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# What’s new in 1.4.0 (January 22, 2022)

These are the changes in pandas 1.4.0. See Release notes for a full changelog including other versions of pandas.

## Enhancements

### Improved warning messages

Previously, warning messages may have pointed to lines within the pandas library. Running the script `setting_with_copy_warning.py`

    import pandas as pd

    df = pd.DataFrame({'a': [1, 2, 3]})
    df[:2].loc[:, 'a'] = 5

with pandas 1.3 resulted in:

    .../site-packages/pandas/core/indexing.py:1951: SettingWithCopyWarning:
    A value is trying to be set on a copy of a slice from a DataFrame.

This made it difficult to determine where the warning was being generated from. Now pandas will inspect the call stack, reporting the first line outside of the pandas library that gave rise to the warning. The output of the above script is now:

    setting_with_copy_warning.py:4: SettingWithCopyWarning:
    A value is trying to be set on a copy of a slice from a DataFrame.

### Index can hold arbitrary ExtensionArrays

Until now, passing a custom `ExtensionArray` to `pd.Index` would cast the array to `object` dtype. Now `Index` can directly hold arbitrary ExtensionArrays (GH 43930).

Previous behavior:

    In [1]: arr = pd.array([1, 2, pd.NA])

    In [2]: idx = pd.Index(arr)

In the old behavior, `idx` would be object-dtype:

Previous behavior:

    In [1]: idx
    Out[1]: Index([1, 2, <NA>], dtype='object')

With the new behavior, we keep the original dtype:

New behavior:

    In [3]: idx
    Out[3]: Index([1, 2, <NA>], dtype='Int64')

One exception to this is `SparseArray`, which will continue to cast to numpy dtype until pandas 2.0. At that point it will retain its dtype like other ExtensionArrays.

### Styler

`Styler` has been further developed in 1.4.0. The following general enhancements have been made:

> - Styling and formatting of indexes has been added, with `Styler.apply_index()`, `Styler.applymap_index()` and `Styler.format_index()`. These mirror the signature of the methods already used to style and format data values, and work with both HTML, LaTeX and Excel format (GH 41893, GH 43101, GH 41993, GH 41995)

> - The new method `Styler.hide()` deprecates `Styler.hide_index()` and `Styler.hide_columns()` (GH 43758)

> - The keyword arguments `level` and `names` have been added to `Styler.hide()` (and implicitly to the deprecated methods `Styler.hide_index()` and `Styler.hide_columns()`) for additional control of visibility of MultiIndexes and of Index names (GH 25475, GH 43404, GH 43346)

> - The `Styler.export()` and `Styler.use()` have been updated to address all of the added functionality from v1.2.0 and v1.3.0 (GH 40675)

> - Global options under the category `pd.options.styler` have been extended to configure default `Styler` properties which address formatting, encoding, and HTML and LaTeX rendering. Note that formerly `Styler` relied on `display.html.use_mathjax`, which has now been replaced by `styler.html.mathjax` (GH 41395)

> - Validation of certain keyword arguments, e.g. `caption` (GH 43368)

> - Various bug fixes as recorded below

Additionally there are specific enhancements to the HTML specific rendering:

> - `Styler.bar()` introduces additional arguments to control alignment and display (GH 26070, GH 36419), and it also validates the input arguments `width` and `height` (GH 42511)

> - `Styler.to_html()` introduces keyword arguments `sparse_index`, `sparse_columns`, `bold_headers`, `caption`, `max_rows` and `max_columns` (GH 41946, GH 43149, GH 42972)

> - `Styler.to_html()` omits CSSStyle rules for hidden table elements as a performance enhancement (GH 43619)

> - Custom CSS classes can now be directly specified without string replacement (GH 43686)

> - Ability to render hyperlinks automatically via a new `hyperlinks` formatting keyword argument (GH 45058)

There are also some LaTeX specific enhancements:

> - `Styler.to_latex()` introduces keyword argument `environment`, which also allows a specific “longtable” entry through a separate jinja2 template (GH 41866)

> - Naive sparsification is now possible for LaTeX without the necessity of including the multirow package (GH 43369)

> - cline support has been added for `MultiIndex` row sparsification through a keyword argument (GH 45138)

### Multi-threaded CSV reading with a new CSV Engine based on pyarrow

`pandas.read_csv()` now accepts `engine="pyarrow"` (requires at least `pyarrow` 1.0.1) as an argument, allowing for faster csv parsing on multicore machines with pyarrow installed. See the I/O docs for more info. (GH 23697, GH 43706)

### Rank function for rolling and expanding windows

Added `rank` function to `Rolling` and `Expanding`. The new function supports the `method`, `ascending`, and `pct` flags of `DataFrame.rank()`. The `method` argument supports `min`, `max`, and `average` ranking methods. Example:

    In [4]: s = pd.Series([1, 4, 2, 3, 5, 3])

    In [5]: s.rolling(3).rank()
    Out[5]:
    0    NaN
    1    NaN
    2    2.0
    3    2.0
    4    3.0
    5    1.5
    dtype: float64

    In [6]: s.rolling(3).rank(method="max")
    Out[6]:
    0    NaN
    1    NaN
    2    2.0
    3    2.0
    4    3.0
    5    2.0
    dtype: float64

### Groupby positional indexing

It is now possible to specify positional ranges relative to the ends of each group.

Negative arguments for `DataFrameGroupBy.head()`, `SeriesGroupBy.head()`, `DataFrameGroupBy.tail()`, and `SeriesGroupBy.tail()` now work correctly and result in ranges relative to the end and start of each group, respectively. Previously, negative arguments returned empty frames.

    In [7]: df = pd.DataFrame([["g", "g0"], ["g", "g1"], ["g", "g2"], ["g", "g3"],
       ...:                    ["h", "h0"], ["h", "h1"]], columns=["A", "B"])
       ...:

    In [8]: df.groupby("A").head(-1)
    Out[8]:
       A   B
    0  g  g0
    1  g  g1
    2  g  g2
    4  h  h0

`DataFrameGroupBy.nth()` and `SeriesGroupBy.nth()` now accept a slice or list of integers and slices.

    In [9]: df.groupby("A").nth(slice(1, -1))
    Out[9]:
       A   B
    1  g  g1
    2  g  g2

    In [10]: df.groupby("A").nth([slice(None, 1), slice(-1, None)])
    Out[10]:
       A   B
    0  g  g0
    3  g  g3
    4  h  h0
    5  h  h1

`DataFrameGroupBy.nth()` and `SeriesGroupBy.nth()` now accept index notation.

    In [11]: df.groupby("A").nth[1, -1]
    Out[11]:
       A   B
    1  g  g1
    3  g  g3
    5  h  h1

    In [12]: df.groupby("A").nth[1:-1]
    Out[12]:
       A   B
    1  g  g1
    2  g  g2

    In [13]: df.groupby("A").nth[:1, -1:]
    Out[13]:
       A   B
    0  g  g0
    3  g  g3
    4  h  h0
    5  h  h1

### DataFrame.from_dict and DataFrame.to_dict have new `'tight'` option

A new `'tight'` dictionary format that preserves `MultiIndex` entries and names is now available with the `DataFrame.from_dict()` and `DataFrame.to_dict()` methods and can be used with the standard `json` library to produce a tight representation of `DataFrame` objects (GH 4889).

    In [14]: df = pd.DataFrame.from_records(
       ....:     [[1, 3], [2, 4]],
       ....:     index=pd.MultiIndex.from_tuples([("a", "b"), ("a", "c")],
       ....:                                     names=["n1", "n2"]),
       ....:     columns=pd.MultiIndex.from_tuples([("x", 1), ("y", 2)],
       ....:                                       names=["z1", "z2"]),
       ....: )
       ....:

    In [15]: df
    Out[15]:
    z1     x  y
    z2     1  2
    n1 n2
    a  b   1  3
       c   2  4

    In [16]: df.to_dict(orient='tight')
    Out[16]:
    {'index': [('a', 'b'), ('a', 'c')],
     'columns': [('x', 1), ('y', 2)],
     'data': [[1, 3], [2, 4]],
     'index_names': ['n1', 'n2'],
     'column_names': ['z1', 'z2']}

### Other enhancements

- `concat()` will preserve the `attrs` when it is the same for all objects and discard the `attrs` when they are different (GH 41828)

- `DataFrameGroupBy` operations with `as_index=False` now correctly retain `ExtensionDtype` dtypes for columns being grouped on (GH 41373)

- Add support for assigning values to `by` argument in `DataFrame.plot.hist()` and `DataFrame.plot.box()` (GH 15079)

- `Series.sample()`, `DataFrame.sample()`, `DataFrameGroupBy.sample()`, and `SeriesGroupBy.sample()` now accept a `np.random.Generator` as input to `random_state`. A generator will be more performant, especially with `replace=False` (GH 38100)

- `Series.ewm()` and `DataFrame.ewm()` now support a `method` argument with a `'table'` option that performs the windowing operation over an entire `DataFrame`. See Window Overview for performance and functional benefits (GH 42273)

- `DataFrameGroupBy.cummin()`, `SeriesGroupBy.cummin()`, `DataFrameGroupBy.cummax()`, and `SeriesGroupBy.cummax()` now support the argument `skipna` (GH 34047)

- `read_table()` now supports the argument `storage_options` (GH 39167)

- `DataFrame.to_stata()` and `StataWriter()` now accept the keyword only argument `value_labels` to save labels for non-categorical columns (GH 38454)

- Methods that relied on hashmap based algos such as `DataFrameGroupBy.value_counts()`, `DataFrameGroupBy.count()` and `factorize()` ignored imaginary component for complex numbers (GH 17927)

- Add `Series.str.removeprefix()` and `Series.str.removesuffix()` introduced in Python 3.9 to remove pre-/suffixes from string-type `Series` (GH 36944)

- Attempting to write into a file in missing parent directory with `DataFrame.to_csv()`, `DataFrame.to_html()`, `DataFrame.to_excel()`, `DataFrame.to_feather()`, `DataFrame.to_parquet()`, `DataFrame.to_stata()`, `DataFrame.to_json()`, `DataFrame.to_pickle()`, and `DataFrame.to_xml()` now explicitly mentions missing parent directory, the same is true for `Series` counterparts (GH 24306)

- Indexing with `.loc` and `.iloc` now supports `Ellipsis` (GH 37750)

- `IntegerArray.all()` , `IntegerArray.any()`, `FloatingArray.any()`, and `FloatingArray.all()` use Kleene logic (GH 41967)

- Added support for nullable boolean and integer types in `DataFrame.to_stata()`, `StataWriter`, `StataWriter117`, and `StataWriterUTF8` (GH 40855)

- `DataFrame.__pos__()` and `DataFrame.__neg__()` now retain `ExtensionDtype` dtypes (GH 43883)

- The error raised when an optional dependency can’t be imported now includes the original exception, for easier investigation (GH 43882)

- Added `ExponentialMovingWindow.sum()` (GH 13297)

- `Series.str.split()` now supports a `regex` argument that explicitly specifies whether the pattern is a regular expression. Default is `None` (GH 43563, GH 32835, GH 25549)

- `DataFrame.dropna()` now accepts a single label as `subset` along with array-like (GH 41021)

- Added `DataFrameGroupBy.value_counts()` (GH 43564)

- `read_csv()` now accepts a `callable` function in `on_bad_lines` when `engine="python"` for custom handling of bad lines (GH 5686)

- `ExcelWriter` argument `if_sheet_exists="overlay"` option added (GH 40231)

- `read_excel()` now accepts a `decimal` argument that allow the user to specify the decimal point when parsing string columns to numeric (GH 14403)

- `DataFrameGroupBy.mean()`, `SeriesGroupBy.mean()`, `DataFrameGroupBy.std()`, `SeriesGroupBy.std()`, `DataFrameGroupBy.var()`, `SeriesGroupBy.var()`, `DataFrameGroupBy.sum()`, and `SeriesGroupBy.sum()` now support Numba execution with the `engine` keyword (GH 43731, GH 44862, GH 44939)

- `Timestamp.isoformat()` now handles the `timespec` argument from the base `datetime` class (GH 26131)

- `NaT.to_numpy()` `dtype` argument is now respected, so `np.timedelta64` can be returned (GH 44460)

- New option `display.max_dir_items` customizes the number of columns added to `Dataframe.__dir__()` and suggested for tab completion (GH 37996)

- Added “Juneteenth National Independence Day” to `USFederalHolidayCalendar` (GH 44574)

- `Rolling.var()`, `Expanding.var()`, `Rolling.std()`, and `Expanding.std()` now support Numba execution with the `engine` keyword (GH 44461)

- `Series.info()` has been added, for compatibility with `DataFrame.info()` (GH 5167)

- Implemented `IntervalArray.min()` and `IntervalArray.max()`, as a result of which `min` and `max` now work for `IntervalIndex`, `Series` and `DataFrame` with `IntervalDtype` (GH 44746)

- `UInt64Index.map()` now retains `dtype` where possible (GH 44609)

- `read_json()` can now parse unsigned long long integers (GH 26068)

- `DataFrame.take()` now raises a `TypeError` when passed a scalar for the indexer (GH 42875)

- `is_list_like()` now identifies duck-arrays as list-like unless `.ndim == 0` (GH 35131)

- `ExtensionDtype` and `ExtensionArray` are now (de)serialized when exporting a `DataFrame` with `DataFrame.to_json()` using `orient='table'` (GH 20612, GH 44705)

- Add support for Zstandard compression to `DataFrame.to_pickle()`/`read_pickle()` and friends (GH 43925)

- `DataFrame.to_sql()` now returns an `int` of the number of written rows (GH 23998)

## Notable bug fixes

These are bug fixes that might have notable behavior changes.

### Inconsistent date string parsing

The `dayfirst` option of `to_datetime()` isn’t strict, and this can lead to surprising behavior:

    In [17]: pd.to_datetime(["31-12-2021"], dayfirst=False)
    Out[17]: DatetimeIndex(['2021-12-31'], dtype='datetime64[us]', freq=None)

Now, a warning will be raised if a date string cannot be parsed accordance to the given `dayfirst` value when the value is a delimited date string (e.g. `31-12-2012`).

### Ignoring dtypes in concat with empty or all-NA columns

Note

This behaviour change has been reverted in pandas 1.4.3.

When using `concat()` to concatenate two or more `DataFrame` objects, if one of the DataFrames was empty or had all-NA values, its dtype was sometimes ignored when finding the concatenated dtype. These are now consistently not ignored (GH 43507).

    In [3]: df1 = pd.DataFrame({"bar": [pd.Timestamp("2013-01-01")]}, index=range(1))
    In [4]: df2 = pd.DataFrame({"bar": np.nan}, index=range(1, 2))
    In [5]: res = pd.concat([df1, df2])

Previously, the float-dtype in `df2` would be ignored so the result dtype would be `datetime64[ns]`. As a result, the `np.nan` would be cast to `NaT`.

Previous behavior:

    In [6]: res
    Out[6]:
             bar
    0 2013-01-01
    1        NaT

Now the float-dtype is respected. Since the common dtype for these DataFrames is object, the `np.nan` is retained.

New behavior:

    In [6]: res
    Out[6]:
                       bar
    0  2013-01-01 00:00:00
    1                  NaN

### Null-values are no longer coerced to NaN-value in value_counts and mode

`Series.value_counts()` and `Series.mode()` no longer coerce `None`, `NaT` and other null-values to a NaN-value for `np.object_`-dtype. This behavior is now consistent with `unique`, `isin` and others (GH 42688).

    In [18]: s = pd.Series([True, None, pd.NaT, None, pd.NaT, None])

    In [19]: res = s.value_counts(dropna=False)

Previously, all null-values were replaced by a NaN-value.

Previous behavior:

    In [3]: res
    Out[3]:
    NaN     5
    True    1
    dtype: int64

Now null-values are no longer mangled.

New behavior:

    In [20]: res
    Out[20]:
    None    3
    NaT     2
    True    1
    Name: count, dtype: int64

### mangle_dupe_cols in read_csv no longer renames unique columns conflicting with target names

`read_csv()` no longer renames unique column labels which conflict with the target names of duplicated columns. Already existing columns are skipped, i.e. the next available index is used for the target column name (GH 14704).

    In [21]: import io

    In [22]: data = "a,a,a.1\n1,2,3"

    In [23]: res = pd.read_csv(io.StringIO(data))

Previously, the second column was called `a.1`, while the third column was also renamed to `a.1.1`.

Previous behavior:

    In [3]: res
    Out[3]:
        a  a.1  a.1.1
    0   1    2      3

Now the renaming checks if `a.1` already exists when changing the name of the second column and jumps this index. The second column is instead renamed to `a.2`.

New behavior:

    In [24]: res
    Out[24]:
       a  a.2  a.1
    0  1    2    3

### unstack and pivot_table no longer raises ValueError for result that would exceed int32 limit

Previously `DataFrame.pivot_table()` and `DataFrame.unstack()` would raise a `ValueError` if the operation could produce a result with more than `2**31 - 1` elements. This operation now raises a `errors.PerformanceWarning` instead (GH 26314).

Previous behavior:

    In [3]: df = DataFrame({"ind1": np.arange(2 ** 16), "ind2": np.arange(2 ** 16), "count": 0})
    In [4]: df.pivot_table(index="ind1", columns="ind2", values="count", aggfunc="count")
    ValueError: Unstacked DataFrame is too big, causing int32 overflow

New behavior:

    In [4]: df.pivot_table(index="ind1", columns="ind2", values="count", aggfunc="count")
    PerformanceWarning: The following operation may generate 4294967296 cells in the resulting pandas object.

### groupby.apply consistent transform detection

`DataFrameGroupBy.apply()` and `SeriesGroupBy.apply()` are designed to be flexible, allowing users to perform aggregations, transformations, filters, and use it with user-defined functions that might not fall into any of these categories. As part of this, apply will attempt to detect when an operation is a transform, and in such a case, the result will have the same index as the input. In order to determine if the operation is a transform, pandas compares the input’s index to the result’s and determines if it has been mutated. Previously in pandas 1.3, different code paths used different definitions of “mutated”: some would use Python’s `is` whereas others would test only up to equality.

This inconsistency has been removed, pandas now tests up to equality.

    In [25]: def func(x):
       ....:     return x.copy()
       ....:

    In [26]: df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})

    In [27]: df
    Out[27]:
       a  b  c
    0  1  3  5
    1  2  4  6

Previous behavior:

    In [3]: df.groupby(['a']).apply(func)
    Out[3]:
         a  b  c
    a
    1 0  1  3  5
    2 1  2  4  6

    In [4]: df.set_index(['a', 'b']).groupby(['a']).apply(func)
    Out[4]:
         c
    a b
    1 3  5
    2 4  6

In the examples above, the first uses a code path where pandas uses `is` and determines that `func` is not a transform whereas the second tests up to equality and determines that `func` is a transform. In the first case, the result’s index is not the same as the input’s.

New behavior:

    In [5]: df.groupby(['a']).apply(func)
    Out[5]:
       a  b  c
    0  1  3  5
    1  2  4  6

    In [6]: df.set_index(['a', 'b']).groupby(['a']).apply(func)
    Out[6]:
         c
    a b
    1 3  5
    2 4  6

Now in both cases it is determined that `func` is a transform. In each case, the result has the same index as the input.

## Backwards incompatible API changes

### Increased minimum version for Python

pandas 1.4.0 supports Python 3.8 and higher.

### Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated. If installed, we now require:

Package | Minimum Version | Required | Changed
--- | --- | --- | ---
numpy | 1.18.5 | X | X
pytz | 2020.1 | X | X
python-dateutil | 2.8.1 | X | X
bottleneck | 1.3.1 |  | X
numexpr | 2.7.1 |  | X
pytest (dev) | 6.0 |  |
mypy (dev) | 0.930 |  | X

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package | Minimum Version | Changed
--- | --- | ---
beautifulsoup4 | 4.8.2 | X
fastparquet | 0.4.0 |
fsspec | 0.7.4 |
gcsfs | 0.6.0 |
lxml | 4.5.0 | X
matplotlib | 3.3.2 | X
numba | 0.50.1 | X
openpyxl | 3.0.3 | X
pandas-gbq | 0.14.0 | X
pyarrow | 1.0.1 | X
pymysql | 0.10.1 | X
pytables | 3.6.1 | X
s3fs | 0.4.0 |
scipy | 1.4.1 | X
sqlalchemy | 1.4.0 | X
tabulate | 0.8.7 |
xarray | 0.15.1 | X
xlrd | 2.0.1 | X
xlsxwriter | 1.2.2 | X
xlwt | 1.3.0 |

See Dependencies and Optional dependencies for more.

### Other API changes

- `Index.get_indexer_for()` no longer accepts keyword arguments (other than `target`); in the past these would be silently ignored if the index was not unique (GH 42310)

- Change in the position of the `min_rows` argument in `DataFrame.to_string()` due to change in the docstring (GH 44304)

- Reduction operations for `DataFrame` or `Series` now raising a `ValueError` when `None` is passed for `skipna` (GH 44178)

- `read_csv()` and `read_html()` no longer raising an error when one of the header rows consists only of `Unnamed:` columns (GH 13054)

- Changed the `name` attribute of several holidays in `USFederalHolidayCalendar` to match official federal holiday names specifically:

  > - “New Year’s Day” gains the possessive apostrophe

  > - “Presidents Day” becomes “Washington’s Birthday”

  > - “Martin Luther King Jr. Day” is now “Birthday of Martin Luther King, Jr.”

  > - “July 4th” is now “Independence Day”

  > - “Thanksgiving” is now “Thanksgiving Day”

  > - “Christmas” is now “Christmas Day”

  > - Added “Juneteenth National Independence Day”

## Deprecations

### Deprecated Int64Index, UInt64Index & Float64Index

`Int64Index`, `UInt64Index` and `Float64Index` have been deprecated in favor of the base `Index` class and will be removed in pandas 2.0 (GH 43028).

For constructing a numeric index, you can use the base `Index` class instead specifying the data type (which will also work on older pandas releases):

    # replace
    pd.Int64Index([1, 2, 3])
    # with
    pd.Index([1, 2, 3], dtype="int64")

For checking the data type of an index object, you can replace `isinstance` checks with checking the `dtype`:

    # replace
    isinstance(idx, pd.Int64Index)
    # with
    idx.dtype == "int64"

Currently, in order to maintain backward compatibility, calls to `Index` will continue to return `Int64Index`, `UInt64Index` and `Float64Index` when given numeric data, but in the future, an `Index` will be returned.

Current behavior:

    In [1]: pd.Index([1, 2, 3], dtype="int32")
    Out [1]: Int64Index([1, 2, 3], dtype='int64')
    In [1]: pd.Index([1, 2, 3], dtype="uint64")
    Out [1]: UInt64Index([1, 2, 3], dtype='uint64')

Future behavior:

    In [3]: pd.Index([1, 2, 3], dtype="int32")
    Out [3]: Index([1, 2, 3], dtype='int32')
    In [4]: pd.Index([1, 2, 3], dtype="uint64")
    Out [4]: Index([1, 2, 3], dtype='uint64')

### Deprecated DataFrame.append and Series.append

`DataFrame.append()` and `Series.append()` have been deprecated and will be removed in a future version. Use `pandas.concat()` instead (GH 35407).

Deprecated syntax

    In [1]: pd.Series([1, 2]).append(pd.Series([3, 4]))
    Out [1]:
    <stdin>:1: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
    0    1
    1    2
    0    3
    1    4
    dtype: int64

    In [2]: df1 = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
    In [3]: df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
    In [4]: df1.append(df2)
    Out [4]:
    <stdin>:1: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
       A  B
    0  1  2
    1  3  4
    0  5  6
    1  7  8

Recommended syntax

    In [28]: pd.concat([pd.Series([1, 2]), pd.Series([3, 4])])
    Out[28]:
    0    1
    1    2
    0    3
    1    4
    dtype: int64

    In [29]: df1 = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

    In [30]: df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))

    In [31]: pd.concat([df1, df2])
    Out[31]:
       A  B
    0  1  2
    1  3  4
    0  5  6
    1  7  8

### Other Deprecations

- Deprecated `Index.is_type_compatible()` (GH 42113)

- Deprecated `method` argument in `Index.get_loc()`, use `index.get_indexer([label], method=...)` instead (GH 42269)

- Deprecated treating integer keys in `Series.__setitem__()` as positional when the index is a `Float64Index` not containing the key, a `IntervalIndex` with no entries containing the key, or a `MultiIndex` with leading `Float64Index` level not containing the key (GH 33469)

- Deprecated treating `numpy.datetime64` objects as UTC times when passed to the `Timestamp` constructor along with a timezone. In a future version, these will be treated as wall-times. To retain the old behavior, use `Timestamp(dt64).tz_localize("UTC").tz_convert(tz)` (GH 24559)

- Deprecated ignoring missing labels when indexing with a sequence of labels on a level of a `MultiIndex` (GH 42351)

- Creating an empty `Series` without a `dtype` will now raise a more visible `FutureWarning` instead of a `DeprecationWarning` (GH 30017)

- Deprecated the `kind` argument in `Index.get_slice_bound()`, `Index.slice_indexer()`, and `Index.slice_locs()`; in a future version passing `kind` will raise (GH 42857)

- Deprecated dropping of nuisance columns in `Rolling`, `Expanding`, and `EWM` aggregations (GH 42738)

- Deprecated `Index.reindex()` with a non-unique `Index` (GH 42568)

- Deprecated `Styler.render()` in favor of `Styler.to_html()` (GH 42140)

- Deprecated `Styler.hide_index()` and `Styler.hide_columns()` in favor of `Styler.hide()` (GH 43758)

- Deprecated passing in a string column label into `times` in `DataFrame.ewm()` (GH 43265)

- Deprecated the `include_start` and `include_end` arguments in `DataFrame.between_time()`; in a future version passing `include_start` or `include_end` will raise (GH 40245)

- Deprecated the `squeeze` argument to `read_csv()`, `read_table()`, and `read_excel()`. Users should squeeze the `DataFrame` afterwards with `.squeeze("columns")` instead (GH 43242)

- Deprecated the `index` argument to `SparseArray` construction (GH 23089)

- Deprecated the `closed` argument in `date_range()` and `bdate_range()` in favor of `inclusive` argument; In a future version passing `closed` will raise (GH 40245)

- Deprecated `Rolling.validate()`, `Expanding.validate()`, and `ExponentialMovingWindow.validate()` (GH 43665)

- Deprecated silent dropping of columns that raised a `TypeError` in `Series.transform` and `DataFrame.transform` when used with a dictionary (GH 43740)

- Deprecated silent dropping of columns that raised a `TypeError`, `DataError`, and some cases of `ValueError` in `Series.aggregate()`, `DataFrame.aggregate()`, `Series.groupby.aggregate()`, and `DataFrame.groupby.aggregate()` when used with a list (GH 43740)

- Deprecated casting behavior when setting timezone-aware value(s) into a timezone-aware `Series` or `DataFrame` column when the timezones do not match. Previously this cast to object dtype. In a future version, the values being inserted will be converted to the series or column’s existing timezone (GH 37605)

- Deprecated casting behavior when passing an item with mismatched-timezone to `DatetimeIndex.insert()`, `DatetimeIndex.putmask()`, `DatetimeIndex.where()` `DatetimeIndex.fillna()`, `Series.mask()`, `Series.where()`, `Series.fillna()`, `Series.shift()`, `Series.replace()`, `Series.reindex()` (and `DataFrame` column analogues). In the past this has cast to object `dtype`. In a future version, these will cast the passed item to the index or series’s timezone (GH 37605, GH 44940)

- Deprecated the `prefix` keyword argument in `read_csv()` and `read_table()`, in a future version the argument will be removed (GH 43396)

- Deprecated passing non boolean argument to `sort` in `concat()` (GH 41518)

- Deprecated passing arguments as positional for `read_fwf()` other than `filepath_or_buffer` (GH 41485)

- Deprecated passing arguments as positional for `read_xml()` other than `path_or_buffer` (GH 45133)

- Deprecated passing `skipna=None` for `DataFrame.mad()` and `Series.mad()`, pass `skipna=True` instead (GH 44580)

- Deprecated the behavior of `to_datetime()` with the string “now” with `utc=False`; in a future version this will match `Timestamp("now")`, which in turn matches `Timestamp.now()` returning the local time (GH 18705)

- Deprecated `DateOffset.apply()`, use `offset + other` instead (GH 44522)

- Deprecated parameter `names` in `Index.copy()` (GH 44916)

- A deprecation warning is now shown for `DataFrame.to_latex()` indicating the arguments signature may change and emulate more the arguments to `Styler.to_latex()` in future versions (GH 44411)

- Deprecated behavior of `concat()` between objects with bool-dtype and numeric-dtypes; in a future version these will cast to object dtype instead of coercing bools to numeric values (GH 39817)

- Deprecated `Categorical.replace()`, use `Series.replace()` instead (GH 44929)

- Deprecated passing `set` or `dict` as indexer for `DataFrame.loc.__setitem__()`, `DataFrame.loc.__getitem__()`, `Series.loc.__setitem__()`, `Series.loc.__getitem__()`, `DataFrame.__getitem__()`, `Series.__getitem__()` and `Series.__setitem__()` (GH 42825)

- Deprecated `Index.__getitem__()` with a bool key; use `index.values[key]` to get the old behavior (GH 44051)

- Deprecated downcasting column-by-column in `DataFrame.where()` with integer-dtypes (GH 44597)

- Deprecated `DatetimeIndex.union_many()`, use `DatetimeIndex.union()` instead (GH 44091)

- Deprecated `Groupby.pad()` in favor of `Groupby.ffill()` (GH 33396)

- Deprecated `Groupby.backfill()` in favor of `Groupby.bfill()` (GH 33396)

- Deprecated `Resample.pad()` in favor of `Resample.ffill()` (GH 33396)

- Deprecated `Resample.backfill()` in favor of `Resample.bfill()` (GH 33396)

- Deprecated `numeric_only=None` in `DataFrame.rank()`; in a future version `numeric_only` must be either `True` or `False` (the default) (GH 45036)

- Deprecated the behavior of `Timestamp.utcfromtimestamp()`, in the future it will return a timezone-aware UTC `Timestamp` (GH 22451)

- Deprecated `NaT.freq()` (GH 45071)

- Deprecated behavior of `Series` and `DataFrame` construction when passed float-dtype data containing `NaN` and an integer dtype ignoring the dtype argument; in a future version this will raise (GH 40110)

- Deprecated the behaviour of `Series.to_frame()` and `Index.to_frame()` to ignore the `name` argument when `name=None`. Currently, this means to preserve the existing name, but in the future explicitly passing `name=None` will set `None` as the name of the column in the resulting DataFrame (GH 44212)

## Performance improvements

- Performance improvement in `DataFrameGroupBy.sample()` and `SeriesGroupBy.sample()`, especially when `weights` argument provided (GH 34483)

- Performance improvement when converting non-string arrays to string arrays (GH 34483)

- Performance improvement in `DataFrameGroupBy.transform()` and `SeriesGroupBy.transform()` for user-defined functions (GH 41598)

- Performance improvement in constructing `DataFrame` objects (GH 42631, GH 43142, GH 43147, GH 43307, GH 43144, GH 44826)

- Performance improvement in `DataFrameGroupBy.shift()` and `SeriesGroupBy.shift()` when `fill_value` argument is provided (GH 26615)

- Performance improvement in `DataFrame.corr()` for `method=pearson` on data without missing values (GH 40956)

- Performance improvement in some `DataFrameGroupBy.apply()` and `SeriesGroupBy.apply()` operations (GH 42992, GH 43578)

- Performance improvement in `read_stata()` (GH 43059, GH 43227)

- Performance improvement in `read_sas()` (GH 43333)

- Performance improvement in `to_datetime()` with `uint` dtypes (GH 42606)

- Performance improvement in `to_datetime()` with `infer_datetime_format` set to `True` (GH 43901)

- Performance improvement in `Series.sparse.to_coo()` (GH 42880)

- Performance improvement in indexing with a `UInt64Index` (GH 43862)

- Performance improvement in indexing with a `Float64Index` (GH 43705)

- Performance improvement in indexing with a non-unique `Index` (GH 43792)

- Performance improvement in indexing with a listlike indexer on a `MultiIndex` (GH 43370)

- Performance improvement in indexing with a `MultiIndex` indexer on another `MultiIndex` (GH 43370)

- Performance improvement in `DataFrameGroupBy.quantile()` and `SeriesGroupBy.quantile()` (GH 43469, GH 43725)

- Performance improvement in `DataFrameGroupBy.count()` and `SeriesGroupBy.count()` (GH 43730, GH 43694)

- Performance improvement in `DataFrameGroupBy.any()`, `SeriesGroupBy.any()`, `DataFrameGroupBy.all()`, and `SeriesGroupBy.all()` (GH 43675, GH 42841)

- Performance improvement in `DataFrameGroupBy.std()` and `SeriesGroupBy.std()` (GH 43115, GH 43576)

- Performance improvement in `DataFrameGroupBy.cumsum()` and `SeriesGroupBy.cumsum()` (GH 43309)

- `SparseArray.min()` and `SparseArray.max()` no longer require converting to a dense array (GH 43526)

- Indexing into a `SparseArray` with a `slice` with `step=1` no longer requires converting to a dense array (GH 43777)

- Performance improvement in `SparseArray.take()` with `allow_fill=False` (GH 43654)

- Performance improvement in `Rolling.mean()`, `Expanding.mean()`, `Rolling.sum()`, `Expanding.sum()`, `Rolling.max()`, `Expanding.max()`, `Rolling.min()` and `Expanding.min()` with `engine="numba"` (GH 43612, GH 44176, GH 45170)

- Improved performance of `pandas.read_csv()` with `memory_map=True` when file encoding is UTF-8 (GH 43787)

- Performance improvement in `RangeIndex.sort_values()` overriding `Index.sort_values()` (GH 43666)

- Performance improvement in `RangeIndex.insert()` (GH 43988)

- Performance improvement in `Index.insert()` (GH 43953)

- Performance improvement in `DatetimeIndex.tolist()` (GH 43823)

- Performance improvement in `DatetimeIndex.union()` (GH 42353)

- Performance improvement in `Series.nsmallest()` (GH 43696)

- Performance improvement in `DataFrame.insert()` (GH 42998)

- Performance improvement in `DataFrame.dropna()` (GH 43683)

- Performance improvement in `DataFrame.fillna()` (GH 43316)

- Performance improvement in `DataFrame.values()` (GH 43160)

- Performance improvement in `DataFrame.select_dtypes()` (GH 42611)

- Performance improvement in `DataFrame` reductions (GH 43185, GH 43243, GH 43311, GH 43609)

- Performance improvement in `Series.unstack()` and `DataFrame.unstack()` (GH 43335, GH 43352, GH 42704, GH 43025)

- Performance improvement in `Series.to_frame()` (GH 43558)

- Performance improvement in `Series.mad()` (GH 43010)

- Performance improvement in `merge()` (GH 43332)

- Performance improvement in `to_csv()` when index column is a datetime and is formatted (GH 39413)

- Performance improvement in `to_csv()` when `MultiIndex` contains a lot of unused levels (GH 37484)

- Performance improvement in `read_csv()` when `index_col` was set with a numeric column (GH 44158)

- Performance improvement in `concat()` (GH 43354)

- Performance improvement in `SparseArray.__getitem__()` (GH 23122)

- Performance improvement in constructing a `DataFrame` from array-like objects like a `Pytorch` tensor (GH 44616)

## Bug fixes

### Categorical

- Bug in setting dtype-incompatible values into a `Categorical` (or `Series` or `DataFrame` backed by `Categorical`) raising `ValueError` instead of `TypeError` (GH 41919)

- Bug in `Categorical.searchsorted()` when passing a dtype-incompatible value raising `KeyError` instead of `TypeError` (GH 41919)

- Bug in `Categorical.astype()` casting datetimes and `Timestamp` to int for dtype `object` (GH 44930)

- Bug in `Series.where()` with `CategoricalDtype` when passing a dtype-incompatible value raising `ValueError` instead of `TypeError` (GH 41919)

- Bug in `Categorical.fillna()` when passing a dtype-incompatible value raising `ValueError` instead of `TypeError` (GH 41919)

- Bug in `Categorical.fillna()` with a tuple-like category raising `ValueError` instead of `TypeError` when filling with a non-category tuple (GH 41919)

### Datetimelike

- Bug in `DataFrame` constructor unnecessarily copying non-datetimelike 2D object arrays (GH 39272)

- Bug in `to_datetime()` with `format` and `pandas.NA` was raising `ValueError` (GH 42957)

- `to_datetime()` would silently swap `MM/DD/YYYY` and `DD/MM/YYYY` formats if the given `dayfirst` option could not be respected - now, a warning is raised in the case of delimited date strings (e.g. `31-12-2012`) (GH 12585)

- Bug in `date_range()` and `bdate_range()` do not return right bound when `start` = `end` and set is closed on one side (GH 43394)

- Bug in inplace addition and subtraction of `DatetimeIndex` or `TimedeltaIndex` with `DatetimeArray` or `TimedeltaArray` (GH 43904)

- Bug in calling `np.isnan`, `np.isfinite`, or `np.isinf` on a timezone-aware `DatetimeIndex` incorrectly raising `TypeError` (GH 43917)

- Bug in constructing a `Series` from datetime-like strings with mixed timezones incorrectly partially-inferring datetime values (GH 40111)

- Bug in addition of a `Tick` object and a `np.timedelta64` object incorrectly raising instead of returning `Timedelta` (GH 44474)

- `np.maximum.reduce` and `np.minimum.reduce` now correctly return `Timestamp` and `Timedelta` objects when operating on `Series`, `DataFrame`, or `Index` with `datetime64[ns]` or `timedelta64[ns]` dtype (GH 43923)

- Bug in adding a `np.timedelta64` object to a `BusinessDay` or `CustomBusinessDay` object incorrectly raising (GH 44532)

- Bug in `Index.insert()` for inserting `np.datetime64`, `np.timedelta64` or `tuple` into `Index` with `dtype='object'` with negative loc adding `None` and replacing existing value (GH 44509)

- Bug in `Timestamp.to_pydatetime()` failing to retain the `fold` attribute (GH 45087)

- Bug in `Series.mode()` with `DatetimeTZDtype` incorrectly returning timezone-naive and `PeriodDtype` incorrectly raising (GH 41927)

- Fixed regression in `reindex()` raising an error when using an incompatible fill value with a datetime-like dtype (or not raising a deprecation warning for using a `datetime.date` as fill value) (GH 42921)

- Bug in `DateOffset` addition with `Timestamp` where `offset.nanoseconds` would not be included in the result (GH 43968, GH 36589)

- Bug in `Timestamp.fromtimestamp()` not supporting the `tz` argument (GH 45083)

- Bug in `DataFrame` construction from dict of `Series` with mismatched index dtypes sometimes raising depending on the ordering of the passed dict (GH 44091)

- Bug in `Timestamp` hashing during some DST transitions caused a segmentation fault (GH 33931 and GH 40817)

### Timedelta

- Bug in division of all-`NaT` `TimeDeltaIndex`, `Series` or `DataFrame` column with object-dtype array like of numbers failing to infer the result as timedelta64-dtype (GH 39750)

- Bug in floor division of `timedelta64[ns]` data with a scalar returning garbage values (GH 44466)

- Bug in `Timedelta` now properly taking into account any nanoseconds contribution of any kwarg (GH 43764, GH 45227)

### Time Zones

- Bug in `to_datetime()` with `infer_datetime_format=True` failing to parse zero UTC offset (`Z`) correctly (GH 41047)

- Bug in `Series.dt.tz_convert()` resetting index in a `Series` with `CategoricalIndex` (GH 43080)

- Bug in `Timestamp` and `DatetimeIndex` incorrectly raising a `TypeError` when subtracting two timezone-aware objects with mismatched timezones (GH 31793)

### Numeric

- Bug in floor-dividing a list or tuple of integers by a `Series` incorrectly raising (GH 44674)

- Bug in `DataFrame.rank()` raising `ValueError` with `object` columns and `method="first"` (GH 41931)

- Bug in `DataFrame.rank()` treating missing values and extreme values as equal (for example `np.nan` and `np.inf`), causing incorrect results when `na_option="bottom"` or `na_option="top` used (GH 41931)

- Bug in `numexpr` engine still being used when the option `compute.use_numexpr` is set to `False` (GH 32556)

- Bug in `DataFrame` arithmetic ops with a subclass whose `_constructor()` attribute is a callable other than the subclass itself (GH 43201)

- Bug in arithmetic operations involving `RangeIndex` where the result would have the incorrect `name` (GH 43962)

- Bug in arithmetic operations involving `Series` where the result could have the incorrect `name` when the operands having matching NA or matching tuple names (GH 44459)

- Bug in division with `IntegerDtype` or `BooleanDtype` array and NA scalar incorrectly raising (GH 44685)

- Bug in multiplying a `Series` with `FloatingDtype` with a timedelta-like scalar incorrectly raising (GH 44772)

### Conversion

- Bug in `UInt64Index` constructor when passing a list containing both positive integers small enough to cast to int64 and integers too large to hold in int64 (GH 42201)

- Bug in `Series` constructor returning 0 for missing values with dtype `int64` and `False` for dtype `bool` (GH 43017, GH 43018)

- Bug in constructing a `DataFrame` from a `PandasArray` containing `Series` objects behaving differently than an equivalent `np.ndarray` (GH 43986)

- Bug in `IntegerDtype` not allowing coercion from string dtype (GH 25472)

- Bug in `to_datetime()` with `arg:xr.DataArray` and `unit="ns"` specified raises `TypeError` (GH 44053)

- Bug in `DataFrame.convert_dtypes()` not returning the correct type when a subclass does not overload `_constructor_sliced()` (GH 43201)

- Bug in `DataFrame.astype()` not propagating `attrs` from the original `DataFrame` (GH 44414)

- Bug in `DataFrame.convert_dtypes()` result losing `columns.names` (GH 41435)

- Bug in constructing a `IntegerArray` from pyarrow data failing to validate dtypes (GH 44891)

- Bug in `Series.astype()` not allowing converting from a `PeriodDtype` to `datetime64` dtype, inconsistent with the `PeriodIndex` behavior (GH 45038)

### Strings

- Bug in checking for `string[pyarrow]` dtype incorrectly raising an `ImportError` when pyarrow is not installed (GH 44276)

### Interval

- Bug in `Series.where()` with `IntervalDtype` incorrectly raising when the `where` call should not replace anything (GH 44181)

### Indexing

- Bug in `Series.rename()` with `MultiIndex` and `level` is provided (GH 43659)

- Bug in `DataFrame.truncate()` and `Series.truncate()` when the object’s `Index` has a length greater than one but only one unique value (GH 42365)

- Bug in `Series.loc()` and `DataFrame.loc()` with a `MultiIndex` when indexing with a tuple in which one of the levels is also a tuple (GH 27591)

- Bug in `Series.loc()` with a `MultiIndex` whose first level contains only `np.nan` values (GH 42055)

- Bug in indexing on a `Series` or `DataFrame` with a `DatetimeIndex` when passing a string, the return type depended on whether the index was monotonic (GH 24892)

- Bug in indexing on a `MultiIndex` failing to drop scalar levels when the indexer is a tuple containing a datetime-like string (GH 42476)

- Bug in `DataFrame.sort_values()` and `Series.sort_values()` when passing an ascending value, failed to raise or incorrectly raising `ValueError` (GH 41634)

- Bug in updating values of `pandas.Series` using boolean index, created by using `pandas.DataFrame.pop()` (GH 42530)

- Bug in `Index.get_indexer_non_unique()` when index contains multiple `np.nan` (GH 35392)

- Bug in `DataFrame.query()` did not handle the degree sign in a backticked column name, such as `Temp(°C)`, used in an expression to query a `DataFrame` (GH 42826)

- Bug in `DataFrame.drop()` where the error message did not show missing labels with commas when raising `KeyError` (GH 42881)

- Bug in `DataFrame.query()` where method calls in query strings led to errors when the `numexpr` package was installed (GH 22435)

- Bug in `DataFrame.nlargest()` and `Series.nlargest()` where sorted result did not count indexes containing `np.nan` (GH 28984)

- Bug in indexing on a non-unique object-dtype `Index` with an NA scalar (e.g. `np.nan`) (GH 43711)

- Bug in `DataFrame.__setitem__()` incorrectly writing into an existing column’s array rather than setting a new array when the new dtype and the old dtype match (GH 43406)

- Bug in setting floating-dtype values into a `Series` with integer dtype failing to set inplace when those values can be losslessly converted to integers (GH 44316)

- Bug in `Series.__setitem__()` with object dtype when setting an array with matching size and dtype=’datetime64[ns]’ or dtype=’timedelta64[ns]’ incorrectly converting the datetime/timedeltas to integers (GH 43868)

- Bug in `DataFrame.sort_index()` where `ignore_index=True` was not being respected when the index was already sorted (GH 43591)

- Bug in `Index.get_indexer_non_unique()` when index contains multiple `np.datetime64("NaT")` and `np.timedelta64("NaT")` (GH 43869)

- Bug in setting a scalar `Interval` value into a `Series` with `IntervalDtype` when the scalar’s sides are floats and the values’ sides are integers (GH 44201)

- Bug when setting string-backed `Categorical` values that can be parsed to datetimes into a `DatetimeArray` or `Series` or `DataFrame` column backed by `DatetimeArray` failing to parse these strings (GH 44236)

- Bug in `Series.__setitem__()` with an integer dtype other than `int64` setting with a `range` object unnecessarily upcasting to `int64` (GH 44261)

- Bug in `Series.__setitem__()` with a boolean mask indexer setting a listlike value of length 1 incorrectly broadcasting that value (GH 44265)

- Bug in `Series.reset_index()` not ignoring `name` argument when `drop` and `inplace` are set to `True` (GH 44575)

- Bug in `DataFrame.loc.__setitem__()` and `DataFrame.iloc.__setitem__()` with mixed dtypes sometimes failing to operate in-place (GH 44345)

- Bug in `DataFrame.loc.__getitem__()` incorrectly raising `KeyError` when selecting a single column with a boolean key (GH 44322).

- Bug in setting `DataFrame.iloc()` with a single `ExtensionDtype` column and setting 2D values e.g. `df.iloc[:] = df.values` incorrectly raising (GH 44514)

- Bug in setting values with `DataFrame.iloc()` with a single `ExtensionDtype` column and a tuple of arrays as the indexer (GH 44703)

- Bug in indexing on columns with `loc` or `iloc` using a slice with a negative step with `ExtensionDtype` columns incorrectly raising (GH 44551)

- Bug in `DataFrame.loc.__setitem__()` changing dtype when indexer was completely `False` (GH 37550)

- Bug in `IntervalIndex.get_indexer_non_unique()` returning boolean mask instead of array of integers for a non unique and non monotonic index (GH 44084)

- Bug in `IntervalIndex.get_indexer_non_unique()` not handling targets of `dtype` ‘object’ with NaNs correctly (GH 44482)

- Fixed regression where a single column `np.matrix` was no longer coerced to a 1d `np.ndarray` when added to a `DataFrame` (GH 42376)

- Bug in `Series.__getitem__()` with a `CategoricalIndex` of integers treating lists of integers as positional indexers, inconsistent with the behavior with a single scalar integer (GH 15470, GH 14865)

- Bug in `Series.__setitem__()` when setting floats or integers into integer-dtype `Series` failing to upcast when necessary to retain precision (GH 45121)

- Bug in `DataFrame.iloc.__setitem__()` ignores axis argument (GH 45032)

### Missing

- Bug in `DataFrame.fillna()` with `limit` and no `method` ignores `axis='columns'` or `axis = 1` (GH 40989, GH 17399)

- Bug in `DataFrame.fillna()` not replacing missing values when using a dict-like `value` and duplicate column names (GH 43476)

- Bug in constructing a `DataFrame` with a dictionary `np.datetime64` as a value and `dtype='timedelta64[ns]'`, or vice-versa, incorrectly casting instead of raising (GH 44428)

- Bug in `Series.interpolate()` and `DataFrame.interpolate()` with `inplace=True` not writing to the underlying array(s) in-place (GH 44749)

- Bug in `Index.fillna()` incorrectly returning an unfilled `Index` when NA values are present and `downcast` argument is specified. This now raises `NotImplementedError` instead; do not pass `downcast` argument (GH 44873)

- Bug in `DataFrame.dropna()` changing `Index` even if no entries were dropped (GH 41965)

- Bug in `Series.fillna()` with an object-dtype incorrectly ignoring `downcast="infer"` (GH 44241)

### MultiIndex

- Bug in `MultiIndex.get_loc()` where the first level is a `DatetimeIndex` and a string key is passed (GH 42465)

- Bug in `MultiIndex.reindex()` when passing a `level` that corresponds to an `ExtensionDtype` level (GH 42043)

- Bug in `MultiIndex.get_loc()` raising `TypeError` instead of `KeyError` on nested tuple (GH 42440)

- Bug in `MultiIndex.union()` setting wrong `sortorder` causing errors in subsequent indexing operations with slices (GH 44752)

- Bug in `MultiIndex.putmask()` where the other value was also a `MultiIndex` (GH 43212)

- Bug in `MultiIndex.dtypes()` duplicate level names returned only one dtype per name (GH 45174)

### I/O

- Bug in `read_excel()` attempting to read chart sheets from .xlsx files (GH 41448)

- Bug in `json_normalize()` where `errors=ignore` could fail to ignore missing values of `meta` when `record_path` has a length greater than one (GH 41876)

- Bug in `read_csv()` with multi-header input and arguments referencing column names as tuples (GH 42446)

- Bug in `read_fwf()`, where difference in lengths of `colspecs` and `names` was not raising `ValueError` (GH 40830)

- Bug in `Series.to_json()` and `DataFrame.to_json()` where some attributes were skipped when serializing plain Python objects to JSON (GH 42768, GH 33043)

- Column headers are dropped when constructing a `DataFrame` from a sqlalchemy’s `Row` object (GH 40682)

- Bug in unpickling an `Index` with object dtype incorrectly inferring numeric dtypes (GH 43188)

- Bug in `read_csv()` where reading multi-header input with unequal lengths incorrectly raised `IndexError` (GH 43102)

- Bug in `read_csv()` raising `ParserError` when reading file in chunks and some chunk blocks have fewer columns than header for `engine="c"` (GH 21211)

- Bug in `read_csv()`, changed exception class when expecting a file path name or file-like object from `OSError` to `TypeError` (GH 43366)

- Bug in `read_csv()` and `read_fwf()` ignoring all `skiprows` except first when `nrows` is specified for `engine='python'` (GH 44021, GH 10261)

- Bug in `read_csv()` keeping the original column in object format when `keep_date_col=True` is set (GH 13378)

- Bug in `read_json()` not handling non-numpy dtypes correctly (especially `category`) (GH 21892, GH 33205)

- Bug in `json_normalize()` where multi-character `sep` parameter is incorrectly prefixed to every key (GH 43831)

- Bug in `json_normalize()` where reading data with missing multi-level metadata would not respect `errors="ignore"` (GH 44312)

- Bug in `read_csv()` used second row to guess implicit index if `header` was set to `None` for `engine="python"` (GH 22144)

- Bug in `read_csv()` not recognizing bad lines when `names` were given for `engine="c"` (GH 22144)

- Bug in `read_csv()` with `float_precision="round_trip"` which did not skip initial/trailing whitespace (GH 43713)

- Bug when Python is built without the lzma module: a warning was raised at the pandas import time, even if the lzma capability isn’t used (GH 43495)

- Bug in `read_csv()` not applying dtype for `index_col` (GH 9435)

- Bug in dumping/loading a `DataFrame` with `yaml.dump(frame)` (GH 42748)

- Bug in `read_csv()` raising `ValueError` when `names` was longer than `header` but equal to data rows for `engine="python"` (GH 38453)

- Bug in `ExcelWriter`, where `engine_kwargs` were not passed through to all engines (GH 43442)

- Bug in `read_csv()` raising `ValueError` when `parse_dates` was used with `MultiIndex` columns (GH 8991)

- Bug in `read_csv()` not raising an `ValueError` when `\n` was specified as `delimiter` or `sep` which conflicts with `lineterminator` (GH 43528)

- Bug in `to_csv()` converting datetimes in categorical `Series` to integers (GH 40754)

- Bug in `read_csv()` converting columns to numeric after date parsing failed (GH 11019)

- Bug in `read_csv()` not replacing `NaN` values with `np.nan` before attempting date conversion (GH 26203)

- Bug in `read_csv()` raising `AttributeError` when attempting to read a .csv file and infer index column dtype from an nullable integer type (GH 44079)

- Bug in `to_csv()` always coercing datetime columns with different formats to the same format (GH 21734)

- `DataFrame.to_csv()` and `Series.to_csv()` with `compression` set to `'zip'` no longer create a zip file containing a file ending with “.zip”. Instead, they try to infer the inner file name more smartly (GH 39465)

- Bug in `read_csv()` where reading a mixed column of booleans and missing values to a float type results in the missing values becoming 1.0 rather than NaN (GH 42808, GH 34120)

- Bug in `to_xml()` raising error for `pd.NA` with extension array dtype (GH 43903)

- Bug in `read_csv()` when passing simultaneously a parser in `date_parser` and `parse_dates=False`, the parsing was still called (GH 44366)

- Bug in `read_csv()` not setting name of `MultiIndex` columns correctly when `index_col` is not the first column (GH 38549)

- Bug in `read_csv()` silently ignoring errors when failing to create a memory-mapped file (GH 44766)

- Bug in `read_csv()` when passing a `tempfile.SpooledTemporaryFile` opened in binary mode (GH 44748)

- Bug in `read_json()` raising `ValueError` when attempting to parse json strings containing “://” (GH 36271)

- Bug in `read_csv()` when `engine="c"` and `encoding_errors=None` which caused a segfault (GH 45180)

- Bug in `read_csv()` an invalid value of `usecols` leading to an unclosed file handle (GH 45384)

- Bug in `DataFrame.to_json()` fix memory leak (GH 43877)

### Period

- Bug in adding a `Period` object to a `np.timedelta64` object incorrectly raising `TypeError` (GH 44182)

- Bug in `PeriodIndex.to_timestamp()` when the index has `freq="B"` inferring `freq="D"` for its result instead of `freq="B"` (GH 44105)

- Bug in `Period` constructor incorrectly allowing `np.timedelta64("NaT")` (GH 44507)

- Bug in `PeriodIndex.to_timestamp()` giving incorrect values for indexes with non-contiguous data (GH 44100)

- Bug in `Series.where()` with `PeriodDtype` incorrectly raising when the `where` call should not replace anything (GH 45135)

### Plotting

- When given non-numeric data, `DataFrame.boxplot()` now raises a `ValueError` rather than a cryptic `KeyError` or `ZeroDivisionError`, in line with other plotting functions like `DataFrame.hist()` (GH 43480)

### Groupby/resample/rolling

- Bug in `SeriesGroupBy.apply()` where passing an unrecognized string argument failed to raise `TypeError` when the underlying `Series` is empty (GH 42021)

- Bug in `Series.rolling.apply()`, `DataFrame.rolling.apply()`, `Series.expanding.apply()` and `DataFrame.expanding.apply()` with `engine="numba"` where `*args` were being cached with the user passed function (GH 42287)

- Bug in `DataFrameGroupBy.max()`, `SeriesGroupBy.max()`, `DataFrameGroupBy.min()`, and `SeriesGroupBy.min()` with nullable integer dtypes losing precision (GH 41743)

- Bug in `DataFrame.groupby.rolling.var()` would calculate the rolling variance only on the first group (GH 42442)

- Bug in `DataFrameGroupBy.shift()` and `SeriesGroupBy.shift()` that would return the grouping columns if `fill_value` was not `None` (GH 41556)

- Bug in `SeriesGroupBy.nlargest()` and `SeriesGroupBy.nsmallest()` would have an inconsistent index when the input `Series` was sorted and `n` was greater than or equal to all group sizes (GH 15272, GH 16345, GH 29129)

- Bug in `pandas.DataFrame.ewm()`, where non-float64 dtypes were silently failing (GH 42452)

- Bug in `pandas.DataFrame.rolling()` operation along rows (`axis=1`) incorrectly omits columns containing `float16` and `float32` (GH 41779)

- Bug in `Resampler.aggregate()` did not allow the use of Named Aggregation (GH 32803)

- Bug in `Series.rolling()` when the `Series` `dtype` was `Int64` (GH 43016)

- Bug in `DataFrame.rolling.corr()` when the `DataFrame` columns was a `MultiIndex` (GH 21157)

- Bug in `DataFrame.groupby.rolling()` when specifying `on` and calling `__getitem__` would subsequently return incorrect results (GH 43355)

- Bug in `DataFrameGroupBy.apply()` and `SeriesGroupBy.apply()` with time-based `Grouper` objects incorrectly raising `ValueError` in corner cases where the grouping vector contains a `NaT` (GH 43500, GH 43515)

- Bug in `DataFrameGroupBy.mean()` and `SeriesGroupBy.mean()` failing with `complex` dtype (GH 43701)

- Bug in `Series.rolling()` and `DataFrame.rolling()` not calculating window bounds correctly for the first row when `center=True` and index is decreasing (GH 43927)

- Bug in `Series.rolling()` and `DataFrame.rolling()` for centered datetimelike windows with uneven nanosecond (GH 43997)

- Bug in `DataFrameGroupBy.mean()` and `SeriesGroupBy.mean()` raising `KeyError` when column was selected at least twice (GH 44924)

- Bug in `DataFrameGroupBy.nth()` and `SeriesGroupBy.nth()` failing on `axis=1` (GH 43926)

- Bug in `Series.rolling()` and `DataFrame.rolling()` not respecting right bound on centered datetime-like windows, if the index contain duplicates (GH 3944)

- Bug in `Series.rolling()` and `DataFrame.rolling()` when using a `pandas.api.indexers.BaseIndexer` subclass that returned unequal start and end arrays would segfault instead of raising a `ValueError` (GH 44470)

- Bug in `Groupby.nunique()` not respecting `observed=True` for `categorical` grouping columns (GH 45128)

- Bug in `DataFrameGroupBy.head()`, `SeriesGroupBy.head()`, `DataFrameGroupBy.tail()`, and `SeriesGroupBy.tail()` not dropping groups with `NaN` when `dropna=True` (GH 45089)

- Bug in `GroupBy.__iter__()` after selecting a subset of columns in a `GroupBy` object, which returned all columns instead of the chosen subset (GH 44821)

- Bug in `Groupby.rolling()` when non-monotonic data passed, fails to correctly raise `ValueError` (GH 43909)

- Bug where grouping by a `Series` that has a `categorical` data type and length unequal to the axis of grouping raised `ValueError` (GH 44179)

### Reshaping

- Improved error message when creating a `DataFrame` column from a multi-dimensional `numpy.ndarray` (GH 42463)

- Bug in `concat()` creating `MultiIndex` with duplicate level entries when concatenating a `DataFrame` with duplicates in `Index` and multiple keys (GH 42651)

- Bug in `pandas.cut()` on `Series` with duplicate indices and non-exact `pandas.CategoricalIndex()` (GH 42185, GH 42425)

- Bug in `DataFrame.append()` failing to retain dtypes when appended columns do not match (GH 43392)

- Bug in `concat()` of `bool` and `boolean` dtypes resulting in `object` dtype instead of `boolean` dtype (GH 42800)

- Bug in `crosstab()` when inputs are categorical `Series`, there are categories that are not present in one or both of the `Series`, and `margins=True`. Previously the margin value for missing categories was `NaN`. It is now correctly reported as 0 (GH 43505)

- Bug in `concat()` would fail when the `objs` argument all had the same index and the `keys` argument contained duplicates (GH 43595)

- Bug in `concat()` which ignored the `sort` parameter (GH 43375)

- Bug in `merge()` with `MultiIndex` as column index for the `on` argument returning an error when assigning a column internally (GH 43734)

- Bug in `crosstab()` would fail when inputs are lists or tuples (GH 44076)

- Bug in `DataFrame.append()` failing to retain `index.name` when appending a list of `Series` objects (GH 44109)

- Fixed metadata propagation in `Dataframe.apply()` method, consequently fixing the same issue for `Dataframe.transform()`, `Dataframe.nunique()` and `Dataframe.mode()` (GH 28283)

- Bug in `concat()` casting levels of `MultiIndex` to float if all levels only consist of missing values (GH 44900)

- Bug in `DataFrame.stack()` with `ExtensionDtype` columns incorrectly raising (GH 43561)

- Bug in `merge()` raising `KeyError` when joining over differently named indexes with on keywords (GH 45094)

- Bug in `Series.unstack()` with object doing unwanted type inference on resulting columns (GH 44595)

- Bug in `MultiIndex.join()` with overlapping `IntervalIndex` levels (GH 44096)

- Bug in `DataFrame.replace()` and `Series.replace()` results is different `dtype` based on `regex` parameter (GH 44864)

- Bug in `DataFrame.pivot()` with `index=None` when the `DataFrame` index was a `MultiIndex` (GH 23955)

### Sparse

- Bug in `DataFrame.sparse.to_coo()` raising `AttributeError` when column names are not unique (GH 29564)

- Bug in `SparseArray.max()` and `SparseArray.min()` raising `ValueError` for arrays with 0 non-null elements (GH 43527)

- Bug in `DataFrame.sparse.to_coo()` silently converting non-zero fill values to zero (GH 24817)

- Bug in `SparseArray` comparison methods with an array-like operand of mismatched length raising `AssertionError` or unclear `ValueError` depending on the input (GH 43863)

- Bug in `SparseArray` arithmetic methods `floordiv` and `mod` behaviors when dividing by zero not matching the non-sparse `Series` behavior (GH 38172)

- Bug in `SparseArray` unary methods as well as `SparseArray.isna()` doesn’t recalculate indexes (GH 44955)

### ExtensionArray

- Bug in `array()` failing to preserve `PandasArray` (GH 43887)

- NumPy ufuncs `np.abs`, `np.positive`, `np.negative` now correctly preserve dtype when called on ExtensionArrays that implement `__abs__, __pos__, __neg__`, respectively. In particular this is fixed for `TimedeltaArray` (GH 43899, GH 23316)

- NumPy ufuncs `np.minimum.reduce` `np.maximum.reduce`, `np.add.reduce`, and `np.prod.reduce` now work correctly instead of raising `NotImplementedError` on `Series` with `IntegerDtype` or `FloatDtype` (GH 43923, GH 44793)

- NumPy ufuncs with `out` keyword are now supported by arrays with `IntegerDtype` and `FloatingDtype` (GH 45122)

- Avoid raising `PerformanceWarning` about fragmented `DataFrame` when using many columns with an extension dtype (GH 44098)

- Bug in `IntegerArray` and `FloatingArray` construction incorrectly coercing mismatched NA values (e.g. `np.timedelta64("NaT")`) to numeric NA (GH 44514)

- Bug in `BooleanArray.__eq__()` and `BooleanArray.__ne__()` raising `TypeError` on comparison with an incompatible type (like a string). This caused `DataFrame.replace()` to sometimes raise a `TypeError` if a nullable boolean column was included (GH 44499)

- Bug in `array()` incorrectly raising when passed a `ndarray` with `float16` dtype (GH 44715)

- Bug in calling `np.sqrt` on `BooleanArray` returning a malformed `FloatingArray` (GH 44715)

- Bug in `Series.where()` with `ExtensionDtype` when `other` is a NA scalar incompatible with the `Series` dtype (e.g. `NaT` with a numeric dtype) incorrectly casting to a compatible NA value (GH 44697)

- Bug in `Series.replace()` where explicitly passing `value=None` is treated as if no `value` was passed, and `None` not being in the result (GH 36984, GH 19998)

- Bug in `Series.replace()` with unwanted downcasting being done in no-op replacements (GH 44498)

- Bug in `Series.replace()` with `FloatDtype`, `string[python]`, or `string[pyarrow]` dtype not being preserved when possible (GH 33484, GH 40732, GH 31644, GH 41215, GH 25438)

### Styler

- Bug in `Styler` where the `uuid` at initialization maintained a floating underscore (GH 43037)

- Bug in `Styler.to_html()` where the `Styler` object was updated if the `to_html` method was called with some args (GH 43034)

- Bug in `Styler.copy()` where `uuid` was not previously copied (GH 40675)

- Bug in `Styler.apply()` where functions which returned `Series` objects were not correctly handled in terms of aligning their index labels (GH 13657, GH 42014)

- Bug when rendering an empty `DataFrame` with a named `Index` (GH 43305)

- Bug when rendering a single level `MultiIndex` (GH 43383)

- Bug when combining non-sparse rendering and `Styler.hide_columns()` or `Styler.hide_index()` (GH 43464)

- Bug setting a table style when using multiple selectors in `Styler` (GH 44011)

- Bugs where row trimming and column trimming failed to reflect hidden rows (GH 43703, GH 44247)

### Other

- Bug in `DataFrame.astype()` with non-unique columns and a `Series` `dtype` argument (GH 44417)

- Bug in `CustomBusinessMonthBegin.__add__()` (`CustomBusinessMonthEnd.__add__()`) not applying the extra `offset` parameter when beginning (end) of the target month is already a business day (GH 41356)

- Bug in `RangeIndex.union()` with another `RangeIndex` with matching (even) `step` and starts differing by strictly less than `step / 2` (GH 44019)

- Bug in `RangeIndex.difference()` with `sort=None` and `step<0` failing to sort (GH 44085)

- Bug in `Series.replace()` and `DataFrame.replace()` with `value=None` and ExtensionDtypes (GH 44270, GH 37899)

- Bug in `FloatingArray.equals()` failing to consider two arrays equal if they contain `np.nan` values (GH 44382)

- Bug in `DataFrame.shift()` with `axis=1` and `ExtensionDtype` columns incorrectly raising when an incompatible `fill_value` is passed (GH 44564)

- Bug in `DataFrame.shift()` with `axis=1` and `periods` larger than `len(frame.columns)` producing an invalid `DataFrame` (GH 44978)

- Bug in `DataFrame.diff()` when passing a NumPy integer object instead of an `int` object (GH 44572)

- Bug in `Series.replace()` raising `ValueError` when using `regex=True` with a `Series` containing `np.nan` values (GH 43344)

- Bug in `DataFrame.to_records()` where an incorrect `n` was used when missing names were replaced by `level_n` (GH 44818)

- Bug in `DataFrame.eval()` where `resolvers` argument was overriding the default resolvers (GH 34966)

- `Series.__repr__()` and `DataFrame.__repr__()` no longer replace all null-values in indexes with “NaN” but use their real string-representations. “NaN” is used only for `float("nan")` (GH 45263)

## Contributors

A total of 275 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

- Abhishek R

- Albert Villanova del Moral

- Alessandro Bisiani +

- Alex Lim

- Alex-Gregory-1 +

- Alexander Gorodetsky

- Alexander Regueiro +

- Alexey Györi

- Alexis Mignon

- Aleš Erjavec

- Ali McMaster

- Alibi +

- Andrei Batomunkuev +

- Andrew Eckart +

- Andrew Hawyrluk

- Andrew Wood

- Anton Lodder +

- Armin Berres +

- Arushi Sharma +

- Benedikt Heidrich +

- Beni Bienz +

- Benoît Vinot

- Bert Palm +

- Boris Rumyantsev +

- Brian Hulette

- Brock

- Bruno Costa +

- Bryan Racic +

- Caleb Epstein

- Calvin Ho

- ChristofKaufmann +

- Christopher Yeh +

- Chuliang Xiao +

- ClaudiaSilver +

- DSM

- Daniel Coll +

- Daniel Schmidt +

- Dare Adewumi

- David +

- David Sanders +

- David Wales +

- Derzan Chiang +

- DeviousLab +

- Dhruv B Shetty +

- Digres45 +

- Dominik Kutra +

- Drew Levitt +

- DriesS

- EdAbati

- Elle

- Elliot Rampono

- Endre Mark Borza

- Erfan Nariman

- Evgeny Naumov +

- Ewout ter Hoeven +

- Fangchen Li

- Felix Divo

- Felix Dulys +

- Francesco Andreuzzi +

- Francois Dion +

- Frans Larsson +

- Fred Reiss

- GYvan

- Gabriel Di Pardi Arruda +

- Gesa Stupperich

- Giacomo Caria +

- Greg Siano +

- Griffin Ansel

- Hiroaki Ogasawara +

- Horace +

- Horace Lai +

- Irv Lustig

- Isaac Virshup

- JHM Darbyshire (MBP)

- JHM Darbyshire (iMac)

- JHM Darbyshire +

- Jack Liu

- Jacob Skwirsk +

- Jaime Di Cristina +

- James Holcombe +

- Janosh Riebesell +

- Jarrod Millman

- Jason Bian +

- Jeff Reback

- Jernej Makovsek +

- Jim Bradley +

- Joel Gibson +

- Joeperdefloep +

- Johannes Mueller +

- John S Bogaardt +

- John Zangwill +

- Jon Haitz Legarreta Gorroño +

- Jon Wiggins +

- Jonas Haag +

- Joris Van den Bossche

- Josh Friedlander

- José Duarte +

- Julian Fleischer +

- Julien de la Bruère-T

- Justin McOmie

- Kadatatlu Kishore +

- Kaiqi Dong

- Kashif Khan +

- Kavya9986 +

- Kendall +

- Kevin Sheppard

- Kiley Hewitt

- Koen Roelofs +

- Krishna Chivukula

- KrishnaSai2020

- Leonardo Freua +

- Leonardus Chen

- Liang-Chi Hsieh +

- Loic Diridollou +

- Lorenzo Maffioli +

- Luke Manley +

- LunarLanding +

- Marc Garcia

- Marcel Bittar +

- Marcel Gerber +

- Marco Edward Gorelli

- Marco Gorelli

- MarcoGorelli

- Marvin +

- Mateusz Piotrowski +

- Mathias Hauser +

- Matt Richards +

- Matthew Davis +

- Matthew Roeschke

- Matthew Zeitlin

- Matthias Bussonnier

- Matti Picus

- Mauro Silberberg +

- Maxim Ivanov

- Maximilian Carr +

- MeeseeksMachine

- Michael Sarrazin +

- Michael Wang +

- Michał Górny +

- Mike Phung +

- Mike Taves +

- Mohamad Hussein Rkein +

- NJOKU OKECHUKWU VALENTINE +

- Neal McBurnett +

- Nick Anderson +

- Nikita Sobolev +

- Olivier Cavadenti +

- PApostol +

- Pandas Development Team

- Patrick Hoefler

- Peter

- Peter Tillmann +

- Prabha Arivalagan +

- Pradyumna Rahul

- Prerana Chakraborty

- Prithvijit +

- Rahul Gaikwad +

- Ray Bell

- Ricardo Martins +

- Richard Shadrach

- Robbert-jan ‘t Hoen +

- Robert Voyer +

- Robin Raymond +

- Rohan Sharma +

- Rohan Sirohia +

- Roman Yurchak

- Ruan Pretorius +

- Sam James +

- Scott Talbert

- Shashwat Sharma +

- Sheogorath27 +

- Shiv Gupta

- Shoham Debnath

- Simon Hawkins

- Soumya +

- Stan West +

- Stefanie Molin +

- Stefano Alberto Russo +

- Stephan Heßelmann

- Stephen

- Suyash Gupta +

- Sven

- Swanand01 +

- Sylvain Marié +

- TLouf

- Tania Allard +

- Terji Petersen

- TheDerivator +

- Thomas Dickson

- Thomas Kastl +

- Thomas Kluyver

- Thomas Li

- Thomas Smith

- Tim Swast

- Tim Tran +

- Tobias McNulty +

- Tobias Pitters

- Tomoki Nakagawa +

- Tony Hirst +

- Torsten Wörtwein

- V.I. Wood +

- Vaibhav K +

- Valentin Oliver Loftsson +

- Varun Shrivastava +

- Vivek Thazhathattil +

- Vyom Pathak

- Wenjun Si

- William Andrea +

- William Bradley +

- Wojciech Sadowski +

- Yao-Ching Huang +

- Yash Gupta +

- Yiannis Hadjicharalambous +

- Yoshiki Vázquez Baeza

- Yuanhao Geng

- Yury Mikhaylov

- Yvan Gatete +

- Yves Delley +

- Zach Rait

- Zbyszek Królikowski +

- Zero +

- Zheyuan

- Zhiyi Wu +

- aiudirog

- ali sayyah +

- aneesh98 +

- aptalca

- arw2019 +

- attack68

- brendandrury +

- bubblingoak +

- calvinsomething +

- claws +

- deponovo +

- dicristina

- el-g-1 +

- evensure +

- fotino21 +

- fshi01 +

- gfkang +

- github-actions[bot]

- i-aki-y

- jbrockmendel

- jreback

- juliandwain +

- jxb4892 +

- kendall smith +

- lmcindewar +

- lrepiton

- maximilianaccardo +

- michal-gh

- neelmraman

- partev

- phofl +

- pratyushsharan +

- quantumalaviya +

- rafael +

- realead

- rocabrera +

- rosagold

- saehuihwang +

- salomondush +

- shubham11941140 +

- srinivasan +

- stphnlyd

- suoniq

- trevorkask +

- tushushu

- tyuyoshi +

- usersblock +

- vernetya +

- vrserpa +

- willie3838 +

- zeitlinv +

- zhangxiaoxing +