataFrame.assign() now accepts dependent keyword arguments for python version later than 3.6 (see also PEP 468). Later keyword arguments may now refer to earlier ones if the argument is a callable. See the documentation here (GH14207)

In [13]: df = pd.DataFrame({'A': [1, 2, 3]})

In [14]: df
Out[14]: 
   A
0  1
1  2
2  3

In [15]: df.assign(B=df.A, C=lambda x:x['A']+ x['B'])
Out[15]: 
   A  B  C
0  1  1  2
1  2  2  4
2  3  3  6

Warning

This may subtly change the behavior of your code when you’re using .assign() to update an existing column. Previously, callables referring to other variables being updated would get the “old” values

Previous Behavior:

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

In [3]: df.assign(A=lambda df: df.A + 1, C=lambda df: df.A * -1)
Out[3]:
   A  C
0  2 -1
1  3 -2
2  4 -3

New Behavior:

In [16]: df.assign(A=df.A+1, C= lambda df: df.A* -1)
Out[16]: 
   A  C
0  2 -2
1  3 -3
2  4 -4

Merging on a combination of columns and index levels

Strings passed to DataFrame.merge() as the on, left_on, and right_on parameters may now refer to either column names or index level names. This enables merging DataFrame instances on a combination of index levels and columns without resetting indexes. See the Merge on columns and levels documentation section. (GH14355)

In [17]: left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')

In [18]: left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
   ....:                      'B': ['B0', 'B1', 'B2', 'B3'],
   ....:                      'key2': ['K0', 'K1', 'K0', 'K1']},
   ....:                     index=left_index)
   ....: 

In [19]: right_index = pd.Index(['K0', 'K1', 'K2', 'K2'], name='key1')

In [20]: right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
   ....:                       'D': ['D0', 'D1', 'D2', 'D3'],
   ....:                       'key2': ['K0', 'K0', 'K0', 'K1']},
   ....:                      index=right_index)
   ....: 

In [21]: left.merge(right, on=['key1', 'key2'])
Out[21]: 
       A   B key2   C   D
key1                     
K0    A0  B0   K0  C0  D0
K1    A2  B2   K0  C1  D1
K2    A3  B3   K1  C3  D3

Sorting by a combination of columns and index levels

Strings passed to DataFrame.sort_values() as the by parameter may now refer to either column names or index level names. This enables sorting DataFrame instances by a combination of index levels and columns without resetting indexes. See the Sorting by Indexes and Values documentation section. (GH14353)

# Build MultiIndex
In [22]: idx = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('a', 2),
   ....:                                  ('b', 2), ('b', 1), ('b', 1)])
   ....: 

In [23]: idx.names = ['first', 'second']

# Build DataFrame
In [24]: df_multi = pd.DataFrame({'A': np.arange(6, 0, -1)},
   ....:                         index=idx)
   ....: 

In [25]: df_multi
Out[25]: 
              A
first second   
a     1       6
      2       5
      2       4
b     2       3
      1       2
      1       1

# Sort by 'second' (index) and 'A' (column)
In [26]: df_multi.sort_values(by=['second', 'A'])
Out[26]: 
              A
first second   
b     1       1
      1       2
a     1       6
b     2       3
a     2       4
      2       5

Extending Pandas with Custom Types (Experimental)

Pandas now supports storing array-like objects that aren’t necessarily 1-D NumPy arrays as columns in a DataFrame or values in a Series. This allows third-party libraries to implement extensions to NumPy’s types, similar to how pandas implemented categoricals, datetimes with timezones, periods, and intervals.

As a demonstration, we’ll use cyberpandas, which provides an IPArray type for storing ip addresses.

In [1]: from cyberpandas import IPArray

In [2]: values = IPArray([
   ...:     0,
   ...:     3232235777,
   ...:     42540766452641154071740215577757643572
   ...: ])
   ...:
   ...:

IPArray isn’t a normal 1-D NumPy array, but because it’s a pandas ExtensionArray, it can be stored properly inside pandas’ containers.

In [3]: ser = pd.Series(values)

In [4]: ser
Out[4]:
0                         0.0.0.0
1                     192.168.1.1
2    2001:db8:85a3::8a2e:370:7334
dtype: ip

Notice that the dtype is ip. The missing value semantics of the underlying array are respected:

In [5]: ser.isna()
Out[5]:
0     True
1    False
2    False
dtype: bool

For more, see the extension types documentation. If you build an extension array, publicize it on our ecosystem page.

New observed keyword for excluding unobserved categories in groupby

Grouping by a categorical includes the unobserved categories in the output. When grouping by multiple categorical columns, this means you get the cartesian product of all the categories, including combinations where there are no observations, which can result in a large number of groups. We have added a keyword observed to control this behavior, it defaults to observed=False for backward-compatibility. (GH14942, GH8138, GH15217, GH17594, GH8669, GH20583, GH20902)

In [27]: cat1 = pd.Categorical(["a", "a", "b", "b"],
   ....:                       categories=["a", "b", "z"], ordered=True)
   ....: 

In [28]: cat2 = pd.Categorical(["c", "d", "c", "d"],
   ....:                       categories=["c", "d", "y"], ordered=True)
   ....: 

In [29]: df = pd.DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})

In [30]: df['C'] = ['foo', 'bar'] * 2

In [31]: df
Out[31]: 
   A  B  values    C
0  a  c       1  foo
1  a  d       2  bar
2  b  c       3  foo
3  b  d       4  bar

To show all values, the previous behavior:

In [32]: df.groupby(['A', 'B', 'C'], observed=False).count()
Out[32]: 
         values
A B C          
a c bar     NaN
    foo     1.0
  d bar     1.0
    foo     NaN
  y bar     NaN
    foo     NaN
b c bar     NaN
...         ...
  y foo     NaN
z c bar     NaN
    foo     NaN
  d bar     NaN
    foo     NaN
  y bar     NaN
    foo     NaN

[18 rows x 1 columns]

To show only observed values:

In [33]: df.groupby(['A', 'B', 'C'], observed=True).count()
Out[33]: 
         values
A B C          
a c foo       1
  d bar       1
b c foo       1
  d bar       1

For pivotting operations, this behavior is already controlled by the dropna keyword:

In [34]: cat1 = pd.Categorical(["a", "a", "b", "b"],
   ....:                       categories=["a", "b", "z"], ordered=True)
   ....: 

In [35]: cat2 = pd.Categorical(["c", "d", "c", "d"],
   ....:                       categories=["c", "d", "y"], ordered=True)
   ....: 

In [36]: df = DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})

In [37]: df
Out[37]: 
   A  B  values
0  a  c       1
1  a  d       2
2  b  c       3
3  b  d       4
In [38]: pd.pivot_table(df, values='values', index=['A', 'B'],
   ....:                dropna=True)
   ....: 
Out[38]: 
     values
A B        
a c       1
  d       2
b c       3
  d       4

In [39]: pd.pivot_table(df, values='values', index=['A', 'B'],
   ....:                dropna=False)
   ....: 
Out[39]: 
     values
A B        
a c     1.0
  d     2.0
  y     NaN
b c     3.0
  d     4.0
  y     NaN
z c     NaN
  d     NaN
  y     NaN

Rolling/Expanding.apply() accepts raw=False to pass a Series to the function

Series.rolling().apply(), DataFrame.rolling().apply(), Series.expanding().apply(), and DataFrame.expanding().apply() have gained a raw=None parameter. This is similar to DataFame.apply(). This parameter, if True allows one to send a np.ndarray to the applied function. If False a Series will be passed. The default is None, which preserves backward compatibility, so this will default to True, sending an np.ndarray. In a future version the default will be changed to False, sending a Series. (GH5071, GH20584)

In [40]: s = pd.Series(np.arange(5), np.arange(5) + 1)

In [41]: s
Out[41]: 
1    0
2    1
3    2
4    3
5    4
dtype: int64

Pass a Series:

In [42]: s.rolling(2, min_periods=1).apply(lambda x: x.iloc[-1], raw=False)
Out[42]: 
1    0.0
2    1.0
3    2.0
4    3.0
5    4.0
dtype: float64

Mimic the original behavior of passing a ndarray:

In [43]: s.rolling(2, min_periods=1).apply(lambda x: x[-1], raw=True)
Out[43]: 
1    0.0
2    1.0
3    2.0
4    3.0
5    4.0
dtype: float64

DataFrame.interpolate has gained the limit_area kwarg

DataFrame.interpolate() has gained a limit_area parameter to allow further control of which NaN s are replaced. Use limit_area='inside' to fill only NaNs surrounded by valid values or use limit_area='outside' to fill only NaN s outside the existing valid values while preserving those inside. (GH16284) See the full documentation here.

In [44]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13, np.nan, np.nan])

In [45]: ser
Out[45]: 
0     NaN
1     NaN
2     5.0
3     NaN
4     NaN
5     NaN
6    13.0
7     NaN
8     NaN
dtype: float64

Fill one consecutive inside value in both directions

In [46]: ser.interpolate(limit_direction='both', limit_area='inside', limit=1)
Out[46]: 
0     NaN
1     NaN
2     5.0
3     7.0
4     NaN
5    11.0
6    13.0
7     NaN
8     NaN
dtype: float64

Fill all consecutive outside values backward

In [47]: ser.interpolate(limit_direction='backward', limit_area='outside')
Out[47]: 
0     5.0
1     5.0
2     5.0
3     NaN
4     NaN
5     NaN
6    13.0
7     NaN
8     NaN
dtype: float64

Fill all consecutive outside values in both directions

In [48]: ser.interpolate(limit_direction='both', limit_area='outside')
Out[48]: 
0     5.0
1     5.0
2     5.0
3     NaN
4     NaN
5     NaN
6    13.0
7    13.0
8    13.0
dtype: float64

get_dummies now supports dtype argument

The get_dummies() now accepts a dtype argument, which specifies a dtype for the new columns. The default remains uint8. (GH18330)

In [49]: df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})

In [50]: pd.get_dummies(df, columns=['c']).dtypes
Out[50]: 
a      int64
b      int64
c_5    uint8
c_6    uint8
dtype: object

In [51]: pd.get_dummies(df, columns=['c'], dtype=bool).dtypes
Out[51]: 
a      int64
b      int64
c_5     bool
c_6     bool
dtype: object

Timedelta mod method

mod (%) and divmod operations are now defined on Timedelta objects when operating with either timedelta-like or with numeric arguments. See the documentation here. (GH19365)

In [52]: td = pd.Timedelta(hours=37)

In [53]: td % pd.Timedelta(minutes=45)
Out[53]: Timedelta('0 days 00:15:00')

.rank() handles inf values when NaN are present

In previous versions, .rank() would assign inf elements NaN as their ranks. Now ranks are calculated properly. (GH6945)

In [54]: s = pd.Series([-np.inf, 0, 1, np.nan, np.inf])

In [55]: s
Out[55]: 
0        -inf
1    0.000000
2    1.000000
3         NaN
4         inf
dtype: float64

Previous Behavior:

In [11]: s.rank()
Out[11]:
0    1.0
1    2.0
2    3.0
3    NaN
4    NaN
dtype: float64

Current Behavior:

In [56]: s.rank()
Out[56]: 
0    1.0
1    2.0
2    3.0
3    NaN
4    4.0
dtype: float64

Furthermore, previously if you rank inf or -inf values together with NaN values, the calculation won’t distinguish NaN from infinity when using ‘top’ or ‘bottom’ argument.

In [57]: s = pd.Series([np.nan, np.nan, -np.inf, -np.inf])

In [58]: s
Out[58]: 
0    NaN
1    NaN
2   -inf
3   -inf
dtype: float64

Previous Behavior:

In [15]: s.rank(na_option='top')
Out[15]:
0    2.5
1    2.5
2    2.5
3    2.5
dtype: float64

Current Behavior:

In [59]: s.rank(na_option='top')
Out[59]: 
0    1.5
1    1.5
2    3.5
3    3.5
dtype: float64

These bugs were squashed:

  • Bug in DataFrame.rank() and Series.rank() when method='dense' and pct=True in which percentile ranks were not being used with the number of distinct observations (GH15630)
  • Bug in Series.rank() and DataFrame.rank() when ascending='False' failed to return correct ranks for infinity if NaN were present (GH19538)
  • Bug in DataFrameGroupBy.rank() where ranks were incorrect when both infinity and NaN were present (GH20561)

Series.str.cat has gained the join kwarg

Previously, Series.str.cat() did not – in contrast to most of pandas – align Series on their index before concatenation (see GH18657). The method has now gained a keyword join to control the manner of alignment, see examples below and here.

In v.0.23 join will default to None (meaning no alignment), but this default will change to 'left' in a future version of pandas.

In [60]: s = pd.Series(['a', 'b', 'c', 'd'])

In [61]: t = pd.Series(['b', 'd', 'e', 'c'], index=[1, 3, 4, 2])

In [62]: s.str.cat(t)
Out[62]: 
0    ab
1    bd
2    ce
3    dc
dtype: object

In [63]: s.str.cat(t, join='left', na_rep='-')
Out[63]: 
0    a-
1    bb
2    cc
3    dd
dtype: object

Furthermore, Series.str.cat() now works for CategoricalIndex as well (previously raised a ValueError; see GH20842).

DataFrame.astype performs column-wise conversion to Categorical

DataFrame.astype() can now perform column-wise conversion to Categorical by supplying the string 'category' or a CategoricalDtype. Previously, attempting this would raise a NotImplementedError. See the Object Creation section of the documentation for more details and examples. (GH12860, GH18099)

Supplying the string 'category' performs column-wise conversion, with only labels appearing in a given column set as categories:

In [64]: df = pd.DataFrame({'A': list('abca'), 'B': list('bccd')})

In [65]: df = df.astype('category')

In [66]: df['A'].dtype
Out[66]: CategoricalDtype(categories=['a', 'b', 'c'], ordered=False)

In [67]: df['B'].dtype
Out[67]: CategoricalDtype(categories=['b', 'c', 'd'], ordered=False)

Supplying a CategoricalDtype will make the categories in each column consistent with the supplied dtype:

In [68]: from pandas.api.types import CategoricalDtype

In [69]: df = pd.DataFrame({'A': list('abca'), 'B': list('bccd')})

In [70]: cdt = CategoricalDtype(categories=list('abcd'), ordered=True)

In [71]: df = df.astype(cdt)

In [72]: df['A'].dtype
Out[72]: CategoricalDtype(categories=['a', 'b', 'c', 'd'], ordered=True)

In [73]: df['B'].dtype
Out[73]: CategoricalDtype(categories=['a', 'b', 'c', 'd'], ordered=True)

Other Enhancements

Backwards incompatible API changes

Dependencies have increased minimum versions

We have updated our minimum supported versions of dependencies (GH15184). If installed, we now require:

Package Minimum Version Required Issue
python-dateutil 2.5.0 X GH15184
openpyxl 2.4.0   GH15184
beautifulsoup4 4.2.1   GH20082
setuptools 24.2.0   GH20698

Instantiation from dicts preserves dict insertion order for python 3.6+

Until Python 3.6, dicts in Python had no formally defined ordering. For Python version 3.6 and later, dicts are ordered by insertion order, see PEP 468. Pandas will use the dict’s insertion order, when creating a Series or DataFrame from a dict and you’re using Python version 3.6 or higher. (GH19884)

Previous Behavior (and current behavior if on Python < 3.6):

pd.Series({'Income': 2000,
           'Expenses': -1500,
           'Taxes': -200,
           'Net result': 300})
Expenses     -1500
Income        2000
Net result     300
Taxes         -200
dtype: int64

Note the Series above is ordered alphabetically by the index values.

New Behavior (for Python >= 3.6):

In [74]: pd.Series({'Income': 2000,
   ....:            'Expenses': -1500,
   ....:            'Taxes': -200,
   ....:            'Net result': 300})
   ....: 
Out[74]: 
Income        2000
Expenses     -1500
Taxes         -200
Net result     300
dtype: int64

Notice that the Series is now ordered by insertion order. This new behavior is used for all relevant pandas types (Series, DataFrame, SparseSeries and SparseDataFrame).

If you wish to retain the old behavior while using Python >= 3.6, you can use .sort_index():

In [75]: pd.Series({'Income': 2000,
   ....:            'Expenses': -1500,
   ....:            'Taxes': -200,
   ....:            'Net result': 300}).sort_index()
   ....: 
Out[75]: 
Expenses     -1500
Income        2000
Net result     300
Taxes         -200
dtype: int64

Deprecate Panel

Panel was deprecated in the 0.20.x release, showing as a DeprecationWarning. Using Panel will now show a FutureWarning. The recommended way to represent 3-D data are with a MultiIndex on a DataFrame via the to_frame() or with the xarray package. Pandas provides a to_xarray() method to automate this conversion. For more details see Deprecate Panel documentation. (GH13563, GH18324).

In [76]: p = tm.makePanel()

In [77]: p
Out[77]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D

Convert to a MultiIndex DataFrame

In [78]: p.to_frame()
Out[78]: 
                     ItemA     ItemB     ItemC
major      minor                              
2000-01-03 A      1.474071 -0.964980 -1.197071
           B      0.781836  1.846883 -0.858447
           C      2.353925 -1.717693  0.384316
           D     -0.744471  0.901805  0.476720
2000-01-04 A     -0.064034 -0.845696 -1.066969
           B     -1.071357 -1.328865  0.306996
           C      0.583787  0.888782  1.574159
           D      0.758527  1.171216  0.473424
2000-01-05 A     -1.282782 -1.340896 -0.303421
           B      0.441153  1.682706 -0.028665
           C      0.221471  0.228440  1.588931
           D      1.729689  0.520260 -0.242861

Convert to an xarray DataArray

In [79]: p.to_xarray()
Out[79]: 
<xarray.DataArray (items: 3, major_axis: 3, minor_axis: 4)>
array([[[ 1.474071,  0.781836,  2.353925, -0.744471],
        [-0.064034, -1.071357,  0.583787,  0.758527],
        [-1.282782,  0.441153,  0.221471,  1.729689]],

       [[-0.96498 ,  1.846883, -1.717693,  0.901805],
        [-0.845696, -1.328865,  0.888782,  1.171216],
        [-1.340896,  1.682706,  0.22844 ,  0.52026 ]],

       [[-1.197071, -0.858447,  0.384316,  0.47672 ],
        [-1.066969,  0.306996,  1.574159,  0.473424],
        [-0.303421, -0.028665,  1.588931, -0.242861]]])
Coordinates:
  * items       (items) object 'ItemA' 'ItemB' 'ItemC'
  * major_axis  (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05
  * minor_axis  (minor_axis) object 'A' 'B' 'C' 'D'

pandas.core.common removals

The following error & warning messages are removed from pandas.core.common (GH13634, GH19769):

  • PerformanceWarning
  • UnsupportedFunctionCall
  • UnsortedIndexError
  • AbstractMethodError

These are available from import from pandas.errors (since 0.19.0).

Changes to make output of DataFrame.apply consistent

DataFrame.apply() was inconsistent when applying an arbitrary user-defined-function that returned a list-like with axis=1. Several bugs and inconsistencies are resolved. If the applied function returns a Series, then pandas will return a DataFrame; otherwise a Series will be returned, this includes the case where a list-like (e.g. tuple or list is returned) (GH16353, GH17437, GH17970, GH17348, GH17892, GH18573, GH17602, GH18775, GH18901, GH18919).

In [80]: df = pd.DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1, columns=['A', 'B', 'C'])

In [81]: df
Out[81]: 
   A  B  C
0  1  2  3
1  1  2  3
2  1  2  3
3  1  2  3
4  1  2  3
5  1  2  3

Previous Behavior: if the returned shape happened to match the length of original columns, this would return a DataFrame. If the return shape did not match, a Series with lists was returned.

In [3]: df.apply(lambda x: [1, 2, 3], axis=1)
Out[3]:
   A  B  C
0  1  2  3
1  1  2  3
2  1  2  3
3  1  2  3
4  1  2  3
5  1  2  3

In [4]: df.apply(lambda x: [1, 2], axis=1)
Out[4]:
0    [1, 2]
1    [1, 2]
2    [1, 2]
3    [1, 2]
4    [1, 2]
5    [1, 2]
dtype: object

New Behavior: When the applied function returns a list-like, this will now always return a Series.

In [82]: df.apply(lambda x: [1, 2, 3], axis=1)
Out[82]: 
0    [1, 2, 3]
1    [1, 2, 3]
2    [1, 2, 3]
3    [1, 2, 3]
4    [1, 2, 3]
5    [1, 2, 3]
dtype: object

In [83]: df.apply(lambda x: [1, 2], axis=1)
Out[83]: 
0    [1, 2]
1    [1, 2]
2    [1, 2]
3    [1, 2]
4    [1, 2]
5    [1, 2]
dtype: object

To have expanded columns, you can use result_type='expand'

In [84]: df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')
Out[84]: 
   0  1  2
0  1  2  3
1  1  2  3
2  1  2  3
3  1  2  3
4  1  2  3
5  1  2  3

To broadcast the result across the original columns (the old behaviour for list-likes of the correct length), you can use result_type='broadcast'. The shape must match the original columns.

In [85]: df.apply(lambda x: [1, 2, 3], axis=1, result_type='broadcast')
Out[85]: 
   A  B  C
0  1  2  3
1  1  2  3
2  1  2  3
3  1  2  3
4  1  2  3
5  1  2  3

Returning a Series allows one to control the exact return structure and column names:

In [86]: df.apply(lambda x: Series([1, 2, 3], index=['D', 'E', 'F']), axis=1)
Out[86]: 
   D  E  F
0  1  2  3
1  1  2  3
2  1  2  3
3  1  2  3
4  1  2  3
5  1  2  3

Concatenation will no longer sort

In a future version of pandas pandas.concat() will no longer sort the non-concatenation axis when it is not already aligned. The current behavior is the same as the previous (sorting), but now a warning is issued when sort is not specified and the non-concatenation axis is not aligned (GH4588).

In [87]: df1 = pd.DataFrame({"a": [1, 2], "b": [1, 2]}, columns=['b', 'a'])

In [88]: df2 = pd.DataFrame({"a": [4, 5]})

In [89]: pd.concat([df1, df2])
Out[89]: 
   a    b
0  1  1.0
1  2  2.0
0  4  NaN
1  5  NaN

To keep the previous behavior (sorting) and silence the warning, pass sort=True

In [90]: pd.concat([df1, df2], sort=True)
Out[90]: 
   a    b
0  1  1.0
1  2  2.0
0  4  NaN
1  5  NaN

To accept the future behavior (no sorting), pass sort=False

Note that this change also applies to DataFrame.append(), which has also received a sort keyword for controlling this behavior.

Build Changes

  • Building pandas for development now requires cython >= 0.24 (GH18613)
  • Building from source now explicitly requires setuptools in setup.py (GH18113)
  • Updated conda recipe to be in compliance with conda-build 3.0+ (GH18002)

Index Division By Zero Fills Correctly

Division operations on Index and subclasses will now fill division of positive numbers by zero with np.inf, division of negative numbers by zero with -np.inf and 0 / 0 with np.nan. This matches existing Series behavior. (GH19322, GH19347)

Previous Behavior:

In [6]: index = pd.Int64Index([-1, 0, 1])

In [7]: index / 0
Out[7]: Int64Index([0, 0, 0], dtype='int64')

# Previous behavior yielded different results depending on the type of zero in the divisor
In [8]: index / 0.0
Out[8]: Float64Index([-inf, nan, inf], dtype='float64')

In [9]: index = pd.UInt64Index([0, 1])

In [10]: index / np.array([0, 0], dtype=np.uint64)
Out[10]: UInt64Index([0, 0], dtype='uint64')

In [11]: pd.RangeIndex(1, 5) / 0
ZeroDivisionError: integer division or modulo by zero

Current Behavior:

In [91]: index = pd.Int64Index([-1, 0, 1])

# division by zero gives -infinity where negative, +infinity where positive, and NaN for 0 / 0
In [92]: index / 0
Out[92]: Float64Index([-inf, nan, inf], dtype='float64')

# The result of division by zero should not depend on whether the zero is int or float
In [93]: index / 0.0
Out[93]: Float64Index([-inf, nan, inf], dtype='float64')

In [94]: index = pd.UInt64Index([0, 1])

In [95]: index / np.array([0, 0], dtype=np.uint64)
Out[95]: Float64Index([nan, inf], dtype='float64')

In [96]: pd.RangeIndex(1, 5) / 0
Out[96]: Float64Index([inf, inf, inf, inf], dtype='float64')

Extraction of matching patterns from strings

By default, extracting matching patterns from strings with str.extract() used to return a Series if a single group was being extracted (a DataFrame if more than one group was extracted). As of Pandas 0.23.0 str.extract() always returns a DataFrame, unless expand is set to False. Finally, None was an accepted value for the expand parameter (which was equivalent to False), but now raises a ValueError. (GH11386)

Previous Behavior:

In [1]: s = pd.Series(['number 10', '12 eggs'])

In [2]: extracted = s.str.extract('.*(\d\d).*')

In [3]: extracted
Out [3]:
0    10
1    12
dtype: object

In [4]: type(extracted)
Out [4]:
pandas.core.series.Series

New Behavior:

In [97]: s = pd.Series(['number 10', '12 eggs'])

In [98]: extracted = s.str.extract('.*(\d\d).*')

In [99]: extracted
Out[99]: 
    0
0  10
1  12

In [100]: type(extracted)
Out[100]: pandas.core.frame.DataFrame

To restore previous behavior, simply set expand to False:

In [101]: s = pd.Series(['number 10', '12 eggs'])

In [102]: extracted = s.str.extract('.*(\d\d).*', expand=False)

In [103]: extracted
Out[103]: 
0    10
1    12
dtype: object

In [104]: type(extracted)
Out[104]: pandas.core.series.Series

Default value for the ordered parameter of CategoricalDtype

The default value of the ordered parameter for CategoricalDtype has changed from False to None to allow updating of categories without impacting ordered. Behavior should remain consistent for downstream objects, such as Categorical (GH18790)

In previous versions, the default value for the ordered parameter was False. This could potentially lead to the ordered parameter unintentionally being changed from True to False when users attempt to update categories if ordered is not explicitly specified, as it would silently default to False. The new behavior for ordered=None is to retain the existing value of ordered.

New Behavior:

In [105]: from pandas.api.types import CategoricalDtype

In [106]: cat = pd.Categorical(list('abcaba'), ordered=True, categories=list('cba'))

In [107]: cat
Out[107]: 
[a, b, c, a, b, a]
Categories (3, object): [c < b < a]

In [108]: cdt = CategoricalDtype(categories=list('cbad'))

In [109]: cat.astype(cdt)
Out[109]: 
[a, b, c, a, b, a]
Categories (4, object): [c < b < a < d]

Notice in the example above that the converted Categorical has retained ordered=True. Had the default value for ordered remained as False, the converted Categorical would have become unordered, despite ordered=False never being explicitly specified. To change the value of ordered, explicitly pass it to the new dtype, e.g. CategoricalDtype(categories=list('cbad'), ordered=False).

Note that the unintentional conversion of ordered discussed above did not arise in previous versions due to separate bugs that prevented astype from doing any type of category to category conversion (GH10696, GH18593). These bugs have been fixed in this release, and motivated changing the default value of ordered.

Better pretty-printing of DataFrames in a terminal

Previously, the default value for the maximum number of columns was pd.options.display.max_columns=20. This meant that relatively wide data frames would not fit within the terminal width, and pandas would introduce line breaks to display these 20 columns. This resulted in an output that was relatively difficult to read:

_images/print_df_old.png

If Python runs in a terminal, the maximum number of columns is now determined automatically so that the printed data frame fits within the current terminal width (pd.options.display.max_columns=0) (GH17023). If Python runs as a Jupyter kernel (such as the Jupyter QtConsole or a Jupyter notebook, as well as in many IDEs), this value cannot be inferred automatically and is thus set to 20 as in previous versions. In a terminal, this results in a much nicer output:

_images/print_df_new.png

Note that if you don’t like the new default, you can always set this option yourself. To revert to the old setting, you can run this line:

pd.options.display.max_columns = 20

Datetimelike API Changes

  • The default Timedelta constructor now accepts an ISO 8601 Duration string as an argument (GH19040)
  • Subtracting NaT from a Series with dtype='datetime64[ns]' returns a Series with dtype='timedelta64[ns]' instead of dtype='datetime64[ns]' (GH18808)
  • Addition or subtraction of NaT from TimedeltaIndex will return TimedeltaIndex instead of DatetimeIndex (GH19124)
  • DatetimeIndex.shift() and TimedeltaIndex.shift() will now raise NullFrequencyError (which subclasses ValueError, which was raised in older versions) when the index object frequency is None (GH19147)
  • Addition and subtraction of NaN from a Series with dtype='timedelta64[ns]' will raise a TypeError instead of treating the NaN as NaT (GH19274)
  • NaT division with datetime.timedelta will now return NaN instead of raising (GH17876)
  • Operations between a Series with dtype dtype='datetime64[ns]' and a PeriodIndex will correctly raises TypeError (GH18850)
  • Subtraction of Series with timezone-aware dtype='datetime64[ns]' with mis-matched timezones will raise TypeError instead of ValueError (GH18817)
  • Timestamp will no longer silently ignore unused or invalid tz or tzinfo keyword arguments (GH17690)
  • Timestamp will no longer silently ignore invalid freq arguments (GH5168)
  • CacheableOffset and WeekDay are no longer available in the pandas.tseries.offsets module (GH17830)
  • pandas.tseries.frequencies.get_freq_group() and pandas.tseries.frequencies.DAYS are removed from the public API (GH18034)
  • Series.truncate() and DataFrame.truncate() will raise a ValueError if the index is not sorted instead of an unhelpful KeyError (GH17935)
  • Series.first and DataFrame.first will now raise a TypeError rather than NotImplementedError when index is not a DatetimeIndex (GH20725).
  • Series.last and DataFrame.last will now raise a TypeError rather than NotImplementedError when index is not a DatetimeIndex (GH20725).
  • Restricted DateOffset keyword arguments. Previously, DateOffset subclasses allowed arbitrary keyword arguments which could lead to unexpected behavior. Now, only valid arguments will be accepted. (GH17176, GH18226).
  • pandas.merge() provides a more informative error message when trying to merge on timezone-aware and timezone-naive columns (GH15800)
  • For DatetimeIndex and TimedeltaIndex with freq=None, addition or subtraction of integer-dtyped array or Index will raise NullFrequencyError instead of TypeError (GH19895)
  • Timestamp constructor now accepts a nanosecond keyword or positional argument (GH18898)
  • DatetimeIndex will now raise an AttributeError when the tz attribute is set after instantiation (GH3746)
  • DatetimeIndex with a pytz timezone will now return a consistent pytz timezone (GH18595)

Other API Changes

  • Series.astype() and Index.astype() with an incompatible dtype will now raise a TypeError rather than a ValueError (GH18231)
  • Series construction with an object dtyped tz-aware datetime and dtype=object specified, will now return an object dtyped Series, previously this would infer the datetime dtype (GH18231)
  • A Series of dtype=category constructed from an empty dict will now have categories of dtype=object rather than dtype=float64, consistently with the case in which an empty list is passed (GH18515)
  • All-NaN levels in a MultiIndex are now assigned float rather than object dtype, promoting consistency with Index (GH17929).
  • Levels names of a MultiIndex (when not None) are now required to be unique: trying to create a MultiIndex with repeated names will raise a ValueError (GH18872)
  • Both construction and renaming of Index/MultiIndex with non-hashable name/names will now raise TypeError (GH20527)
  • Index.map() can now accept Series and dictionary input objects (GH12756, GH18482, GH18509).
  • DataFrame.unstack() will now default to filling with np.nan for object columns. (GH12815)
  • IntervalIndex constructor will raise if the closed parameter conflicts with how the input data is inferred to be closed (GH18421)
  • Inserting missing values into indexes will work for all types of indexes and automatically insert the correct type of missing value (NaN, NaT, etc.) regardless of the type passed in (GH18295)
  • When created with duplicate labels, MultiIndex now raises a ValueError. (GH17464)
  • Series.fillna() now raises a TypeError instead of a ValueError when passed a list, tuple or DataFrame as a value (GH18293)
  • pandas.DataFrame.merge() no longer casts a float column to object when merging on int and float columns (GH16572)
  • pandas.merge() now raises a ValueError when trying to merge on incompatible data types (GH9780)
  • The default NA value for UInt64Index has changed from 0 to NaN, which impacts methods that mask with NA, such as UInt64Index.where() (GH18398)
  • Refactored setup.py to use find_packages instead of explicitly listing out all subpackages (GH18535)
  • Rearranged the order of keyword arguments in read_excel() to align with read_csv() (GH16672)
  • wide_to_long() previously kept numeric-like suffixes as object dtype. Now they are cast to numeric if possible (GH17627)
  • In read_excel(), the comment argument is now exposed as a named parameter (GH18735)
  • Rearranged the order of keyword arguments in read_excel() to align with read_csv() (GH16672)
  • The options html.border and mode.use_inf_as_null were deprecated in prior versions, these will now show FutureWarning rather than a DeprecationWarning (GH19003)
  • IntervalIndex and IntervalDtype no longer support categorical, object, and string subtypes (GH19016)
  • IntervalDtype now returns True when compared against 'interval' regardless of subtype, and IntervalDtype.name now returns 'interval' regardless of subtype (GH18980)
  • KeyError now raises instead of ValueError in drop(), drop(), drop(), drop() when dropping a non-existent element in an axis with duplicates (GH19186)
  • Series.to_csv() now accepts a compression argument that works in the same way as the compression argument in DataFrame.to_csv() (GH18958)
  • Set operations (union, difference…) on IntervalIndex with incompatible index types will now raise a TypeError rather than a ValueError (GH19329)
  • DateOffset objects render more simply, e.g. <DateOffset: days=1> instead of <DateOffset: kwds={'days': 1}> (GH19403)
  • Categorical.fillna now validates its value and method keyword arguments. It now raises when both or none are specified, matching the behavior of Series.fillna() (GH19682)
  • pd.to_datetime('today') now returns a datetime, consistent with pd.Timestamp('today'); previously pd.to_datetime('today') returned a .normalized() datetime (GH19935)
  • Series.str.replace() now takes an optional regex keyword which, when set to False, uses literal string replacement rather than regex replacement (GH16808)
  • DatetimeIndex.strftime() and PeriodIndex.strftime() now return an Index instead of a numpy array to be consistent with similar accessors (GH20127)
  • Constructing a Series from a list of length 1 no longer broadcasts this list when a longer index is specified (GH19714, GH20391).
  • DataFrame.to_dict() with orient='index' no longer casts int columns to float for a DataFrame with only int and float columns (GH18580)
  • A user-defined-function that is passed to Series.rolling().aggregate(), DataFrame.rolling().aggregate(), or its expanding cousins, will now always be passed a Series, rather than a np.array; .apply() only has the raw keyword, see here. This is consistent with the signatures of .aggregate() across pandas (GH20584)
  • Rolling and Expanding types raise NotImplementedError upon iteration (GH11704).

Deprecations

  • Series.from_array and SparseSeries.from_array are deprecated. Use the normal constructor Series(..) and SparseSeries(..) instead (GH18213).
  • DataFrame.as_matrix is deprecated. Use DataFrame.values instead (GH18458).
  • Series.asobject, DatetimeIndex.asobject, PeriodIndex.asobject and TimeDeltaIndex.asobject have been deprecated. Use .astype(object) instead (GH18572)
  • Grouping by a tuple of keys now emits a FutureWarning and is deprecated. In the future, a tuple passed to 'by' will always refer to a single key that is the actual tuple, instead of treating the tuple as multiple keys. To retain the previous behavior, use a list instead of a tuple (GH18314)
  • Series.valid is deprecated. Use Series.dropna() instead (GH18800).
  • read_excel() has deprecated the skip_footer parameter. Use skipfooter instead (GH18836)
  • ExcelFile.parse() has deprecated sheetname in favor of sheet_name for consistency with read_excel() (GH20920).
  • The is_copy attribute is deprecated and will be removed in a future version (GH18801).
  • IntervalIndex.from_intervals is deprecated in favor of the IntervalIndex constructor (GH19263)
  • DataFrame.from_items is deprecated. Use DataFrame.from_dict() instead, or DataFrame.from_dict(OrderedDict()) if you wish to preserve the key order (GH17320, GH17312)
  • Indexing a MultiIndex or a FloatIndex with a list containing some missing keys will now show a FutureWarning, which is consistent with other types of indexes (GH17758).
  • The broadcast parameter of .apply() is deprecated in favor of result_type='broadcast' (GH18577)
  • The reduce parameter of .apply() is deprecated in favor of result_type='reduce' (GH18577)
  • The order parameter of factorize() is deprecated and will be removed in a future release (GH19727)
  • Timestamp.weekday_name, DatetimeIndex.weekday_name, and Series.dt.weekday_name are deprecated in favor of Timestamp.day_name(), DatetimeIndex.day_name(), and Series.dt.day_name() (GH12806)
  • pandas.tseries.plotting.tsplot is deprecated. Use Series.plot() instead (GH18627)
  • Index.summary() is deprecated and will be removed in a future version (GH18217)
  • NDFrame.get_ftype_counts() is deprecated and will be removed in a future version (GH18243)
  • The convert_datetime64 parameter in DataFrame.to_records() has been deprecated and will be removed in a future version. The NumPy bug motivating this parameter has been resolved. The default value for this parameter has also changed from True to None (GH18160).
  • Series.rolling().apply(), DataFrame.rolling().apply(), Series.expanding().apply(), and DataFrame.expanding().apply() have deprecated passing an np.array by default. One will need to pass the new raw parameter to be explicit about what is passed (GH20584)
  • The data, base, strides, flags and itemsize properties of the Series and Index classes have been deprecated and will be removed in a future version (GH20419).
  • DatetimeIndex.offset is deprecated. Use DatetimeIndex.freq instead (GH20716)
  • Floor division between an integer ndarray and a Timedelta is deprecated. Divide by Timedelta.value instead (GH19761)
  • Setting PeriodIndex.freq (which was not guaranteed to work correctly) is deprecated. Use PeriodIndex.asfreq() instead (GH20678)
  • Index.get_duplicates() is deprecated and will be removed in a future version (GH20239)
  • The previous default behavior of negative indices in Categorical.take is deprecated. In a future version it will change from meaning missing values to meaning positional indices from the right. The future behavior is consistent with Series.take() (GH20664).
  • Passing multiple axes to the axis parameter in DataFrame.dropna() has been deprecated and will be removed in a future version (GH20987)

Removal of prior version deprecations/changes

  • Warnings against the obsolete usage Categorical(codes, categories), which were emitted for instance when the first two arguments to Categorical() had different dtypes, and recommended the use of Categorical.from_codes, have now been removed (GH8074)
  • The levels and labels attributes of a MultiIndex can no longer be set directly (GH4039).
  • pd.tseries.util.pivot_annual has been removed (deprecated since v0.19). Use pivot_table instead (GH18370)
  • pd.tseries.util.isleapyear has been removed (deprecated since v0.19). Use .is_leap_year property in Datetime-likes instead (GH18370)
  • pd.ordered_merge has been removed (deprecated since v0.19). Use pd.merge_ordered instead (GH18459)
  • The SparseList class has been removed (GH14007)
  • The pandas.io.wb and pandas.io.data stub modules have been removed (GH13735)
  • Categorical.from_array has been removed (GH13854)
  • The freq and how parameters have been removed from the rolling/expanding/ewm methods of DataFrame and Series (deprecated since v0.18). Instead, resample before calling the methods. (GH18601 & GH18668)
  • DatetimeIndex.to_datetime, Timestamp.to_datetime, PeriodIndex.to_datetime, and Index.to_datetime have been removed (GH8254, GH14096, GH14113)
  • read_csv() has dropped the skip_footer parameter (GH13386)
  • read_csv() has dropped the as_recarray parameter (GH13373)
  • read_csv() has dropped the buffer_lines parameter (GH13360)
  • read_csv() has dropped the compact_ints and use_unsigned parameters (GH13323)
  • The Timestamp class has dropped the offset attribute in favor of freq (GH13593)
  • The Series, Categorical, and Index classes have dropped the reshape method (GH13012)
  • pandas.tseries.frequencies.get_standard_freq has been removed in favor of pandas.tseries.frequencies.to_offset(freq).rule_code (GH13874)
  • The freqstr keyword has been removed from pandas.tseries.frequencies.to_offset in favor of freq (GH13874)
  • The Panel4D and PanelND classes have been removed (GH13776)
  • The Panel class has dropped the to_long and toLong methods (GH19077)
  • The options display.line_with and display.height are removed in favor of display.width and display.max_rows respectively (GH4391, GH19107)
  • The labels attribute of the Categorical class has been removed in favor of Categorical.codes (GH7768)
  • The flavor parameter have been removed from func:to_sql method (GH13611)
  • The modules pandas.tools.hashing and pandas.util.hashing have been removed (GH16223)
  • The top-level functions pd.rolling_*, pd.expanding_* and pd.ewm* have been removed (Deprecated since v0.18). Instead, use the DataFrame/Series methods rolling, expanding and ewm (GH18723)
  • Imports from pandas.core.common for functions such as is_datetime64_dtype are now removed. These are located in pandas.api.types. (GH13634, GH19769)
  • The infer_dst keyword in Series.tz_localize(), DatetimeIndex.tz_localize() and DatetimeIndex have been removed. infer_dst=True is equivalent to ambiguous='infer', and infer_dst=False to ambiguous='raise' (GH7963).
  • When .resample() was changed from an eager to a lazy operation, like .groupby() in v0.18.0, we put in place compatibility (with a FutureWarning), so operations would continue to work. This is now fully removed, so a Resampler will no longer forward compat operations (GH20554)
  • Remove long deprecated axis=None parameter from .replace() (GH20271)

Performance Improvements

Documentation Changes

Thanks to all of the contributors who participated in the Pandas Documentation Sprint, which took place on March 10th. We had about 500 participants from over 30 locations across the world. You should notice that many of the API docstrings have greatly improved.

There were too many simultaneous contributions to include a release note for each improvement, but this GitHub search should give you an idea of how many docstrings were improved.

Special thanks to Marc Garcia for organizing the sprint. For more information, read the NumFOCUS blogpost recapping the sprint.

  • Changed spelling of “numpy” to “NumPy”, and “python” to “Python”. (GH19017)
  • Consistency when introducing code samples, using either colon or period. Rewrote some sentences for greater clarity, added more dynamic references to functions, methods and classes. (GH18941, GH18948, GH18973, GH19017)
  • Added a reference to DataFrame.assign() in the concatenate section of the merging documentation (GH18665)

Bug Fixes

Categorical

Warning

A class of bugs were introduced in pandas 0.21 with CategoricalDtype that affects the correctness of operations like merge, concat, and indexing when comparing multiple unordered Categorical arrays that have the same categories, but in a different order. We highly recommend upgrading or manually aligning your categories before doing these operations.

  • Bug in Categorical.equals returning the wrong result when comparing two unordered Categorical arrays with the same categories, but in a different order (GH16603)
  • Bug in pandas.api.types.union_categoricals() returning the wrong result when for unordered categoricals with the categories in a different order. This affected pandas.concat() with Categorical data (GH19096).
  • Bug in pandas.merge() returning the wrong result when joining on an unordered Categorical that had the same categories but in a different order (GH19551)
  • Bug in CategoricalIndex.get_indexer() returning the wrong result when target was an unordered Categorical that had the same categories as self but in a different order (GH19551)
  • Bug in Index.astype() with a categorical dtype where the resultant index is not converted to a CategoricalIndex for all types of index (GH18630)
  • Bug in Series.astype() and Categorical.astype() where an existing categorical data does not get updated (GH10696, GH18593)
  • Bug in Series.str.split() with expand=True incorrectly raising an IndexError on empty strings (GH20002).
  • Bug in Index constructor with dtype=CategoricalDtype(...) where categories and ordered are not maintained (GH19032)
  • Bug in Series constructor with scalar and dtype=CategoricalDtype(...) where categories and ordered are not maintained (GH19565)
  • Bug in Categorical.__iter__ not converting to Python types (GH19909)
  • Bug in pandas.factorize() returning the unique codes for the uniques. This now returns a Categorical with the same dtype as the input (GH19721)
  • Bug in pandas.factorize() including an item for missing values in the uniques return value (GH19721)
  • Bug in Series.take() with categorical data interpreting -1 in indices as missing value markers, rather than the last element of the Series (GH20664)

Datetimelike

  • Bug in Series.__sub__() subtracting a non-nanosecond np.datetime64 object from a Series gave incorrect results (GH7996)
  • Bug in DatetimeIndex, TimedeltaIndex addition and subtraction of zero-dimensional integer arrays gave incorrect results (GH19012)
  • Bug in DatetimeIndex and TimedeltaIndex where adding or subtracting an array-like of DateOffset objects either raised (np.array, pd.Index) or broadcast incorrectly (pd.Series) (GH18849)
  • Bug in Series.__add__() adding Series with dtype timedelta64[ns] to a timezone-aware DatetimeIndex incorrectly dropped timezone information (GH13905)
  • Adding a Period object to a datetime or Timestamp object will now correctly raise a TypeError (GH17983)
  • Bug in Timestamp where comparison with an array of Timestamp objects would result in a RecursionError (GH15183)
  • Bug in Series floor-division where operating on a scalar timedelta raises an exception (GH18846)
  • Bug in DatetimeIndex where the repr was not showing high-precision time values at the end of a day (e.g., 23:59:59.999999999) (GH19030)
  • Bug in .astype() to non-ns timedelta units would hold the incorrect dtype (GH19176, GH19223, GH12425)
  • Bug in subtracting Series from NaT incorrectly returning NaT (GH19158)
  • Bug in Series.truncate() which raises TypeError with a monotonic PeriodIndex (GH17717)
  • Bug in pct_change() using periods and freq returned different length outputs (GH7292)
  • Bug in comparison of DatetimeIndex against None or datetime.date objects raising TypeError for == and != comparisons instead of all-False and all-True, respectively (GH19301)
  • Bug in Timestamp and to_datetime() where a string representing a barely out-of-bounds timestamp would be incorrectly rounded down instead of raising OutOfBoundsDatetime (GH19382)
  • Bug in Timestamp.floor() DatetimeIndex.floor() where time stamps far in the future and past were not rounded correctly (GH19206)
  • Bug in to_datetime() where passing an out-of-bounds datetime with errors='coerce' and utc=True would raise OutOfBoundsDatetime instead of parsing to NaT (GH19612)
  • Bug in DatetimeIndex and TimedeltaIndex addition and subtraction where name of the returned object was not always set consistently. (GH19744)
  • Bug in DatetimeIndex and TimedeltaIndex addition and subtraction where operations with numpy arrays raised TypeError (GH19847)
  • Bug in DatetimeIndex and TimedeltaIndex where setting the freq attribute was not fully supported (GH20678)

Timedelta

  • Bug in Timedelta.__mul__() where multiplying by NaT returned NaT instead of raising a TypeError (GH19819)
  • Bug in Series with dtype='timedelta64[ns]' where addition or subtraction of TimedeltaIndex had results cast to dtype='int64' (GH17250)
  • Bug in Series with dtype='timedelta64[ns]' where addition or subtraction of TimedeltaIndex could return a Series with an incorrect name (GH19043)
  • Bug in Timedelta.__floordiv__() and Timedelta.__rfloordiv__() dividing by many incompatible numpy objects was incorrectly allowed (GH18846)
  • Bug where dividing a scalar timedelta-like object with TimedeltaIndex performed the reciprocal operation (GH19125)
  • Bug in TimedeltaIndex where division by a Series would return a TimedeltaIndex instead of a Series (GH19042)
  • Bug in Timedelta.__add__(), Timedelta.__sub__() where adding or subtracting a np.timedelta64 object would return another np.timedelta64 instead of a Timedelta (GH19738)
  • Bug in Timedelta.__floordiv__(), Timedelta.__rfloordiv__() where operating with a Tick object would raise a TypeError instead of returning a numeric value (GH19738)
  • Bug in Period.asfreq() where periods near datetime(1, 1, 1) could be converted incorrectly (GH19643, GH19834)
  • Bug in Timedelta.total_seconds() causing precision errors, for example Timedelta('30S').total_seconds()==30.000000000000004 (GH19458)
  • Bug in Timedelta.__rmod__() where operating with a numpy.timedelta64 returned a timedelta64 object instead of a Timedelta (GH19820)
  • Multiplication of TimedeltaIndex by TimedeltaIndex will now raise TypeError instead of raising ValueError in cases of length mis-match (GH19333)
  • Bug in indexing a TimedeltaIndex with a np.timedelta64 object which was raising a TypeError (GH20393)

Timezones

  • Bug in creating a Series from an array that contains both tz-naive and tz-aware values will result in a Series whose dtype is tz-aware instead of object (GH16406)
  • Bug in comparison of timezone-aware DatetimeIndex against NaT incorrectly raising TypeError (GH19276)
  • Bug in DatetimeIndex.astype() when converting between timezone aware dtypes, and converting from timezone aware to naive (GH18951)
  • Bug in comparing DatetimeIndex, which failed to raise TypeError when attempting to compare timezone-aware and timezone-naive datetimelike objects (GH18162)
  • Bug in localization of a naive, datetime string in a Series constructor with a datetime64[ns, tz] dtype (GH174151)
  • Timestamp.replace() will now handle Daylight Savings transitions gracefully (GH18319)
  • Bug in tz-aware DatetimeIndex where addition/subtraction with a TimedeltaIndex or array with dtype='timedelta64[ns]' was incorrect (GH17558)
  • Bug in DatetimeIndex.insert() where inserting NaT into a timezone-aware index incorrectly raised (GH16357)
  • Bug in DataFrame constructor, where tz-aware Datetimeindex and a given column name will result in an empty DataFrame (GH19157)
  • Bug in Timestamp.tz_localize() where localizing a timestamp near the minimum or maximum valid values could overflow and return a timestamp with an incorrect nanosecond value (GH12677)
  • Bug when iterating over DatetimeIndex that was localized with fixed timezone offset that rounded nanosecond precision to microseconds (GH19603)
  • Bug in DataFrame.diff() that raised an IndexError with tz-aware values (GH18578)
  • Bug in melt() that converted tz-aware dtypes to tz-naive (GH15785)
  • Bug in Dataframe.count() that raised an ValueError, if Dataframe.dropna() was called for a single column with timezone-aware values. (GH13407)

Offsets

  • Bug in WeekOfMonth and Week where addition and subtraction did not roll correctly (GH18510, GH18672, GH18864)
  • Bug in WeekOfMonth and LastWeekOfMonth where default keyword arguments for constructor raised ValueError (GH19142)
  • Bug in FY5253Quarter, LastWeekOfMonth where rollback and rollforward behavior was inconsistent with addition and subtraction behavior (GH18854)
  • Bug in FY5253 where datetime addition and subtraction incremented incorrectly for dates on the year-end but not normalized to midnight (GH18854)
  • Bug in FY5253 where date offsets could incorrectly raise an AssertionError in arithmetic operations (GH14774)

Numeric

  • Bug in Series constructor with an int or float list where specifying dtype=str, dtype='str' or dtype='U' failed to convert the data elements to strings (GH16605)
  • Bug in Index multiplication and division methods where operating with a Series would return an Index object instead of a Series object (GH19042)
  • Bug in the DataFrame constructor in which data containing very large positive or very large negative numbers was causing OverflowError (GH18584)
  • Bug in Index constructor with dtype='uint64' where int-like floats were not coerced to UInt64Index (GH18400)
  • Bug in DataFrame flex arithmetic (e.g. df.add(other, fill_value=foo)) with a fill_value other than None failed to raise NotImplementedError in corner cases where either the frame or other has length zero (GH19522)
  • Multiplication and division of numeric-dtyped Index objects with timedelta-like scalars returns TimedeltaIndex instead of raising TypeError (GH19333)
  • Bug where NaN was returned instead of 0 by Series.pct_change() and DataFrame.pct_change() when fill_method is not None (GH19873)

Strings

  • Bug in Series.str.get() with a dictionary in the values and the index not in the keys, raising KeyError (GH20671)

Indexing

  • Bug in Index construction from list of mixed type tuples (GH18505)
  • Bug in Index.drop() when passing a list of both tuples and non-tuples (GH18304)
  • Bug in DataFrame.drop(), Panel.drop(), Series.drop(), Index.drop() where no KeyError is raised when dropping a non-existent element from an axis that contains duplicates (GH19186)
  • Bug in indexing a datetimelike Index that raised ValueError instead of IndexError (GH18386).
  • Index.to_series() now accepts index and name kwargs (GH18699)
  • DatetimeIndex.to_series() now accepts index and name kwargs (GH18699)
  • Bug in indexing non-scalar value from Series having non-unique Index will return value flattened (GH17610)
  • Bug in indexing with iterator containing only missing keys, which raised no error (GH20748)
  • Fixed inconsistency in .ix between list and scalar keys when the index has integer dtype and does not include the desired keys (GH20753)
  • Bug in __setitem__ when indexing a DataFrame with a 2-d boolean ndarray (GH18582)
  • Bug in str.extractall when there were no matches empty Index was returned instead of appropriate MultiIndex (GH19034)
  • Bug in IntervalIndex where empty and purely NA data was constructed inconsistently depending on the construction method (GH18421)
  • Bug in IntervalIndex.symmetric_difference() where the symmetric difference with a non-IntervalIndex did not raise (GH18475)
  • Bug in IntervalIndex where set operations that returned an empty IntervalIndex had the wrong dtype (GH19101)
  • Bug in DataFrame.drop_duplicates() where no KeyError is raised when passing in columns that don’t exist on the DataFrame (GH19726)
  • Bug in Index subclasses constructors that ignore unexpected keyword arguments (GH19348)
  • Bug in Index.difference() when taking difference of an Index with itself (GH20040)
  • Bug in DataFrame.first_valid_index() and DataFrame.last_valid_index() in presence of entire rows of NaNs in the middle of values (GH20499).
  • Bug in IntervalIndex where some indexing operations were not supported for overlapping or non-monotonic uint64 data (GH20636)
  • Bug in Series.is_unique where extraneous output in stderr is shown if Series contains objects with __ne__ defined (GH20661)
  • Bug in .loc assignment with a single-element list-like incorrectly assigns as a list (GH19474)
  • Bug in partial string indexing on a Series/DataFrame with a monotonic decreasing DatetimeIndex (GH19362)
  • Bug in performing in-place operations on a DataFrame with a duplicate Index (GH17105)
  • Bug in IntervalIndex.get_loc() and IntervalIndex.get_indexer() when used with an IntervalIndex containing a single interval (GH17284, GH20921)
  • Bug in .loc with a uint64 indexer (GH20722)

MultiIndex

I/O

Plotting

  • Better error message when attempting to plot but matplotlib is not installed (GH19810).
  • DataFrame.plot() now raises a ValueError when the x or y argument is improperly formed (GH18671)
  • Bug in DataFrame.plot() when x and y arguments given as positions caused incorrect referenced columns for line, bar and area plots (GH20056)
  • Bug in formatting tick labels with datetime.time() and fractional seconds (GH18478).
  • Series.plot.kde() has exposed the args ind and bw_method in the docstring (GH18461). The argument ind may now also be an integer (number of sample points).
  • DataFrame.plot() now supports multiple columns to the y argument (GH19699)

Groupby/Resample/Rolling

Sparse

  • Bug in which creating a SparseDataFrame from a dense Series or an unsupported type raised an uncontrolled exception (GH19374)
  • Bug in SparseDataFrame.to_csv causing exception (GH19384)
  • Bug in SparseSeries.memory_usage which caused segfault by accessing non sparse elements (GH19368)
  • Bug in constructing a SparseArray: if data is a scalar and index is defined it will coerce to float64 regardless of scalar’s dtype. (GH19163)

Reshaping

  • Bug in DataFrame.merge() where referencing a CategoricalIndex by name, where the by kwarg would KeyError (GH20777)
  • Bug in DataFrame.stack() which fails trying to sort mixed type levels under Python 3 (GH18310)
  • Bug in DataFrame.unstack() which casts int to float if columns is a MultiIndex with unused levels (GH17845)
  • Bug in DataFrame.unstack() which raises an error if index is a MultiIndex with unused labels on the unstacked level (GH18562)
  • Fixed construction of a Series from a dict containing NaN as key (GH18480)
  • Fixed construction of a DataFrame from a dict containing NaN as key (GH18455)
  • Disabled construction of a Series where len(index) > len(data) = 1, which previously would broadcast the data item, and now raises a ValueError (GH18819)
  • Suppressed error in the construction of a DataFrame from a dict containing scalar values when the corresponding keys are not included in the passed index (GH18600)
  • Fixed (changed from object to float64) dtype of DataFrame initialized with axes, no data, and dtype=int (GH19646)
  • Bug in Series.rank() where Series containing NaT modifies the Series inplace (GH18521)
  • Bug in cut() which fails when using readonly arrays (GH18773)
  • Bug in DataFrame.pivot_table() which fails when the aggfunc arg is of type string. The behavior is now consistent with other methods like agg and apply (GH18713)
  • Bug in DataFrame.merge() in which merging using Index objects as vectors raised an Exception (GH19038)
  • Bug in DataFrame.stack(), DataFrame.unstack(), Series.unstack() which were not returning subclasses (GH15563)
  • Bug in timezone comparisons, manifesting as a conversion of the index to UTC in .concat() (GH18523)
  • Bug in concat() when concatenating sparse and dense series it returns only a SparseDataFrame. Should be a DataFrame. (GH18914, GH18686, and GH16874)
  • Improved error message for DataFrame.merge() when there is no common merge key (GH19427)
  • Bug in DataFrame.join() which does an outer instead of a left join when being called with multiple DataFrames and some have non-unique indices (GH19624)
  • Series.rename() now accepts axis as a kwarg (GH18589)
  • Bug in rename() where an Index of same-length tuples was converted to a MultiIndex (GH19497)
  • Comparisons between Series and Index would return a Series with an incorrect name, ignoring the Index’s name attribute (GH19582)
  • Bug in qcut() where datetime and timedelta data with NaT present raised a ValueError (GH19768)
  • Bug in DataFrame.iterrows(), which would infers strings not compliant to ISO8601 to datetimes (GH19671)
  • Bug in Series constructor with Categorical where a ValueError is not raised when an index of different length is given (GH19342)
  • Bug in DataFrame.astype() where column metadata is lost when converting to categorical or a dictionary of dtypes (GH19920)
  • Bug in cut() and qcut() where timezone information was dropped (GH19872)
  • Bug in Series constructor with a dtype=str, previously raised in some cases (GH19853)
  • Bug in get_dummies(), and select_dtypes(), where duplicate column names caused incorrect behavior (GH20848)
  • Bug in isna(), which cannot handle ambiguous typed lists (GH20675)
  • Bug in concat() which raises an error when concatenating TZ-aware dataframes and all-NaT dataframes (GH12396)
  • Bug in concat() which raises an error when concatenating empty TZ-aware series (GH18447)

Other

v0.22.0 (December 29, 2017)

This is a major release from 0.21.1 and includes a single, API-breaking change. We recommend that all users upgrade to this version after carefully reading the release note (singular!).

Backwards incompatible API changes

Pandas 0.22.0 changes the handling of empty and all-NA sums and products. The summary is that

  • The sum of an empty or all-NA Series is now 0
  • The product of an empty or all-NA Series is now 1
  • We’ve added a min_count parameter to .sum() and .prod() controlling the minimum number of valid values for the result to be valid. If fewer than min_count non-NA values are present, the result is NA. The default is 0. To return NaN, the 0.21 behavior, use min_count=1.

Some background: In pandas 0.21, we fixed a long-standing inconsistency in the return value of all-NA series depending on whether or not bottleneck was installed. See Sum/Prod of all-NaN or empty Series/DataFrames is now consistently NaN. At the same time, we changed the sum and prod of an empty Series to also be NaN.

Based on feedback, we’ve partially reverted those changes.

Arithmetic Operations

The default sum for empty or all-NA Series is now 0.

pandas 0.21.x

In [1]: pd.Series([]).sum()
Out[1]: nan

In [2]: pd.Series([np.nan]).sum()
Out[2]: nan

pandas 0.22.0

In [1]: pd.Series([]).sum()
Out[1]: 0.0

In [2]: pd.Series([np.nan]).sum()
Out[2]: 0.0

The default behavior is the same as pandas 0.20.3 with bottleneck installed. It also matches the behavior of NumPy’s np.nansum on empty and all-NA arrays.

To have the sum of an empty series return NaN (the default behavior of pandas 0.20.3 without bottleneck, or pandas 0.21.x), use the min_count keyword.

In [3]: pd.Series([]).sum(min_count=1)
Out[3]: nan

Thanks to the skipna parameter, the .sum on an all-NA series is conceptually the same as the .sum of an empty one with skipna=True (the default).

In [4]: pd.Series([np.nan]).sum(min_count=1)  # skipna=True by default
Out[4]: nan

The min_count parameter refers to the minimum number of non-null values required for a non-NA sum or product.

Series.prod() has been updated to behave the same as Series.sum(), returning 1 instead.

In [5]: pd.Series([]).prod()
Out[5]: 1.0

In [6]: pd.Series([np.nan]).prod()
Out[6]: 1.0

In [7]: pd.Series([]).prod(min_count=1)
Out[7]: nan

These changes affect DataFrame.sum() and DataFrame.prod() as well. Finally, a few less obvious places in pandas are affected by this change.

Grouping by a Categorical

Grouping by a Categorical and summing now returns 0 instead of NaN for categories with no observations. The product now returns 1 instead of NaN.

pandas 0.21.x

In [8]: grouper = pd.Categorical(['a', 'a'], categories=['a', 'b'])

In [9]: pd.Series([1, 2]).groupby(grouper).sum()
Out[9]:
a    3.0
b    NaN
dtype: float64

pandas 0.22

In [8]: grouper = pd.Categorical(['a', 'a'], categories=['a', 'b'])

In [9]: pd.Series([1, 2]).groupby(grouper).sum()
Out[9]: 
a    3
b    0
dtype: int64

To restore the 0.21 behavior of returning NaN for unobserved groups, use min_count>=1.

In [10]: pd.Series([1, 2]).groupby(grouper).sum(min_count=1)
Out[10]: 
a    3.0
b    NaN
dtype: float64

Resample

The sum and product of all-NA bins has changed from NaN to 0 for sum and 1 for product.

pandas 0.21.x

In [11]: s = pd.Series([1, 1, np.nan, np.nan],
   ...:                index=pd.date_range('2017', periods=4))
   ...:  s
Out[11]:
2017-01-01    1.0
2017-01-02    1.0
2017-01-03    NaN
2017-01-04    NaN
Freq: D, dtype: float64

In [12]: s.resample('2d').sum()
Out[12]:
2017-01-01    2.0
2017-01-03    NaN
Freq: 2D, dtype: float64

pandas 0.22.0

In [11]: s = pd.Series([1, 1, np.nan, np.nan],
   ....:               index=pd.date_range('2017', periods=4))
   ....: 

In [12]: s.resample('2d').sum()
Out[12]: 
2017-01-01    2.0
2017-01-03    0.0
dtype: float64

To restore the 0.21 behavior of returning NaN, use min_count>=1.

In [13]: s.resample('2d').sum(min_count=1)
Out[13]: 
2017-01-01    2.0
2017-01-03    NaN
dtype: float64

In particular, upsampling and taking the sum or product is affected, as upsampling introduces missing values even if the original series was entirely valid.

pandas 0.21.x

In [14]: idx = pd.DatetimeIndex(['2017-01-01', '2017-01-02'])

In [15]: pd.Series([1, 2], index=idx).resample('12H').sum()
Out[15]:
2017-01-01 00:00:00    1.0
2017-01-01 12:00:00    NaN
2017-01-02 00:00:00    2.0
Freq: 12H, dtype: float64

pandas 0.22.0

In [14]: idx = pd.DatetimeIndex(['2017-01-01', '2017-01-02'])

In [15]: pd.Series([1, 2], index=idx).resample("12H").sum()
Out[15]: 
2017-01-01 00:00:00    1
2017-01-01 12:00:00    0
2017-01-02 00:00:00    2
Freq: 12H, dtype: int64

Once again, the min_count keyword is available to restore the 0.21 behavior.

In [16]: pd.Series([1, 2], index=idx).resample("12H").sum(min_count=1)
Out[16]: 
2017-01-01 00:00:00    1.0
2017-01-01 12:00:00    NaN
2017-01-02 00:00:00    2.0
Freq: 12H, dtype: float64

Rolling and Expanding

Rolling and expanding already have a min_periods keyword that behaves similar to min_count. The only case that changes is when doing a rolling or expanding sum with min_periods=0. Previously this returned NaN, when fewer than min_periods non-NA values were in the window. Now it returns 0.

pandas 0.21.1

In [17]: s = pd.Series([np.nan, np.nan])

In [18]: s.rolling(2, min_periods=0).sum()
Out[18]:
0   NaN
1   NaN
dtype: float64

pandas 0.22.0

In [17]: s = pd.Series([np.nan, np.nan])

In [18]: s.rolling(2, min_periods=0).sum()
Out[18]: 
0    0.0
1    0.0
dtype: float64

The default behavior of min_periods=None, implying that min_periods equals the window size, is unchanged.

Compatibility

If you maintain a library that should work across pandas versions, it may be easiest to exclude pandas 0.21 from your requirements. Otherwise, all your sum() calls would need to check if the Series is empty before summing.

With setuptools, in your setup.py use:

install_requires=['pandas!=0.21.*', ...]

With conda, use

requirements:
  run:
    - pandas !=0.21.0,!=0.21.1

Note that the inconsistency in the return value for all-NA series is still there for pandas 0.20.3 and earlier. Avoiding pandas 0.21 will only help with the empty case.

v0.21.1 (December 12, 2017)

This is a minor bug-fix release in the 0.21.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend that all users upgrade to this version.

Highlights include:

  • Temporarily restore matplotlib datetime plotting functionality. This should resolve issues for users who implicitly relied on pandas to plot datetimes with matplotlib. See here.
  • Improvements to the Parquet IO functions introduced in 0.21.0. See here.

Restore Matplotlib datetime Converter Registration

Pandas implements some matplotlib converters for nicely formatting the axis labels on plots with datetime or Period values. Prior to pandas 0.21.0, these were implicitly registered with matplotlib, as a side effect of import pandas.

In pandas 0.21.0, we required users to explicitly register the converter. This caused problems for some users who relied on those converters being present for regular matplotlib.pyplot plotting methods, so we’re temporarily reverting that change; pandas 0.21.1 again registers the converters on import, just like before 0.21.0.

We’ve added a new option to control the converters: pd.options.plotting.matplotlib.register_converters. By default, they are registered. Toggling this to False removes pandas’ formatters and restore any converters we overwrote when registering them (GH18301).

We’re working with the matplotlib developers to make this easier. We’re trying to balance user convenience (automatically registering the converters) with import performance and best practices (importing pandas shouldn’t have the side effect of overwriting any custom converters you’ve already set). In the future we hope to have most of the datetime formatting functionality in matplotlib, with just the pandas-specific converters in pandas. We’ll then gracefully deprecate the automatic registration of converters in favor of users explicitly registering them when they want them.

New features

Improvements to the Parquet IO functionality

Other Enhancements

Deprecations

Performance Improvements

  • Improved performance of plotting large series/dataframes (GH18236).

Bug Fixes

Conversion

  • Bug in TimedeltaIndex subtraction could incorrectly overflow when NaT is present (GH17791)
  • Bug in DatetimeIndex subtracting datetimelike from DatetimeIndex could fail to overflow (GH18020)
  • Bug in IntervalIndex.copy() when copying and IntervalIndex with non-default closed (GH18339)
  • Bug in DataFrame.to_dict() where columns of datetime that are tz-aware were not converted to required arrays when used with orient='records', raising TypeError (GH18372)
  • Bug in DateTimeIndex and date_range() where mismatching tz-aware start and end timezones would not raise an err if end.tzinfo is None (GH18431)
  • Bug in Series.fillna() which raised when passed a long integer on Python 2 (GH18159).

Indexing

  • Bug in a boolean comparison of a datetime.datetime and a datetime64[ns] dtype Series (GH17965)
  • Bug where a MultiIndex with more than a million records was not raising AttributeError when trying to access a missing attribute (GH18165)
  • Bug in IntervalIndex constructor when a list of intervals is passed with non-default closed (GH18334)
  • Bug in Index.putmask when an invalid mask passed (GH18368)
  • Bug in masked assignment of a timedelta64[ns] dtype Series, incorrectly coerced to float (GH18493)

I/O

  • Bug in class:~pandas.io.stata.StataReader not converting date/time columns with display formatting addressed (GH17990). Previously columns with display formatting were normally left as ordinal numbers and not converted to datetime objects.
  • Bug in read_csv() when reading a compressed UTF-16 encoded file (GH18071)
  • Bug in read_csv() for handling null values in index columns when specifying na_filter=False (GH5239)
  • Bug in read_csv() when reading numeric category fields with high cardinality (GH18186)
  • Bug in DataFrame.to_csv() when the table had MultiIndex columns, and a list of strings was passed in for header (GH5539)
  • Bug in parsing integer datetime-like columns with specified format in read_sql (GH17855).
  • Bug in DataFrame.to_msgpack() when serializing data of the numpy.bool_ datatype (GH18390)
  • Bug in read_json() not decoding when reading line delimited JSON from S3 (GH17200)
  • Bug in pandas.io.json.json_normalize() to avoid modification of meta (GH18610)
  • Bug in to_latex() where repeated MultiIndex values were not printed even though a higher level index differed from the previous row (GH14484)
  • Bug when reading NaN-only categorical columns in HDFStore (GH18413)
  • Bug in DataFrame.to_latex() with longtable=True where a latex multicolumn always spanned over three columns (GH17959)

Plotting

  • Bug in DataFrame.plot() and Series.plot() with DatetimeIndex where a figure generated by them is not pickleable in Python 3 (GH18439)

Groupby/Resample/Rolling

  • Bug in DataFrame.resample(...).apply(...) when there is a callable that returns different columns (GH15169)
  • Bug in DataFrame.resample(...) when there is a time change (DST) and resampling frequency is 12h or higher (GH15549)
  • Bug in pd.DataFrameGroupBy.count() when counting over a datetimelike column (GH13393)
  • Bug in rolling.var where calculation is inaccurate with a zero-valued array (GH18430)

Reshaping

  • Error message in pd.merge_asof() for key datatype mismatch now includes datatype of left and right key (GH18068)
  • Bug in pd.concat when empty and non-empty DataFrames or Series are concatenated (GH18178 GH18187)
  • Bug in DataFrame.filter(...) when unicode is passed as a condition in Python 2 (GH13101)
  • Bug when merging empty DataFrames when np.seterr(divide='raise') is set (GH17776)

Numeric

  • Bug in pd.Series.rolling.skew() and rolling.kurt() with all equal values has floating issue (GH18044)

Categorical

  • Bug in DataFrame.astype() where casting to ‘category’ on an empty DataFrame causes a segmentation fault (GH18004)
  • Error messages in the testing module have been improved when items have different CategoricalDtype (GH18069)
  • CategoricalIndex can now correctly take a pd.api.types.CategoricalDtype as its dtype (GH18116)
  • Bug in Categorical.unique() returning read-only codes array when all categories were NaN (GH18051)
  • Bug in DataFrame.groupby(axis=1) with a CategoricalIndex (GH18432)

String

v0.21.0 (October 27, 2017)

This is a major release from 0.20.3 and includes a number of API changes, deprecations, 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:

  • Integration with Apache Parquet, including a new top-level read_parquet() function and DataFrame.to_parquet() method, see here.
  • New user-facing pandas.api.types.CategoricalDtype for specifying categoricals independent of the data, see here.
  • The behavior of sum and prod on all-NaN Series/DataFrames is now consistent and no longer depends on whether bottleneck is installed, and sum and prod on empty Series now return NaN instead of 0, see here.
  • Compatibility fixes for pypy, see here.
  • Additions to the drop, reindex and rename API to make them more consistent, see here.
  • Addition of the new methods DataFrame.infer_objects (see here) and GroupBy.pipe (see here).
  • Indexing with a list of labels, where one or more of the labels is missing, is deprecated and will raise a KeyError in a future version, see here.

Check the API Changes and deprecations before updating.

New features

Integration with Apache Parquet file format

Integration with Apache Parquet, including a new top-level read_parquet() and DataFrame.to_parquet() method, see here (GH15838, GH17438).

Apache Parquet provides a cross-language, binary file format for reading and writing data frames efficiently. Parquet is designed to faithfully serialize and de-serialize DataFrame s, supporting all of the pandas dtypes, including extension dtypes such as datetime with timezones.

This functionality depends on either the pyarrow or fastparquet library. For more details, see see the IO docs on Parquet.

infer_objects type conversion

The DataFrame.infer_objects() and Series.infer_objects() methods have been added to perform dtype inference on object columns, replacing some of the functionality of the deprecated convert_objects method. See the documentation here for more details. (GH11221)

This method only performs soft conversions on object columns, converting Python objects to native types, but not any coercive conversions. For example:

In [1]: df = pd.DataFrame({'A': [1, 2, 3],
   ...:                    'B': np.array([1, 2, 3], dtype='object'),
   ...:                    'C': ['1', '2', '3']})
   ...: 

In [2]: df.dtypes
Out[2]: 
A     int64
B    object
C    object
dtype: object

In [3]: df.infer_objects().dtypes
Out[3]: 
A     int64
B     int64
C    object
dtype: object

Note that column 'C' was not converted - only scalar numeric types will be converted to a new type. Other types of conversion should be accomplished using the to_numeric() function (or to_datetime(), to_timedelta()).

In [4]: df = df.infer_objects()

In [5]: df['C'] = pd.to_numeric(df['C'], errors='coerce')

In [6]: df.dtypes
Out[6]: 
A    int64
B    int64
C    int64
dtype: object

Improved warnings when attempting to create columns

New users are often puzzled by the relationship between column operations and attribute access on DataFrame instances (GH7175). One specific instance of this confusion is attempting to create a new column by setting an attribute on the DataFrame:

In[1]: df = pd.DataFrame({'one': [1., 2., 3.]})
In[2]: df.two = [4, 5, 6]

This does not raise any obvious exceptions, but also does not create a new column:

In[3]: df
Out[3]:
    one
0  1.0
1  2.0
2  3.0

Setting a list-like data structure into a new attribute now raises a UserWarning about the potential for unexpected behavior. See Attribute Access.

drop now also accepts index/columns keywords

The drop() method has gained index/columns keywords as an alternative to specifying the axis. This is similar to the behavior of reindex (GH12392).

For example:

In [7]: df = pd.DataFrame(np.arange(8).reshape(2,4),
   ...:                   columns=['A', 'B', 'C', 'D'])
   ...: 

In [8]: df
Out[8]: 
   A  B  C  D
0  0  1  2  3
1  4  5  6  7

In [9]: df.drop(['B', 'C'], axis=1)
Out[9]: 
   A  D
0  0  3
1  4  7

# the following is now equivalent
In [10]: df.drop(columns=['B', 'C'])
Out[10]: 
   A  D
0  0  3
1  4  7

rename, reindex now also accept axis keyword

The DataFrame.rename() and DataFrame.reindex() methods have gained the axis keyword to specify the axis to target with the operation (GH12392).

Here’s rename:

In [11]: df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})

In [12]: df.rename(str.lower, axis='columns')
Out[12]: 
   a  b
0  1  4
1  2  5
2  3  6

In [13]: df.rename(id, axis='index')
Out[13]: 
                A  B
93904703662592  1  4
93904703662624  2  5
93904703662656  3  6

And reindex:

In [14]: df.reindex(['A', 'B', 'C'], axis='columns')
Out[14]: 
   A  B   C
0  1  4 NaN
1  2  5 NaN
2  3  6 NaN

In [15]: df.reindex([0, 1, 3], axis='index')
Out[15]: 
     A    B
0  1.0  4.0
1  2.0  5.0
3  NaN  NaN

The “index, columns” style continues to work as before.

In [16]: df.rename(index=id, columns=str.lower)
Out[16]: 
                a  b
93904703662592  1  4
93904703662624  2  5
93904703662656  3  6

In [17]: df.reindex(index=[0, 1, 3], columns=['A', 'B', 'C'])
Out[17]: 
     A    B   C
0  1.0  4.0 NaN
1  2.0  5.0 NaN
3  NaN  NaN NaN

We highly encourage using named arguments to avoid confusion when using either style.

CategoricalDtype for specifying categoricals

pandas.api.types.CategoricalDtype has been added to the public API and expanded to include the categories and ordered attributes. A CategoricalDtype can be used to specify the set of categories and orderedness of an array, independent of the data. This can be useful for example, when converting string data to a Categorical (GH14711, GH15078, GH16015, GH17643):

In [18]: from pandas.api.types import CategoricalDtype

In [19]: s = pd.Series(['a', 'b', 'c', 'a'])  # strings

In [20]: dtype = CategoricalDtype(categories=['a', 'b', 'c', 'd'], ordered=True)

In [21]: s.astype(dtype)
Out[21]: 
0    a
1    b
2    c
3    a
dtype: category
Categories (4, object): [a < b < c < d]

One place that deserves special mention is in read_csv(). Previously, with dtype={'col': 'category'}, the returned values and categories would always be strings.

In [22]: data = 'A,B\na,1\nb,2\nc,3'

In [23]: pd.read_csv(StringIO(data), dtype={'B': 'category'}).B.cat.categories
Out[23]: Index(['1', '2', '3'], dtype='object')

Notice the “object” dtype.

With a CategoricalDtype of all numerics, datetimes, or timedeltas, we can automatically convert to the correct type

In [24]: dtype = {'B': CategoricalDtype([1, 2, 3])}

In [25]: pd.read_csv(StringIO(data), dtype=dtype).B.cat.categories
Out[25]: Int64Index([1, 2, 3], dtype='int64')

The values have been correctly interpreted as integers.

The .dtype property of a Categorical, CategoricalIndex or a Series with categorical type will now return an instance of CategoricalDtype. While the repr has changed, str(CategoricalDtype()) is still the string 'category'. We’ll take this moment to remind users that the preferred way to detect categorical data is to use pandas.api.types.is_categorical_dtype(), and not str(dtype) == 'category'.

See the CategoricalDtype docs for more.

GroupBy objects now have a pipe method

GroupBy objects now have a pipe method, similar to the one on DataFrame and Series, that allow for functions that take a GroupBy to be composed in a clean, readable syntax. (GH17871)

For a concrete example on combining .groupby and .pipe , imagine having a DataFrame with columns for stores, products, revenue and sold quantity. We’d like to do a groupwise calculation of prices (i.e. revenue/quantity) per store and per product. We could do this in a multi-step operation, but expressing it in terms of piping can make the code more readable.

First we set the data:

In [26]: import numpy as np

In [27]: n = 1000

In [28]: df = pd.DataFrame({'Store': np.random.choice(['Store_1', 'Store_2'], n),
   ....:                    'Product': np.random.choice(['Product_1', 'Product_2', 'Product_3'], n),
   ....:                    'Revenue': (np.random.random(n)*50+10).round(2),
   ....:                    'Quantity': np.random.randint(1, 10, size=n)})
   ....: 

In [29]: df.head(2)
Out[29]: 
     Store    Product  Revenue  Quantity
0  Store_1  Product_3    54.28         3
1  Store_2  Product_2    30.91         1

Now, to find prices per store/product, we can simply do:

In [30]: (df.groupby(['Store', 'Product'])
   ....:    .pipe(lambda grp: grp.Revenue.sum()/grp.Quantity.sum())
   ....:    .unstack().round(2))
   ....: 
Out[30]: 
Product  Product_1  Product_2  Product_3
Store                                   
Store_1       6.37       6.98       7.49
Store_2       7.60       7.01       7.13

See the documentation for more.

Categorical.rename_categories accepts a dict-like

rename_categories() now accepts a dict-like argument for new_categories. The previous categories are looked up in the dictionary’s keys and replaced if found. The behavior of missing and extra keys is the same as in DataFrame.rename().

In [31]: c = pd.Categorical(['a', 'a', 'b'])

In [32]: c.rename_categories({"a": "eh", "b": "bee"})
Out[32]: 
[eh, eh, bee]
Categories (2, object): [eh, bee]

Warning

To assist with upgrading pandas, rename_categories treats Series as list-like. Typically, Series are considered to be dict-like (e.g. in .rename, .map). In a future version of pandas rename_categories will change to treat them as dict-like. Follow the warning message’s recommendations for writing future-proof code.

In [33]: c.rename_categories(pd.Series([0, 1], index=['a', 'c']))
FutureWarning: Treating Series 'new_categories' as a list-like and using the values.
In a future version, 'rename_categories' will treat Series like a dictionary.
For dict-like, use 'new_categories.to_dict()'
For list-like, use 'new_categories.values'.
Out[33]:
[0, 0, 1]
Categories (2, int64): [0, 1]

Other Enhancements

New functions or methods
New keywords
Various enhancements

Backwards incompatible API changes

Dependencies have increased minimum versions

We have updated our minimum supported versions of dependencies (GH15206, GH15543, GH15214). If installed, we now require:

Package Minimum Version Required
Numpy 1.9.0 X
Matplotlib 1.4.3  
Scipy 0.14.0  
Bottleneck 1.0.0  

Additionally, support has been dropped for Python 3.4 (GH15251).

Sum/Prod of all-NaN or empty Series/DataFrames is now consistently NaN

Note

The changes described here have been partially reverted. See the v0.22.0 Whatsnew for more.

The behavior of sum and prod on all-NaN Series/DataFrames no longer depends on whether bottleneck is installed, and return value of sum and prod on an empty Series has changed (GH9422, GH15507).

Calling sum or prod on an empty or all-NaN Series, or columns of a DataFrame, will result in NaN. See the docs.

In [33]: s = Series([np.nan])

Previously WITHOUT bottleneck installed:

In [2]: s.sum()
Out[2]: np.nan

Previously WITH bottleneck:

In [2]: s.sum()
Out[2]: 0.0

New Behavior, without regard to the bottleneck installation:

In [34]: s.sum()
Out[34]: 0.0

Note that this also changes the sum of an empty Series. Previously this always returned 0 regardless of a bottlenck installation:

In [1]: pd.Series([]).sum()
Out[1]: 0

but for consistency with the all-NaN case, this was changed to return NaN as well:

In [35]: pd.Series([]).sum()
Out[35]: 0.0

Indexing with a list with missing labels is Deprecated

Previously, selecting with a list of labels, where one or more labels were missing would always succeed, returning NaN for missing labels. This will now show a FutureWarning. In the future this will raise a KeyError (GH15747). This warning will trigger on a DataFrame or a Series for using .loc[] or [[]] when passing a list-of-labels with at least 1 missing label. See the deprecation docs.

In [36]: s = pd.Series([1, 2, 3])

In [37]: s
Out[37]: 
0    1
1    2
2    3
dtype: int64

Previous Behavior

In [4]: s.loc[[1, 2, 3]]
Out[4]:
1    2.0
2    3.0
3    NaN
dtype: float64

Current Behavior

In [4]: s.loc[[1, 2, 3]]
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike

Out[4]:
1    2.0
2    3.0
3    NaN
dtype: float64

The idiomatic way to achieve selecting potentially not-found elements is via .reindex()

In [38]: s.reindex([1, 2, 3])
Out[38]: 
1    2.0
2    3.0
3    NaN
dtype: float64

Selection with all keys found is unchanged.

In [39]: s.loc[[1, 2]]
Out[39]: 
1    2
2    3
dtype: int64

NA naming Changes

In order to promote more consistency among the pandas API, we have added additional top-level functions isna() and notna() that are aliases for isnull() and notnull(). The naming scheme is now more consistent with methods like .dropna() and .fillna(). Furthermore in all cases where .isnull() and .notnull() methods are defined, these have additional methods named .isna() and .notna(), these are included for classes Categorical, Index, Series, and DataFrame. (GH15001).

The configuration option pd.options.mode.use_inf_as_null is deprecated, and pd.options.mode.use_inf_as_na is added as a replacement.

Iteration of Series/Index will now return Python scalars

Previously, when using certain iteration methods for a Series with dtype int or float, you would receive a numpy scalar, e.g. a np.int64, rather than a Python int. Issue (GH10904) corrected this for Series.tolist() and list(Series). This change makes all iteration methods consistent, in particular, for __iter__() and .map(); note that this only affects int/float dtypes. (GH13236, GH13258, GH14216).

In [40]: s = pd.Series([1, 2, 3])

In [41]: s
Out[41]: 
0    1
1    2
2    3
dtype: int64

Previously:

In [2]: type(list(s)[0])
Out[2]: numpy.int64

New Behaviour:

In [42]: type(list(s)[0])
Out[42]: int

Furthermore this will now correctly box the results of iteration for DataFrame.to_dict() as well.

In [43]: d = {'a':[1], 'b':['b']}

In [44]: df = pd.DataFrame(d)

Previously:

In [8]: type(df.to_dict()['a'][0])
Out[8]: numpy.int64

New Behaviour:

In [45]: type(df.to_dict()['a'][0])
Out[45]: int

Indexing with a Boolean Index

Previously when passing a boolean Index to .loc, if the index of the Series/DataFrame had boolean labels, you would get a label based selection, potentially duplicating result labels, rather than a boolean indexing selection (where True selects elements), this was inconsistent how a boolean numpy array indexed. The new behavior is to act like a boolean numpy array indexer. (GH17738)

Previous Behavior:

In [46]: s = pd.Series([1, 2, 3], index=[False, True, False])

In [47]: s
Out[47]: 
False    1
True     2
False    3
dtype: int64
In [59]: s.loc[pd.Index([True, False, True])]
Out[59]:
True     2
False    1
False    3
True     2
dtype: int64

Current Behavior

In [48]: s.loc[pd.Index([True, False, True])]
Out[48]: 
False    1
False    3
dtype: int64

Furthermore, previously if you had an index that was non-numeric (e.g. strings), then a boolean Index would raise a KeyError. This will now be treated as a boolean indexer.

Previously Behavior:

In [49]: s = pd.Series([1,2,3], index=['a', 'b', 'c'])

In [50]: s
Out[50]: 
a    1
b    2
c    3
dtype: int64
In [39]: s.loc[pd.Index([True, False, True])]
KeyError: "None of [Index([True, False, True], dtype='object')] are in the [index]"

Current Behavior

In [51]: s.loc[pd.Index([True, False, True])]
Out[51]: 
a    1
c    3
dtype: int64

PeriodIndex resampling

In previous versions of pandas, resampling a Series/DataFrame indexed by a PeriodIndex returned a DatetimeIndex in some cases (GH12884). Resampling to a multiplied frequency now returns a PeriodIndex (GH15944). As a minor enhancement, resampling a PeriodIndex can now handle NaT values (GH13224)

Previous Behavior:

In [1]: pi = pd.period_range('2017-01', periods=12, freq='M')

In [2]: s = pd.Series(np.arange(12), index=pi)

In [3]: resampled = s.resample('2Q').mean()

In [4]: resampled
Out[4]:
2017-03-31     1.0
2017-09-30     5.5
2018-03-31    10.0
Freq: 2Q-DEC, dtype: float64

In [5]: resampled.index
Out[5]: DatetimeIndex(['2017-03-31', '2017-09-30', '2018-03-31'], dtype='datetime64[ns]', freq='2Q-DEC')

New Behavior:

In [52]: pi = pd.period_range('2017-01', periods=12, freq='M')

In [53]: s = pd.Series(np.arange(12), index=pi)

In [54]: resampled = s.resample('2Q').mean()

In [55]: resampled
Out[55]: 
2017Q1    2.5
2017Q3    8.5
Freq: 2Q-DEC, dtype: float64

In [56]: resampled.index
Out[56]: PeriodIndex(['2017Q1', '2017Q3'], dtype='period[2Q-DEC]', freq='2Q-DEC')

Upsampling and calling .ohlc() previously returned a Series, basically identical to calling .asfreq(). OHLC upsampling now returns a DataFrame with columns open, high, low and close (GH13083). This is consistent with downsampling and DatetimeIndex behavior.

Previous Behavior:

In [1]: pi = pd.PeriodIndex(start='2000-01-01', freq='D', periods=10)

In [2]: s = pd.Series(np.arange(10), index=pi)

In [3]: s.resample('H').ohlc()
Out[3]:
2000-01-01 00:00    0.0
                ...
2000-01-10 23:00    NaN
Freq: H, Length: 240, dtype: float64

In [4]: s.resample('M').ohlc()
Out[4]:
         open  high  low  close
2000-01     0     9    0      9

New Behavior:

In [57]: pi = pd.PeriodIndex(start='2000-01-01', freq='D', periods=10)

In [58]: s = pd.Series(np.arange(10), index=pi)

In [59]: s.resample('H').ohlc()
Out[59]: 
                  open  high  low  close
2000-01-01 00:00   0.0   0.0  0.0    0.0
2000-01-01 01:00   NaN   NaN  NaN    NaN
2000-01-01 02:00   NaN   NaN  NaN    NaN
2000-01-01 03:00   NaN   NaN  NaN    NaN
2000-01-01 04:00   NaN   NaN  NaN    NaN
2000-01-01 05:00   NaN   NaN  NaN    NaN
2000-01-01 06:00   NaN   NaN  NaN    NaN
...                ...   ...  ...    ...
2000-01-10 17:00   NaN   NaN  NaN    NaN
2000-01-10 18:00   NaN   NaN  NaN    NaN
2000-01-10 19:00   NaN   NaN  NaN    NaN
2000-01-10 20:00   NaN   NaN  NaN    NaN
2000-01-10 21:00   NaN   NaN  NaN    NaN
2000-01-10 22:00   NaN   NaN  NaN    NaN
2000-01-10 23:00   NaN   NaN  NaN    NaN

[240 rows x 4 columns]

In [60]: s.resample('M').ohlc()
Out[60]: 
         open  high  low  close
2000-01     0     9    0      9

Improved error handling during item assignment in pd.eval

eval() will now raise a ValueError when item assignment malfunctions, or inplace operations are specified, but there is no item assignment in the expression (GH16732)

In [61]: arr = np.array([1, 2, 3])

Previously, if you attempted the following expression, you would get a not very helpful error message:

In [3]: pd.eval("a = 1 + 2", target=arr, inplace=True)
...
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`)
and integer or boolean arrays are valid indices

This is a very long way of saying numpy arrays don’t support string-item indexing. With this change, the error message is now this:

In [3]: pd.eval("a = 1 + 2", target=arr, inplace=True)
...
ValueError: Cannot assign expression output to target

It also used to be possible to evaluate expressions inplace, even if there was no item assignment:

In [4]: pd.eval("1 + 2", target=arr, inplace=True)
Out[4]: 3

However, this input does not make much sense because the output is not being assigned to the target. Now, a ValueError will be raised when such an input is passed in:

In [4]: pd.eval("1 + 2", target=arr, inplace=True)
...
ValueError: Cannot operate inplace if there is no assignment

Dtype Conversions

Previously assignments, .where() and .fillna() with a bool assignment, would coerce to same the type (e.g. int / float), or raise for datetimelikes. These will now preserve the bools with object dtypes. (GH16821).

In [62]: s = Series([1, 2, 3])
In [5]: s[1] = True

In [6]: s
Out[6]:
0    1
1    1
2    3
dtype: int64

New Behavior

In [63]: s[1] = True

In [64]: s
Out[64]: 
0       1
1    True
2       3
dtype: object

Previously, as assignment to a datetimelike with a non-datetimelike would coerce the non-datetime-like item being assigned (GH14145).

In [65]: s = pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2012-01-01')])
In [1]: s[1] = 1

In [2]: s
Out[2]:
0   2011-01-01 00:00:00.000000000
1   1970-01-01 00:00:00.000000001
dtype: datetime64[ns]

These now coerce to object dtype.

In [66]: s[1] = 1

In [67]: s
Out[67]: 
0    2011-01-01 00:00:00
1                      1
dtype: object
  • Inconsistent behavior in .where() with datetimelikes which would raise rather than coerce to object (GH16402)
  • Bug in assignment against int64 data with np.ndarray with float64 dtype may keep int64 dtype (GH14001)

MultiIndex Constructor with a Single Level

The MultiIndex constructors no longer squeezes a MultiIndex with all length-one levels down to a regular Index. This affects all the MultiIndex constructors. (GH17178)

Previous behavior:

In [2]: pd.MultiIndex.from_tuples([('a',), ('b',)])
Out[2]: Index(['a', 'b'], dtype='object')

Length 1 levels are no longer special-cased. They behave exactly as if you had length 2+ levels, so a MultiIndex is always returned from all of the MultiIndex constructors:

In [68]: pd.MultiIndex.from_tuples([('a',), ('b',)])
Out[68]: 
MultiIndex(levels=[['a', 'b']],
           labels=[[0, 1]])

UTC Localization with Series

Previously, to_datetime() did not localize datetime Series data when utc=True was passed. Now, to_datetime() will correctly localize Series with a datetime64[ns, UTC] dtype to be consistent with how list-like and Index data are handled. (GH6415).

Previous Behavior

In [69]: s = Series(['20130101 00:00:00'] * 3)
In [12]: pd.to_datetime(s, utc=True)
Out[12]:
0   2013-01-01
1   2013-01-01
2   2013-01-01
dtype: datetime64[ns]

New Behavior

In [70]: pd.to_datetime(s, utc=True)
Out[70]: 
0   2013-01-01 00:00:00+00:00
1   2013-01-01 00:00:00+00:00
2   2013-01-01 00:00:00+00:00
dtype: datetime64[ns, UTC]

Additionally, DataFrames with datetime columns that were parsed by read_sql_table() and read_sql_query() will also be localized to UTC only if the original SQL columns were timezone aware datetime columns.

Consistency of Range Functions

In previous versions, there were some inconsistencies between the various range functions: date_range(), bdate_range(), period_range(), timedelta_range(), and interval_range(). (GH17471).

One of the inconsistent behaviors occurred when the start, end and period parameters were all specified, potentially leading to ambiguous ranges. When all three parameters were passed, interval_range ignored the period parameter, period_range ignored the end parameter, and the other range functions raised. To promote consistency among the range functions, and avoid potentially ambiguous ranges, interval_range and period_range will now raise when all three parameters are passed.

Previous Behavior:

In [2]: pd.interval_range(start=0, end=4, periods=6)
Out[2]:
IntervalIndex([(0, 1], (1, 2], (2, 3]]
              closed='right',
              dtype='interval[int64]')

In [3]: pd.period_range(start='2017Q1', end='2017Q4', periods=6, freq='Q')
Out[3]: PeriodIndex(['2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1', '2018Q2'], dtype='period[Q-DEC]', freq='Q-DEC')

New Behavior:

In [2]: pd.interval_range(start=0, end=4, periods=6)
---------------------------------------------------------------------------
ValueError: Of the three parameters: start, end, and periods, exactly two must be specified

In [3]: pd.period_range(start='2017Q1', end='2017Q4', periods=6, freq='Q')
---------------------------------------------------------------------------
ValueError: Of the three parameters: start, end, and periods, exactly two must be specified

Additionally, the endpoint parameter end was not included in the intervals produced by interval_range. However, all other range functions include end in their output. To promote consistency among the range functions, interval_range will now include end as the right endpoint of the final interval, except if freq is specified in a way which skips end.

Previous Behavior:

In [4]: pd.interval_range(start=0, end=4)
Out[4]:
IntervalIndex([(0, 1], (1, 2], (2, 3]]
              closed='right',
              dtype='interval[int64]')

New Behavior:

In [71]: pd.interval_range(start=0, end=4)
Out[71]: 
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4]]
              closed='right',
              dtype='interval[int64]')

No Automatic Matplotlib Converters

Pandas no longer registers our date, time, datetime, datetime64, and Period converters with matplotlib when pandas is imported. Matplotlib plot methods (plt.plot, ax.plot, …), will not nicely format the x-axis for DatetimeIndex or PeriodIndex values. You must explicitly register these methods:

Pandas built-in Series.plot and DataFrame.plot will register these converters on first-use (GH17710).

Note

This change has been temporarily reverted in pandas 0.21.1, for more details see here.

Other API Changes

  • The Categorical constructor no longer accepts a scalar for the categories keyword. (GH16022)
  • Accessing a non-existent attribute on a closed HDFStore will now raise an AttributeError rather than a ClosedFileError (GH16301)
  • read_csv() now issues a UserWarning if the names parameter contains duplicates (GH17095)
  • read_csv() now treats 'null' and 'n/a' strings as missing values by default (GH16471, GH16078)
  • pandas.HDFStore’s string representation is now faster and less detailed. For the previous behavior, use pandas.HDFStore.info(). (GH16503).
  • Compression defaults in HDF stores now follow pytables standards. Default is no compression and if complib is missing and complevel > 0 zlib is used (GH15943)
  • Index.get_indexer_non_unique() now returns a ndarray indexer rather than an Index; this is consistent with Index.get_indexer() (GH16819)
  • Removed the @slow decorator from pandas.util.testing, which caused issues for some downstream packages’ test suites. Use @pytest.mark.slow instead, which achieves the same thing (GH16850)
  • Moved definition of MergeError to the pandas.errors module.
  • The signature of Series.set_axis() and DataFrame.set_axis() has been changed from set_axis(axis, labels) to set_axis(labels, axis=0), for consistency with the rest of the API. The old signature is deprecated and will show a FutureWarning (GH14636)
  • Series.argmin() and Series.argmax() will now raise a TypeError when used with object dtypes, instead of a ValueError (GH13595)
  • Period is now immutable, and will now raise an AttributeError when a user tries to assign a new value to the ordinal or freq attributes (GH17116).
  • to_datetime() when passed a tz-aware origin= kwarg will now raise a more informative ValueError rather than a TypeError (GH16842)
  • to_datetime() now raises a ValueError when format includes %W or %U without also including day of the week and calendar year (GH16774)
  • Renamed non-functional index to index_col in read_stata() to improve API consistency (GH16342)
  • Bug in DataFrame.drop() caused boolean labels False and True to be treated as labels 0 and 1 respectively when dropping indices from a numeric index. This will now raise a ValueError (GH16877)
  • Restricted DateOffset keyword arguments. Previously, DateOffset subclasses allowed arbitrary keyword arguments which could lead to unexpected behavior. Now, only valid arguments will be accepted. (GH17176).

Deprecations

Series.select and DataFrame.select

The Series.select() and DataFrame.select() methods are deprecated in favor of using df.loc[labels.map(crit)] (GH12401)

In [72]: df = DataFrame({'A': [1, 2, 3]}, index=['foo', 'bar', 'baz'])
In [3]: df.select(lambda x: x in ['bar', 'baz'])
FutureWarning: select is deprecated and will be removed in a future release. You can use .loc[crit] as a replacement
Out[3]:
     A
bar  2
baz  3
In [73]: df.loc[df.index.map(lambda x: x in ['bar', 'baz'])]
Out[73]: 
     A
bar  2
baz  3

Series.argmax and Series.argmin

The behavior of Series.argmax() and Series.argmin() have been deprecated in favor of Series.idxmax() and Series.idxmin(), respectively (GH16830).

For compatibility with NumPy arrays, pd.Series implements argmax and argmin. Since pandas 0.13.0, argmax has been an alias for pandas.Series.idxmax(), and argmin has been an alias for pandas.Series.idxmin(). They return the label of the maximum or minimum, rather than the position.

We’ve deprecated the current behavior of Series.argmax and Series.argmin. Using either of these will emit a FutureWarning. Use Series.idxmax() if you want the label of the maximum. Use Series.values.argmax() if you want the position of the maximum. Likewise for the minimum. In a future release Series.argmax and Series.argmin will return the position of the maximum or minimum.

Removal of prior version deprecations/changes

  • read_excel() has dropped the has_index_names parameter (GH10967)
  • The pd.options.display.height configuration has been dropped (GH3663)
  • The pd.options.display.line_width configuration has been dropped (GH2881)
  • The pd.options.display.mpl_style configuration has been dropped (GH12190)
  • Index has dropped the .sym_diff() method in favor of .symmetric_difference() (GH12591)
  • Categorical has dropped the .order() and .sort() methods in favor of .sort_values() (GH12882)
  • eval() and DataFrame.eval() have changed the default of inplace from None to False (GH11149)
  • The function get_offset_name has been dropped in favor of the .freqstr attribute for an offset (GH11834)
  • pandas no longer tests for compatibility with hdf5-files created with pandas < 0.11 (GH17404).

Performance Improvements

  • Improved performance of instantiating SparseDataFrame (GH16773)
  • Series.dt no longer performs frequency inference, yielding a large speedup when accessing the attribute (GH17210)
  • Improved performance of set_categories() by not materializing the values (GH17508)
  • Timestamp.microsecond no longer re-computes on attribute access (GH17331)
  • Improved performance of the CategoricalIndex for data that is already categorical dtype (GH17513)
  • Improved performance of RangeIndex.min() and RangeIndex.max() by using RangeIndex properties to perform the computations (GH17607)

Documentation Changes

  • Several NaT method docstrings (e.g. NaT.ctime()) were incorrect (GH17327)
  • The documentation has had references to versions < v0.17 removed and cleaned up (GH17442, GH17442, GH17404 & GH17504)

Bug Fixes

Conversion

  • Bug in assignment against datetime-like data with int may incorrectly convert to datetime-like (GH14145)
  • Bug in assignment against int64 data with np.ndarray with float64 dtype may keep int64 dtype (GH14001)
  • Fixed the return type of IntervalIndex.is_non_overlapping_monotonic to be a Python bool for consistency with similar attributes/methods. Previously returned a numpy.bool_. (GH17237)
  • Bug in IntervalIndex.is_non_overlapping_monotonic when intervals are closed on both sides and overlap at a point (GH16560)
  • Bug in Series.fillna() returns frame when inplace=True and value is dict (GH16156)
  • Bug in Timestamp.weekday_name returning a UTC-based weekday name when localized to a timezone (GH17354)
  • Bug in Timestamp.replace when replacing tzinfo around DST changes (GH15683)
  • Bug in Timedelta construction and arithmetic that would not propagate the Overflow exception (GH17367)
  • Bug in astype() converting to object dtype when passed extension type classes (DatetimeTZDtype, CategoricalDtype) rather than instances. Now a TypeError is raised when a class is passed (GH17780).
  • Bug in to_numeric() in which elements were not always being coerced to numeric when errors='coerce' (GH17007, GH17125)
  • Bug in DataFrame and Series constructors where range objects are converted to int32 dtype on Windows instead of int64 (GH16804)

Indexing

  • When called with a null slice (e.g. df.iloc[:]), the .iloc and .loc indexers return a shallow copy of the original object. Previously they returned the original object. (GH13873).
  • When called on an unsorted MultiIndex, the loc indexer now will raise UnsortedIndexError only if proper slicing is used on non-sorted levels (GH16734).
  • Fixes regression in 0.20.3 when indexing with a string on a TimedeltaIndex (GH16896).
  • Fixed TimedeltaIndex.get_loc() handling of np.timedelta64 inputs (GH16909).
  • Fix MultiIndex.sort_index() ordering when ascending argument is a list, but not all levels are specified, or are in a different order (GH16934).
  • Fixes bug where indexing with np.inf caused an OverflowError to be raised (GH16957)
  • Bug in reindexing on an empty CategoricalIndex (GH16770)
  • Fixes DataFrame.loc for setting with alignment and tz-aware DatetimeIndex (GH16889)
  • Avoids IndexError when passing an Index or Series to .iloc with older numpy (GH17193)
  • Allow unicode empty strings as placeholders in multilevel columns in Python 2 (GH17099)
  • Bug in .iloc when used with inplace addition or assignment and an int indexer on a MultiIndex causing the wrong indexes to be read from and written to (GH17148)
  • Bug in .isin() in which checking membership in empty Series objects raised an error (GH16991)
  • Bug in CategoricalIndex reindexing in which specified indices containing duplicates were not being respected (GH17323)
  • Bug in intersection of RangeIndex with negative step (GH17296)
  • Bug in IntervalIndex where performing a scalar lookup fails for included right endpoints of non-overlapping monotonic decreasing indexes (GH16417, GH17271)
  • Bug in DataFrame.first_valid_index() and DataFrame.last_valid_index() when no valid entry (GH17400)
  • Bug in Series.rename() when called with a callable, incorrectly alters the name of the Series, rather than the name of the Index. (GH17407)
  • Bug in String.str_get() raises IndexError instead of inserting NaNs when using a negative index. (GH17704)

I/O

  • Bug in read_hdf() when reading a timezone aware index from fixed format HDFStore (GH17618)
  • Bug in read_csv() in which columns were not being thoroughly de-duplicated (GH17060)
  • Bug in read_csv() in which specified column names were not being thoroughly de-duplicated (GH17095)
  • Bug in read_csv() in which non integer values for the header argument generated an unhelpful / unrelated error message (GH16338)
  • Bug in read_csv() in which memory management issues in exception handling, under certain conditions, would cause the interpreter to segfault (GH14696, GH16798).
  • Bug in read_csv() when called with low_memory=False in which a CSV with at least one column > 2GB in size would incorrectly raise a MemoryError (GH16798).
  • Bug in read_csv() when called with a single-element list header would return a DataFrame of all NaN values (GH7757)
  • Bug in DataFrame.to_csv() defaulting to ‘ascii’ encoding in Python 3, instead of ‘utf-8’ (GH17097)
  • Bug in read_stata() where value labels could not be read when using an iterator (GH16923)
  • Bug in read_stata() where the index was not set (GH16342)
  • Bug in read_html() where import check fails when run in multiple threads (GH16928)
  • Bug in read_csv() where automatic delimiter detection caused a TypeError to be thrown when a bad line was encountered rather than the correct error message (GH13374)
  • Bug in DataFrame.to_html() with notebook=True where DataFrames with named indices or non-MultiIndex indices had undesired horizontal or vertical alignment for column or row labels, respectively (GH16792)
  • Bug in DataFrame.to_html() in which there was no validation of the justify parameter (GH17527)
  • Bug in HDFStore.select() when reading a contiguous mixed-data table featuring VLArray (GH17021)
  • Bug in to_json() where several conditions (including objects with unprintable symbols, objects with deep recursion, overlong labels) caused segfaults instead of raising the appropriate exception (GH14256)

Plotting

  • Bug in plotting methods using secondary_y and fontsize not setting secondary axis font size (GH12565)
  • Bug when plotting timedelta and datetime dtypes on y-axis (GH16953)
  • Line plots no longer assume monotonic x data when calculating xlims, they show the entire lines now even for unsorted x data. (GH11310, GH11471)
  • With matplotlib 2.0.0 and above, calculation of x limits for line plots is left to matplotlib, so that its new default settings are applied. (GH15495)
  • Bug in Series.plot.bar or DataFrame.plot.bar with y not respecting user-passed color (GH16822)
  • Bug causing plotting.parallel_coordinates to reset the random seed when using random colors (GH17525)

Groupby/Resample/Rolling

  • Bug in DataFrame.resample(...).size() where an empty DataFrame did not return a Series (GH14962)
  • Bug in infer_freq() causing indices with 2-day gaps during the working week to be wrongly inferred as business daily (GH16624)
  • Bug in .rolling(...).quantile() which incorrectly used different defaults than Series.quantile() and DataFrame.quantile() (GH9413, GH16211)
  • Bug in groupby.transform() that would coerce boolean dtypes back to float (GH16875)
  • Bug in Series.resample(...).apply() where an empty Series modified the source index and did not return the name of a Series (GH14313)
  • Bug in .rolling(...).apply(...) with a DataFrame with a DatetimeIndex, a window of a timedelta-convertible and min_periods >= 1 (GH15305)
  • Bug in DataFrame.groupby where index and column keys were not recognized correctly when the number of keys equaled the number of elements on the groupby axis (GH16859)
  • Bug in groupby.nunique() with TimeGrouper which cannot handle NaT correctly (GH17575)
  • Bug in DataFrame.groupby where a single level selection from a MultiIndex unexpectedly sorts (GH17537)
  • Bug in DataFrame.groupby where spurious warning is raised when Grouper object is used to override ambiguous column name (GH17383)
  • Bug in TimeGrouper differs when passes as a list and as a scalar (GH17530)

Sparse

  • Bug in SparseSeries raises AttributeError when a dictionary is passed in as data (GH16905)
  • Bug in SparseDataFrame.fillna() not filling all NaNs when frame was instantiated from SciPy sparse matrix (GH16112)
  • Bug in SparseSeries.unstack() and SparseDataFrame.stack() (GH16614, GH15045)
  • Bug in make_sparse() treating two numeric/boolean data, which have same bits, as same when array dtype is object (GH17574)
  • SparseArray.all() and SparseArray.any() are now implemented to handle SparseArray, these were used but not implemented (GH17570)

Reshaping

  • Joining/Merging with a non unique PeriodIndex raised a TypeError (GH16871)
  • Bug in crosstab() where non-aligned series of integers were casted to float (GH17005)
  • Bug in merging with categorical dtypes with datetimelikes incorrectly raised a TypeError (GH16900)
  • Bug when using isin() on a large object series and large comparison array (GH16012)
  • Fixes regression from 0.20, Series.aggregate() and DataFrame.aggregate() allow dictionaries as return values again (GH16741)
  • Fixes dtype of result with integer dtype input, from pivot_table() when called with margins=True (GH17013)
  • Bug in crosstab() where passing two Series with the same name raised a KeyError (GH13279)
  • Series.argmin(), Series.argmax(), and their counterparts on DataFrame and groupby objects work correctly with floating point data that contains infinite values (GH13595).
  • Bug in unique() where checking a tuple of strings raised a TypeError (GH17108)
  • Bug in concat() where order of result index was unpredictable if it contained non-comparable elements (GH17344)
  • Fixes regression when sorting by multiple columns on a datetime64 dtype Series with NaT values (GH16836)
  • Bug in pivot_table() where the result’s columns did not preserve the categorical dtype of columns when dropna was False (GH17842)
  • Bug in DataFrame.drop_duplicates where dropping with non-unique column names raised a ValueError (GH17836)
  • Bug in unstack() which, when called on a list of levels, would discard the fillna argument (GH13971)
  • Bug in the alignment of range objects and other list-likes with DataFrame leading to operations being performed row-wise instead of column-wise (GH17901)

Numeric

  • Bug in .clip() with axis=1 and a list-like for threshold is passed; previously this raised ValueError (GH15390)
  • Series.clip() and DataFrame.clip() now treat NA values for upper and lower arguments as None instead of raising ValueError (GH17276).

Categorical

  • Bug in Series.isin() when called with a categorical (GH16639)
  • Bug in the categorical constructor with empty values and categories causing the .categories to be an empty Float64Index rather than an empty Index with object dtype (GH17248)
  • Bug in categorical operations with Series.cat not preserving the original Series’ name (GH17509)
  • Bug in DataFrame.merge() failing for categorical columns with boolean/int data types (GH17187)
  • Bug in constructing a Categorical/CategoricalDtype when the specified categories are of categorical type (GH17884).

PyPy

  • Compatibility with PyPy in read_csv() with usecols=[<unsorted ints>] and read_json() (GH17351)
  • Split tests into cases for CPython and PyPy where needed, which highlights the fragility of index matching with float('nan'), np.nan and NAT (GH17351)
  • Fix DataFrame.memory_usage() to support PyPy. Objects on PyPy do not have a fixed size, so an approximation is used instead (GH17228)

Other

  • Bug where some inplace operators were not being wrapped and produced a copy when invoked (GH12962)
  • Bug in eval() where the inplace parameter was being incorrectly handled (GH16732)

v0.20.3 (July 7, 2017)

This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes and bug fixes. We recommend that all users upgrade to this version.

Bug Fixes

  • Fixed a bug in failing to compute rolling computations of a column-MultiIndexed DataFrame (GH16789, GH16825)
  • Fixed a pytest marker failing downstream packages’ tests suites (GH16680)

Conversion

  • Bug in pickle compat prior to the v0.20.x series, when UTC is a timezone in a Series/DataFrame/Index (GH16608)
  • Bug in Series construction when passing a Series with dtype='category' (GH16524).
  • Bug in DataFrame.astype() when passing a Series as the dtype kwarg. (GH16717).

Indexing

  • Bug in Float64Index causing an empty array instead of None to be returned from .get(np.nan) on a Series whose index did not contain any NaN s (GH8569)
  • Bug in MultiIndex.isin causing an error when passing an empty iterable (GH16777)
  • Fixed a bug in a slicing DataFrame/Series that have a TimedeltaIndex (GH16637)

I/O

  • Bug in read_csv() in which files weren’t opened as binary files by the C engine on Windows, causing EOF characters mid-field, which would fail (GH16039, GH16559, GH16675)
  • Bug in read_hdf() in which reading a Series saved to an HDF file in ‘fixed’ format fails when an explicit mode='r' argument is supplied (GH16583)
  • Bug in DataFrame.to_latex() where bold_rows was wrongly specified to be True by default, whereas in reality row labels remained non-bold whatever parameter provided. (GH16707)
  • Fixed an issue with DataFrame.style() where generated element ids were not unique (GH16780)
  • Fixed loading a DataFrame with a PeriodIndex, from a format='fixed' HDFStore, in Python 3, that was written in Python 2 (GH16781)

Plotting

  • Fixed regression that prevented RGB and RGBA tuples from being used as color arguments (GH16233)
  • Fixed an issue with DataFrame.plot.scatter() that incorrectly raised a KeyError when categorical data is used for plotting (GH16199)

Reshaping

  • PeriodIndex / TimedeltaIndex.join was missing the sort= kwarg (GH16541)
  • Bug in joining on a MultiIndex with a category dtype for a level (GH16627).
  • Bug in merge() when merging/joining with multiple categorical columns (GH16767)

Categorical

  • Bug in DataFrame.sort_values not respecting the kind parameter with categorical data (GH16793)

v0.20.2 (June 4, 2017)

This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend that all users upgrade to this version.

Enhancements

  • Unblocked access to additional compression types supported in pytables: ‘blosc:blosclz, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’ (GH14478)
  • Series provides a to_latex method (GH16180)
  • A new groupby method ngroup(), parallel to the existing cumcount(), has been added to return the group order (GH11642); see here.

Performance Improvements

  • Performance regression fix when indexing with a list-like (GH16285)
  • Performance regression fix for MultiIndexes (GH16319, GH16346)
  • Improved performance of .clip() with scalar arguments (GH15400)
  • Improved performance of groupby with categorical groupers (GH16413)
  • Improved performance of MultiIndex.remove_unused_levels() (GH16556)

Bug Fixes

  • Silenced a warning on some Windows environments about “tput: terminal attributes: No such device or address” when detecting the terminal size. This fix only applies to python 3 (GH16496)
  • Bug in using pathlib.Path or py.path.local objects with io functions (GH16291)
  • Bug in Index.symmetric_difference() on two equal MultiIndex’s, results in a TypeError (GH13490)
  • Bug in DataFrame.update() with overwrite=False and NaN values (GH15593)
  • Passing an invalid engine to read_csv() now raises an informative ValueError rather than UnboundLocalError. (GH16511)
  • Bug in unique() on an array of tuples (GH16519)
  • Bug in cut() when labels are set, resulting in incorrect label ordering (GH16459)
  • Fixed a compatibility issue with IPython 6.0’s tab completion showing deprecation warnings on Categoricals (GH16409)

Conversion

  • Bug in to_numeric() in which empty data inputs were causing a segfault of the interpreter (GH16302)
  • Silence numpy warnings when broadcasting DataFrame to Series with comparison ops (GH16378, GH16306)

Indexing

  • Bug in DataFrame.reset_index(level=) with single level index (GH16263)
  • Bug in partial string indexing with a monotonic, but not strictly-monotonic, index incorrectly reversing the slice bounds (GH16515)
  • Bug in MultiIndex.remove_unused_levels() that would not return a MultiIndex equal to the original. (GH16556)

I/O

  • Bug in read_csv() when comment is passed in a space delimited text file (GH16472)
  • Bug in read_csv() not raising an exception with nonexistent columns in usecols when it had the correct length (GH14671)
  • Bug that would force importing of the clipboard routines unnecessarily, potentially causing an import error on startup (GH16288)
  • Bug that raised IndexError when HTML-rendering an empty DataFrame (GH15953)
  • Bug in read_csv() in which tarfile object inputs were raising an error in Python 2.x for the C engine (GH16530)
  • Bug where DataFrame.to_html() ignored the index_names parameter (GH16493)
  • Bug where pd.read_hdf() returns numpy strings for index names (GH13492)
  • Bug in HDFStore.select_as_multiple() where start/stop arguments were not respected (GH16209)

Plotting

  • Bug in DataFrame.plot with a single column and a list-like color (GH3486)
  • Bug in plot where NaT in DatetimeIndex results in Timestamp.min (GH12405)
  • Bug in DataFrame.boxplot where figsize keyword was not respected for non-grouped boxplots (GH11959)

Groupby/Resample/Rolling

  • Bug in creating a time-based rolling window on an empty DataFrame (GH15819)
  • Bug in rolling.cov() with offset window (GH16058)
  • Bug in .resample() and .groupby() when aggregating on integers (GH16361)

Sparse

  • Bug in construction of SparseDataFrame from scipy.sparse.dok_matrix (GH16179)

Reshaping

  • Bug in DataFrame.stack with unsorted levels in MultiIndex columns (GH16323)
  • Bug in pd.wide_to_long() where no error was raised when i was not a unique identifier (GH16382)
  • Bug in Series.isin(..) with a list of tuples (GH16394)
  • Bug in construction of a DataFrame with mixed dtypes including an all-NaT column. (GH16395)
  • Bug in DataFrame.agg() and Series.agg() with aggregating on non-callable attributes (GH16405)

Numeric

  • Bug in .interpolate(), where limit_direction was not respected when limit=None (default) was passed (GH16282)

Categorical

  • Fixed comparison operations considering the order of the categories when both categoricals are unordered (GH16014)

Other

  • Bug in DataFrame.drop() with an empty-list with non-unique indices (GH16270)

v0.20.1 (May 5, 2017)

This is a major release from 0.19.2 and includes a number of API changes, deprecations, 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:

  • New .agg() API for Series/DataFrame similar to the groupby-rolling-resample API’s, see here
  • Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() method, see here.
  • The .ix indexer has been deprecated, see here
  • Panel has been deprecated, see here
  • Addition of an IntervalIndex and Interval scalar type, see here
  • Improved user API when grouping by index levels in .groupby(), see here
  • Improved support for UInt64 dtypes, see here
  • A new orient for JSON serialization, orient='table', that uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here
  • Experimental support for exporting styled DataFrames (DataFrame.style) to Excel, see here
  • Window binary corr/cov operations now return a MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see here
  • Support for S3 handling now uses s3fs, see here
  • Google BigQuery support now uses the pandas-gbq library, see here

Warning

Pandas has changed the internal structure and layout of the code base. This can affect imports that are not from the top-level pandas.* namespace, please see the changes here.

Check the API Changes and deprecations before updating.

Note

This is a combined release for 0.20.0 and and 0.20.1. Version 0.20.1 contains one additional change for backwards-compatibility with downstream projects using pandas’ utils routines. (GH16250)

What’s new in v0.20.0

New features

agg API for DataFrame/Series

Series & DataFrame have been enhanced to support the aggregation API. This is a familiar API from groupby, window operations, and resampling. This allows aggregation operations in a concise way by using agg() and transform(). The full documentation is here (GH1623).

Here is a sample

In [1]: df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
   ...:                  index=pd.date_range('1/1/2000', periods=10))
   ...: 

In [2]: df.iloc[3:7] = np.nan

In [3]: df
Out[3]: 
                   A         B         C
2000-01-01  1.682600  0.413582  1.689516
2000-01-02 -2.099110 -1.180182  1.595661
2000-01-03 -0.419048  0.522165 -1.208946
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.955435 -0.133009  2.011466
2000-01-09  0.578780  0.897126 -0.980013
2000-01-10 -0.045748  0.361601 -0.208039

One can operate using string function names, callables, lists, or dictionaries of these.

Using a single function is equivalent to .apply.

In [4]: df.agg('sum')
Out[4]: 
A    0.652908
B    0.881282
C    2.899645
dtype: float64

Multiple aggregations with a list of functions.

In [5]: df.agg(['sum', 'min'])
Out[5]: 
            A         B         C
sum  0.652908  0.881282  2.899645
min -2.099110 -1.180182 -1.208946

Using a dict provides the ability to apply specific aggregations per column. You will get a matrix-like output of all of the aggregators. The output has one column per unique function. Those functions applied to a particular column will be NaN:

In [6]: df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
Out[6]: 
            A         B
max       NaN  0.897126
min -2.099110 -1.180182
sum  0.652908       NaN

The API also supports a .transform() function for broadcasting results.

In [7]: df.transform(['abs', lambda x: x - x.min()])
Out[7]: 
                   A                   B                   C          
                 abs  <lambda>       abs  <lambda>       abs  <lambda>
2000-01-01  1.682600  3.781710  0.413582  1.593764  1.689516  2.898461
2000-01-02  2.099110  0.000000  1.180182  0.000000  1.595661  2.804606
2000-01-03  0.419048  1.680062  0.522165  1.702346  1.208946  0.000000
2000-01-04       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-08  0.955435  3.054545  0.133009  1.047173  2.011466  3.220412
2000-01-09  0.578780  2.677890  0.897126  2.077307  0.980013  0.228932
2000-01-10  0.045748  2.053362  0.361601  1.541782  0.208039  1.000907

When presented with mixed dtypes that cannot be aggregated, .agg() will only take the valid aggregations. This is similar to how groupby .agg() works. (GH15015)

In [8]: df = pd.DataFrame({'A': [1, 2, 3],
   ...:                    'B': [1., 2., 3.],
   ...:                    'C': ['foo', 'bar', 'baz'],
   ...:                    'D': pd.date_range('20130101', periods=3)})
   ...: 

In [9]: df.dtypes
Out[9]: 
A             int64
B           float64
C            object
D    datetime64[ns]
dtype: object
In [10]: df.agg(['min', 'sum'])
Out[10]: 
     A    B          C          D
min  1  1.0        bar 2013-01-01
sum  6  6.0  foobarbaz        NaT

dtype keyword for data IO

The 'python' engine for read_csv(), as well as the read_fwf() function for parsing fixed-width text files and read_excel() for parsing Excel files, now accept the dtype keyword argument for specifying the types of specific columns (GH14295). See the io docs for more information.

In [11]: data = "a  b\n1  2\n3  4"

In [12]: pd.read_fwf(StringIO(data)).dtypes
Out[12]: 
a    int64
b    int64
dtype: object

In [13]: pd.read_fwf(StringIO(data), dtype={'a':'float64', 'b':'object'}).dtypes
Out[13]: 
a    float64
b     object
dtype: object

.to_datetime() has gained an origin parameter

to_datetime() has gained a new parameter, origin, to define a reference date from where to compute the resulting timestamps when parsing numerical values with a specific unit specified. (GH11276, GH11745)

For example, with 1960-01-01 as the starting date:

In [14]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
Out[14]: DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None)

The default is set at origin='unix', which defaults to 1970-01-01 00:00:00, which is commonly called ‘unix epoch’ or POSIX time. This was the previous default, so this is a backward compatible change.

In [15]: pd.to_datetime([1, 2, 3], unit='D')
Out[15]: DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)

Groupby Enhancements

Strings passed to DataFrame.groupby() as the by parameter may now reference either column names or index level names. Previously, only column names could be referenced. This allows to easily group by a column and index level at the same time. (GH5677)

In [16]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
   ....:           ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
   ....: 

In [17]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])

In [18]: df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3, 3],
   ....:                    'B': np.arange(8)},
   ....:                   index=index)
   ....: 

In [19]: df
Out[19]: 
              A  B
first second      
bar   one     1  0
      two     1  1
baz   one     1  2
      two     1  3
foo   one     2  4
      two     2  5
qux   one     3  6
      two     3  7

In [20]: df.groupby(['second', 'A']).sum()
Out[20]: 
          B
second A   
one    1  2
       2  4
       3  6
two    1  4
       2  5
       3  7

Better support for compressed URLs in read_csv

The compression code was refactored (GH12688). As a result, reading dataframes from URLs in read_csv() or read_table() now supports additional compression methods: xz, bz2, and zip (GH14570). Previously, only gzip compression was supported. By default, compression of URLs and paths are now inferred using their file extensions. Additionally, support for bz2 compression in the python 2 C-engine improved (GH14874).

In [21]: url = 'https://github.com/{repo}/raw/{branch}/{path}'.format(
   ....:     repo = 'pandas-dev/pandas',
   ....:     branch = 'master',
   ....:     path = 'pandas/tests/io/parser/data/salaries.csv.bz2',
   ....: )
   ....: 

In [22]: df = pd.read_table(url, compression='infer')  # default, infer compression

In [23]: df = pd.read_table(url, compression='bz2')  # explicitly specify compression

In [24]: df.head(2)
Out[24]: 
       S  X  E  M
0  13876  1  1  1
1  11608  1  3  0

Pickle file I/O now supports compression

read_pickle(), DataFrame.to_pickle() and Series.to_pickle() can now read from and write to compressed pickle files. Compression methods can be an explicit parameter or be inferred from the file extension. See the docs here.

In [25]: df = pd.DataFrame({
   ....:     'A': np.random.randn(1000),
   ....:     'B': 'foo',
   ....:     'C': pd.date_range('20130101', periods=1000, freq='s')})
   ....: 

Using an explicit compression type

In [26]: df.to_pickle("data.pkl.compress", compression="gzip")

In [27]: rt = pd.read_pickle("data.pkl.compress", compression="gzip")

In [28]: rt.head()
Out[28]: 
          A    B                   C
0  1.578227  foo 2013-01-01 00:00:00
1 -0.230575  foo 2013-01-01 00:00:01
2  0.695530  foo 2013-01-01 00:00:02
3 -0.466001  foo 2013-01-01 00:00:03
4 -0.154972  foo 2013-01-01 00:00:04

The default is to infer the compression type from the extension (compression='infer'):

In [29]: df.to_pickle("data.pkl.gz")

In [30]: rt = pd.read_pickle("data.pkl.gz")

In [31]: rt.head()
Out[31]: 
          A    B                   C
0  1.578227  foo 2013-01-01 00:00:00
1 -0.230575  foo 2013-01-01 00:00:01
2  0.695530  foo 2013-01-01 00:00:02
3 -0.466001  foo 2013-01-01 00:00:03
4 -0.154972  foo 2013-01-01 00:00:04

In [32]: df["A"].to_pickle("s1.pkl.bz2")

In [33]: rt = pd.read_pickle("s1.pkl.bz2")

In [34]: rt.head()
Out[34]: 
0    1.578227
1   -0.230575
2    0.695530
3   -0.466001
4   -0.154972
Name: A, dtype: float64

UInt64 Support Improved

Pandas has significantly improved support for operations involving unsigned, or purely non-negative, integers. Previously, handling these integers would result in improper rounding or data-type casting, leading to incorrect results. Notably, a new numerical index, UInt64Index, has been created (GH14937)

In [35]: idx = pd.UInt64Index([1, 2, 3])

In [36]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)

In [37]: df.index
Out[37]: UInt64Index([1, 2, 3], dtype='uint64')
  • Bug in converting object elements of array-like objects to unsigned 64-bit integers (GH4471, GH14982)
  • Bug in Series.unique() in which unsigned 64-bit integers were causing overflow (GH14721)
  • Bug in DataFrame construction in which unsigned 64-bit integer elements were being converted to objects (GH14881)
  • Bug in pd.read_csv() in which unsigned 64-bit integer elements were being improperly converted to the wrong data types (GH14983)
  • Bug in pd.unique() in which unsigned 64-bit integers were causing overflow (GH14915)
  • Bug in pd.value_counts() in which unsigned 64-bit integers were being erroneously truncated in the output (GH14934)

GroupBy on Categoricals

In previous versions, .groupby(..., sort=False) would fail with a ValueError when grouping on a categorical series with some categories not appearing in the data. (GH13179)

In [38]: chromosomes = np.r_[np.arange(1, 23).astype(str), ['X', 'Y']]

In [39]: df = pd.DataFrame({
   ....:     'A': np.random.randint(100),
   ....:     'B': np.random.randint(100),
   ....:     'C': np.random.randint(100),
   ....:     'chromosomes': pd.Categorical(np.random.choice(chromosomes, 100),
   ....:                                   categories=chromosomes,
   ....:                                   ordered=True)})
   ....: 

In [40]: df
Out[40]: 
     A   B   C chromosomes
0   80  36  94          12
1   80  36  94           X
2   80  36  94          19
3   80  36  94          22
4   80  36  94          17
5   80  36  94           6
6   80  36  94          13
..  ..  ..  ..         ...
93  80  36  94          21
94  80  36  94          20
95  80  36  94          11
96  80  36  94          16
97  80  36  94          21
98  80  36  94          18
99  80  36  94           8

[100 rows x 4 columns]

Previous Behavior:

In [3]: df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum()
---------------------------------------------------------------------------
ValueError: items in new_categories are not the same as in old categories

New Behavior:

In [41]: df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum()
Out[41]: 
               A    B    C
chromosomes               
2            320  144  376
3            400  180  470
4            240  108  282
5            240  108  282
6            400  180  470
7            400  180  470
8            480  216  564
...          ...  ...  ...
19           400  180  470
20           160   72  188
21           480  216  564
22           160   72  188
X            400  180  470
Y            320  144  376
1              0    0    0

[24 rows x 3 columns]

Table Schema Output

The new orient 'table' for DataFrame.to_json() will generate a Table Schema compatible string representation of the data.

In [42]: df = pd.DataFrame(
   ....:     {'A': [1, 2, 3],
   ....:      'B': ['a', 'b', 'c'],
   ....:      'C': pd.date_range('2016-01-01', freq='d', periods=3),
   ....:     }, index=pd.Index(range(3), name='idx'))
   ....: 

In [43]: df
Out[43]: 
     A  B          C
idx                 
0    1  a 2016-01-01
1    2  b 2016-01-02
2    3  c 2016-01-03

In [44]: df.to_json(orient='table')
Out[44]: '{"schema": {"fields":[{"name":"idx","type":"integer"},{"name":"A","type":"integer"},{"name":"B","type":"string"},{"name":"C","type":"datetime"}],"primaryKey":["idx"],"pandas_version":"0.20.0"}, "data": [{"idx":0,"A":1,"B":"a","C":"2016-01-01T00:00:00.000Z"},{"idx":1,"A":2,"B":"b","C":"2016-01-02T00:00:00.000Z"},{"idx":2,"A":3,"B":"c","C":"2016-01-03T00:00:00.000Z"}]}'

See IO: Table Schema for more information.

Additionally, the repr for DataFrame and Series can now publish this JSON Table schema representation of the Series or DataFrame if you are using IPython (or another frontend like nteract using the Jupyter messaging protocol). This gives frontends like the Jupyter notebook and nteract more flexibility in how they display pandas objects, since they have more information about the data. You must enable this by setting the display.html.table_schema option to True.

SciPy sparse matrix from/to SparseDataFrame

Pandas now supports creating sparse dataframes directly from scipy.sparse.spmatrix instances. See the documentation for more information. (GH4343)

All sparse formats are supported, but matrices that are not in COOrdinate format will be converted, copying data as needed.

In [45]: from scipy.sparse import csr_matrix

In [46]: arr = np.random.random(size=(1000, 5))

In [47]: arr[arr < .9] = 0

In [48]: sp_arr = csr_matrix(arr)

In [49]: sp_arr
Out[49]: 
<1000x5 sparse matrix of type '<class 'numpy.float64'>'
	with 521 stored elements in Compressed Sparse Row format>

In [50]: sdf = pd.SparseDataFrame(sp_arr)

In [51]: sdf
Out[51]: 
      0         1        2         3         4
0   NaN       NaN      NaN       NaN       NaN
1   NaN       NaN      NaN  0.955103       NaN
2   NaN       NaN      NaN  0.900469       NaN
3   NaN       NaN      NaN       NaN       NaN
4   NaN  0.924771      NaN       NaN       NaN
5   NaN       NaN      NaN       NaN       NaN
6   NaN       NaN      NaN       NaN       NaN
..   ..       ...      ...       ...       ...
993 NaN       NaN      NaN       NaN       NaN
994 NaN       NaN      NaN       NaN  0.972191
995 NaN  0.979898  0.97901       NaN       NaN
996 NaN       NaN      NaN       NaN       NaN
997 NaN       NaN      NaN       NaN       NaN
998 NaN       NaN      NaN       NaN       NaN
999 NaN       NaN      NaN       NaN       NaN

[1000 rows x 5 columns]

To convert a SparseDataFrame back to sparse SciPy matrix in COO format, you can use:

In [52]: sdf.to_coo()
Out[52]: 
<1000x5 sparse matrix of type '<class 'numpy.float64'>'
	with 521 stored elements in COOrdinate format>

Excel output for styled DataFrames

Experimental support has been added to export DataFrame.style formats to Excel using the openpyxl engine. (GH15530)

For example, after running the following, styled.xlsx renders as below:

In [53]: np.random.seed(24)

In [54]: df = pd.DataFrame({'A': np.linspace(1, 10, 10)})

In [55]: df = pd.concat([df, pd.DataFrame(np.random.RandomState(24).randn(10, 4),
   ....:                                  columns=list('BCDE'))],
   ....:                axis=1)
   ....: 

In [56]: df.iloc[0, 2] = np.nan

In [57]: df
Out[57]: 
      A         B         C         D         E
0   1.0  1.329212       NaN -0.316280 -0.990810
1   2.0 -1.070816 -1.438713  0.564417  0.295722
2   3.0 -1.626404  0.219565  0.678805  1.889273
3   4.0  0.961538  0.104011 -0.481165  0.850229
4   5.0  1.453425  1.057737  0.165562  0.515018
5   6.0 -1.336936  0.562861  1.392855 -0.063328
6   7.0  0.121668  1.207603 -0.002040  1.627796
7   8.0  0.354493  1.037528 -0.385684  0.519818
8   9.0  1.686583 -1.325963  1.428984 -2.089354
9  10.0 -0.129820  0.631523 -0.586538  0.290720

In [58]: styled = df.style.\
   ....:     applymap(lambda val: 'color: %s' % 'red' if val < 0 else 'black').\
   ....:     highlight_max()
   ....: 

In [59]: styled.to_excel('styled.xlsx', engine='openpyxl')
_images/style-excel.png

See the Style documentation for more detail.

IntervalIndex

pandas has gained an IntervalIndex with its own dtype, interval as well as the Interval scalar type. These allow first-class support for interval notation, specifically as a return type for the categories in cut() and qcut(). The IntervalIndex allows some unique indexing, see the docs. (GH7640, GH8625)

Warning

These indexing behaviors of the IntervalIndex are provisional and may change in a future version of pandas. Feedback on usage is welcome.

Previous behavior:

The returned categories were strings, representing Intervals

In [1]: c = pd.cut(range(4), bins=2)

In [2]: c
Out[2]:
[(-0.003, 1.5], (-0.003, 1.5], (1.5, 3], (1.5, 3]]
Categories (2, object): [(-0.003, 1.5] < (1.5, 3]]

In [3]: c.categories
Out[3]: Index(['(-0.003, 1.5]', '(1.5, 3]'], dtype='object')

New behavior:

In [60]: c = pd.cut(range(4), bins=2)

In [61]: c
Out[61]: 
[(-0.003, 1.5], (-0.003, 1.5], (1.5, 3.0], (1.5, 3.0]]
Categories (2, interval[float64]): [(-0.003, 1.5] < (1.5, 3.0]]

In [62]: c.categories
Out[62]: 
IntervalIndex([(-0.003, 1.5], (1.5, 3.0]]
              closed='right',
              dtype='interval[float64]')

Furthermore, this allows one to bin other data with these same bins, with NaN representing a missing value similar to other dtypes.

In [63]: pd.cut([0, 3, 5, 1], bins=c.categories)
Out[63]: 
[(-0.003, 1.5], (1.5, 3.0], NaN, (-0.003, 1.5]]
Categories (2, interval[float64]): [(-0.003, 1.5] < (1.5, 3.0]]

An IntervalIndex can also be used in Series and DataFrame as the index.

In [64]: df = pd.DataFrame({'A': range(4),
   ....:                    'B': pd.cut([0, 3, 1, 1], bins=c.categories)}
   ....:                  ).set_index('B')
   ....: 

In [65]: df
Out[65]: 
               A
B               
(-0.003, 1.5]  0
(1.5, 3.0]     1
(-0.003, 1.5]  2
(-0.003, 1.5]  3

Selecting via a specific interval:

In [66]: df.loc[pd.Interval(1.5, 3.0)]
Out[66]: 
A    1
Name: (1.5, 3.0], dtype: int64

Selecting via a scalar value that is contained in the intervals.

In [67]: df.loc[0]
Out[67]: 
               A
B               
(-0.003, 1.5]  0
(-0.003, 1.5]  2
(-0.003, 1.5]  3

Other Enhancements

  • DataFrame.rolling() now accepts the parameter closed='right'|'left'|'both'|'neither' to choose the rolling window-endpoint closedness. See the documentation (GH13965)
  • Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() method, see here.
  • Series.str.replace() now accepts a callable, as replacement, which is passed to re.sub (GH15055)
  • Series.str.replace() now accepts a compiled regular expression as a pattern (GH15446)
  • Series.sort_index accepts parameters kind and na_position (GH13589, GH14444)
  • DataFrame and DataFrame.groupby() have gained a nunique() method to count the distinct values over an axis (GH14336, GH15197).
  • DataFrame has gained a melt() method, equivalent to pd.melt(), for unpivoting from a wide to long format (GH12640).
  • pd.read_excel() now preserves sheet order when using sheetname=None (GH9930)
  • Multiple offset aliases with decimal points are now supported (e.g. 0.5min is parsed as 30s) (GH8419)
  • .isnull() and .notnull() have been added to Index object to make them more consistent with the Series API (GH15300)
  • New UnsortedIndexError (subclass of KeyError) raised when indexing/slicing into an unsorted MultiIndex (GH11897). This allows differentiation between errors due to lack of sorting or an incorrect key. See here
  • MultiIndex has gained a .to_frame() method to convert to a DataFrame (GH12397)
  • pd.cut and pd.qcut now support datetime64 and timedelta64 dtypes (GH14714, GH14798)
  • pd.qcut has gained the duplicates='raise'|'drop' option to control whether to raise on duplicated edges (GH7751)
  • Series provides a to_excel method to output Excel files (GH8825)
  • The usecols argument in pd.read_csv() now accepts a callable function as a value (GH14154)
  • The skiprows argument in pd.read_csv() now accepts a callable function as a value (GH10882)
  • The nrows and chunksize arguments in pd.read_csv() are supported if both are passed (GH6774, GH15755)
  • DataFrame.plot now prints a title above each subplot if suplots=True and title is a list of strings (GH14753)
  • DataFrame.plot can pass the matplotlib 2.0 default color cycle as a single string as color parameter, see here. (GH15516)
  • Series.interpolate() now supports timedelta as an index type with method='time' (GH6424)
  • Addition of a level keyword to DataFrame/Series.rename to rename labels in the specified level of a MultiIndex (GH4160).
  • DataFrame.reset_index() will now interpret a tuple index.name as a key spanning across levels of columns, if this is a MultiIndex (GH16164)
  • Timedelta.isoformat method added for formatting Timedeltas as an ISO 8601 duration. See the Timedelta docs (GH15136)
  • .select_dtypes() now allows the string datetimetz to generically select datetimes with tz (GH14910)
  • The .to_latex() method will now accept multicolumn and multirow arguments to use the accompanying LaTeX enhancements
  • pd.merge_asof() gained the option direction='backward'|'forward'|'nearest' (GH14887)
  • Series/DataFrame.asfreq() have gained a fill_value parameter, to fill missing values (GH3715).
  • Series/DataFrame.resample.asfreq have gained a fill_value parameter, to fill missing values during resampling (GH3715).
  • pandas.util.hash_pandas_object() has gained the ability to hash a MultiIndex (GH15224)
  • Series/DataFrame.squeeze() have gained the axis parameter. (GH15339)
  • DataFrame.to_excel() has a new freeze_panes parameter to turn on Freeze Panes when exporting to Excel (GH15160)
  • pd.read_html() will parse multiple header rows, creating a MutliIndex header. (GH13434).
  • HTML table output skips colspan or rowspan attribute if equal to 1. (GH15403)
  • pandas.io.formats.style.Styler template now has blocks for easier extension, see the example notebook (GH15649)
  • Styler.render() now accepts **kwargs to allow user-defined variables in the template (GH15649)
  • Compatibility with Jupyter notebook 5.0; MultiIndex column labels are left-aligned and MultiIndex row-labels are top-aligned (GH15379)
  • TimedeltaIndex now has a custom date-tick formatter specifically designed for nanosecond level precision (GH8711)
  • pd.api.types.union_categoricals gained the ignore_ordered argument to allow ignoring the ordered attribute of unioned categoricals (GH13410). See the categorical union docs for more information.
  • DataFrame.to_latex() and DataFrame.to_string() now allow optional header aliases. (GH15536)
  • Re-enable the parse_dates keyword of pd.read_excel() to parse string columns as dates (GH14326)
  • Added .empty property to subclasses of Index. (GH15270)
  • Enabled floor division for Timedelta and TimedeltaIndex (GH15828)
  • pandas.io.json.json_normalize() gained the option errors='ignore'|'raise'; the default is errors='raise' which is backward compatible. (GH14583)
  • pandas.io.json.json_normalize() with an empty list will return an empty DataFrame (GH15534)
  • pandas.io.json.json_normalize() has gained a sep option that accepts str to separate joined fields; the default is “.”, which is backward compatible. (GH14883)
  • MultiIndex.remove_unused_levels() has been added to facilitate removing unused levels. (GH15694)
  • pd.read_csv() will now raise a ParserError error whenever any parsing error occurs (GH15913, GH15925)
  • pd.read_csv() now supports the error_bad_lines and warn_bad_lines arguments for the Python parser (GH15925)
  • The display.show_dimensions option can now also be used to specify whether the length of a Series should be shown in its repr (GH7117).
  • parallel_coordinates() has gained a sort_labels keyword argument that sorts class labels and the colors assigned to them (GH15908)
  • Options added to allow one to turn on/off using bottleneck and numexpr, see here (GH16157)
  • DataFrame.style.bar() now accepts two more options to further customize the bar chart. Bar alignment is set with align='left'|'mid'|'zero', the default is “left”, which is backward compatible; You can now pass a list of color=[color_negative, color_positive]. (GH14757)

Backwards incompatible API changes

Possible incompatibility for HDF5 formats created with pandas < 0.13.0

pd.TimeSeries was deprecated officially in 0.17.0, though has already been an alias since 0.13.0. It has been dropped in favor of pd.Series. (GH15098).

This may cause HDF5 files that were created in prior versions to become unreadable if pd.TimeSeries was used. This is most likely to be for pandas < 0.13.0. If you find yourself in this situation. You can use a recent prior version of pandas to read in your HDF5 files, then write them out again after applying the procedure below.

In [2]: s = pd.TimeSeries([1,2,3], index=pd.date_range('20130101', periods=3))

In [3]: s
Out[3]:
2013-01-01    1
2013-01-02    2
2013-01-03    3
Freq: D, dtype: int64

In [4]: type(s)
Out[4]: pandas.core.series.TimeSeries

In [5]: s = pd.Series(s)

In [6]: s
Out[6]:
2013-01-01    1
2013-01-02    2
2013-01-03    3
Freq: D, dtype: int64

In [7]: type(s)
Out[7]: pandas.core.series.Series

Map on Index types now return other Index types

map on an Index now returns an Index, not a numpy array (GH12766)

In [68]: idx = Index([1, 2])

In [69]: idx
Out[69]: Int64Index([1, 2], dtype='int64')

In [70]: mi = MultiIndex.from_tuples([(1, 2), (2, 4)])

In [71]: mi
Out[71]: 
MultiIndex(levels=[[1, 2], [2, 4]],
           labels=[[0, 1], [0, 1]])

Previous Behavior:

In [5]: idx.map(lambda x: x * 2)
Out[5]: array([2, 4])

In [6]: idx.map(lambda x: (x, x * 2))
Out[6]: array([(1, 2), (2, 4)], dtype=object)

In [7]: mi.map(lambda x: x)
Out[7]: array([(1, 2), (2, 4)], dtype=object)

In [8]: mi.map(lambda x: x[0])
Out[8]: array([1, 2])

New Behavior:

In [72]: idx.map(lambda x: x * 2)
Out[72]: Int64Index([2, 4], dtype='int64')

In [73]: idx.map(lambda x: (x, x * 2))
Out[73]: 
MultiIndex(levels=[[1, 2], [2, 4]],
           labels=[[0, 1], [0, 1]])

In [74]: mi.map(lambda x: x)
Out[74]: 
MultiIndex(levels=[[1, 2], [2, 4]],
           labels=[[0, 1], [0, 1]])

In [75]: mi.map(lambda x: x[0])
Out[75]: Int64Index([1, 2], dtype='int64')

map on a Series with datetime64 values may return int64 dtypes rather than int32

In [76]: s = Series(date_range('2011-01-02T00:00', '2011-01-02T02:00', freq='H').tz_localize('Asia/Tokyo'))

In [77]: s
Out[77]: 
0   2011-01-02 00:00:00+09:00
1   2011-01-02 01:00:00+09:00
2   2011-01-02 02:00:00+09:00
dtype: datetime64[ns, Asia/Tokyo]

Previous Behavior:

In [9]: s.map(lambda x: x.hour)
Out[9]:
0    0
1    1
2    2
dtype: int32

New Behavior:

In [78]: s.map(lambda x: x.hour)
Out[78]: 
0    0
1    1
2    2
dtype: int64

Accessing datetime fields of Index now return Index

The datetime-related attributes (see here for an overview) of DatetimeIndex, PeriodIndex and TimedeltaIndex previously returned numpy arrays. They will now return a new Index object, except in the case of a boolean field, where the result will still be a boolean ndarray. (GH15022)

Previous behaviour:

In [1]: idx = pd.date_range("2015-01-01", periods=5, freq='10H')

In [2]: idx.hour
Out[2]: array([ 0, 10, 20,  6, 16], dtype=int32)

New Behavior:

In [79]: idx = pd.date_range("2015-01-01", periods=5, freq='10H')

In [80]: idx.hour
Out[80]: Int64Index([0, 10, 20, 6, 16], dtype='int64')

This has the advantage that specific Index methods are still available on the result. On the other hand, this might have backward incompatibilities: e.g. compared to numpy arrays, Index objects are not mutable. To get the original ndarray, you can always convert explicitly using np.asarray(idx.hour).

pd.unique will now be consistent with extension types

In prior versions, using Series.unique() and pandas.unique() on Categorical and tz-aware data-types would yield different return types. These are now made consistent. (GH15903)

  • Datetime tz-aware

    Previous behaviour:

    # Series
    In [5]: pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
                       pd.Timestamp('20160101', tz='US/Eastern')]).unique()
    Out[5]: array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')], dtype=object)
    
    In [6]: pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
                                 pd.Timestamp('20160101', tz='US/Eastern')]))
    Out[6]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]')
    
    # Index
    In [7]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
                      pd.Timestamp('20160101', tz='US/Eastern')]).unique()
    Out[7]: DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)
    
    In [8]: pd.unique([pd.Timestamp('20160101', tz='US/Eastern'),
                       pd.Timestamp('20160101', tz='US/Eastern')])
    Out[8]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]')
    

    New Behavior:

    # Series, returns an array of Timestamp tz-aware
    In [81]: pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
       ....:           pd.Timestamp('20160101', tz='US/Eastern')]).unique()
       ....: 
    Out[81]: array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')], dtype=object)
    
    In [82]: pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
       ....:                      pd.Timestamp('20160101', tz='US/Eastern')]))
       ....: 
    Out[82]: array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')], dtype=object)
    
    # Index, returns a DatetimeIndex
    In [83]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
       ....:           pd.Timestamp('20160101', tz='US/Eastern')]).unique()
       ....: 
    Out[83]: DatetimeIndex(['2016-01-01 05:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)
    
    In [84]: pd.unique(pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
       ....:                     pd.Timestamp('20160101', tz='US/Eastern')]))
       ....: 
    Out[84]: DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)
    
  • Categoricals

    Previous behaviour:

    In [1]: pd.Series(list('baabc'), dtype='category').unique()
    Out[1]:
    [b, a, c]
    Categories (3, object): [b, a, c]
    
    In [2]: pd.unique(pd.Series(list('baabc'), dtype='category'))
    Out[2]: array(['b', 'a', 'c'], dtype=object)
    

    New Behavior:

    # returns a Categorical
    In [85]: pd.Series(list('baabc'), dtype='category').unique()
    Out[85]: 
    [b, a, c]
    Categories (3, object): [b, a, c]
    
    In [86]: pd.unique(pd.Series(list('baabc'), dtype='category'))
    Out[86]: 
    [b, a, c]
    Categories (3, object): [b, a, c]
    

S3 File Handling

pandas now uses s3fs for handling S3 connections. This shouldn’t break any code. However, since s3fs is not a required dependency, you will need to install it separately, like boto in prior versions of pandas. (GH11915).

Partial String Indexing Changes

DatetimeIndex Partial String Indexing now works as an exact match, provided that string resolution coincides with index resolution, including a case when both are seconds (GH14826). See Slice vs. Exact Match for details.

In [87]: df = DataFrame({'a': [1, 2, 3]}, DatetimeIndex(['2011-12-31 23:59:59',
   ....:                                                 '2012-01-01 00:00:00',
   ....:                                                 '2012-01-01 00:00:01']))
   ....: 

Previous Behavior:

In [4]: df['2011-12-31 23:59:59']
Out[4]:
                       a
2011-12-31 23:59:59  1

In [5]: df['a']['2011-12-31 23:59:59']
Out[5]:
2011-12-31 23:59:59    1
Name: a, dtype: int64

New Behavior:

In [4]: df['2011-12-31 23:59:59']
KeyError: '2011-12-31 23:59:59'

In [5]: df['a']['2011-12-31 23:59:59']
Out[5]: 1

Concat of different float dtypes will not automatically upcast

Previously, concat of multiple objects with different float dtypes would automatically upcast results to a dtype of float64. Now the smallest acceptable dtype will be used (GH13247)

In [88]: df1 = pd.DataFrame(np.array([1.0], dtype=np.float32, ndmin=2))

In [89]: df1.dtypes
Out[89]: 
0    float32
dtype: object

In [90]: df2 = pd.DataFrame(np.array([np.nan], dtype=np.float32, ndmin=2))

In [91]: df2.dtypes
Out[91]: 
0    float32
dtype: object

Previous Behavior:

In [7]: pd.concat([df1, df2]).dtypes
Out[7]:
0    float64
dtype: object

New Behavior:

In [92]: pd.concat([df1, df2]).dtypes
Out[92]: 
0    float32
dtype: object

Pandas Google BigQuery support has moved

pandas has split off Google BigQuery support into a separate package pandas-gbq. You can conda install pandas-gbq -c conda-forge or pip install pandas-gbq to get it. The functionality of read_gbq() and DataFrame.to_gbq() remain the same with the currently released version of pandas-gbq=0.1.4. Documentation is now hosted here (GH15347)

Memory Usage for Index is more Accurate

In previous versions, showing .memory_usage() on a pandas structure that has an index, would only include actual index values and not include structures that facilitated fast indexing. This will generally be different for Index and MultiIndex and less-so for other index types. (GH15237)

Previous Behavior:

In [8]: index = Index(['foo', 'bar', 'baz'])

In [9]: index.memory_usage(deep=True)
Out[9]: 180

In [10]: index.get_loc('foo')
Out[10]: 0

In [11]: index.memory_usage(deep=True)
Out[11]: 180

New Behavior:

In [8]: index = Index(['foo', 'bar', 'baz'])

In [9]: index.memory_usage(deep=True)
Out[9]: 180

In [10]: index.get_loc('foo')
Out[10]: 0

In [11]: index.memory_usage(deep=True)
Out[11]: 260

DataFrame.sort_index changes

In certain cases, calling .sort_index() on a MultiIndexed DataFrame would return the same DataFrame without seeming to sort. This would happen with a lexsorted, but non-monotonic levels. (GH15622, GH15687, GH14015, GH13431, GH15797)

This is unchanged from prior versions, but shown for illustration purposes:

In [93]: df = DataFrame(np.arange(6), columns=['value'], index=MultiIndex.from_product([list('BA'), range(3)]))

In [94]: df
Out[94]: 
     value
B 0      0
  1      1
  2      2
A 0      3
  1      4
  2      5
In [95]: df.index.is_lexsorted()
Out[95]: False

In [96]: df.index.is_monotonic
Out[96]: False

Sorting works as expected

In [97]: df.sort_index()
Out[97]: 
     value
A 0      3
  1      4
  2      5
B 0      0
  1      1
  2      2
In [98]: df.sort_index().index.is_lexsorted()
Out[98]: True

In [99]: df.sort_index().index.is_monotonic
Out[99]: True

However, this example, which has a non-monotonic 2nd level, doesn’t behave as desired.

In [100]: df = pd.DataFrame(
   .....:         {'value': [1, 2, 3, 4]},
   .....:          index=pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
   .....:                              labels=[[0, 0, 1, 1], [0, 1, 0, 1]]))
   .....: 

In [101]: df
Out[101]: 
      value
a bb      1
  aa      2
b bb      3
  aa      4

Previous Behavior:

In [11]: df.sort_index()
Out[11]:
      value
a bb      1
  aa      2
b bb      3
  aa      4

In [14]: df.sort_index().index.is_lexsorted()
Out[14]: True

In [15]: df.sort_index().index.is_monotonic
Out[15]: False

New Behavior:

In [102]: df.sort_index()
Out[102]: 
      value
a aa      2
  bb      1
b aa      4
  bb      3

In [103]: df.sort_index().index.is_lexsorted()
Out[103]: True

In [104]: df.sort_index().index.is_monotonic
Out[104]: True

Groupby Describe Formatting

The output formatting of groupby.describe() now labels the describe() metrics in the columns instead of the index. This format is consistent with groupby.agg() when applying multiple functions at once. (GH4792)

Previous Behavior:

In [1]: df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})

In [2]: df.groupby('A').describe()
Out[2]:
                B
A
1 count  2.000000
  mean   1.500000
  std    0.707107
  min    1.000000
  25%    1.250000
  50%    1.500000
  75%    1.750000
  max    2.000000
2 count  2.000000
  mean   3.500000
  std    0.707107
  min    3.000000
  25%    3.250000
  50%    3.500000
  75%    3.750000
  max    4.000000

In [3]: df.groupby('A').agg([np.mean, np.std, np.min, np.max])
Out[3]:
     B
  mean       std amin amax
A
1  1.5  0.707107    1    2
2  3.5  0.707107    3    4

New Behavior:

In [105]: df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})

In [106]: df.groupby('A').describe()
Out[106]: 
      B                                          
  count mean       std  min   25%  50%   75%  max
A                                                
1   2.0  1.5  0.707107  1.0  1.25  1.5  1.75  2.0
2   2.0  3.5  0.707107  3.0  3.25  3.5  3.75  4.0

In [107]: df.groupby('A').agg([np.mean, np.std, np.min, np.max])
Out[107]: 
     B                    
  mean       std amin amax
A                         
1  1.5  0.707107    1    2
2  3.5  0.707107    3    4

Window Binary Corr/Cov operations return a MultiIndex DataFrame

A binary window operation, like .corr() or .cov(), when operating on a .rolling(..), .expanding(..), or .ewm(..) object, will now return a 2-level MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see here. These are equivalent in function, but a MultiIndexed DataFrame enjoys more support in pandas. See the section on Windowed Binary Operations for more information. (GH15677)

In [108]: np.random.seed(1234)

In [109]: df = pd.DataFrame(np.random.rand(100, 2),
   .....:                   columns=pd.Index(['A', 'B'], name='bar'),
   .....:                   index=pd.date_range('20160101',
   .....:                                       periods=100, freq='D', name='foo'))
   .....: 

In [110]: df.tail()
Out[110]: 
bar                A         B
foo                           
2016-04-05  0.640880  0.126205
2016-04-06  0.171465  0.737086
2016-04-07  0.127029  0.369650
2016-04-08  0.604334  0.103104
2016-04-09  0.802374  0.945553

Previous Behavior:

In [2]: df.rolling(12).corr()
Out[2]:
<class 'pandas.core.panel.Panel'>
Dimensions: 100 (items) x 2 (major_axis) x 2 (minor_axis)
Items axis: 2016-01-01 00:00:00 to 2016-04-09 00:00:00
Major_axis axis: A to B
Minor_axis axis: A to B

New Behavior:

In [111]: res = df.rolling(12).corr()

In [112]: res.tail()
Out[112]: 
bar                    A         B
foo        bar                    
2016-04-07 B   -0.132090  1.000000
2016-04-08 A    1.000000 -0.145775
           B   -0.145775  1.000000
2016-04-09 A    1.000000  0.119645
           B    0.119645  1.000000

Retrieving a correlation matrix for a cross-section

In [113]: df.rolling(12).corr().loc['2016-04-07']
Out[113]: 
bar                   A        B
foo        bar                  
2016-04-07 A    1.00000 -0.13209
           B   -0.13209  1.00000

HDFStore where string comparison

In previous versions most types could be compared to string column in a HDFStore usually resulting in an invalid comparison, returning an empty result frame. These comparisons will now raise a TypeError (GH15492)

In [114]: df = pd.DataFrame({'unparsed_date': ['2014-01-01', '2014-01-01']})

In [115]: df.to_hdf('store.h5', 'key', format='table', data_columns=True)

In [116]: df.dtypes
Out[116]: 
unparsed_date    object
dtype: object

Previous Behavior:

In [4]: pd.read_hdf('store.h5', 'key', where='unparsed_date > ts')
File "<string>", line 1
  (unparsed_date > 1970-01-01 00:00:01.388552400)
                        ^
SyntaxError: invalid token

New Behavior:

In [18]: ts = pd.Timestamp('2014-01-01')

In [19]: pd.read_hdf('store.h5', 'key', where='unparsed_date > ts')
TypeError: Cannot compare 2014-01-01 00:00:00 of
type <class 'pandas.tslib.Timestamp'> to string column

Index.intersection and inner join now preserve the order of the left Index

Index.intersection() now preserves the order of the calling Index (left) instead of the other Index (right) (GH15582). This affects inner joins, DataFrame.join() and merge(), and the .align method.

  • Index.intersection

    In [117]: left = pd.Index([2, 1, 0])
    
    In [118]: left
    Out[118]: Int64Index([2, 1, 0], dtype='int64')
    
    In [119]: right = pd.Index([1, 2, 3])
    
    In [120]: right
    Out[120]: Int64Index([1, 2, 3], dtype='int64')
    

    Previous Behavior:

    In [4]: left.intersection(right)
    Out[4]: Int64Index([1, 2], dtype='int64')
    

    New Behavior:

    In [121]: left.intersection(right)
    Out[121]: Int64Index([2, 1], dtype='int64')
    
  • DataFrame.join and pd.merge

    In [122]: left = pd.DataFrame({'a': [20, 10, 0]}, index=[2, 1, 0])
    
    In [123]: left
    Out[123]: 
        a
    2  20
    1  10
    0   0
    
    In [124]: right = pd.DataFrame({'b': [100, 200, 300]}, index=[1, 2, 3])
    
    In [125]: right
    Out[125]: 
         b
    1  100
    2  200
    3  300
    

    Previous Behavior:

    In [4]: left.join(right, how='inner')
    Out[4]:
        a    b
    1  10  100
    2  20  200
    

    New Behavior:

    In [126]: left.join(right, how='inner')
    Out[126]: 
        a    b
    2  20  200
    1  10  100
    

Pivot Table always returns a DataFrame

The documentation for pivot_table() states that a DataFrame is always returned. Here a bug is fixed that allowed this to return a Series under certain circumstance. (GH4386)

In [127]: df = DataFrame({'col1': [3, 4, 5],
   .....:                 'col2': ['C', 'D', 'E'],
   .....:                 'col3': [1, 3, 9]})
   .....: 

In [128]: df
Out[128]: 
   col1 col2  col3
0     3    C     1
1     4    D     3
2     5    E     9

Previous Behavior:

In [2]: df.pivot_table('col1', index=['col3', 'col2'], aggfunc=np.sum)
Out[2]:
col3  col2
1     C       3
3     D       4
9     E       5
Name: col1, dtype: int64

New Behavior:

In [129]: df.pivot_table('col1', index=['col3', 'col2'], aggfunc=np.sum)
Out[129]: 
           col1
col3 col2      
1    C        3
3    D        4
9    E        5

Other API Changes

  • numexpr version is now required to be >= 2.4.6 and it will not be used at all if this requisite is not fulfilled (GH15213).
  • CParserError has been renamed to ParserError in pd.read_csv() and will be removed in the future (GH12665)
  • SparseArray.cumsum() and SparseSeries.cumsum() will now always return SparseArray and SparseSeries respectively (GH12855)
  • DataFrame.applymap() with an empty DataFrame will return a copy of the empty DataFrame instead of a Series (GH8222)
  • Series.map() now respects default values of dictionary subclasses with a __missing__ method, such as collections.Counter (GH15999)
  • .loc has compat with .ix for accepting iterators, and NamedTuples (GH15120)
  • interpolate() and fillna() will raise a ValueError if the limit keyword argument is not greater than 0. (GH9217)
  • pd.read_csv() will now issue a ParserWarning whenever there are conflicting values provided by the dialect parameter and the user (GH14898)
  • pd.read_csv() will now raise a ValueError for the C engine if the quote character is larger than than one byte (GH11592)
  • inplace arguments now require a boolean value, else a ValueError is thrown (GH14189)
  • pandas.api.types.is_datetime64_ns_dtype will now report True on a tz-aware dtype, similar to pandas.api.types.is_datetime64_any_dtype
  • DataFrame.asof() will return a null filled Series instead the scalar NaN if a match is not found (GH15118)
  • Specific support for copy.copy() and copy.deepcopy() functions on NDFrame objects (GH15444)
  • Series.sort_values() accepts a one element list of bool for consistency with the behavior of DataFrame.sort_values() (GH15604)
  • .merge() and .join() on category dtype columns will now preserve the category dtype when possible (GH10409)
  • SparseDataFrame.default_fill_value will be 0, previously was nan in the return from pd.get_dummies(..., sparse=True) (GH15594)
  • The default behaviour of Series.str.match has changed from extracting groups to matching the pattern. The extracting behaviour was deprecated since pandas version 0.13.0 and can be done with the Series.str.extract method (GH5224). As a consequence, the as_indexer keyword is ignored (no longer needed to specify the new behaviour) and is deprecated.
  • NaT will now correctly report False for datetimelike boolean operations such as is_month_start (GH15781)
  • NaT will now correctly return np.nan for Timedelta and Period accessors such as days and quarter (GH15782)
  • NaT will now returns NaT for tz_localize and tz_convert methods (GH15830)
  • DataFrame and Panel constructors with invalid input will now raise ValueError rather than PandasError, if called with scalar inputs and not axes (GH15541)
  • DataFrame and Panel constructors with invalid input will now raise ValueError rather than pandas.core.common.PandasError, if called with scalar inputs and not axes; The exception PandasError is removed as well. (GH15541)
  • The exception pandas.core.common.AmbiguousIndexError is removed as it is not referenced (GH15541)

Reorganization of the library: Privacy Changes

Modules Privacy Has Changed

Some formerly public python/c/c++/cython extension modules have been moved and/or renamed. These are all removed from the public API. Furthermore, the pandas.core, pandas.compat, and pandas.util top-level modules are now considered to be PRIVATE. If indicated, a deprecation warning will be issued if you reference theses modules. (GH12588)

Previous Location New Location Deprecated
pandas.lib pandas._libs.lib X
pandas.tslib pandas._libs.tslib X
pandas.computation pandas.core.computation X
pandas.msgpack pandas.io.msgpack  
pandas.index pandas._libs.index  
pandas.algos pandas._libs.algos  
pandas.hashtable pandas._libs.hashtable  
pandas.indexes pandas.core.indexes  
pandas.json pandas._libs.json / pandas.io.json X
pandas.parser pandas._libs.parsers X
pandas.formats pandas.io.formats  
pandas.sparse pandas.core.sparse  
pandas.tools pandas.core.reshape X
pandas.types pandas.core.dtypes X
pandas.io.sas.saslib pandas.io.sas._sas  
pandas._join pandas._libs.join  
pandas._hash pandas._libs.hashing  
pandas._period pandas._libs.period  
pandas._sparse pandas._libs.sparse  
pandas._testing pandas._libs.testing  
pandas._window pandas._libs.window  

Some new subpackages are created with public functionality that is not directly exposed in the top-level namespace: pandas.errors, pandas.plotting and pandas.testing (more details below). Together with pandas.api.types and certain functions in the pandas.io and pandas.tseries submodules, these are now the public subpackages.

Further changes:

  • The function union_categoricals() is now importable from pandas.api.types, formerly from pandas.types.concat (GH15998)
  • The type import pandas.tslib.NaTType is deprecated and can be replaced by using type(pandas.NaT) (GH16146)
  • The public functions in pandas.tools.hashing deprecated from that locations, but are now importable from pandas.util (GH16223)
  • The modules in pandas.util: decorators, print_versions, doctools, validators, depr_module are now private. Only the functions exposed in pandas.util itself are public (GH16223)

pandas.errors

We are adding a standard public module for all pandas exceptions & warnings pandas.errors. (GH14800). Previously these exceptions & warnings could be imported from pandas.core.common or pandas.io.common. These exceptions and warnings will be removed from the *.common locations in a future release. (GH15541)

The following are now part of this API:

['DtypeWarning',
 'EmptyDataError',
 'OutOfBoundsDatetime',
 'ParserError',
 'ParserWarning',
 'PerformanceWarning',
 'UnsortedIndexError',
 'UnsupportedFunctionCall']

pandas.testing

We are adding a standard module that exposes the public testing functions in pandas.testing (GH9895). Those functions can be used when writing tests for functionality using pandas objects.

The following testing functions are now part of this API:

pandas.plotting

A new public pandas.plotting module has been added that holds plotting functionality that was previously in either pandas.tools.plotting or in the top-level namespace. See the deprecations sections for more details.

Other Development Changes

  • Building pandas for development now requires cython >= 0.23 (GH14831)
  • Require at least 0.23 version of cython to avoid problems with character encodings (GH14699)
  • Switched the test framework to use pytest (GH13097)
  • Reorganization of tests directory layout (GH14854, GH15707).

Deprecations

Deprecate .ix

The .ix indexer is deprecated, in favor of the more strict .iloc and .loc indexers. .ix offers a lot of magic on the inference of what the user wants to do. To wit, .ix can decide to index positionally OR via labels, depending on the data type of the index. This has caused quite a bit of user confusion over the years. The full indexing documentation is here. (GH14218)

The recommended methods of indexing are:

  • .loc if you want to label index
  • .iloc if you want to positionally index.

Using .ix will now show a DeprecationWarning with a link to some examples of how to convert code here.

In [130]: df = pd.DataFrame({'A': [1, 2, 3],
   .....:                    'B': [4, 5, 6]},
   .....:                   index=list('abc'))
   .....: 

In [131]: df
Out[131]: 
   A  B
a  1  4
b  2  5
c  3  6

Previous Behavior, where you wish to get the 0th and the 2nd elements from the index in the ‘A’ column.

In [3]: df.ix[[0, 2], 'A']
Out[3]:
a    1
c    3
Name: A, dtype: int64

Using .loc. Here we will select the appropriate indexes from the index, then use label indexing.

In [132]: df.loc[df.index[[0, 2]], 'A']
Out[132]: 
a    1
c    3
Name: A, dtype: int64

Using .iloc. Here we will get the location of the ‘A’ column, then use positional indexing to select things.

In [133]: df.iloc[[0, 2], df.columns.get_loc('A')]
Out[133]: 
a    1
c    3
Name: A, dtype: int64

Deprecate Panel

Panel is deprecated and will be removed in a future version. The recommended way to represent 3-D data are with a MultiIndex on a DataFrame via the to_frame() or with the xarray package. Pandas provides a to_xarray() method to automate this conversion. For more details see Deprecate Panel documentation. (GH13563).

In [134]: p = tm.makePanel()

In [135]: p
Out[135]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D

Convert to a MultiIndex DataFrame

In [136]: p.to_frame()
Out[136]: 
                     ItemA     ItemB     ItemC
major      minor                              
2000-01-03 A      0.628776 -1.409432  0.209395
           B      0.988138 -1.347533 -0.896581
           C     -0.938153  1.272395 -0.161137
           D     -0.223019 -0.591863 -1.051539
2000-01-04 A      0.186494  1.422986 -0.592886
           B     -0.072608  0.363565  1.104352
           C     -1.239072 -1.449567  0.889157
           D      2.123692 -0.414505 -0.319561
2000-01-05 A      0.952478 -2.147855 -1.473116
           B     -0.550603 -0.014752 -0.431550
           C      0.139683 -1.195524  0.288377
           D      0.122273 -1.425795 -0.619993

Convert to an xarray DataArray

In [137]: p.to_xarray()
Out[137]: 
<xarray.DataArray (items: 3, major_axis: 3, minor_axis: 4)>
array([[[ 0.628776,  0.988138, -0.938153, -0.223019],
        [ 0.186494, -0.072608, -1.239072,  2.123692],
        [ 0.952478, -0.550603,  0.139683,  0.122273]],

       [[-1.409432, -1.347533,  1.272395, -0.591863],
        [ 1.422986,  0.363565, -1.449567, -0.414505],
        [-2.147855, -0.014752, -1.195524, -1.425795]],

       [[ 0.209395, -0.896581, -0.161137, -1.051539],
        [-0.592886,  1.104352,  0.889157, -0.319561],
        [-1.473116, -0.43155 ,  0.288377, -0.619993]]])
Coordinates:
  * items       (items) object 'ItemA' 'ItemB' 'ItemC'
  * major_axis  (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05
  * minor_axis  (minor_axis) object 'A' 'B' 'C' 'D'

Deprecate groupby.agg() with a dictionary when renaming

The .groupby(..).agg(..), .rolling(..).agg(..), and .resample(..).agg(..) syntax can accept a variable of inputs, including scalars, list, and a dict of column names to scalars or lists. This provides a useful syntax for constructing multiple (potentially different) aggregations.

However, .agg(..) can also accept a dict that allows ‘renaming’ of the result columns. This is a complicated and confusing syntax, as well as not consistent between Series and DataFrame. We are deprecating this ‘renaming’ functionality.

  • We are deprecating passing a dict to a grouped/rolled/resampled Series. This allowed one to rename the resulting aggregation, but this had a completely different meaning than passing a dictionary to a grouped DataFrame, which accepts column-to-aggregations.
  • We are deprecating passing a dict-of-dicts to a grouped/rolled/resampled DataFrame in a similar manner.

This is an illustrative example:

In [138]: df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
   .....:                    'B': range(5),
   .....:                    'C': range(5)})
   .....: 

In [139]: df
Out[139]: 
   A  B  C
0  1  0  0
1  1  1  1
2  1  2  2
3  2  3  3
4  2  4  4

Here is a typical useful syntax for computing different aggregations for different columns. This is a natural, and useful syntax. We aggregate from the dict-to-list by taking the specified columns and applying the list of functions. This returns a MultiIndex for the columns (this is not deprecated).

In [140]: df.groupby('A').agg({'B': 'sum', 'C': 'min'})
Out[140]: 
   B  C
A      
1  3  0
2  7  3

Here’s an example of the first deprecation, passing a dict to a grouped Series. This is a combination aggregation & renaming:

In [6]: df.groupby('A').B.agg({'foo': 'count'})
FutureWarning: using a dict on a Series for aggregation
is deprecated and will be removed in a future version

Out[6]:
   foo
A
1    3
2    2

You can accomplish the same operation, more idiomatically by:

In [141]: df.groupby('A').B.agg(['count']).rename(columns={'count': 'foo'})
Out[141]: 
   foo
A     
1    3
2    2

Here’s an example of the second deprecation, passing a dict-of-dict to a grouped DataFrame:

In [23]: (df.groupby('A')
            .agg({'B': {'foo': 'sum'}, 'C': {'bar': 'min'}})
         )
FutureWarning: using a dict with renaming is deprecated and
will be removed in a future version

Out[23]:
     B   C
   foo bar
A
1   3   0
2   7   3

You can accomplish nearly the same by:

In [142]: (df.groupby('A')
   .....:    .agg({'B': 'sum', 'C': 'min'})
   .....:    .rename(columns={'B': 'foo', 'C': 'bar'})
   .....: )
   .....: 
Out[142]: 
   foo  bar
A          
1    3    0
2    7    3

Deprecate .plotting

The pandas.tools.plotting module has been deprecated, in favor of the top level pandas.plotting module. All the public plotting functions are now available from pandas.plotting (GH12548).

Furthermore, the top-level pandas.scatter_matrix and pandas.plot_params are deprecated. Users can import these from pandas.plotting as well.

Previous script:

pd.tools.plotting.scatter_matrix(df)
pd.scatter_matrix(df)

Should be changed to:

pd.plotting.scatter_matrix(df)

Other Deprecations

  • SparseArray.to_dense() has deprecated the fill parameter, as that parameter was not being respected (GH14647)
  • SparseSeries.to_dense() has deprecated the sparse_only parameter (GH14647)
  • Series.repeat() has deprecated the reps parameter in favor of repeats (GH12662)
  • The Series constructor and .astype method have deprecated accepting timestamp dtypes without a frequency (e.g. np.datetime64) for the dtype parameter (GH15524)
  • Index.repeat() and MultiIndex.repeat() have deprecated the n parameter in favor of repeats (GH12662)
  • Categorical.searchsorted() and Series.searchsorted() have deprecated the v parameter in favor of value (GH12662)
  • TimedeltaIndex.searchsorted(), DatetimeIndex.searchsorted(), and PeriodIndex.searchsorted() have deprecated the key parameter in favor of value (GH12662)
  • DataFrame.astype() has deprecated the raise_on_error parameter in favor of errors (GH14878)
  • Series.sortlevel and DataFrame.sortlevel have been deprecated in favor of Series.sort_index and DataFrame.sort_index (GH15099)
  • importing concat from pandas.tools.merge has been deprecated in favor of imports from the pandas namespace. This should only affect explicit imports (GH15358)
  • Series/DataFrame/Panel.consolidate() been deprecated as a public method. (GH15483)
  • The as_indexer keyword of Series.str.match() has been deprecated (ignored keyword) (GH15257).
  • The following top-level pandas functions have been deprecated and will be removed in a future version (GH13790, GH15940)
    • pd.pnow(), replaced by Period.now()
    • pd.Term, is removed, as it is not applicable to user code. Instead use in-line string expressions in the where clause when searching in HDFStore
    • pd.Expr, is removed, as it is not applicable to user code.
    • pd.match(), is removed.
    • pd.groupby(), replaced by using the .groupby() method directly on a Series/DataFrame
    • pd.get_store(), replaced by a direct call to pd.HDFStore(...)
  • is_any_int_dtype, is_floating_dtype, and is_sequence are deprecated from pandas.api.types (GH16042)

Removal of prior version deprecations/changes

  • The pandas.rpy module is removed. Similar functionality can be accessed through the rpy2 project. See the R interfacing docs for more details.
  • The pandas.io.ga module with a google-analytics interface is removed (GH11308). Similar functionality can be found in the Google2Pandas package.
  • pd.to_datetime and pd.to_timedelta have dropped the coerce parameter in favor of errors (GH13602)
  • pandas.stats.fama_macbeth, pandas.stats.ols, pandas.stats.plm and pandas.stats.var, as well as the top-level pandas.fama_macbeth and pandas.ols routines are removed. Similar functionality can be found in the statsmodels package. (GH11898)
  • The TimeSeries and SparseTimeSeries classes, aliases of Series and SparseSeries, are removed (GH10890, GH15098).
  • Series.is_time_series is dropped in favor of Series.index.is_all_dates (GH15098)
  • The deprecated irow, icol, iget and iget_value methods are removed in favor of iloc and iat as explained here (GH10711).
  • The deprecated DataFrame.iterkv() has been removed in favor of DataFrame.iteritems() (GH10711)
  • The Categorical constructor has dropped the name parameter (GH10632)
  • Categorical has dropped support for NaN categories (GH10748)
  • The take_last parameter has been dropped from duplicated(), drop_duplicates(), nlargest(), and nsmallest() methods (GH10236, GH10792, GH10920)
  • Series, Index, and DataFrame have dropped the sort and order methods (GH10726)
  • Where clauses in pytables are only accepted as strings and expressions types and not other data-types (GH12027)
  • DataFrame has dropped the combineAdd and combineMult methods in favor of add and mul respectively (GH10735)

Performance Improvements

  • Improved performance of pd.wide_to_long() (GH14779)
  • Improved performance of pd.factorize() by releasing the GIL with object dtype when inferred as strings (GH14859, GH16057)
  • Improved performance of timeseries plotting with an irregular DatetimeIndex (or with compat_x=True) (GH15073).
  • Improved performance of groupby().cummin() and groupby().cummax() (GH15048, GH15109, GH15561, GH15635)
  • Improved performance and reduced memory when indexing with a MultiIndex (GH15245)
  • When reading buffer object in read_sas() method without specified format, filepath string is inferred rather than buffer object. (GH14947)
  • Improved performance of .rank() for categorical data (GH15498)
  • Improved performance when using .unstack() (GH15503)
  • Improved performance of merge/join on category columns (GH10409)
  • Improved performance of drop_duplicates() on bool columns (GH12963)
  • Improve performance of pd.core.groupby.GroupBy.apply when the applied function used the .name attribute of the group DataFrame (GH15062).
  • Improved performance of iloc indexing with a list or array (GH15504).
  • Improved performance of Series.sort_index() with a monotonic index (GH15694)
  • Improved performance in pd.read_csv() on some platforms with buffered reads (GH16039)

Bug Fixes

Conversion

  • Bug in Timestamp.replace now raises TypeError when incorrect argument names are given; previously this raised ValueError (GH15240)
  • Bug in Timestamp.replace with compat for passing long integers (GH15030)
  • Bug in Timestamp returning UTC based time/date attributes when a timezone was provided (GH13303, GH6538)
  • Bug in Timestamp incorrectly localizing timezones during construction (GH11481, GH15777)
  • Bug in TimedeltaIndex addition where overflow was being allowed without error (GH14816)
  • Bug in TimedeltaIndex raising a ValueError when boolean indexing with loc (GH14946)
  • Bug in catching an overflow in Timestamp + Timedelta/Offset operations (GH15126)
  • Bug in DatetimeIndex.round() and Timestamp.round() floating point accuracy when rounding by milliseconds or less (GH14440, GH15578)
  • Bug in astype() where inf values were incorrectly converted to integers. Now raises error now with astype() for Series and DataFrames (GH14265)
  • Bug in DataFrame(..).apply(to_numeric) when values are of type decimal.Decimal. (GH14827)
  • Bug in describe() when passing a numpy array which does not contain the median to the percentiles keyword argument (GH14908)
  • Cleaned up PeriodIndex constructor, including raising on floats more consistently (GH13277)
  • Bug in using __deepcopy__ on empty NDFrame objects (GH15370)
  • Bug in .replace() may result in incorrect dtypes. (GH12747, GH15765)
  • Bug in Series.replace and DataFrame.replace which failed on empty replacement dicts (GH15289)
  • Bug in Series.replace which replaced a numeric by string (GH15743)
  • Bug in Index construction with NaN elements and integer dtype specified (GH15187)
  • Bug in Series construction with a datetimetz (GH14928)
  • Bug in Series.dt.round() inconsistent behaviour on NaT ‘s with different arguments (GH14940)
  • Bug in Series constructor when both copy=True and dtype arguments are provided (GH15125)
  • Incorrect dtyped Series was returned by comparison methods (e.g., lt, gt, …) against a constant for an empty DataFrame (GH15077)
  • Bug in Series.ffill() with mixed dtypes containing tz-aware datetimes. (GH14956)
  • Bug in DataFrame.fillna() where the argument downcast was ignored when fillna value was of type dict (GH15277)
  • Bug in .asfreq(), where frequency was not set for empty Series (GH14320)
  • Bug in DataFrame construction with nulls and datetimes in a list-like (GH15869)
  • Bug in DataFrame.fillna() with tz-aware datetimes (GH15855)
  • Bug in is_string_dtype, is_timedelta64_ns_dtype, and is_string_like_dtype in which an error was raised when None was passed in (GH15941)
  • Bug in the return type of pd.unique on a Categorical, which was returning an ndarray and not a Categorical (GH15903)
  • Bug in Index.to_series() where the index was not copied (and so mutating later would change the original), (GH15949)
  • Bug in indexing with partial string indexing with a len-1 DataFrame (GH16071)
  • Bug in Series construction where passing invalid dtype didn’t raise an error. (GH15520)

Indexing

  • Bug in Index power operations with reversed operands (GH14973)
  • Bug in DataFrame.sort_values() when sorting by multiple columns where one column is of type int64 and contains NaT (GH14922)
  • Bug in DataFrame.reindex() in which method was ignored when passing columns (GH14992)
  • Bug in DataFrame.loc with indexing a MultiIndex with a Series indexer (GH14730, GH15424)
  • Bug in DataFrame.loc with indexing a MultiIndex with a numpy array (GH15434)
  • Bug in Series.asof which raised if the series contained all np.nan (GH15713)
  • Bug in .at when selecting from a tz-aware column (GH15822)
  • Bug in Series.where() and DataFrame.where() where array-like conditionals were being rejected (GH15414)
  • Bug in Series.where() where TZ-aware data was converted to float representation (GH15701)
  • Bug in .loc that would not return the correct dtype for scalar access for a DataFrame (GH11617)
  • Bug in output formatting of a MultiIndex when names are integers (GH12223, GH15262)
  • Bug in Categorical.searchsorted() where alphabetical instead of the provided categorical order was used (GH14522)
  • Bug in Series.iloc where a Categorical object for list-like indexes input was returned, where a Series was expected. (GH14580)
  • Bug in DataFrame.isin comparing datetimelike to empty frame (GH15473)
  • Bug in .reset_index() when an all NaN level of a MultiIndex would fail (GH6322)
  • Bug in .reset_index() when raising error for index name already present in MultiIndex columns (GH16120)
  • Bug in creating a MultiIndex with tuples and not passing a list of names; this will now raise ValueError (GH15110)
  • Bug in the HTML display with with a MultiIndex and truncation (GH14882)
  • Bug in the display of .info() where a qualifier (+) would always be displayed with a MultiIndex that contains only non-strings (GH15245)
  • Bug in pd.concat() where the names of MultiIndex of resulting DataFrame are not handled correctly when None is presented in the names of MultiIndex of input DataFrame (GH15787)
  • Bug in DataFrame.sort_index() and Series.sort_index() where na_position doesn’t work with a MultiIndex (GH14784, GH16604)
  • Bug in in pd.concat() when combining objects with a CategoricalIndex (GH16111)
  • Bug in indexing with a scalar and a CategoricalIndex (GH16123)

I/O

  • Bug in pd.to_numeric() in which float and unsigned integer elements were being improperly casted (GH14941, GH15005)
  • Bug in pd.read_fwf() where the skiprows parameter was not being respected during column width inference (GH11256)
  • Bug in pd.read_csv() in which the dialect parameter was not being verified before processing (GH14898)
  • Bug in pd.read_csv() in which missing data was being improperly handled with usecols (GH6710)
  • Bug in pd.read_csv() in which a file containing a row with many columns followed by rows with fewer columns would cause a crash (GH14125)
  • Bug in pd.read_csv() for the C engine where usecols were being indexed incorrectly with parse_dates (GH14792)
  • Bug in pd.read_csv() with parse_dates when multi-line headers are specified (GH15376)
  • Bug in pd.read_csv() with float_precision='round_trip' which caused a segfault when a text entry is parsed (GH15140)
  • Bug in pd.read_csv() when an index was specified and no values were specified as null values (GH15835)
  • Bug in pd.read_csv() in which certain invalid file objects caused the Python interpreter to crash (GH15337)
  • Bug in pd.read_csv() in which invalid values for nrows and chunksize were allowed (GH15767)
  • Bug in pd.read_csv() for the Python engine in which unhelpful error messages were being raised when parsing errors occurred (GH15910)
  • Bug in pd.read_csv() in which the skipfooter parameter was not being properly validated (GH15925)
  • Bug in pd.to_csv() in which there was numeric overflow when a timestamp index was being written (GH15982)
  • Bug in pd.util.hashing.hash_pandas_object() in which hashing of categoricals depended on the ordering of categories, instead of just their values. (GH15143)
  • Bug in .to_json() where lines=True and contents (keys or values) contain escaped characters (GH15096)
  • Bug in .to_json() causing single byte ascii characters to be expanded to four byte unicode (GH15344)
  • Bug in .to_json() for the C engine where rollover was not correctly handled for case where frac is odd and diff is exactly 0.5 (GH15716, GH15864)
  • Bug in pd.read_json() for Python 2 where lines=True and contents contain non-ascii unicode characters (GH15132)
  • Bug in pd.read_msgpack() in which Series categoricals were being improperly processed (GH14901)
  • Bug in pd.read_msgpack() which did not allow loading of a dataframe with an index of type CategoricalIndex (GH15487)
  • Bug in pd.read_msgpack() when deserializing a CategoricalIndex (GH15487)
  • Bug in DataFrame.to_records() with converting a DatetimeIndex with a timezone (GH13937)
  • Bug in DataFrame.to_records() which failed with unicode characters in column names (GH11879)
  • Bug in .to_sql() when writing a DataFrame with numeric index names (GH15404).
  • Bug in DataFrame.to_html() with index=False and max_rows raising in IndexError (GH14998)
  • Bug in pd.read_hdf() passing a Timestamp to the where parameter with a non date column (GH15492)
  • Bug in DataFrame.to_stata() and StataWriter which produces incorrectly formatted files to be produced for some locales (GH13856)
  • Bug in