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# Version 0.20.1 (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 0.20.1. Version 0.20.1 contains one additional change for backwards-compatibility with downstream projects using pandas’ `utils` routines. (GH 16250)

## New features

### Method `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 (GH 1623).

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  0.469112 -0.282863 -1.509059
    2000-01-02 -1.135632  1.212112 -0.173215
    2000-01-03  0.119209 -1.044236 -0.861849
    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  0.113648 -1.478427  0.524988
    2000-01-09  0.404705  0.577046 -1.715002
    2000-01-10 -1.039268 -0.370647 -1.157892

    [10 rows x 3 columns]

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   -1.068226
    B   -1.387015
    C   -4.892029
    Length: 3, dtype: float64

Multiple aggregations with a list of functions.

    In [5]: df.agg(['sum', 'min'])
    Out[5]:
                A         B         C
    sum -1.068226 -1.387015 -4.892029
    min -1.135632 -1.478427 -1.715002

    [2 rows x 3 columns]

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
    sum -1.068226       NaN
    min -1.135632 -1.478427
    max       NaN  1.212112

    [3 rows x 2 columns]

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  0.469112  1.604745  0.282863  1.195563  1.509059  0.205944
    2000-01-02  1.135632  0.000000  1.212112  2.690539  0.173215  1.541787
    2000-01-03  0.119209  1.254841  1.044236  0.434191  0.861849  0.853153
    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  0.113648  1.249281  1.478427  0.000000  0.524988  2.239990
    2000-01-09  0.404705  1.540338  0.577046  2.055473  1.715002  0.000000
    2000-01-10  1.039268  0.096364  0.370647  1.107780  1.157892  0.557110

    [10 rows x 6 columns]

When presented with mixed dtypes that cannot be aggregated, `.agg()` will only take the valid aggregations. This is similar to how groupby `.agg()` works. (GH 15015)

    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               str
    D    datetime64[us]
    Length: 4, 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

### Keyword argument `dtype` 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 (GH 14295). See the io docs for more information.

    In [10]: data = "a  b\n1  2\n3  4"

    In [11]: pd.read_fwf(StringIO(data)).dtypes
    Out[11]:
    a    int64
    b    int64
    Length: 2, dtype: object

    In [12]: pd.read_fwf(StringIO(data), dtype={'a': 'float64', 'b': 'object'}).dtypes
    Out[12]:
    a    float64
    b     object
    Length: 2, dtype: object

### Method `.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. (GH 11276, GH 11745)

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

    In [13]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
    Out[13]: DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[s]', 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 [14]: pd.to_datetime([1, 2, 3], unit='D')
    Out[14]: DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[s]', 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. (GH 5677)

    In [15]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
       ....:           ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
       ....:

    In [16]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])

    In [17]: df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3, 3],
       ....:                    'B': np.arange(8)},
       ....:                   index=index)
       ....:

    In [18]: df
    Out[18]:
                  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

    [8 rows x 2 columns]

    In [19]: df.groupby(['second', 'A']).sum()
    Out[19]:
              B
    second A
    one    1  2
           2  4
           3  6
    two    1  4
           2  5
           3  7

    [6 rows x 1 columns]

### Better support for compressed URLs in `read_csv`

The compression code was refactored (GH 12688). As a result, reading dataframes from URLs in `read_csv()` or `read_table()` now supports additional compression methods: `xz`, `bz2`, and `zip` (GH 14570). 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 (GH 14874).

    In [20]: url = ('https://github.com/{repo}/raw/{branch}/{path}'
       ....:        .format(repo='pandas-dev/pandas',
       ....:                branch='main',
       ....:                path='pandas/tests/io/parser/data/salaries.csv.bz2'))
       ....:

    # default, infer compression
    In [21]: df = pd.read_csv(url, sep='\t', compression='infer')

    # explicitly specify compression
    In [22]: df = pd.read_csv(url, sep='\t', compression='bz2')

    In [23]: df.head(2)
    Out[23]:
           S  X  E  M
    0  13876  1  1  1
    1  11608  1  3  0

    [2 rows x 4 columns]

### Pickle file IO 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 [24]: 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 [25]: df.to_pickle("data.pkl.compress", compression="gzip")

    In [26]: rt = pd.read_pickle("data.pkl.compress", compression="gzip")

    In [27]: rt.head()
    Out[27]:
              A    B                   C
    0 -1.344312  foo 2013-01-01 00:00:00
    1  0.844885  foo 2013-01-01 00:00:01
    2  1.075770  foo 2013-01-01 00:00:02
    3 -0.109050  foo 2013-01-01 00:00:03
    4  1.643563  foo 2013-01-01 00:00:04

    [5 rows x 3 columns]

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

    In [28]: df.to_pickle("data.pkl.gz")

    In [29]: rt = pd.read_pickle("data.pkl.gz")

    In [30]: rt.head()
    Out[30]:
              A    B                   C
    0 -1.344312  foo 2013-01-01 00:00:00
    1  0.844885  foo 2013-01-01 00:00:01
    2  1.075770  foo 2013-01-01 00:00:02
    3 -0.109050  foo 2013-01-01 00:00:03
    4  1.643563  foo 2013-01-01 00:00:04

    [5 rows x 3 columns]

    In [31]: df["A"].to_pickle("s1.pkl.bz2")

    In [32]: rt = pd.read_pickle("s1.pkl.bz2")

    In [33]: rt.head()
    Out[33]:
    0   -1.344312
    1    0.844885
    2    1.075770
    3   -0.109050
    4    1.643563
    Name: A, Length: 5, 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 (GH 14937)

    In [1]: idx = pd.UInt64Index([1, 2, 3])
    In [2]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)
    In [3]: df.index
    Out[3]: UInt64Index([1, 2, 3], dtype='uint64')

- Bug in converting object elements of array-like objects to unsigned 64-bit integers (GH 4471, GH 14982)

- Bug in `Series.unique()` in which unsigned 64-bit integers were causing overflow (GH 14721)

- Bug in `DataFrame` construction in which unsigned 64-bit integer elements were being converted to objects (GH 14881)

- Bug in `pd.read_csv()` in which unsigned 64-bit integer elements were being improperly converted to the wrong data types (GH 14983)

- Bug in `pd.unique()` in which unsigned 64-bit integers were causing overflow (GH 14915)

- Bug in `pd.value_counts()` in which unsigned 64-bit integers were being erroneously truncated in the output (GH 14934)

### 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. (GH 13179)

    In [34]: chromosomes = np.r_[np.arange(1, 23).astype(str), ['X', 'Y']]

    In [35]: 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 [36]: df
    Out[36]:
         A   B   C chromosomes
    0   87  22  81           4
    1   87  22  81          13
    2   87  22  81          22
    3   87  22  81           2
    4   87  22  81           6
    ..  ..  ..  ..         ...
    95  87  22  81           8
    96  87  22  81          11
    97  87  22  81           X
    98  87  22  81           1
    99  87  22  81          19

    [100 rows x 4 columns]

Previous behavior:

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

New behavior:

    In [37]: df[df.chromosomes != '1'].groupby('chromosomes', observed=False, sort=False).sum()
    Out[37]:
                   A   B    C
    chromosomes
    4            348  88  324
    13           261  66  243
    22           348  88  324
    2            348  88  324
    6            174  44  162
    ...          ...  ..  ...
    3            348  88  324
    11           348  88  324
    19           174  44  162
    1              0   0    0
    21             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 [38]: 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 [39]: df
    Out[39]:
         A  B          C
    idx
    0    1  a 2016-01-01
    1    2  b 2016-01-02
    2    3  c 2016-01-03

    [3 rows x 3 columns]

    In [40]: df.to_json(orient='table')
    Out[40]:
    '{"schema":{"fields":[{"name":"idx","type":"integer"},{"name":"A","type":"integer"},{"name":"B","type":"string"},{"name":"C","type":"datetime"}],"primaryKey":["idx"],"pandas_version":"1.4.0"},"data":[{"idx":0,"A":1,"B":"a","C":"2016-01-01T00:00:00.000"},{"idx":1,"A":2,"B":"b","C":"2016-01-02T00:00:00.000"},{"idx":2,"A":3,"B":"c","C":"2016-01-03T00:00:00.000"}]}'

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. (GH 4343)

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

    from scipy.sparse import csr_matrix
    arr = np.random.random(size=(1000, 5))
    arr[arr < .9] = 0
    sp_arr = csr_matrix(arr)
    sp_arr
    sdf = pd.SparseDataFrame(sp_arr)
    sdf

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

    sdf.to_coo()

### Excel output for styled DataFrames

Experimental support has been added to export `DataFrame.style` formats to Excel using the `openpyxl` engine. (GH 15530)

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

    In [38]: np.random.seed(24)

    In [39]: df = pd.DataFrame({'A': np.linspace(1, 10, 10)})

    In [40]: df = pd.concat([df, pd.DataFrame(np.random.RandomState(24).randn(10, 4),
       ....:                                  columns=list('BCDE'))],
       ....:                axis=1)
       ....:

    In [41]: df.iloc[0, 2] = np.nan

    In [42]: df
    Out[42]:
          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

    [10 rows x 5 columns]

    In [43]: styled = (df.style
       ....:           .map(lambda val: 'color:red;' if val < 0 else 'color:black;')
       ....:           .highlight_max())
       ....:

    In [44]: styled.to_excel('styled.xlsx', engine='openpyxl')

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. (GH 7640, GH 8625)

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

    In [46]: c
    Out[46]:
    [(-0.003, 1.5], (-0.003, 1.5], (1.5, 3.0], (1.5, 3.0]]
    Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

    In [47]: c.categories
    Out[47]: IntervalIndex([(-0.003, 1.5], (1.5, 3.0]], dtype='interval[float64, right]')

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

    In [48]: pd.cut([0, 3, 5, 1], bins=c.categories)
    Out[48]:
    [(-0.003, 1.5], (1.5, 3.0], NaN, (-0.003, 1.5]]
    Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

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

    In [49]: df = pd.DataFrame({'A': range(4),
       ....:                    'B': pd.cut([0, 3, 1, 1], bins=c.categories)
       ....:                    }).set_index('B')
       ....:

    In [50]: df
    Out[50]:
                   A
    B
    (-0.003, 1.5]  0
    (1.5, 3.0]     1
    (-0.003, 1.5]  2
    (-0.003, 1.5]  3

    [4 rows x 1 columns]

Selecting via a specific interval:

    In [51]: df.loc[pd.Interval(1.5, 3.0)]
    Out[51]:
    A    1
    Name: (1.5, 3.0], Length: 1, dtype: int64

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

    In [52]: df.loc[0]
    Out[52]:
                   A
    B
    (-0.003, 1.5]  0
    (-0.003, 1.5]  2
    (-0.003, 1.5]  3

    [3 rows x 1 columns]

### Other enhancements

- `DataFrame.rolling()` now accepts the parameter `closed='right'|'left'|'both'|'neither'` to choose the rolling window-endpoint closedness. See the documentation (GH 13965)

- 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` (GH 15055)

- `Series.str.replace()` now accepts a compiled regular expression as a pattern (GH 15446)

- `Series.sort_index` accepts parameters `kind` and `na_position` (GH 13589, GH 14444)

- `DataFrame` and `DataFrame.groupby()` have gained a `nunique()` method to count the distinct values over an axis (GH 14336, GH 15197).

- `DataFrame` has gained a `melt()` method, equivalent to `pd.melt()`, for unpivoting from a wide to long format (GH 12640).

- `pd.read_excel()` now preserves sheet order when using `sheetname=None` (GH 9930)

- Multiple offset aliases with decimal points are now supported (e.g. `0.5min` is parsed as `30s`) (GH 8419)

- `.isnull()` and `.notnull()` have been added to `Index` object to make them more consistent with the `Series` API (GH 15300)

- New `UnsortedIndexError` (subclass of `KeyError`) raised when indexing/slicing into an unsorted MultiIndex (GH 11897). 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` (GH 12397)

- `pd.cut` and `pd.qcut` now support datetime64 and timedelta64 dtypes (GH 14714, GH 14798)

- `pd.qcut` has gained the `duplicates='raise'|'drop'` option to control whether to raise on duplicated edges (GH 7751)

- `Series` provides a `to_excel` method to output Excel files (GH 8825)

- The `usecols` argument in `pd.read_csv()` now accepts a callable function as a value (GH 14154)

- The `skiprows` argument in `pd.read_csv()` now accepts a callable function as a value (GH 10882)

- The `nrows` and `chunksize` arguments in `pd.read_csv()` are supported if both are passed (GH 6774, GH 15755)

- `DataFrame.plot` now prints a title above each subplot if `suplots=True` and `title` is a list of strings (GH 14753)

- `DataFrame.plot` can pass the matplotlib 2.0 default color cycle as a single string as color parameter, see here. (GH 15516)

- `Series.interpolate()` now supports timedelta as an index type with `method='time'` (GH 6424)

- Addition of a `level` keyword to `DataFrame/Series.rename` to rename labels in the specified level of a MultiIndex (GH 4160).

- `DataFrame.reset_index()` will now interpret a tuple `index.name` as a key spanning across levels of `columns`, if this is a `MultiIndex` (GH 16164)

- `Timedelta.isoformat` method added for formatting Timedeltas as an ISO 8601 duration. See the Timedelta docs (GH 15136)

- `.select_dtypes()` now allows the string `datetimetz` to generically select datetimes with tz (GH 14910)

- 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'` (GH 14887)

- `Series/DataFrame.asfreq()` have gained a `fill_value` parameter, to fill missing values (GH 3715).

- `Series/DataFrame.resample.asfreq` have gained a `fill_value` parameter, to fill missing values during resampling (GH 3715).

- `pandas.util.hash_pandas_object()` has gained the ability to hash a `MultiIndex` (GH 15224)

- `Series/DataFrame.squeeze()` have gained the `axis` parameter. (GH 15339)

- `DataFrame.to_excel()` has a new `freeze_panes` parameter to turn on Freeze Panes when exporting to Excel (GH 15160)

- `pd.read_html()` will parse multiple header rows, creating a MultiIndex header. (GH 13434).

- HTML table output skips `colspan` or `rowspan` attribute if equal to 1. (GH 15403)

- `pandas.io.formats.style.Styler` template now has blocks for easier extension, see the example notebook (GH 15649)

- `Styler.render()` now accepts `**kwargs` to allow user-defined variables in the template (GH 15649)

- Compatibility with Jupyter notebook 5.0; MultiIndex column labels are left-aligned and MultiIndex row-labels are top-aligned (GH 15379)

- `TimedeltaIndex` now has a custom date-tick formatter specifically designed for nanosecond level precision (GH 8711)

- `pd.api.types.union_categoricals` gained the `ignore_ordered` argument to allow ignoring the ordered attribute of unioned categoricals (GH 13410). See the categorical union docs for more information.

- `DataFrame.to_latex()` and `DataFrame.to_string()` now allow optional header aliases. (GH 15536)

- Re-enable the `parse_dates` keyword of `pd.read_excel()` to parse string columns as dates (GH 14326)

- Added `.empty` property to subclasses of `Index`. (GH 15270)

- Enabled floor division for `Timedelta` and `TimedeltaIndex` (GH 15828)

- `pandas.io.json.json_normalize()` gained the option `errors='ignore'|'raise'`; the default is `errors='raise'` which is backward compatible. (GH 14583)

- `pandas.io.json.json_normalize()` with an empty `list` will return an empty `DataFrame` (GH 15534)

- `pandas.io.json.json_normalize()` has gained a `sep` option that accepts `str` to separate joined fields; the default is “.”, which is backward compatible. (GH 14883)

- `MultiIndex.remove_unused_levels()` has been added to facilitate removing unused levels. (GH 15694)

- `pd.read_csv()` will now raise a `ParserError` error whenever any parsing error occurs (GH 15913, GH 15925)

- `pd.read_csv()` now supports the `error_bad_lines` and `warn_bad_lines` arguments for the Python parser (GH 15925)

- The `display.show_dimensions` option can now also be used to specify whether the length of a `Series` should be shown in its repr (GH 7117).

- `parallel_coordinates()` has gained a `sort_labels` keyword argument that sorts class labels and the colors assigned to them (GH 15908)

- Options added to allow one to turn on/off using `bottleneck` and `numexpr`, see here (GH 16157)

- `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]`. (GH 14757)

## 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`. (GH 15098).

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 (GH 12766)

    In [53]: idx = pd.Index([1, 2])

    In [54]: idx
    Out[54]: Index([1, 2], dtype='int64')

    In [55]: mi = pd.MultiIndex.from_tuples([(1, 2), (2, 4)])

    In [56]: mi
    Out[56]:
    MultiIndex([(1, 2),
                (2, 4)],
               )

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 [57]: idx.map(lambda x: x * 2)
    Out[57]: Index([2, 4], dtype='int64')

    In [58]: idx.map(lambda x: (x, x * 2))
    Out[58]:
    MultiIndex([(1, 2),
                (2, 4)],
               )

    In [59]: mi.map(lambda x: x)
    Out[59]:
    MultiIndex([(1, 2),
                (2, 4)],
               )

    In [60]: mi.map(lambda x: x[0])
    Out[60]: Index([1, 2], dtype='int64')

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

    In [64]: s = pd.Series(pd.date_range('2011-01-02T00:00', '2011-01-02T02:00', freq='H')
       ....:               .tz_localize('Asia/Tokyo'))
       ....:

    In [65]: s
    Out[65]:
    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
    Length: 3, 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 [66]: s.map(lambda x: x.hour)
    Out[66]:
    0    0
    1    1
    2    2
    Length: 3, 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. (GH 15022)

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

    In [68]: idx.hour
    Out[68]: Index([0, 10, 20, 6, 16], dtype='int32')

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. (GH 15903)

- 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 [61]: pd.Series([pd.Timestamp(r'20160101', tz=r'US/Eastern'),
         ....:            pd.Timestamp(r'20160101', tz=r'US/Eastern')]).unique()
         ....:
      Out[61]:
      <DatetimeArray>
      ['2016-01-01 00:00:00-05:00']
      Length: 1, dtype: datetime64[us, US/Eastern]

      In [62]: pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
         ....:           pd.Timestamp('20160101', tz='US/Eastern')]))
         ....:
      Out[62]:
      <DatetimeArray>
      ['2016-01-01 00:00:00-05:00']
      Length: 1, dtype: datetime64[us, US/Eastern]

      # Index, returns a DatetimeIndex
      In [63]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
         ....:           pd.Timestamp('20160101', tz='US/Eastern')]).unique()
         ....:
      Out[63]: DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[us, US/Eastern]', freq=None)

      In [64]: pd.unique(pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
         ....:                     pd.Timestamp('20160101', tz='US/Eastern')]))
         ....:
      Out[64]: DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[us, 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 [65]: pd.Series(list('baabc'), dtype='category').unique()
      Out[65]:
      ['b', 'a', 'c']
      Categories (3, str): ['a', 'b', 'c']

      In [66]: pd.unique(pd.Series(list('baabc'), dtype='category'))
      Out[66]:
      ['b', 'a', 'c']
      Categories (3, str): ['a', 'b', '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. (GH 11915).

### 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 (GH 14826). See Slice vs. Exact Match for details.

    In [67]: df = pd.DataFrame({'a': [1, 2, 3]}, pd.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 (GH 13247)

    In [68]: df1 = pd.DataFrame(np.array([1.0], dtype=np.float32, ndmin=2))

    In [69]: df1.dtypes
    Out[69]:
    0    float32
    Length: 1, dtype: object

    In [70]: df2 = pd.DataFrame(np.array([np.nan], dtype=np.float32, ndmin=2))

    In [71]: df2.dtypes
    Out[71]:
    0    float32
    Length: 1, dtype: object

Previous behavior:

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

New behavior:

    In [72]: pd.concat([df1, df2]).dtypes
    Out[72]:
    0    float32
    Length: 1, 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 (GH 15347)

### 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. (GH 15237)

Previous behavior:

    In [8]: index = pd.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 = pd.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. (GH 15622, GH 15687, GH 14015, GH 13431, GH 15797)

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

    In [81]: df = pd.DataFrame(np.arange(6), columns=['value'],
       ....:                   index=pd.MultiIndex.from_product([list('BA'), range(3)]))
       ....:
    In [82]: df

    Out[82]:
         value
    B 0      0
      1      1
      2      2
    A 0      3
      1      4
      2      5

    [6 rows x 1 columns]

    In [87]: df.index.is_lexsorted()
    Out[87]: False

    In [88]: df.index.is_monotonic
    Out[88]: False

Sorting works as expected

    In [73]: df.sort_index()
    Out[73]:
                         a
    2011-12-31 23:59:59  1
    2012-01-01 00:00:00  2
    2012-01-01 00:00:01  3

    [3 rows x 1 columns]

    In [90]: df.sort_index().index.is_lexsorted()
    Out[90]: True

    In [91]: df.sort_index().index.is_monotonic
    Out[91]: True

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

    In [74]: df = pd.DataFrame({'value': [1, 2, 3, 4]},
       ....:                   index=pd.MultiIndex([['a', 'b'], ['bb', 'aa']],
       ....:                                       [[0, 0, 1, 1], [0, 1, 0, 1]]))
       ....:

    In [75]: df
    Out[75]:
          value
    a bb      1
      aa      2
    b bb      3
      aa      4

    [4 rows x 1 columns]

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 [94]: df.sort_index()
    Out[94]:
          value
    a aa      2
      bb      1
    b aa      4
      bb      3

    [4 rows x 1 columns]

    In [95]: df.sort_index().index.is_lexsorted()
    Out[95]: True

    In [96]: df.sort_index().index.is_monotonic
    Out[96]: 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. (GH 4792)

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(["mean", "std", "min", "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 [76]: df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})

    In [77]: df.groupby('A').describe()
    Out[77]:
          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

    [2 rows x 8 columns]

    In [78]: df.groupby('A').agg(["mean", "std", "min", "max"])
    Out[78]:
         B
      mean       std min max
    A
    1  1.5  0.707107   1   2
    2  3.5  0.707107   3   4

    [2 rows x 4 columns]

### 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. (GH 15677)

    In [79]: np.random.seed(1234)

    In [80]: 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 [81]: df.tail()
    Out[81]:
    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

    [5 rows x 2 columns]

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

    In [83]: res.tail()
    Out[83]:
    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

    [5 rows x 2 columns]

Retrieving a correlation matrix for a cross-section

    In [84]: df.rolling(12).corr().loc['2016-04-07']
    Out[84]:
    bar        A        B
    bar
    A    1.00000 -0.13209
    B   -0.13209  1.00000

    [2 rows x 2 columns]

### 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` (GH 15492)

    In [85]: df = pd.DataFrame({'unparsed_date': ['2014-01-01', '2014-01-01']})

    In [86]: df.to_hdf('store.h5', key='key', format='table', data_columns=True)

    In [87]: df.dtypes
    Out[87]:
    unparsed_date    str
    Length: 1, 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) (GH 15582). This affects inner joins, `DataFrame.join()` and `merge()`, and the `.align` method.

- `Index.intersection`

      In [88]: left = pd.Index([2, 1, 0])

      In [89]: left
      Out[89]: Index([2, 1, 0], dtype='int64')

      In [90]: right = pd.Index([1, 2, 3])

      In [91]: right
      Out[91]: Index([1, 2, 3], dtype='int64')

  Previous behavior:

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

  New behavior:

      In [92]: left.intersection(right)
      Out[92]: Index([2, 1], dtype='int64')

- `DataFrame.join` and `pd.merge`

      In [93]: left = pd.DataFrame({'a': [20, 10, 0]}, index=[2, 1, 0])

      In [94]: left
      Out[94]:
          a
      2  20
      1  10
      0   0

      [3 rows x 1 columns]

      In [95]: right = pd.DataFrame({'b': [100, 200, 300]}, index=[1, 2, 3])

      In [96]: right
      Out[96]:
           b
      1  100
      2  200
      3  300

      [3 rows x 1 columns]

  Previous behavior:

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

  New behavior:

      In [97]: left.join(right, how='inner')
      Out[97]:
          a    b
      2  20  200
      1  10  100

      [2 rows x 2 columns]

### 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. (GH 4386)

    In [98]: df = pd.DataFrame({'col1': [3, 4, 5],
       ....:                    'col2': ['C', 'D', 'E'],
       ....:                    'col3': [1, 3, 9]})
       ....:

    In [99]: df
    Out[99]:
       col1 col2  col3
    0     3    C     1
    1     4    D     3
    2     5    E     9

    [3 rows x 3 columns]

Previous behavior:

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

New behavior:

    In [100]: df.pivot_table('col1', index=['col3', 'col2'], aggfunc="sum")
    Out[100]:
               col1
    col3 col2
    1    C        3
    3    D        4
    9    E        5

    [3 rows x 1 columns]

### 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 (GH 15213).

- `CParserError` has been renamed to `ParserError` in `pd.read_csv()` and will be removed in the future (GH 12665)

- `SparseArray.cumsum()` and `SparseSeries.cumsum()` will now always return `SparseArray` and `SparseSeries` respectively (GH 12855)

- `DataFrame.applymap()` with an empty `DataFrame` will return a copy of the empty `DataFrame` instead of a `Series` (GH 8222)

- `Series.map()` now respects default values of dictionary subclasses with a `__missing__` method, such as `collections.Counter` (GH 15999)

- `.loc` has compat with `.ix` for accepting iterators, and NamedTuples (GH 15120)

- `interpolate()` and `fillna()` will raise a `ValueError` if the `limit` keyword argument is not greater than 0. (GH 9217)

- `pd.read_csv()` will now issue a `ParserWarning` whenever there are conflicting values provided by the `dialect` parameter and the user (GH 14898)

- `pd.read_csv()` will now raise a `ValueError` for the C engine if the quote character is larger than one byte (GH 11592)

- `inplace` arguments now require a boolean value, else a `ValueError` is thrown (GH 14189)

- `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 (GH 15118)

- Specific support for `copy.copy()` and `copy.deepcopy()` functions on NDFrame objects (GH 15444)

- `Series.sort_values()` accepts a one element list of bool for consistency with the behavior of `DataFrame.sort_values()` (GH 15604)

- `.merge()` and `.join()` on `category` dtype columns will now preserve the category dtype when possible (GH 10409)

- `SparseDataFrame.default_fill_value` will be 0, previously was `nan` in the return from `pd.get_dummies(..., sparse=True)` (GH 15594)

- 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 (GH 5224). 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` (GH 15781)

- `NaT` will now correctly return `np.nan` for `Timedelta` and `Period` accessors such as `days` and `quarter` (GH 15782)

- `NaT` will now returns `NaT` for `tz_localize` and `tz_convert` methods (GH 15830)

- `DataFrame` and `Panel` constructors with invalid input will now raise `ValueError` rather than `PandasError`, if called with scalar inputs and not axes (GH 15541)

- `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. (GH 15541)

- The exception `pandas.core.common.AmbiguousIndexError` is removed as it is not referenced (GH 15541)

## 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 these modules. (GH 12588)

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` (GH 15998)

- The type import `pandas.tslib.NaTType` is deprecated and can be replaced by using `type(pandas.NaT)` (GH 16146)

- The public functions in `pandas.tools.hashing` deprecated from that locations, but are now importable from `pandas.util` (GH 16223)

- 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 (GH 16223)

### `pandas.errors`

We are adding a standard public module for all pandas exceptions & warnings `pandas.errors`. (GH 14800). 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. (GH 15541)

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` (GH 9895). Those functions can be used when writing tests for functionality using pandas objects.

The following testing functions are now part of this API:

- `testing.assert_frame_equal()`

- `testing.assert_series_equal()`

- `testing.assert_index_equal()`

### `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` (GH 14831)

- Require at least 0.23 version of cython to avoid problems with character encodings (GH 14699)

- Switched the test framework to use pytest (GH 13097)

- Reorganization of tests directory layout (GH 14854, GH 15707).

## 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. More specifically, `.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. (GH 14218)

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

    In [102]: df
    Out[102]:
       A  B
    a  1  4
    b  2  5
    c  3  6

    [3 rows x 2 columns]

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 [103]: df.loc[df.index[[0, 2]], 'A']
    Out[103]:
    a    1
    c    3
    Name: A, Length: 2, dtype: int64

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

    In [104]: df.iloc[[0, 2], df.columns.get_loc('A')]
    Out[104]:
    a    1
    c    3
    Name: A, Length: 2, 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 (GH 13563).

    In [133]: import pandas._testing as tm

    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

    [12 rows x 3 columns]

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

    In [106]: df
    Out[106]:
       A  B  C
    0  1  0  0
    1  1  1  1
    2  1  2  2
    3  2  3  3
    4  2  4  4

    [5 rows x 3 columns]

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 [107]: df.groupby('A').agg({'B': 'sum', 'C': 'min'})
    Out[107]:
       B  C
    A
    1  3  0
    2  7  3

    [2 rows x 2 columns]

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 [108]: df.groupby('A').B.agg(['count']).rename(columns={'count': 'foo'})
    Out[108]:
       foo
    A
    1    3
    2    2

    [2 rows x 1 columns]

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 [109]: (df.groupby('A')
       .....:    .agg({'B': 'sum', 'C': 'min'})
       .....:    .rename(columns={'B': 'foo', 'C': 'bar'})
       .....:  )
       .....:
    Out[109]:
       foo  bar
    A
    1    3    0
    2    7    3

    [2 rows x 2 columns]

### 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` (GH 12548).

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 (GH 14647)

- `SparseSeries.to_dense()` has deprecated the `sparse_only` parameter (GH 14647)

- `Series.repeat()` has deprecated the `reps` parameter in favor of `repeats` (GH 12662)

- The `Series` constructor and `.astype` method have deprecated accepting timestamp dtypes without a frequency (e.g. `np.datetime64`) for the `dtype` parameter (GH 15524)

- `Index.repeat()` and `MultiIndex.repeat()` have deprecated the `n` parameter in favor of `repeats` (GH 12662)

- `Categorical.searchsorted()` and `Series.searchsorted()` have deprecated the `v` parameter in favor of `value` (GH 12662)

- `TimedeltaIndex.searchsorted()`, `DatetimeIndex.searchsorted()`, and `PeriodIndex.searchsorted()` have deprecated the `key` parameter in favor of `value` (GH 12662)

- `DataFrame.astype()` has deprecated the `raise_on_error` parameter in favor of `errors` (GH 14878)

- `Series.sortlevel` and `DataFrame.sortlevel` have been deprecated in favor of `Series.sort_index` and `DataFrame.sort_index` (GH 15099)

- importing `concat` from `pandas.tools.merge` has been deprecated in favor of imports from the `pandas` namespace. This should only affect explicit imports (GH 15358)

- `Series/DataFrame/Panel.consolidate()` been deprecated as a public method. (GH 15483)

- The `as_indexer` keyword of `Series.str.match()` has been deprecated (ignored keyword) (GH 15257).

- The following top-level pandas functions have been deprecated and will be removed in a future version (GH 13790, GH 15940)

  - `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` (GH 16042)

## 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 (GH 11308). 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` (GH 13602)

- `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. (GH 11898)

- The `TimeSeries` and `SparseTimeSeries` classes, aliases of `Series` and `SparseSeries`, are removed (GH 10890, GH 15098).

- `Series.is_time_series` is dropped in favor of `Series.index.is_all_dates` (GH 15098)

- The deprecated `irow`, `icol`, `iget` and `iget_value` methods are removed in favor of `iloc` and `iat` as explained here (GH 10711).

- The deprecated `DataFrame.iterkv()` has been removed in favor of `DataFrame.iteritems()` (GH 10711)

- The `Categorical` constructor has dropped the `name` parameter (GH 10632)

- `Categorical` has dropped support for `NaN` categories (GH 10748)

- The `take_last` parameter has been dropped from `duplicated()`, `drop_duplicates()`, `nlargest()`, and `nsmallest()` methods (GH 10236, GH 10792, GH 10920)

- `Series`, `Index`, and `DataFrame` have dropped the `sort` and `order` methods (GH 10726)

- Where clauses in `pytables` are only accepted as strings and expressions types and not other data-types (GH 12027)

- `DataFrame` has dropped the `combineAdd` and `combineMult` methods in favor of `add` and `mul` respectively (GH 10735)

## Performance improvements

- Improved performance of `pd.wide_to_long()` (GH 14779)

- Improved performance of `pd.factorize()` by releasing the GIL with `object` dtype when inferred as strings (GH 14859, GH 16057)

- Improved performance of timeseries plotting with an irregular DatetimeIndex (or with `compat_x=True`) (GH 15073).

- Improved performance of `groupby().cummin()` and `groupby().cummax()` (GH 15048, GH 15109, GH 15561, GH 15635)

- Improved performance and reduced memory when indexing with a `MultiIndex` (GH 15245)

- When reading buffer object in `read_sas()` method without specified format, filepath string is inferred rather than buffer object. (GH 14947)

- Improved performance of `.rank()` for categorical data (GH 15498)

- Improved performance when using `.unstack()` (GH 15503)

- Improved performance of merge/join on `category` columns (GH 10409)

- Improved performance of `drop_duplicates()` on `bool` columns (GH 12963)

- Improve performance of `pd.core.groupby.GroupBy.apply` when the applied function used the `.name` attribute of the group DataFrame (GH 15062).

- Improved performance of `iloc` indexing with a list or array (GH 15504).

- Improved performance of `Series.sort_index()` with a monotonic index (GH 15694)

- Improved performance in `pd.read_csv()` on some platforms with buffered reads (GH 16039)

## Bug fixes

### Conversion

- Bug in `Timestamp.replace` now raises `TypeError` when incorrect argument names are given; previously this raised `ValueError` (GH 15240)

- Bug in `Timestamp.replace` with compat for passing long integers (GH 15030)

- Bug in `Timestamp` returning UTC based time/date attributes when a timezone was provided (GH 13303, GH 6538)

- Bug in `Timestamp` incorrectly localizing timezones during construction (GH 11481, GH 15777)

- Bug in `TimedeltaIndex` addition where overflow was being allowed without error (GH 14816)

- Bug in `TimedeltaIndex` raising a `ValueError` when boolean indexing with `loc` (GH 14946)

- Bug in catching an overflow in `Timestamp` + `Timedelta/Offset` operations (GH 15126)

- Bug in `DatetimeIndex.round()` and `Timestamp.round()` floating point accuracy when rounding by milliseconds or less (GH 14440, GH 15578)

- Bug in `astype()` where `inf` values were incorrectly converted to integers. Now raises error now with `astype()` for Series and DataFrames (GH 14265)

- Bug in `DataFrame(..).apply(to_numeric)` when values are of type decimal.Decimal. (GH 14827)

- Bug in `describe()` when passing a numpy array which does not contain the median to the `percentiles` keyword argument (GH 14908)

- Cleaned up `PeriodIndex` constructor, including raising on floats more consistently (GH 13277)

- Bug in using `__deepcopy__` on empty NDFrame objects (GH 15370)

- Bug in `.replace()` may result in incorrect dtypes. (GH 12747, GH 15765)

- Bug in `Series.replace` and `DataFrame.replace` which failed on empty replacement dicts (GH 15289)

- Bug in `Series.replace` which replaced a numeric by string (GH 15743)

- Bug in `Index` construction with `NaN` elements and integer dtype specified (GH 15187)

- Bug in `Series` construction with a datetimetz (GH 14928)

- Bug in `Series.dt.round()` inconsistent behaviour on `NaT` ‘s with different arguments (GH 14940)

- Bug in `Series` constructor when both `copy=True` and `dtype` arguments are provided (GH 15125)

- Incorrect dtyped `Series` was returned by comparison methods (e.g., `lt`, `gt`, …) against a constant for an empty `DataFrame` (GH 15077)

- Bug in `Series.ffill()` with mixed dtypes containing tz-aware datetimes. (GH 14956)

- Bug in `DataFrame.fillna()` where the argument `downcast` was ignored when fillna value was of type `dict` (GH 15277)

- Bug in `.asfreq()`, where frequency was not set for empty `Series` (GH 14320)

- Bug in `DataFrame` construction with nulls and datetimes in a list-like (GH 15869)

- Bug in `DataFrame.fillna()` with tz-aware datetimes (GH 15855)

- 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 (GH 15941)

- Bug in the return type of `pd.unique` on a `Categorical`, which was returning an ndarray and not a `Categorical` (GH 15903)

- Bug in `Index.to_series()` where the index was not copied (and so mutating later would change the original), (GH 15949)

- Bug in indexing with partial string indexing with a len-1 DataFrame (GH 16071)

- Bug in `Series` construction where passing invalid dtype didn’t raise an error. (GH 15520)

### Indexing

- Bug in `Index` power operations with reversed operands (GH 14973)

- Bug in `DataFrame.sort_values()` when sorting by multiple columns where one column is of type `int64` and contains `NaT` (GH 14922)

- Bug in `DataFrame.reindex()` in which `method` was ignored when passing `columns` (GH 14992)

- Bug in `DataFrame.loc` with indexing a `MultiIndex` with a `Series` indexer (GH 14730, GH 15424)

- Bug in `DataFrame.loc` with indexing a `MultiIndex` with a numpy array (GH 15434)

- Bug in `Series.asof` which raised if the series contained all `np.nan` (GH 15713)

- Bug in `.at` when selecting from a tz-aware column (GH 15822)

- Bug in `Series.where()` and `DataFrame.where()` where array-like conditionals were being rejected (GH 15414)

- Bug in `Series.where()` where TZ-aware data was converted to float representation (GH 15701)

- Bug in `.loc` that would not return the correct dtype for scalar access for a DataFrame (GH 11617)

- Bug in output formatting of a `MultiIndex` when names are integers (GH 12223, GH 15262)

- Bug in `Categorical.searchsorted()` where alphabetical instead of the provided categorical order was used (GH 14522)

- Bug in `Series.iloc` where a `Categorical` object for list-like indexes input was returned, where a `Series` was expected. (GH 14580)

- Bug in `DataFrame.isin` comparing datetimelike to empty frame (GH 15473)

- Bug in `.reset_index()` when an all `NaN` level of a `MultiIndex` would fail (GH 6322)

- Bug in `.reset_index()` when raising error for index name already present in `MultiIndex` columns (GH 16120)

- Bug in creating a `MultiIndex` with tuples and not passing a list of names; this will now raise `ValueError` (GH 15110)

- Bug in the HTML display with a `MultiIndex` and truncation (GH 14882)

- Bug in the display of `.info()` where a qualifier (+) would always be displayed with a `MultiIndex` that contains only non-strings (GH 15245)

- 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` (GH 15787)

- Bug in `DataFrame.sort_index()` and `Series.sort_index()` where `na_position` doesn’t work with a `MultiIndex` (GH 14784, GH 16604)

- Bug in `pd.concat()` when combining objects with a `CategoricalIndex` (GH 16111)

- Bug in indexing with a scalar and a `CategoricalIndex` (GH 16123)

### IO

- Bug in `pd.to_numeric()` in which float and unsigned integer elements were being improperly casted (GH 14941, GH 15005)

- Bug in `pd.read_fwf()` where the skiprows parameter was not being respected during column width inference (GH 11256)

- Bug in `pd.read_csv()` in which the `dialect` parameter was not being verified before processing (GH 14898)

- Bug in `pd.read_csv()` in which missing data was being improperly handled with `usecols` (GH 6710)

- 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 (GH 14125)

- Bug in `pd.read_csv()` for the C engine where `usecols` were being indexed incorrectly with `parse_dates` (GH 14792)

- Bug in `pd.read_csv()` with `parse_dates` when multi-line headers are specified (GH 15376)

- Bug in `pd.read_csv()` with `float_precision='round_trip'` which caused a segfault when a text entry is parsed (GH 15140)

- Bug in `pd.read_csv()` when an index was specified and no values were specified as null values (GH 15835)

- Bug in `pd.read_csv()` in which certain invalid file objects caused the Python interpreter to crash (GH 15337)

- Bug in `pd.read_csv()` in which invalid values for `nrows` and `chunksize` were allowed (GH 15767)

- Bug in `pd.read_csv()` for the Python engine in which unhelpful error messages were being raised when parsing errors occurred (GH 15910)

- Bug in `pd.read_csv()` in which the `skipfooter` parameter was not being properly validated (GH 15925)

- Bug in `pd.to_csv()` in which there was numeric overflow when a timestamp index was being written (GH 15982)

- Bug in `pd.util.hashing.hash_pandas_object()` in which hashing of categoricals depended on the ordering of categories, instead of just their values. (GH 15143)

- Bug in `.to_json()` where `lines=True` and contents (keys or values) contain escaped characters (GH 15096)

- Bug in `.to_json()` causing single byte ascii characters to be expanded to four byte unicode (GH 15344)

- 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 (GH 15716, GH 15864)

- Bug in `pd.read_json()` for Python 2 where `lines=True` and contents contain non-ascii unicode characters (GH 15132)

- Bug in `pd.read_msgpack()` in which `Series` categoricals were being improperly processed (GH 14901)

- Bug in `pd.read_msgpack()` which did not allow loading of a dataframe with an index of type `CategoricalIndex` (GH 15487)

- Bug in `pd.read_msgpack()` when deserializing a `CategoricalIndex` (GH 15487)

- Bug in `DataFrame.to_records()` with converting a `DatetimeIndex` with a timezone (GH 13937)

- Bug in `DataFrame.to_records()` which failed with unicode characters in column names (GH 11879)

- Bug in `.to_sql()` when writing a DataFrame with numeric index names (GH 15404).

- Bug in `DataFrame.to_html()` with `index=False` and `max_rows` raising in `IndexError` (GH 14998)

- Bug in `pd.read_hdf()` passing a `Timestamp` to the `where` parameter with a non date column (GH 15492)

- Bug in `DataFrame.to_stata()` and `StataWriter` which produces incorrectly formatted files to be produced for some locales (GH 13856)

- Bug in `StataReader` and `StataWriter` which allows invalid encodings (GH 15723)

- Bug in the `Series` repr not showing the length when the output was truncated (GH 15962).

### Plotting

- Bug in `DataFrame.hist` where `plt.tight_layout` caused an `AttributeError` (use `matplotlib >= 2.0.1`) (GH 9351)

- Bug in `DataFrame.boxplot` where `fontsize` was not applied to the tick labels on both axes (GH 15108)

- Bug in the date and time converters pandas registers with matplotlib not handling multiple dimensions (GH 16026)

- Bug in `pd.scatter_matrix()` could accept either `color` or `c`, but not both (GH 14855)

### GroupBy/resample/rolling

- Bug in `.groupby(..).resample()` when passed the `on=` kwarg. (GH 15021)

- Properly set `__name__` and `__qualname__` for `Groupby.*` functions (GH 14620)

- Bug in `GroupBy.get_group()` failing with a categorical grouper (GH 15155)

- Bug in `.groupby(...).rolling(...)` when `on` is specified and using a `DatetimeIndex` (GH 15130, GH 13966)

- Bug in groupby operations with `timedelta64` when passing `numeric_only=False` (GH 5724)

- Bug in `groupby.apply()` coercing `object` dtypes to numeric types, when not all values were numeric (GH 14423, GH 15421, GH 15670)

- Bug in `resample`, where a non-string `loffset` argument would not be applied when resampling a timeseries (GH 13218)

- Bug in `DataFrame.groupby().describe()` when grouping on `Index` containing tuples (GH 14848)

- Bug in `groupby().nunique()` with a datetimelike-grouper where bins counts were incorrect (GH 13453)

- Bug in `groupby.transform()` that would coerce the resultant dtypes back to the original (GH 10972, GH 11444)

- Bug in `groupby.agg()` incorrectly localizing timezone on `datetime` (GH 15426, GH 10668, GH 13046)

- Bug in `.rolling/expanding()` functions where `count()` was not counting `np.Inf`, nor handling `object` dtypes (GH 12541)

- Bug in `.rolling()` where `pd.Timedelta` or `datetime.timedelta` was not accepted as a `window` argument (GH 15440)

- Bug in `Rolling.quantile` function that caused a segmentation fault when called with a quantile value outside of the range [0, 1] (GH 15463)

- Bug in `DataFrame.resample().median()` if duplicate column names are present (GH 14233)

### Sparse

- Bug in `SparseSeries.reindex` on single level with list of length 1 (GH 15447)

- Bug in repr-formatting a `SparseDataFrame` after a value was set on (a copy of) one of its series (GH 15488)

- Bug in `SparseDataFrame` construction with lists not coercing to dtype (GH 15682)

- Bug in sparse array indexing in which indices were not being validated (GH 15863)

### Reshaping

- Bug in `pd.merge_asof()` where `left_index` or `right_index` caused a failure when multiple `by` was specified (GH 15676)

- Bug in `pd.merge_asof()` where `left_index`/`right_index` together caused a failure when `tolerance` was specified (GH 15135)

- Bug in `DataFrame.pivot_table()` where `dropna=True` would not drop all-NaN columns when the columns was a `category` dtype (GH 15193)

- Bug in `pd.melt()` where passing a tuple value for `value_vars` caused a `TypeError` (GH 15348)

- Bug in `pd.pivot_table()` where no error was raised when values argument was not in the columns (GH 14938)

- Bug in `pd.concat()` in which concatenating with an empty dataframe with `join='inner'` was being improperly handled (GH 15328)

- Bug with `sort=True` in `DataFrame.join` and `pd.merge` when joining on indexes (GH 15582)

- Bug in `DataFrame.nsmallest` and `DataFrame.nlargest` where identical values resulted in duplicated rows (GH 15297)

- Bug in `pandas.pivot_table()` incorrectly raising `UnicodeError` when passing unicode input for `margins` keyword (GH 13292)

### Numeric

- Bug in `.rank()` which incorrectly ranks ordered categories (GH 15420)

- Bug in `.corr()` and `.cov()` where the column and index were the same object (GH 14617)

- Bug in `.mode()` where `mode` was not returned if was only a single value (GH 15714)

- Bug in `pd.cut()` with a single bin on an all 0s array (GH 15428)

- Bug in `pd.qcut()` with a single quantile and an array with identical values (GH 15431)

- Bug in `pandas.tools.utils.cartesian_product()` with large input can cause overflow on windows (GH 15265)

- Bug in `.eval()` which caused multi-line evals to fail with local variables not on the first line (GH 15342)

### Other

- Compat with SciPy 0.19.0 for testing on `.interpolate()` (GH 15662)

- Compat for 32-bit platforms for `.qcut/cut`; bins will now be `int64` dtype (GH 14866)

- Bug in interactions with `Qt` when a `QtApplication` already exists (GH 14372)

- Avoid use of `np.finfo()` during `import pandas` removed to mitigate deadlock on Python GIL misuse (GH 14641)

## Contributors

A total of 204 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

- Adam J. Stewart +

- Adrian +

- Ajay Saxena

- Akash Tandon +

- Albert Villanova del Moral +

- Aleksey Bilogur +

- Alexis Mignon +

- Amol Kahat +

- Andreas Winkler +

- Andrew Kittredge +

- Anthonios Partheniou

- Arco Bast +

- Ashish Singal +

- Baurzhan Muftakhidinov +

- Ben Kandel

- Ben Thayer +

- Ben Welsh +

- Bill Chambers +

- Brandon M. Burroughs

- Brian +

- Brian McFee +

- Carlos Souza +

- Chris

- Chris Ham

- Chris Warth

- Christoph Gohlke

- Christoph Paulik +

- Christopher C. Aycock

- Clemens Brunner +

- D.S. McNeil +

- DaanVanHauwermeiren +

- Daniel Himmelstein

- Dave Willmer

- David Cook +

- David Gwynne +

- David Hoffman +

- David Krych

- Diego Fernandez +

- Dimitris Spathis +

- Dmitry L +

- Dody Suria Wijaya +

- Dominik Stanczak +

- Dr-Irv

- Dr. Irv +

- Elliott Sales de Andrade +

- Ennemoser Christoph +

- Francesc Alted +

- Fumito Hamamura +

- Giacomo Ferroni

- Graham R. Jeffries +

- Greg Williams +

- Guilherme Beltramini +

- Guilherme Samora +

- Hao Wu +

- Harshit Patni +

- Ilya V. Schurov +

- Iván Vallés Pérez

- Jackie Leng +

- Jaehoon Hwang +

- James Draper +

- James Goppert +

- James McBride +

- James Santucci +

- Jan Schulz

- Jeff Carey

- Jeff Reback

- JennaVergeynst +

- Jim +

- Jim Crist

- Joe Jevnik

- Joel Nothman +

- John +

- John Tucker +

- John W. O’Brien

- John Zwinck

- Jon M. Mease

- Jon Mease

- Jonathan Whitmore +

- Jonathan de Bruin +

- Joost Kranendonk +

- Joris Van den Bossche

- Joshua Bradt +

- Julian Santander

- Julien Marrec +

- Jun Kim +

- Justin Solinsky +

- Kacawi +

- Kamal Kamalaldin +

- Kerby Shedden

- Kernc

- Keshav Ramaswamy

- Kevin Sheppard

- Kyle Kelley

- Larry Ren

- Leon Yin +

- Line Pedersen +

- Lorenzo Cestaro +

- Luca Scarabello

- Lukasz +

- Mahmoud Lababidi

- Mark Mandel +

- Matt Roeschke

- Matthew Brett

- Matthew Roeschke +

- Matti Picus

- Maximilian Roos

- Michael Charlton +

- Michael Felt

- Michael Lamparski +

- Michiel Stock +

- Mikolaj Chwalisz +

- Min RK

- Miroslav Šedivý +

- Mykola Golubyev

- Nate Yoder

- Nathalie Rud +

- Nicholas Ver Halen

- Nick Chmura +

- Nolan Nichols +

- Pankaj Pandey +

- Pawel Kordek

- Pete Huang +

- Peter +

- Peter Csizsek +

- Petio Petrov +

- Phil Ruffwind +

- Pietro Battiston

- Piotr Chromiec

- Prasanjit Prakash +

- Rob Forgione +

- Robert Bradshaw

- Robin +

- Rodolfo Fernandez

- Roger Thomas

- Rouz Azari +

- Sahil Dua

- Sam Foo +

- Sami Salonen +

- Sarah Bird +

- Sarma Tangirala +

- Scott Sanderson

- Sebastian Bank

- Sebastian Gsänger +

- Shawn Heide

- Shyam Saladi +

- Sinhrks

- Stephen Rauch +

- Sébastien de Menten +

- Tara Adiseshan

- Thiago Serafim

- Thoralf Gutierrez +

- Thrasibule +

- Tobias Gustafsson +

- Tom Augspurger

- Tong SHEN +

- Tong Shen +

- TrigonaMinima +

- Uwe +

- Wes Turner

- Wiktor Tomczak +

- WillAyd

- Yaroslav Halchenko

- Yimeng Zhang +

- abaldenko +

- adrian-stepien +

- alexandercbooth +

- atbd +

- bastewart +

- bmagnusson +

- carlosdanielcsantos +

- chaimdemulder +

- chris-b1

- dickreuter +

- discort +

- dr-leo +

- dubourg

- dwkenefick +

- funnycrab +

- gfyoung

- goldenbull +

- hesham.shabana@hotmail.com

- jojomdt +

- linebp +

- manu +

- manuels +

- mattip +

- maxalbert +

- mcocdawc +

- nuffe +

- paul-mannino

- pbreach +

- sakkemo +

- scls19fr

- sinhrks

- stijnvanhoey +

- the-nose-knows +

- themrmax +

- tomrod +

- tzinckgraf

- wandersoncferreira

- watercrossing +

- wcwagner

- xgdgsc +

- yui-knk