What’s new in 2.0.0 (April 3, 2023)#

These are the changes in pandas 2.0.0. See Release notes for a full changelog including other versions of pandas.

Enhancements#

Installing optional dependencies with pip extras#

When installing pandas using pip, sets of optional dependencies can also be installed by specifying extras.

pip install "pandas[performance, aws]>=2.0.0"

The available extras, found in the installation guide, are [all, performance, computation, fss, aws, gcp, excel, parquet, feather, hdf5, spss, postgresql, mysql, sql-other, html, xml, plot, output_formatting, clipboard, compression, test] (GH 39164).

Index can now hold numpy numeric dtypes#

It is now possible to use any numpy numeric dtype in a Index (GH 42717).

Previously it was only possible to use int64, uint64 & float64 dtypes:

In [1]: pd.Index([1, 2, 3], dtype=np.int8)
Out[1]: Int64Index([1, 2, 3], dtype="int64")
In [2]: pd.Index([1, 2, 3], dtype=np.uint16)
Out[2]: UInt64Index([1, 2, 3], dtype="uint64")
In [3]: pd.Index([1, 2, 3], dtype=np.float32)
Out[3]: Float64Index([1.0, 2.0, 3.0], dtype="float64")

Int64Index, UInt64Index & Float64Index were deprecated in pandas version 1.4 and have now been removed. Instead Index should be used directly, and can it now take all numpy numeric dtypes, i.e. int8/ int16/int32/int64/uint8/uint16/uint32/uint64/float32/float64 dtypes:

In [1]: pd.Index([1, 2, 3], dtype=np.int8)
Out[1]: Index([1, 2, 3], dtype='int8')

In [2]: pd.Index([1, 2, 3], dtype=np.uint16)
Out[2]: Index([1, 2, 3], dtype='uint16')

In [3]: pd.Index([1, 2, 3], dtype=np.float32)
Out[3]: Index([1.0, 2.0, 3.0], dtype='float32')

The ability for Index to hold the numpy numeric dtypes has meant some changes in Pandas functionality. In particular, operations that previously were forced to create 64-bit indexes, can now create indexes with lower bit sizes, e.g. 32-bit indexes.

Below is a possibly non-exhaustive list of changes:

  1. Instantiating using a numpy numeric array now follows the dtype of the numpy array. Previously, all indexes created from numpy numeric arrays were forced to 64-bit. Now, for example, Index(np.array([1, 2, 3])) will be int32 on 32-bit systems, where it previously would have been int64 even on 32-bit systems. Instantiating Index using a list of numbers will still return 64bit dtypes, e.g. Index([1, 2, 3]) will have a int64 dtype, which is the same as previously.

  2. The various numeric datetime attributes of DatetimeIndex (day, month, year etc.) were previously in of dtype int64, while they were int32 for arrays.DatetimeArray. They are now int32 on DatetimeIndex also:

    In [4]: idx = pd.date_range(start='1/1/2018', periods=3, freq='ME')

In [5]: idx.array.year
Out[5]: array([2018, 2018, 2018], dtype=int32)

In [6]: idx.year
Out[6]: Index([2018, 2018, 2018], dtype='int32')

  3. Level dtypes on Indexes from Series.sparse.from_coo() are now of dtype int32, the same as they are on the rows/cols on a scipy sparse matrix. Previously they were of dtype int64.

    In [7]: from scipy import sparse

In [8]: A = sparse.coo_matrix(
   ...:     ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)
   ...: )
   ...: 

In [9]: ser = pd.Series.sparse.from_coo(A)

In [10]: ser.index.dtypes
Out[10]: 
level_0    int32
level_1    int32
dtype: object

  4. Index cannot be instantiated using a float16 dtype. Previously instantiating an Index using dtype float16 resulted in a Float64Index with a float64 dtype. It now raises a NotImplementedError:

    In [11]: pd.Index([1, 2, 3], dtype=np.float16)
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[11], line 1
----> 1 pd.Index([1, 2, 3], dtype=np.float16)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:576, in Index.__new__(cls, data, dtype, copy, name, tupleize_cols)
    572 arr = ensure_wrapped_if_datetimelike(arr)
    574 klass = cls._dtype_to_subclass(arr.dtype)
--> 576 arr = klass._ensure_array(arr, arr.dtype, copy=False)
    577 result = klass._simple_new(arr, name, refs=refs)
    578 if dtype is None and is_pandas_object and data_dtype == np.object_:

File ~/work/pandas/pandas/pandas/core/indexes/base.py:601, in Index._ensure_array(cls, data, dtype, copy)
    598     raise ValueError("Index data must be 1-dimensional")
    599 elif dtype == np.float16:
    600     # float16 not supported (no indexing engine)
--> 601     raise NotImplementedError("float16 indexes are not supported")
    603 if copy:
    604     # asarray_tuplesafe does not always copy underlying data,
    605     #  so need to make sure that this happens
    606     data = data.copy()

NotImplementedError: float16 indexes are not supported

Argument dtype_backend, to return pyarrow-backed or numpy-backed nullable dtypes#

The following functions gained a new keyword dtype_backend (GH 36712)

When this option is set to "numpy_nullable" it will return a DataFrame that is backed by nullable dtypes.

When this keyword is set to "pyarrow", then these functions will return pyarrow-backed nullable ArrowDtype DataFrames (GH 48957, GH 49997):

In [12]: import io

In [13]: data = io.StringIO("""a,b,c,d,e,f,g,h,i
   ....:     1,2.5,True,a,,,,,
   ....:     3,4.5,False,b,6,7.5,True,a,
   ....: """)
   ....: 

In [14]: df = pd.read_csv(data, dtype_backend="pyarrow")

In [15]: df.dtypes
Out[15]: 
a     int64[pyarrow]
b    double[pyarrow]
c      bool[pyarrow]
d    string[pyarrow]
e     int64[pyarrow]
f    double[pyarrow]
g      bool[pyarrow]
h    string[pyarrow]
i      null[pyarrow]
dtype: object

In [16]: data.seek(0)
Out[16]: 0

In [17]: df_pyarrow = pd.read_csv(data, dtype_backend="pyarrow", engine="pyarrow")

In [18]: df_pyarrow.dtypes
Out[18]: 
a     int64[pyarrow]
b    double[pyarrow]
c      bool[pyarrow]
d    string[pyarrow]
e     int64[pyarrow]
f    double[pyarrow]
g      bool[pyarrow]
h    string[pyarrow]
i      null[pyarrow]
dtype: object

Copy-on-Write improvements#

  • A new lazy copy mechanism that defers the copy until the object in question is modified was added to the methods listed in Copy-on-Write optimizations. These methods return views when Copy-on-Write is enabled, which provides a significant performance improvement compared to the regular execution (GH 49473).

  • Accessing a single column of a DataFrame as a Series (e.g. df["col"]) now always returns a new object every time it is constructed when Copy-on-Write is enabled (not returning multiple times an identical, cached Series object). This ensures that those Series objects correctly follow the Copy-on-Write rules (GH 49450)

  • The Series constructor will now create a lazy copy (deferring the copy until a modification to the data happens) when constructing a Series from an existing Series with the default of copy=False (GH 50471)

  • The DataFrame constructor will now create a lazy copy (deferring the copy until a modification to the data happens) when constructing from an existing DataFrame with the default of copy=False (GH 51239)

  • The DataFrame constructor, when constructing a DataFrame from a dictionary of Series objects and specifying copy=False, will now use a lazy copy of those Series objects for the columns of the DataFrame (GH 50777)

  • The DataFrame constructor, when constructing a DataFrame from a Series or Index and specifying copy=False, will now respect Copy-on-Write.

  • The DataFrame and Series constructors, when constructing from a NumPy array, will now copy the array by default to avoid mutating the DataFrame / Series when mutating the array. Specify copy=False to get the old behavior. When setting copy=False pandas does not guarantee correct Copy-on-Write behavior when the NumPy array is modified after creation of the DataFrame / Series.

  • The DataFrame.from_records() will now respect Copy-on-Write when called with a DataFrame.

  • Trying to set values using chained assignment (for example, df["a"][1:3] = 0) will now always raise a warning when Copy-on-Write is enabled. In this mode, chained assignment can never work because we are always setting into a temporary object that is the result of an indexing operation (getitem), which under Copy-on-Write always behaves as a copy. Thus, assigning through a chain can never update the original Series or DataFrame. Therefore, an informative warning is raised to the user to avoid silently doing nothing (GH 49467)

  • DataFrame.replace() will now respect the Copy-on-Write mechanism when inplace=True.

  • DataFrame.transpose() will now respect the Copy-on-Write mechanism.

  • Arithmetic operations that can be inplace, e.g. ser *= 2 will now respect the Copy-on-Write mechanism.

  • DataFrame.__getitem__() will now respect the Copy-on-Write mechanism when the DataFrame has MultiIndex columns.

  • Series.__getitem__() will now respect the Copy-on-Write mechanism when the

    Series has a MultiIndex.

  • Series.view() will now respect the Copy-on-Write mechanism.

Copy-on-Write can be enabled through one of

pd.set_option("mode.copy_on_write", True)

pd.options.mode.copy_on_write = True

Alternatively, copy on write can be enabled locally through:

with pd.option_context("mode.copy_on_write", True):
    ...

Other enhancements#

Notable bug fixes#

These are bug fixes that might have notable behavior changes.

DataFrameGroupBy.cumsum() and DataFrameGroupBy.cumprod() overflow instead of lossy casting to float#

In previous versions we cast to float when applying cumsum and cumprod which lead to incorrect results even if the result could be hold by int64 dtype. Additionally, the aggregation overflows consistent with numpy and the regular DataFrame.cumprod() and DataFrame.cumsum() methods when the limit of int64 is reached (GH 37493).

Old Behavior

In [1]: df = pd.DataFrame({"key": ["b"] * 7, "value": 625})
In [2]: df.groupby("key")["value"].cumprod()[5]
Out[2]: 5.960464477539062e+16

We return incorrect results with the 6th value.

New Behavior

In [19]: df = pd.DataFrame({"key": ["b"] * 7, "value": 625})

In [20]: df.groupby("key")["value"].cumprod()
Out[20]: 
0                   625
1                390625
2             244140625
3          152587890625
4        95367431640625
5     59604644775390625
6    359414837200037393
Name: value, dtype: int64

We overflow with the 7th value, but the 6th value is still correct.

DataFrameGroupBy.nth() and SeriesGroupBy.nth() now behave as filtrations#

In previous versions of pandas, DataFrameGroupBy.nth() and SeriesGroupBy.nth() acted as if they were aggregations. However, for most inputs n, they may return either zero or multiple rows per group. This means that they are filtrations, similar to e.g. DataFrameGroupBy.head(). pandas now treats them as filtrations (GH 13666).

In [21]: df = pd.DataFrame({"a": [1, 1, 2, 1, 2], "b": [np.nan, 2.0, 3.0, 4.0, 5.0]})

In [22]: gb = df.groupby("a")

Old Behavior

In [5]: gb.nth(n=1)
Out[5]:
   A    B
1  1  2.0
4  2  5.0

New Behavior

In [23]: gb.nth(n=1)
Out[23]: 
   a    b
1  1  2.0
4  2  5.0

In particular, the index of the result is derived from the input by selecting the appropriate rows. Also, when n is larger than the group, no rows instead of NaN is returned.

Old Behavior

In [5]: gb.nth(n=3, dropna="any")
Out[5]:
    B
A
1 NaN
2 NaN

New Behavior

In [24]: gb.nth(n=3, dropna="any")
Out[24]: 
Empty DataFrame
Columns: [a, b]
Index: []

Backwards incompatible API changes#

Construction with datetime64 or timedelta64 dtype with unsupported resolution#

In past versions, when constructing a Series or DataFrame and passing a “datetime64” or “timedelta64” dtype with unsupported resolution (i.e. anything other than “ns”), pandas would silently replace the given dtype with its nanosecond analogue:

Previous behavior:

In [5]: pd.Series(["2016-01-01"], dtype="datetime64[s]")
Out[5]:
0   2016-01-01
dtype: datetime64[ns]

In [6] pd.Series(["2016-01-01"], dtype="datetime64[D]")
Out[6]:
0   2016-01-01
dtype: datetime64[ns]

In pandas 2.0 we support resolutions “s”, “ms”, “us”, and “ns”. When passing a supported dtype (e.g. “datetime64[s]”), the result now has exactly the requested dtype:

New behavior:

In [25]: pd.Series(["2016-01-01"], dtype="datetime64[s]")
Out[25]: 
0   2016-01-01
dtype: datetime64[s]

With an un-supported dtype, pandas now raises instead of silently swapping in a supported dtype:

New behavior:

In [26]: pd.Series(["2016-01-01"], dtype="datetime64[D]")
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[26], line 1
----> 1 pd.Series(["2016-01-01"], dtype="datetime64[D]")

File ~/work/pandas/pandas/pandas/core/series.py:584, in Series.__init__(self, data, index, dtype, name, copy, fastpath)
    582         data = data.copy()
    583 else:
--> 584     data = sanitize_array(data, index, dtype, copy)
    586     manager = _get_option("mode.data_manager", silent=True)
    587     if manager == "block":

File ~/work/pandas/pandas/pandas/core/construction.py:648, in sanitize_array(data, index, dtype, copy, allow_2d)
    645     subarr = np.array([], dtype=np.float64)
    647 elif dtype is not None:
--> 648     subarr = _try_cast(data, dtype, copy)
    650 else:
    651     subarr = maybe_convert_platform(data)

File ~/work/pandas/pandas/pandas/core/construction.py:805, in _try_cast(arr, dtype, copy)
    800     return lib.ensure_string_array(arr, convert_na_value=False, copy=copy).reshape(
    801         shape
    802     )
    804 elif dtype.kind in "mM":
--> 805     return maybe_cast_to_datetime(arr, dtype)
    807 # GH#15832: Check if we are requesting a numeric dtype and
    808 # that we can convert the data to the requested dtype.
    809 elif dtype.kind in "iu":
    810     # this will raise if we have e.g. floats

File ~/work/pandas/pandas/pandas/core/dtypes/cast.py:1219, in maybe_cast_to_datetime(value, dtype)
   1215     raise TypeError("value must be listlike")
   1217 # TODO: _from_sequence would raise ValueError in cases where
   1218 #  _ensure_nanosecond_dtype raises TypeError
-> 1219 _ensure_nanosecond_dtype(dtype)
   1221 if lib.is_np_dtype(dtype, "m"):
   1222     res = TimedeltaArray._from_sequence(value, dtype=dtype)

File ~/work/pandas/pandas/pandas/core/dtypes/cast.py:1276, in _ensure_nanosecond_dtype(dtype)
   1273     raise ValueError(msg)
   1274 # TODO: ValueError or TypeError? existing test
   1275 #  test_constructor_generic_timestamp_bad_frequency expects TypeError
-> 1276 raise TypeError(
   1277     f"dtype={dtype} is not supported. Supported resolutions are 's', "
   1278     "'ms', 'us', and 'ns'"
   1279 )

TypeError: dtype=datetime64[D] is not supported. Supported resolutions are 's', 'ms', 'us', and 'ns'

Value counts sets the resulting name to count#

In past versions, when running Series.value_counts(), the result would inherit the original object’s name, and the result index would be nameless. This would cause confusion when resetting the index, and the column names would not correspond with the column values. Now, the result name will be 'count' (or 'proportion' if normalize=True was passed), and the index will be named after the original object (GH 49497).

Previous behavior:

In [8]: pd.Series(['quetzal', 'quetzal', 'elk'], name='animal').value_counts()

Out[2]:
quetzal    2
elk        1
Name: animal, dtype: int64

New behavior:

In [27]: pd.Series(['quetzal', 'quetzal', 'elk'], name='animal').value_counts()
Out[27]: 
animal
quetzal    2
elk        1
Name: count, dtype: int64

Likewise for other value_counts methods (for example, DataFrame.value_counts()).

Disallow astype conversion to non-supported datetime64/timedelta64 dtypes#

In previous versions, converting a Series or DataFrame from datetime64[ns] to a different datetime64[X] dtype would return with datetime64[ns] dtype instead of the requested dtype. In pandas 2.0, support is added for “datetime64[s]”, “datetime64[ms]”, and “datetime64[us]” dtypes, so converting to those dtypes gives exactly the requested dtype:

Previous behavior:

In [28]: idx = pd.date_range("2016-01-01", periods=3)

In [29]: ser = pd.Series(idx)

Previous behavior:

In [4]: ser.astype("datetime64[s]")
Out[4]:
0   2016-01-01
1   2016-01-02
2   2016-01-03
dtype: datetime64[ns]

With the new behavior, we get exactly the requested dtype:

New behavior:

In [30]: ser.astype("datetime64[s]")
Out[30]: 
0   2016-01-01
1   2016-01-02
2   2016-01-03
dtype: datetime64[s]

For non-supported resolutions e.g. “datetime64[D]”, we raise instead of silently ignoring the requested dtype:

New behavior:

In [31]: ser.astype("datetime64[D]")
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[31], line 1
----> 1 ser.astype("datetime64[D]")

File ~/work/pandas/pandas/pandas/core/generic.py:6640, in NDFrame.astype(self, dtype, copy, errors)
   6634     results = [
   6635         ser.astype(dtype, copy=copy, errors=errors) for _, ser in self.items()
   6636     ]
   6638 else:
   6639     # else, only a single dtype is given
-> 6640     new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
   6641     res = self._constructor_from_mgr(new_data, axes=new_data.axes)
   6642     return res.__finalize__(self, method="astype")

File ~/work/pandas/pandas/pandas/core/internals/managers.py:430, in BaseBlockManager.astype(self, dtype, copy, errors)
    427 elif using_copy_on_write():
    428     copy = False
--> 430 return self.apply(
    431     "astype",
    432     dtype=dtype,
    433     copy=copy,
    434     errors=errors,
    435     using_cow=using_copy_on_write(),
    436 )

File ~/work/pandas/pandas/pandas/core/internals/managers.py:363, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
    361         applied = b.apply(f, **kwargs)
    362     else:
--> 363         applied = getattr(b, f)(**kwargs)
    364     result_blocks = extend_blocks(applied, result_blocks)
    366 out = type(self).from_blocks(result_blocks, self.axes)

File ~/work/pandas/pandas/pandas/core/internals/blocks.py:758, in Block.astype(self, dtype, copy, errors, using_cow, squeeze)
    755         raise ValueError("Can not squeeze with more than one column.")
    756     values = values[0, :]  # type: ignore[call-overload]
--> 758 new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
    760 new_values = maybe_coerce_values(new_values)
    762 refs = None

File ~/work/pandas/pandas/pandas/core/dtypes/astype.py:237, in astype_array_safe(values, dtype, copy, errors)
    234     dtype = dtype.numpy_dtype
    236 try:
--> 237     new_values = astype_array(values, dtype, copy=copy)
    238 except (ValueError, TypeError):
    239     # e.g. _astype_nansafe can fail on object-dtype of strings
    240     #  trying to convert to float
    241     if errors == "ignore":

File ~/work/pandas/pandas/pandas/core/dtypes/astype.py:179, in astype_array(values, dtype, copy)
    175     return values
    177 if not isinstance(values, np.ndarray):
    178     # i.e. ExtensionArray
--> 179     values = values.astype(dtype, copy=copy)
    181 else:
    182     values = _astype_nansafe(values, dtype, copy=copy)

File ~/work/pandas/pandas/pandas/core/arrays/datetimes.py:739, in DatetimeArray.astype(self, dtype, copy)
    737 elif isinstance(dtype, PeriodDtype):
    738     return self.to_period(freq=dtype.freq)
--> 739 return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy)

File ~/work/pandas/pandas/pandas/core/arrays/datetimelike.py:492, in DatetimeLikeArrayMixin.astype(self, dtype, copy)
    488 elif (dtype.kind in "mM" and self.dtype != dtype) or dtype.kind == "f":
    489     # disallow conversion between datetime/timedelta,
    490     # and conversions for any datetimelike to float
    491     msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
--> 492     raise TypeError(msg)
    493 else:
    494     return np.asarray(self, dtype=dtype)

TypeError: Cannot cast DatetimeArray to dtype datetime64[D]

For conversion from timedelta64[ns] dtypes, the old behavior converted to a floating point format.

Previous behavior:

In [32]: idx = pd.timedelta_range("1 Day", periods=3)

In [33]: ser = pd.Series(idx)

Previous behavior:

In [7]: ser.astype("timedelta64[s]")
Out[7]:
0     86400.0
1    172800.0
2    259200.0
dtype: float64

In [8]: ser.astype("timedelta64[D]")
Out[8]:
0    1.0
1    2.0
2    3.0
dtype: float64

The new behavior, as for datetime64, either gives exactly the requested dtype or raises:

New behavior:

In [34]: ser.astype("timedelta64[s]")
Out[34]: 
0   1 days
1   2 days
2   3 days
dtype: timedelta64[s]

In [35]: ser.astype("timedelta64[D]")
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[35], line 1
----> 1 ser.astype("timedelta64[D]")

File ~/work/pandas/pandas/pandas/core/generic.py:6640, in NDFrame.astype(self, dtype, copy, errors)
   6634     results = [
   6635         ser.astype(dtype, copy=copy, errors=errors) for _, ser in self.items()
   6636     ]
   6638 else:
   6639     # else, only a single dtype is given
-> 6640     new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
   6641     res = self._constructor_from_mgr(new_data, axes=new_data.axes)
   6642     return res.__finalize__(self, method="astype")

File ~/work/pandas/pandas/pandas/core/internals/managers.py:430, in BaseBlockManager.astype(self, dtype, copy, errors)
    427 elif using_copy_on_write():
    428     copy = False
--> 430 return self.apply(
    431     "astype",
    432     dtype=dtype,
    433     copy=copy,
    434     errors=errors,
    435     using_cow=using_copy_on_write(),
    436 )

File ~/work/pandas/pandas/pandas/core/internals/managers.py:363, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
    361         applied = b.apply(f, **kwargs)
    362     else:
--> 363         applied = getattr(b, f)(**kwargs)
    364     result_blocks = extend_blocks(applied, result_blocks)
    366 out = type(self).from_blocks(result_blocks, self.axes)

File ~/work/pandas/pandas/pandas/core/internals/blocks.py:758, in Block.astype(self, dtype, copy, errors, using_cow, squeeze)
    755         raise ValueError("Can not squeeze with more than one column.")
    756     values = values[0, :]  # type: ignore[call-overload]
--> 758 new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
    760 new_values = maybe_coerce_values(new_values)
    762 refs = None

File ~/work/pandas/pandas/pandas/core/dtypes/astype.py:237, in astype_array_safe(values, dtype, copy, errors)
    234     dtype = dtype.numpy_dtype
    236 try:
--> 237     new_values = astype_array(values, dtype, copy=copy)
    238 except (ValueError, TypeError):
    239     # e.g. _astype_nansafe can fail on object-dtype of strings
    240     #  trying to convert to float
    241     if errors == "ignore":

File ~/work/pandas/pandas/pandas/core/dtypes/astype.py:179, in astype_array(values, dtype, copy)
    175     return values
    177 if not isinstance(values, np.ndarray):
    178     # i.e. ExtensionArray
--> 179     values = values.astype(dtype, copy=copy)
    181 else:
    182     values = _astype_nansafe(values, dtype, copy=copy)

File ~/work/pandas/pandas/pandas/core/arrays/timedeltas.py:358, in TimedeltaArray.astype(self, dtype, copy)
    354         return type(self)._simple_new(
    355             res_values, dtype=res_values.dtype, freq=self.freq
    356         )
    357     else:
--> 358         raise ValueError(
    359             f"Cannot convert from {self.dtype} to {dtype}. "
    360             "Supported resolutions are 's', 'ms', 'us', 'ns'"
    361         )
    363 return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)

ValueError: Cannot convert from timedelta64[ns] to timedelta64[D]. Supported resolutions are 's', 'ms', 'us', 'ns'

UTC and fixed-offset timezones default to standard-library tzinfo objects#

In previous versions, the default tzinfo object used to represent UTC was pytz.UTC. In pandas 2.0, we default to datetime.timezone.utc instead. Similarly, for timezones represent fixed UTC offsets, we use datetime.timezone objects instead of pytz.FixedOffset objects. See (GH 34916)

Previous behavior:

In [2]: ts = pd.Timestamp("2016-01-01", tz="UTC")
In [3]: type(ts.tzinfo)
Out[3]: pytz.UTC

In [4]: ts2 = pd.Timestamp("2016-01-01 04:05:06-07:00")
In [3]: type(ts2.tzinfo)
Out[5]: pytz._FixedOffset

New behavior:

In [36]: ts = pd.Timestamp("2016-01-01", tz="UTC")

In [37]: type(ts.tzinfo)
Out[37]: datetime.timezone

In [38]: ts2 = pd.Timestamp("2016-01-01 04:05:06-07:00")

In [39]: type(ts2.tzinfo)
Out[39]: datetime.timezone

For timezones that are neither UTC nor fixed offsets, e.g. “US/Pacific”, we continue to default to pytz objects.

Empty DataFrames/Series will now default to have a RangeIndex#

Before, constructing an empty (where data is None or an empty list-like argument) Series or DataFrame without specifying the axes (index=None, columns=None) would return the axes as empty Index with object dtype.

Now, the axes return an empty RangeIndex (GH 49572).

Previous behavior:

In [8]: pd.Series().index
Out[8]:
Index([], dtype='object')

In [9] pd.DataFrame().axes
Out[9]:
[Index([], dtype='object'), Index([], dtype='object')]

New behavior:

In [40]: pd.Series().index
Out[40]: RangeIndex(start=0, stop=0, step=1)

In [41]: pd.DataFrame().axes
Out[41]: [RangeIndex(start=0, stop=0, step=1), RangeIndex(start=0, stop=0, step=1)]

DataFrame to LaTeX has a new render engine#

The existing DataFrame.to_latex() has been restructured to utilise the extended implementation previously available under Styler.to_latex(). The arguments signature is similar, albeit col_space has been removed since it is ignored by LaTeX engines. This render engine also requires jinja2 as a dependency which needs to be installed, since rendering is based upon jinja2 templates.

The pandas latex options below are no longer used and have been removed. The generic max rows and columns arguments remain but for this functionality should be replaced by the Styler equivalents. The alternative options giving similar functionality are indicated below:

  • display.latex.escape: replaced with styler.format.escape,

  • display.latex.longtable: replaced with styler.latex.environment,

  • display.latex.multicolumn, display.latex.multicolumn_format and display.latex.multirow: replaced with styler.sparse.rows, styler.sparse.columns, styler.latex.multirow_align and styler.latex.multicol_align,

  • display.latex.repr: replaced with styler.render.repr,

  • display.max_rows and display.max_columns: replace with styler.render.max_rows, styler.render.max_columns and styler.render.max_elements.

Note that due to this change some defaults have also changed:

  • multirow now defaults to True.

  • multirow_align defaults to “r” instead of “l”.

  • multicol_align defaults to “r” instead of “l”.

  • escape now defaults to False.

Note that the behaviour of _repr_latex_ is also changed. Previously setting display.latex.repr would generate LaTeX only when using nbconvert for a JupyterNotebook, and not when the user is running the notebook. Now the styler.render.repr option allows control of the specific output within JupyterNotebooks for operations (not just on nbconvert). See GH 39911.

Increased minimum versions for dependencies#

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

Package

Minimum Version

Required

Changed

mypy (dev)

1.0

X

pytest (dev)

7.0.0

X

pytest-xdist (dev)

2.2.0

X

hypothesis (dev)

6.34.2

X

python-dateutil

2.8.2

X

X

tzdata

2022.1

X

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

pyarrow

7.0.0

X

matplotlib

3.6.1

X

fastparquet

0.6.3

X

xarray

0.21.0

X

See Dependencies and Optional dependencies for more.

Datetimes are now parsed with a consistent format#

In the past, to_datetime() guessed the format for each element independently. This was appropriate for some cases where elements had mixed date formats - however, it would regularly cause problems when users expected a consistent format but the function would switch formats between elements. As of version 2.0.0, parsing will use a consistent format, determined by the first non-NA value (unless the user specifies a format, in which case that is used).

Old behavior:

In [1]: ser = pd.Series(['13-01-2000', '12-01-2000'])
In [2]: pd.to_datetime(ser)
Out[2]:
0   2000-01-13
1   2000-12-01
dtype: datetime64[ns]

New behavior:

In [42]: ser = pd.Series(['13-01-2000', '12-01-2000'])

In [43]: pd.to_datetime(ser)
Out[43]: 
0   2000-01-13
1   2000-01-12
dtype: datetime64[ns]

Note that this affects read_csv() as well.

If you still need to parse dates with inconsistent formats, you can use format='mixed' (possibly alongside dayfirst)

ser = pd.Series(['13-01-2000', '12 January 2000'])
pd.to_datetime(ser, format='mixed', dayfirst=True)

or, if your formats are all ISO8601 (but possibly not identically-formatted)

ser = pd.Series(['2020-01-01', '2020-01-01 03:00'])
pd.to_datetime(ser, format='ISO8601')

Other API changes#

  • The freq, tz, nanosecond, and unit keywords in the Timestamp constructor are now keyword-only (GH 45307, GH 32526)

  • Passing nanoseconds greater than 999 or less than 0 in Timestamp now raises a ValueError (GH 48538, GH 48255)

  • read_csv(): specifying an incorrect number of columns with index_col of now raises ParserError instead of IndexError when using the c parser.

  • Default value of dtype in get_dummies() is changed to bool from uint8 (GH 45848)

  • DataFrame.astype(), Series.astype(), and DatetimeIndex.astype() casting datetime64 data to any of “datetime64[s]”, “datetime64[ms]”, “datetime64[us]” will return an object with the given resolution instead of coercing back to “datetime64[ns]” (GH 48928)

  • DataFrame.astype(), Series.astype(), and DatetimeIndex.astype() casting timedelta64 data to any of “timedelta64[s]”, “timedelta64[ms]”, “timedelta64[us]” will return an object with the given resolution instead of coercing to “float64” dtype (GH 48963)

  • DatetimeIndex.astype(), TimedeltaIndex.astype(), PeriodIndex.astype() Series.astype(), DataFrame.astype() with datetime64, timedelta64 or PeriodDtype dtypes no longer allow converting to integer dtypes other than “int64”, do obj.astype('int64', copy=False).astype(dtype) instead (GH 49715)

  • Index.astype() now allows casting from float64 dtype to datetime-like dtypes, matching Series behavior (GH 49660)

  • Passing data with dtype of “timedelta64[s]”, “timedelta64[ms]”, or “timedelta64[us]” to TimedeltaIndex, Series, or DataFrame constructors will now retain that dtype instead of casting to “timedelta64[ns]”; timedelta64 data with lower resolution will be cast to the lowest supported resolution “timedelta64[s]” (GH 49014)

  • Passing dtype of “timedelta64[s]”, “timedelta64[ms]”, or “timedelta64[us]” to TimedeltaIndex, Series, or DataFrame constructors will now retain that dtype instead of casting to “timedelta64[ns]”; passing a dtype with lower resolution for Series or DataFrame will be cast to the lowest supported resolution “timedelta64[s]” (GH 49014)

  • Passing a np.datetime64 object with non-nanosecond resolution to Timestamp will retain the input resolution if it is “s”, “ms”, “us”, or “ns”; otherwise it will be cast to the closest supported resolution (GH 49008)

  • Passing datetime64 values with resolution other than nanosecond to to_datetime() will retain the input resolution if it is “s”, “ms”, “us”, or “ns”; otherwise it will be cast to the closest supported resolution (GH 50369)

  • Passing integer values and a non-nanosecond datetime64 dtype (e.g. “datetime64[s]”) DataFrame, Series, or Index will treat the values as multiples of the dtype’s unit, matching the behavior of e.g. Series(np.array(values, dtype="M8[s]")) (GH 51092)

  • Passing a string in ISO-8601 format to Timestamp will retain the resolution of the parsed input if it is “s”, “ms”, “us”, or “ns”; otherwise it will be cast to the closest supported resolution (GH 49737)

  • The other argument in DataFrame.mask() and Series.mask() now defaults to no_default instead of np.nan consistent with DataFrame.where() and Series.where(). Entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes). (GH 49111)

  • Changed behavior of Series.quantile() and DataFrame.quantile() with SparseDtype to retain sparse dtype (GH 49583)

  • When creating a Series with a object-dtype Index of datetime objects, pandas no longer silently converts the index to a DatetimeIndex (GH 39307, GH 23598)

  • pandas.testing.assert_index_equal() with parameter exact="equiv" now considers two indexes equal when both are either a RangeIndex or Index with an int64 dtype. Previously it meant either a RangeIndex or a Int64Index (GH 51098)

  • Series.unique() with dtype “timedelta64[ns]” or “datetime64[ns]” now returns TimedeltaArray or DatetimeArray instead of numpy.ndarray (GH 49176)

  • to_datetime() and DatetimeIndex now allow sequences containing both datetime objects and numeric entries, matching Series behavior (GH 49037, GH 50453)

  • pandas.api.types.is_string_dtype() now only returns True for array-likes with dtype=object when the elements are inferred to be strings (GH 15585)

  • Passing a sequence containing datetime objects and date objects to Series constructor will return with object dtype instead of datetime64[ns] dtype, consistent with Index behavior (GH 49341)

  • Passing strings that cannot be parsed as datetimes to Series or DataFrame with dtype="datetime64[ns]" will raise instead of silently ignoring the keyword and returning object dtype (GH 24435)

  • Passing a sequence containing a type that cannot be converted to Timedelta to to_timedelta() or to the Series or DataFrame constructor with dtype="timedelta64[ns]" or to TimedeltaIndex now raises TypeError instead of ValueError (GH 49525)

  • Changed behavior of Index constructor with sequence containing at least one NaT and everything else either None or NaN to infer datetime64[ns] dtype instead of object, matching Series behavior (GH 49340)

  • read_stata() with parameter index_col set to None (the default) will now set the index on the returned DataFrame to a RangeIndex instead of a Int64Index (GH 49745)

  • Changed behavior of Index, Series, and DataFrame arithmetic methods when working with object-dtypes, the results no longer do type inference on the result of the array operations, use result.infer_objects(copy=False) to do type inference on the result (GH 49999, GH 49714)

  • Changed behavior of Index constructor with an object-dtype numpy.ndarray containing all-bool values or all-complex values, this will now retain object dtype, consistent with the Series behavior (GH 49594)

  • Changed behavior of Series.astype() from object-dtype containing bytes objects to string dtypes; this now does val.decode() on bytes objects instead of str(val), matching Index.astype() behavior (GH 45326)

  • Added "None" to default na_values in read_csv() (GH 50286)

  • Changed behavior of Series and DataFrame constructors when given an integer dtype and floating-point data that is not round numbers, this now raises ValueError instead of silently retaining the float dtype; do Series(data) or DataFrame(data) to get the old behavior, and Series(data).astype(dtype) or DataFrame(data).astype(dtype) to get the specified dtype (GH 49599)

  • Changed behavior of DataFrame.shift() with axis=1, an integer fill_value, and homogeneous datetime-like dtype, this now fills new columns with integer dtypes instead of casting to datetimelike (GH 49842)

  • Files are now closed when encountering an exception in read_json() (GH 49921)

  • Changed behavior of read_csv(), read_json() & read_fwf(), where the index will now always be a RangeIndex, when no index is specified. Previously the index would be a Index with dtype object if the new DataFrame/Series has length 0 (GH 49572)

  • DataFrame.values(), DataFrame.to_numpy(), DataFrame.xs(), DataFrame.reindex(), DataFrame.fillna(), and DataFrame.replace() no longer silently consolidate the underlying arrays; do df = df.copy() to ensure consolidation (GH 49356)

  • Creating a new DataFrame using a full slice on both axes with loc or iloc (thus, df.loc[:, :] or df.iloc[:, :]) now returns a new DataFrame (shallow copy) instead of the original DataFrame, consistent with other methods to get a full slice (for example df.loc[:] or df[:]) (GH 49469)

  • The Series and DataFrame constructors will now return a shallow copy (i.e. share data, but not attributes) when passed a Series and DataFrame, respectively, and with the default of copy=False (and if no other keyword triggers a copy). Previously, the new Series or DataFrame would share the index attribute (e.g. df.index = ... would also update the index of the parent or child) (GH 49523)

  • Disallow computing cumprod for Timedelta object; previously this returned incorrect values (GH 50246)

  • DataFrame objects read from a HDFStore file without an index now have a RangeIndex instead of an int64 index (GH 51076)

  • Instantiating an Index with an numeric numpy dtype with data containing NA and/or NaT now raises a ValueError. Previously a TypeError was raised (GH 51050)

  • Loading a JSON file with duplicate columns using read_json(orient='split') renames columns to avoid duplicates, as read_csv() and the other readers do (GH 50370)

  • The levels of the index of the Series returned from Series.sparse.from_coo now always have dtype int32. Previously they had dtype int64 (GH 50926)

  • to_datetime() with unit of either “Y” or “M” will now raise if a sequence contains a non-round float value, matching the Timestamp behavior (GH 50301)

  • The methods Series.round(), DataFrame.__invert__(), Series.__invert__(), DataFrame.swapaxes(), DataFrame.first(), DataFrame.last(), Series.first(), Series.last() and DataFrame.align() will now always return new objects (GH 51032)

  • DataFrame and DataFrameGroupBy aggregations (e.g. “sum”) with object-dtype columns no longer infer non-object dtypes for their results, explicitly call result.infer_objects(copy=False) on the result to obtain the old behavior (GH 51205, GH 49603)

  • Division by zero with ArrowDtype dtypes returns -inf, nan, or inf depending on the numerator, instead of raising (GH 51541)

  • Added pandas.api.types.is_any_real_numeric_dtype() to check for real numeric dtypes (GH 51152)

  • value_counts() now returns data with ArrowDtype with pyarrow.int64 type instead of "Int64" type (GH 51462)

  • factorize() and unique() preserve the original dtype when passed numpy timedelta64 or datetime64 with non-nanosecond resolution (GH 48670)

Note

A current PDEP proposes the deprecation and removal of the keywords inplace and copy for all but a small subset of methods from the pandas API. The current discussion takes place at here. The keywords won’t be necessary anymore in the context of Copy-on-Write. If this proposal is accepted, both keywords would be deprecated in the next release of pandas and removed in pandas 3.0.

Deprecations#

Removal of prior version deprecations/changes#

  • Removed Int64Index, UInt64Index and Float64Index. See also here for more information (GH 42717)

  • Removed deprecated Timestamp.freq, Timestamp.freqstr and argument freq from the Timestamp constructor and Timestamp.fromordinal() (GH 14146)

  • Removed deprecated CategoricalBlock, Block.is_categorical(), require datetime64 and timedelta64 values to be wrapped in DatetimeArray or TimedeltaArray before passing to Block.make_block_same_class(), require DatetimeTZBlock.values to have the correct ndim when passing to the BlockManager constructor, and removed the “fastpath” keyword from the SingleBlockManager constructor (GH 40226, GH 40571)

  • Removed deprecated global option use_inf_as_null in favor of use_inf_as_na (GH 17126)

  • Removed deprecated module pandas.core.index (GH 30193)

  • Removed deprecated alias pandas.core.tools.datetimes.to_time, import the function directly from pandas.core.tools.times instead (GH 34145)

  • Removed deprecated alias pandas.io.json.json_normalize, import the function directly from pandas.json_normalize instead (GH 27615)

  • Removed deprecated Categorical.to_dense(), use np.asarray(cat) instead (GH 32639)

  • Removed deprecated Categorical.take_nd() (GH 27745)

  • Removed deprecated Categorical.mode(), use Series(cat).mode() instead (GH 45033)

  • Removed deprecated Categorical.is_dtype_equal() and CategoricalIndex.is_dtype_equal() (GH 37545)

  • Removed deprecated CategoricalIndex.take_nd() (GH 30702)

  • Removed deprecated Index.is_type_compatible() (GH 42113)

  • Removed deprecated Index.is_mixed(), check index.inferred_type directly instead (GH 32922)

  • Removed deprecated pandas.api.types.is_categorical(); use pandas.api.types.is_categorical_dtype() instead (GH 33385)

  • Removed deprecated Index.asi8() (GH 37877)

  • Enforced deprecation changing behavior when passing datetime64[ns] dtype data and timezone-aware dtype to Series, interpreting the values as wall-times instead of UTC times, matching DatetimeIndex behavior (GH 41662)

  • Enforced deprecation changing behavior when applying a numpy ufunc on multiple non-aligned (on the index or columns) DataFrame that will now align the inputs first (GH 39239)

  • Removed deprecated DataFrame._AXIS_NUMBERS(), DataFrame._AXIS_NAMES(), Series._AXIS_NUMBERS(), Series._AXIS_NAMES() (GH 33637)

  • Removed deprecated Index.to_native_types(), use obj.astype(str) instead (GH 36418)

  • Removed deprecated Series.iteritems(), DataFrame.iteritems(), use obj.items instead (GH 45321)

  • Removed deprecated DataFrame.lookup() (GH 35224)

  • Removed deprecated Series.append(), DataFrame.append(), use concat() instead (GH 35407)

  • Removed deprecated Series.iteritems(), DataFrame.iteritems() and HDFStore.iteritems() use obj.items instead (GH 45321)

  • Removed deprecated DatetimeIndex.union_many() (GH 45018)

  • Removed deprecated weekofyear and week attributes of DatetimeArray, DatetimeIndex and dt accessor in favor of isocalendar().week (GH 33595)

  • Removed deprecated RangeIndex._start(), RangeIndex._stop(), RangeIndex._step(), use start, stop, step instead (GH 30482)

  • Removed deprecated DatetimeIndex.to_perioddelta(), Use dtindex - dtindex.to_period(freq).to_timestamp() instead (GH 34853)

  • Removed deprecated Styler.hide_index() and Styler.hide_columns() (GH 49397)

  • Removed deprecated Styler.set_na_rep() and Styler.set_precision() (GH 49397)

  • Removed deprecated Styler.where() (GH 49397)

  • Removed deprecated Styler.render() (GH 49397)

  • Removed deprecated argument col_space in DataFrame.to_latex() (GH 47970)

  • Removed deprecated argument null_color in Styler.highlight_null() (GH 49397)

  • Removed deprecated argument check_less_precise in testing.assert_frame_equal(), testing.assert_extension_array_equal(), testing.assert_series_equal(), testing.assert_index_equal() (GH 30562)

  • Removed deprecated null_counts argument in DataFrame.info(). Use show_counts instead (GH 37999)

  • Removed deprecated Index.is_monotonic(), and Series.is_monotonic(); use obj.is_monotonic_increasing instead (GH 45422)

  • Removed deprecated Index.is_all_dates() (GH 36697)

  • Enforced deprecation disallowing passing a timezone-aware Timestamp and dtype="datetime64[ns]" to Series or DataFrame constructors (GH 41555)

  • Enforced deprecation disallowing passing a sequence of timezone-aware values and dtype="datetime64[ns]" to to Series or DataFrame constructors (GH 41555)

  • Enforced deprecation disallowing numpy.ma.mrecords.MaskedRecords in the DataFrame constructor; pass "{name: data[name] for name in data.dtype.names} instead (GH 40363)

  • Enforced deprecation disallowing unit-less “datetime64” dtype in Series.astype() and DataFrame.astype() (GH 47844)

  • Enforced deprecation disallowing using .astype to convert a datetime64[ns] Series, DataFrame, or DatetimeIndex to timezone-aware dtype, use obj.tz_localize or ser.dt.tz_localize instead (GH 39258)

  • Enforced deprecation disallowing using .astype to convert a timezone-aware Series, DataFrame, or DatetimeIndex to timezone-naive datetime64[ns] dtype, use obj.tz_localize(None) or obj.tz_convert("UTC").tz_localize(None) instead (GH 39258)

  • Enforced deprecation disallowing passing non boolean argument to sort in concat() (GH 44629)

  • Removed Date parser functions parse_date_time(), parse_date_fields(), parse_all_fields() and generic_parser() (GH 24518)

  • Removed argument index from the core.arrays.SparseArray constructor (GH 43523)

  • Remove argument squeeze from DataFrame.groupby() and Series.groupby() (GH 32380)

  • Removed deprecated apply, apply_index, __call__, onOffset, and isAnchored attributes from DateOffset (GH 34171)

  • Removed keep_tz argument in DatetimeIndex.to_series() (GH 29731)

  • Remove arguments names and dtype from Index.copy() and levels and codes from MultiIndex.copy() (GH 35853, GH 36685)

  • Remove argument inplace from MultiIndex.set_levels() and MultiIndex.set_codes() (GH 35626)

  • Removed arguments verbose and encoding from DataFrame.to_excel() and Series.to_excel() (GH 47912)

  • Removed argument line_terminator from DataFrame.to_csv() and Series.to_csv(), use lineterminator instead (GH 45302)

  • Removed argument inplace from DataFrame.set_axis() and Series.set_axis(), use obj = obj.set_axis(..., copy=False) instead (GH 48130)

  • Disallow passing positional arguments to MultiIndex.set_levels() and MultiIndex.set_codes() (GH 41485)

  • Disallow parsing to Timedelta strings with components with units “Y”, “y”, or “M”, as these do not represent unambiguous durations (GH 36838)

  • Removed MultiIndex.is_lexsorted() and MultiIndex.lexsort_depth() (GH 38701)

  • Removed argument how from PeriodIndex.astype(), use PeriodIndex.to_timestamp() instead (GH 37982)

  • Removed argument try_cast from DataFrame.mask(), DataFrame.where(), Series.mask() and Series.where() (GH 38836)

  • Removed argument tz from Period.to_timestamp(), use obj.to_timestamp(...).tz_localize(tz) instead (GH 34522)

  • Removed argument sort_columns in DataFrame.plot() and Series.plot() (GH 47563)

  • Removed argument is_copy from DataFrame.take() and Series.take() (GH 30615)

  • Removed argument kind from Index.get_slice_bound(), Index.slice_indexer() and Index.slice_locs() (GH 41378)

  • Removed arguments prefix, squeeze, error_bad_lines and warn_bad_lines from read_csv() (GH 40413, GH 43427)

  • Removed arguments squeeze from read_excel() (GH 43427)

  • Removed argument datetime_is_numeric from DataFrame.describe() and Series.describe() as datetime data will always be summarized as numeric data (GH 34798)

  • Disallow passing list key to Series.xs() and DataFrame.xs(), pass a tuple instead (GH 41789)

  • Disallow subclass-specific keywords (e.g. “freq”, “tz”, “names”, “closed”) in the Index constructor (GH 38597)

  • Removed argument inplace from Categorical.remove_unused_categories() (GH 37918)

  • Disallow passing non-round floats to Timestamp with unit="M" or unit="Y" (GH 47266)

  • Remove keywords convert_float and mangle_dupe_cols from read_excel() (GH 41176)

  • Remove keyword mangle_dupe_cols from read_csv() and read_table() (GH 48137)

  • Removed errors keyword from DataFrame.where(), Series.where(), DataFrame.mask() and Series.mask() (GH 47728)

  • Disallow passing non-keyword arguments to read_excel() except io and sheet_name (GH 34418)

  • Disallow passing non-keyword arguments to DataFrame.drop() and Series.drop() except labels (GH 41486)

  • Disallow passing non-keyword arguments to DataFrame.fillna() and Series.fillna() except value (GH 41485)

  • Disallow passing non-keyword arguments to StringMethods.split() and StringMethods.rsplit() except for pat (GH 47448)

  • Disallow passing non-keyword arguments to DataFrame.set_index() except keys (GH 41495)

  • Disallow passing non-keyword arguments to Resampler.interpolate() except method (GH 41699)

  • Disallow passing non-keyword arguments to DataFrame.reset_index() and Series.reset_index() except level (GH 41496)

  • Disallow passing non-keyword arguments to DataFrame.dropna() and Series.dropna() (GH 41504)

  • Disallow passing non-keyword arguments to ExtensionArray.argsort() (GH 46134)

  • Disallow passing non-keyword arguments to Categorical.sort_values() (GH 47618)

  • Disallow passing non-keyword arguments to Index.drop_duplicates() and Series.drop_duplicates() (GH 41485)

  • Disallow passing non-keyword arguments to DataFrame.drop_duplicates() except for subset (GH 41485)

  • Disallow passing non-keyword arguments to DataFrame.sort_index() and Series.sort_index() (GH 41506)

  • Disallow passing non-keyword arguments to DataFrame.interpolate() and Series.interpolate() except for method (GH 41510)

  • Disallow passing non-keyword arguments to DataFrame.any() and Series.any() (GH 44896)

  • Disallow passing non-keyword arguments to Index.set_names() except for names (GH 41551)

  • Disallow passing non-keyword arguments to Index.join() except for other (GH 46518)

  • Disallow passing non-keyword arguments to concat() except for objs (GH 41485)

  • Disallow passing non-keyword arguments to pivot() except for data (GH 48301)

  • Disallow passing non-keyword arguments to DataFrame.pivot() (GH 48301)

  • Disallow passing non-keyword arguments to read_html() except for io (GH 27573)

  • Disallow passing non-keyword arguments to read_json() except for path_or_buf (GH 27573)

  • Disallow passing non-keyword arguments to read_sas() except for filepath_or_buffer (GH 47154)

  • Disallow passing non-keyword arguments to read_stata() except for filepath_or_buffer (GH 48128)

  • Disallow passing non-keyword arguments to read_csv() except filepath_or_buffer (GH 41485)

  • Disallow passing non-keyword arguments to read_table() except filepath_or_buffer (GH 41485)

  • Disallow passing non-keyword arguments to read_fwf() except filepath_or_buffer (GH 44710)

  • Disallow passing non-keyword arguments to read_xml() except for path_or_buffer (GH 45133)

  • Disallow passing non-keyword arguments to Series.mask() and DataFrame.mask() except cond and other (GH 41580)

  • Disallow passing non-keyword arguments to DataFrame.to_stata() except for path (GH 48128)

  • Disallow passing non-keyword arguments to DataFrame.where() and Series.where() except for cond and other (GH 41523)

  • Disallow passing non-keyword arguments to Series.set_axis() and DataFrame.set_axis() except for labels (GH 41491)

  • Disallow passing non-keyword arguments to Series.rename_axis() and DataFrame.rename_axis() except for mapper (GH 47587)

  • Disallow passing non-keyword arguments to Series.clip() and DataFrame.clip() except lower and upper (GH 41511)

  • Disallow passing non-keyword arguments to Series.bfill(), Series.ffill(), DataFrame.bfill() and DataFrame.ffill() (GH 41508)

  • Disallow passing non-keyword arguments to DataFrame.replace(), Series.replace() except for to_replace and value (GH 47587)

  • Disallow passing non-keyword arguments to DataFrame.sort_values() except for by (GH 41505)

  • Disallow passing non-keyword arguments to Series.sort_values() (GH 41505)

  • Disallow passing non-keyword arguments to DataFrame.reindex() except for labels (GH 17966)

  • Disallow Index.reindex() with non-unique Index objects (GH 42568)

  • Disallowed constructing Categorical with scalar data (GH 38433)

  • Disallowed constructing CategoricalIndex without passing data (GH 38944)

  • Removed Rolling.validate(), Expanding.validate(), and ExponentialMovingWindow.validate() (GH 43665)

  • Removed Rolling.win_type returning "freq" (GH 38963)

  • Removed Rolling.is_datetimelike (GH 38963)

  • Removed the level keyword in DataFrame and Series aggregations; use groupby instead (GH 39983)

  • Removed deprecated Timedelta.delta(), Timedelta.is_populated(), and Timedelta.freq (GH 46430, GH 46476)

  • Removed deprecated NaT.freq (GH 45071)

  • Removed deprecated Categorical.replace(), use Series.replace() instead (GH 44929)

  • Removed the numeric_only keyword from Categorical.min() and Categorical.max() in favor of skipna (GH 48821)

  • Changed behavior of DataFrame.median() and DataFrame.mean() with numeric_only=None to not exclude datetime-like columns THIS NOTE WILL BE IRRELEVANT ONCE numeric_only=None DEPRECATION IS ENFORCED (GH 29941)

  • Removed is_extension_type() in favor of is_extension_array_dtype() (GH 29457)

  • Removed .ExponentialMovingWindow.vol (GH 39220)

  • Removed Index.get_value() and Index.set_value() (GH 33907, GH 28621)

  • Removed Series.slice_shift() and DataFrame.slice_shift() (GH 37601)

  • Remove DataFrameGroupBy.pad() and DataFrameGroupBy.backfill() (GH 45076)

  • Remove numpy argument from read_json() (GH 30636)

  • Disallow passing abbreviations for orient in DataFrame.to_dict() (GH 32516)

  • Disallow partial slicing on an non-monotonic DatetimeIndex with keys which are not in Index. This now raises a KeyError (GH 18531)

  • Removed get_offset in favor of to_offset() (GH 30340)

  • Removed the warn keyword in infer_freq() (GH 45947)

  • Removed the include_start and include_end arguments in DataFrame.between_time() in favor of inclusive (GH 43248)

  • Removed the closed argument in date_range() and bdate_range() in favor of inclusive argument (GH 40245)

  • Removed the center keyword in DataFrame.expanding() (GH 20647)

  • Removed the truediv keyword from eval() (GH 29812)

  • Removed the method and tolerance arguments in Index.get_loc(). Use index.get_indexer([label], method=..., tolerance=...) instead (GH 42269)

  • Removed the pandas.datetime submodule (GH 30489)

  • Removed the pandas.np submodule (GH 30296)

  • Removed pandas.util.testing in favor of pandas.testing (GH 30745)

  • Removed Series.str.__iter__() (GH 28277)

  • Removed pandas.SparseArray in favor of arrays.SparseArray (GH 30642)

  • Removed pandas.SparseSeries and pandas.SparseDataFrame, including pickle support. (GH 30642)

  • Enforced disallowing passing an integer fill_value to DataFrame.shift() and Series.shift`() with datetime64, timedelta64, or period dtypes (GH 32591)

  • Enforced disallowing a string column label into times in DataFrame.ewm() (GH 43265)

  • Enforced disallowing passing True and False into inclusive in Series.between() in favor of "both" and "neither" respectively (GH 40628)

  • Enforced disallowing using usecols with out of bounds indices for read_csv with engine="c" (GH 25623)

  • Enforced disallowing the use of **kwargs in ExcelWriter; use the keyword argument engine_kwargs instead (GH 40430)

  • Enforced disallowing a tuple of column labels into DataFrameGroupBy.__getitem__() (GH 30546)

  • Enforced disallowing missing labels when indexing with a sequence of labels on a level of a MultiIndex. This now raises a KeyError (GH 42351)

  • Enforced disallowing setting values with .loc using a positional slice. Use .loc with labels or .iloc with positions instead (GH 31840)

  • Enforced disallowing positional indexing with a float key even if that key is a round number, manually cast to integer instead (GH 34193)

  • Enforced disallowing using a DataFrame indexer with .iloc, use .loc instead for automatic alignment (GH 39022)

  • Enforced disallowing set or dict indexers in __getitem__ and __setitem__ methods (GH 42825)

  • Enforced disallowing indexing on a Index or positional indexing on a Series producing multi-dimensional objects e.g. obj[:, None], convert to numpy before indexing instead (GH 35141)

  • Enforced disallowing dict or set objects in suffixes in merge() (GH 34810)

  • Enforced disallowing merge() to produce duplicated columns through the suffixes keyword and already existing columns (GH 22818)

  • Enforced disallowing using merge() or join() on a different number of levels (GH 34862)

  • Enforced disallowing value_name argument in DataFrame.melt() to match an element in the DataFrame columns (GH 35003)

  • Enforced disallowing passing showindex into **kwargs in DataFrame.to_markdown() and Series.to_markdown() in favor of index (GH 33091)

  • Removed setting Categorical._codes directly (GH 41429)

  • Removed setting Categorical.categories directly (GH 47834)

  • Removed argument inplace from Categorical.add_categories(), Categorical.remove_categories(), Categorical.set_categories(), Categorical.rename_categories(), Categorical.reorder_categories(), Categorical.set_ordered(), Categorical.as_ordered(), Categorical.as_unordered() (GH 37981, GH 41118, GH 41133, GH 47834)

  • Enforced Rolling.count() with min_periods=None to default to the size of the window (GH 31302)

  • Renamed fname to path in DataFrame.to_parquet(), DataFrame.to_stata() and DataFrame.to_feather() (GH 30338)

  • Enforced disallowing indexing a Series with a single item list with a slice (e.g. ser[[slice(0, 2)]]). Either convert the list to tuple, or pass the slice directly instead (GH 31333)

  • Changed behavior indexing on a DataFrame with a DatetimeIndex index using a string indexer, previously this operated as a slice on rows, now it operates like any other column key; use frame.loc[key] for the old behavior (GH 36179)

  • Enforced the display.max_colwidth option to not accept negative integers (GH 31569)

  • Removed the display.column_space option in favor of df.to_string(col_space=...) (GH 47280)

  • Removed the deprecated method mad from pandas classes (GH 11787)

  • Removed the deprecated method tshift from pandas classes (GH 11631)

  • Changed behavior of empty data passed into Series; the default dtype will be object instead of float64 (GH 29405)

  • Changed the behavior of DatetimeIndex.union(), DatetimeIndex.intersection(), and DatetimeIndex.symmetric_difference() with mismatched timezones to convert to UTC instead of casting to object dtype (GH 39328)

  • Changed the behavior of to_datetime() with argument “now” with utc=False to match Timestamp("now") (GH 18705)

  • Changed the behavior of indexing on a timezone-aware DatetimeIndex with a timezone-naive datetime object or vice-versa; these now behave like any other non-comparable type by raising KeyError (GH 36148)

  • Changed the behavior of Index.reindex(), Series.reindex(), and DataFrame.reindex() with a datetime64 dtype and a datetime.date object for fill_value; these are no longer considered equivalent to datetime.datetime objects so the reindex casts to object dtype (GH 39767)

  • Changed behavior of SparseArray.astype() when given a dtype that is not explicitly SparseDtype, cast to the exact requested dtype rather than silently using a SparseDtype instead (GH 34457)

  • Changed behavior of Index.ravel() to return a view on the original Index instead of a np.ndarray (GH 36900)

  • Changed behavior of Series.to_frame() and Index.to_frame() with explicit name=None to use None for the column name instead of the index’s name or default 0 (GH 45523)

  • Changed behavior of concat() with one array of bool-dtype and another of integer dtype, this now returns object dtype instead of integer dtype; explicitly cast the bool object to integer before concatenating to get the old behavior (GH 45101)

  • Changed behavior of DataFrame constructor given floating-point data and an integer dtype, when the data cannot be cast losslessly, the floating point dtype is retained, matching Series behavior (GH 41170)

  • Changed behavior of Index constructor when given a np.ndarray with object-dtype containing numeric entries; this now retains object dtype rather than inferring a numeric dtype, consistent with Series behavior (GH 42870)

  • Changed behavior of Index.__and__(), Index.__or__() and Index.__xor__() to behave as logical operations (matching Series behavior) instead of aliases for set operations (GH 37374)

  • Changed behavior of DataFrame constructor when passed a list whose first element is a Categorical, this now treats the elements as rows casting to object dtype, consistent with behavior for other types (GH 38845)

  • Changed behavior of DataFrame constructor when passed a dtype (other than int) that the data cannot be cast to; it now raises instead of silently ignoring the dtype (GH 41733)

  • Changed the behavior of Series constructor, it will no longer infer a datetime64 or timedelta64 dtype from string entries (GH 41731)

  • Changed behavior of Timestamp constructor with a np.datetime64 object and a tz passed to interpret the input as a wall-time as opposed to a UTC time (GH 42288)

  • Changed behavior of Timestamp.utcfromtimestamp() to return a timezone-aware object satisfying Timestamp.utcfromtimestamp(val).timestamp() == val (GH 45083)

  • Changed behavior of Index constructor when passed a SparseArray or SparseDtype to retain that dtype instead of casting to numpy.ndarray (GH 43930)

  • Changed behavior of setitem-like operations (__setitem__, fillna, where, mask, replace, insert, fill_value for shift) on an object with DatetimeTZDtype when using a value with a non-matching timezone, the value will be cast to the object’s timezone instead of casting both to object-dtype (GH 44243)

  • Changed behavior of Index, Series, DataFrame constructors with floating-dtype data and a DatetimeTZDtype, the data are now interpreted as UTC-times instead of wall-times, consistent with how integer-dtype data are treated (GH 45573)

  • Changed behavior of Series and DataFrame constructors with integer dtype and floating-point data containing NaN, this now raises IntCastingNaNError (GH 40110)

  • Changed behavior of Series and DataFrame constructors with an integer dtype and values that are too large to losslessly cast to this dtype, this now raises ValueError (GH 41734)

  • Changed behavior of Series and DataFrame constructors with an integer dtype and values having either datetime64 or timedelta64 dtypes, this now raises TypeError, use values.view("int64") instead (GH 41770)

  • Removed the deprecated base and loffset arguments from pandas.DataFrame.resample(), pandas.Series.resample() and pandas.Grouper. Use offset or origin instead (GH 31809)

  • Changed behavior of Series.fillna() and DataFrame.fillna() with timedelta64[ns] dtype and an incompatible fill_value; this now casts to object dtype instead of raising, consistent with the behavior with other dtypes (GH 45746)

  • Change the default argument of regex for Series.str.replace() from True to False. Additionally, a single character pat with regex=True is now treated as a regular expression instead of a string literal. (GH 36695, GH 24804)

  • Changed behavior of DataFrame.any() and DataFrame.all() with bool_only=True; object-dtype columns with all-bool values will no longer be included, manually cast to bool dtype first (GH 46188)

  • Changed behavior of DataFrame.max(), DataFrame.min, DataFrame.mean, DataFrame.median, DataFrame.skew, DataFrame.kurt with axis=None to return a scalar applying the aggregation across both axes (GH 45072)

  • Changed behavior of comparison of a Timestamp with a datetime.date object; these now compare as un-equal and raise on inequality comparisons, matching the datetime.datetime behavior (GH 36131)

  • Changed behavior of comparison of NaT with a datetime.date object; these now raise on inequality comparisons (GH 39196)

  • Enforced deprecation of silently dropping columns that raised a TypeError in Series.transform and DataFrame.transform when used with a list or dictionary (GH 43740)

  • Changed behavior of DataFrame.apply() with list-like so that any partial failure will raise an error (GH 43740)

  • Changed behaviour of DataFrame.to_latex() to now use the Styler implementation via Styler.to_latex() (GH 47970)

  • Changed behavior of Series.__setitem__() with an integer key and a Float64Index when the key is not present in the index; previously we treated the key as positional (behaving like series.iloc[key] = val), now we treat it is a label (behaving like series.loc[key] = val), consistent with Series.__getitem__`() behavior (GH 33469)

  • Removed na_sentinel argument from factorize(), Index.factorize(), and ExtensionArray.factorize() (GH 47157)

  • Changed behavior of Series.diff() and DataFrame.diff() with ExtensionDtype dtypes whose arrays do not implement diff, these now raise TypeError rather than casting to numpy (GH 31025)

  • Enforced deprecation of calling numpy “ufunc”s on DataFrame with method="outer"; this now raises NotImplementedError (GH 36955)

  • Enforced deprecation disallowing passing numeric_only=True to Series reductions (rank, any, all, …) with non-numeric dtype (GH 47500)

  • Changed behavior of DataFrameGroupBy.apply() and SeriesGroupBy.apply() so that group_keys is respected even if a transformer is detected (GH 34998)

  • Comparisons between a DataFrame and a Series where the frame’s columns do not match the series’s index raise ValueError instead of automatically aligning, do left, right = left.align(right, axis=1, copy=False) before comparing (GH 36795)

  • Enforced deprecation numeric_only=None (the default) in DataFrame reductions that would silently drop columns that raised; numeric_only now defaults to False (GH 41480)

  • Changed default of numeric_only to False in all DataFrame methods with that argument (GH 46096, GH 46906)

  • Changed default of numeric_only to False in Series.rank() (GH 47561)

  • Enforced deprecation of silently dropping nuisance columns in groupby and resample operations when numeric_only=False (GH 41475)

  • Enforced deprecation of silently dropping nuisance columns in Rolling, Expanding, and ExponentialMovingWindow ops. This will now raise a errors.DataError (GH 42834)

  • Changed behavior in setting values with df.loc[:, foo] = bar or df.iloc[:, foo] = bar, these now always attempt to set values inplace before falling back to casting (GH 45333)

  • Changed default of numeric_only in various DataFrameGroupBy methods; all methods now default to numeric_only=False (GH 46072)

  • Changed default of numeric_only to False in Resampler methods (GH 47177)

  • Using the method DataFrameGroupBy.transform() with a callable that returns DataFrames will align to the input’s index (GH 47244)

  • When providing a list of columns of length one to DataFrame.groupby(), the keys that are returned by iterating over the resulting DataFrameGroupBy object will now be tuples of length one (GH 47761)

  • Removed deprecated methods ExcelWriter.write_cells(), ExcelWriter.save(), ExcelWriter.cur_sheet(), ExcelWriter.handles(), ExcelWriter.path() (GH 45795)

  • The ExcelWriter attribute book can no longer be set; it is still available to be accessed and mutated (GH 48943)

  • Removed unused *args and **kwargs in Rolling, Expanding, and ExponentialMovingWindow ops (GH 47851)

  • Removed the deprecated argument line_terminator from DataFrame.to_csv() (GH 45302)

  • Removed the deprecated argument label from lreshape() (GH 30219)

  • Arguments after expr in DataFrame.eval() and DataFrame.query() are keyword-only (GH 47587)

  • Removed Index._get_attributes_dict() (GH 50648)

  • Removed Series.__array_wrap__() (GH 50648)

  • Changed behavior of DataFrame.value_counts() to return a Series with MultiIndex for any list-like(one element or not) but an Index for a single label (GH 50829)

Performance improvements#

Bug fixes#

Categorical#

Datetimelike#

Timedelta#

  • Bug in to_timedelta() raising error when input has nullable dtype Float64 (GH 48796)

  • Bug in Timedelta constructor incorrectly raising instead of returning NaT when given a np.timedelta64("nat") (GH 48898)

  • Bug in Timedelta constructor failing to raise when passed both a Timedelta object and keywords (e.g. days, seconds) (GH 48898)

  • Bug in Timedelta comparisons with very large datetime.timedelta objects incorrect raising OutOfBoundsTimedelta (GH 49021)

Timezones#

  • Bug in Series.astype() and DataFrame.astype() with object-dtype containing multiple timezone-aware datetime objects with heterogeneous timezones to a DatetimeTZDtype incorrectly raising (GH 32581)

  • Bug in to_datetime() was failing to parse date strings with timezone name when format was specified with %Z (GH 49748)

  • Better error message when passing invalid values to ambiguous parameter in Timestamp.tz_localize() (GH 49565)

  • Bug in string parsing incorrectly allowing a Timestamp to be constructed with an invalid timezone, which would raise when trying to print (GH 50668)

  • Corrected TypeError message in objects_to_datetime64ns() to inform that DatetimeIndex has mixed timezones (GH 50974)

Numeric#

Conversion#

Strings#

Interval#

Indexing#

Missing#

MultiIndex#

I/O#

Period#

  • Bug in Period.strftime() and PeriodIndex.strftime(), raising UnicodeDecodeError when a locale-specific directive was passed (GH 46319)

  • Bug in adding a Period object to an array of DateOffset objects incorrectly raising TypeError (GH 50162)

  • Bug in Period where passing a string with finer resolution than nanosecond would result in a KeyError instead of dropping the extra precision (GH 50417)

  • Bug in parsing strings representing Week-periods e.g. “2017-01-23/2017-01-29” as minute-frequency instead of week-frequency (GH 50803)

  • Bug in DataFrameGroupBy.sum(), DataFrameGroupByGroupBy.cumsum(), DataFrameGroupByGroupBy.prod(), DataFrameGroupByGroupBy.cumprod() with PeriodDtype failing to raise TypeError (GH 51040)

  • Bug in parsing empty string with Period incorrectly raising ValueError instead of returning NaT (GH 51349)

Plotting#

  • Bug in DataFrame.plot.hist(), not dropping elements of weights corresponding to NaN values in data (GH 48884)

  • ax.set_xlim was sometimes raising UserWarning which users couldn’t address due to set_xlim not accepting parsing arguments - the converter now uses Timestamp() instead (GH 49148)

Groupby/resample/rolling#

Reshaping#

Sparse#

ExtensionArray#

Styler#

Metadata#

Other#

Contributors#

A total of 260 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

  • 5j9 +

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  • Aarni Koskela +

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  • Gaurav Sheni

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  • H L +

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  • Jan Koch

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  • Jonas Haag

  • Joris Van den Bossche

  • João Meirelles +

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  • Kevin Sheppard

  • Khor Chean Wei

  • Kian Eliasi

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  • Lakmal Pinnaduwage +

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  • Lawrence Mitchell +

  • Levi Ob +

  • Loic Diridollou

  • Lorenzo Vainigli +

  • Luca Pizzini +

  • Lucas Damo +

  • Luke Manley

  • Madhuri Patil +

  • Marc Garcia

  • Marco Edward Gorelli

  • Marco Gorelli

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  • Maren Westermann +

  • Maria Stazherova +

  • Marie K +

  • Marielle +

  • Mark Harfouche +

  • Marko Pacak +

  • Martin +

  • Matheus Cerqueira +

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  • Matteo Raso +

  • Matthew Roeschke

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  • Neal Muppidi +

  • Nick Crews

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  • Pandas Development Team

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  • Pedro Nacht +

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  • Sylvain MARIE

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  • Taylor Packard

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  • Thomas H +

  • Thomas Li

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  • William Andrea

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  • Wilson Xing +

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