What’s new in 1.2.0 (December 26, 2020)#

These are the changes in pandas 1.2.0. See Release notes for a full changelog including other versions of pandas.


The xlwt package for writing old-style .xls excel files is no longer maintained. The xlrd package is now only for reading old-style .xls files.

Previously, the default argument engine=None to read_excel() would result in using the xlrd engine in many cases, including new Excel 2007+ (.xlsx) files. If openpyxl is installed, many of these cases will now default to using the openpyxl engine. See the read_excel() documentation for more details.

Thus, it is strongly encouraged to install openpyxl to read Excel 2007+ (.xlsx) files. Please do not report issues when using ``xlrd`` to read ``.xlsx`` files. This is no longer supported, switch to using openpyxl instead.

Attempting to use the xlwt engine will raise a FutureWarning unless the option io.excel.xls.writer is set to "xlwt". While this option is now deprecated and will also raise a FutureWarning, it can be globally set and the warning suppressed. Users are recommended to write .xlsx files using the openpyxl engine instead.


Optionally disallow duplicate labels#

Series and DataFrame can now be created with allows_duplicate_labels=False flag to control whether the index or columns can contain duplicate labels (GH28394). This can be used to prevent accidental introduction of duplicate labels, which can affect downstream operations.

By default, duplicates continue to be allowed.

In [1]: pd.Series([1, 2], index=['a', 'a'])
a    1
a    2
Length: 2, dtype: int64

In [2]: pd.Series([1, 2], index=['a', 'a']).set_flags(allows_duplicate_labels=False)
DuplicateLabelError: Index has duplicates.
a        [0, 1]

pandas will propagate the allows_duplicate_labels property through many operations.

In [3]: a = (
   ...:     pd.Series([1, 2], index=['a', 'b'])
   ...:       .set_flags(allows_duplicate_labels=False)
   ...: )

In [4]: a
a    1
b    2
Length: 2, dtype: int64

# An operation introducing duplicates
In [5]: a.reindex(['a', 'b', 'a'])
DuplicateLabelError: Index has duplicates.
a        [0, 2]

[1 rows x 1 columns]


This is an experimental feature. Currently, many methods fail to propagate the allows_duplicate_labels value. In future versions it is expected that every method taking or returning one or more DataFrame or Series objects will propagate allows_duplicate_labels.

See Duplicate Labels for more.

The allows_duplicate_labels flag is stored in the new DataFrame.flags attribute. This stores global attributes that apply to the pandas object. This differs from DataFrame.attrs, which stores information that applies to the dataset.

Passing arguments to fsspec backends#

Many read/write functions have acquired the storage_options optional argument, to pass a dictionary of parameters to the storage backend. This allows, for example, for passing credentials to S3 and GCS storage. The details of what parameters can be passed to which backends can be found in the documentation of the individual storage backends (detailed from the fsspec docs for builtin implementations and linked to external ones). See Section Reading/writing remote files.

GH35655 added fsspec support (including storage_options) for reading excel files.

Support for binary file handles in to_csv#

to_csv() supports file handles in binary mode (GH19827 and GH35058) with encoding (GH13068 and GH23854) and compression (GH22555). If pandas does not automatically detect whether the file handle is opened in binary or text mode, it is necessary to provide mode="wb".

For example:

In [1]: import io

In [2]: data = pd.DataFrame([0, 1, 2])

In [3]: buffer = io.BytesIO()

In [4]: data.to_csv(buffer, encoding="utf-8", compression="gzip")

Support for short caption and table position in to_latex#

DataFrame.to_latex() now allows one to specify a floating table position (GH35281) and a short caption (GH36267).

The keyword position has been added to set the position.

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

In [6]: table = data.to_latex(position='ht')

In [7]: print(table)
 & a & b \\
0 & 1 & 3 \\
1 & 2 & 4 \\

Usage of the keyword caption has been extended. Besides taking a single string as an argument, one can optionally provide a tuple (full_caption, short_caption) to add a short caption macro.

In [8]: data = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})

In [9]: table = data.to_latex(caption=('the full long caption', 'short caption'))

In [10]: print(table)
\caption[short caption]{the full long caption}
 & a & b \\
0 & 1 & 3 \\
1 & 2 & 4 \\

Change in default floating precision for read_csv and read_table#

For the C parsing engine, the methods read_csv() and read_table() previously defaulted to a parser that could read floating point numbers slightly incorrectly with respect to the last bit in precision. The option floating_precision="high" has always been available to avoid this issue. Beginning with this version, the default is now to use the more accurate parser by making floating_precision=None correspond to the high precision parser, and the new option floating_precision="legacy" to use the legacy parser. The change to using the higher precision parser by default should have no impact on performance. (GH17154)

Experimental nullable data types for float data#

We’ve added Float32Dtype / Float64Dtype and FloatingArray. These are extension data types dedicated to floating point data that can hold the pd.NA missing value indicator (GH32265, GH34307).

While the default float data type already supports missing values using np.nan, these new data types use pd.NA (and its corresponding behavior) as the missing value indicator, in line with the already existing nullable integer and boolean data types.

One example where the behavior of np.nan and pd.NA is different is comparison operations:

# the default NumPy float64 dtype
In [11]: s1 = pd.Series([1.5, None])

In [12]: s1
0    1.5
1    NaN
Length: 2, dtype: float64

In [13]: s1 > 1
0     True
1    False
Length: 2, dtype: bool
# the new nullable float64 dtype
In [14]: s2 = pd.Series([1.5, None], dtype="Float64")

In [15]: s2
0     1.5
1    <NA>
Length: 2, dtype: Float64

In [16]: s2 > 1
0    True
1    <NA>
Length: 2, dtype: boolean

See the Experimental NA scalar to denote missing values doc section for more details on the behavior when using the pd.NA missing value indicator.

As shown above, the dtype can be specified using the “Float64” or “Float32” string (capitalized to distinguish it from the default “float64” data type). Alternatively, you can also use the dtype object:

In [17]: pd.Series([1.5, None], dtype=pd.Float32Dtype())
0     1.5
1    <NA>
Length: 2, dtype: Float32

Operations with the existing integer or boolean nullable data types that give float results will now also use the nullable floating data types (GH38178).


Experimental: the new floating data types are currently experimental, and their behavior or API may still change without warning. Especially the behavior regarding NaN (distinct from NA missing values) is subject to change.

Index/column name preservation when aggregating#

When aggregating using concat() or the DataFrame constructor, pandas will now attempt to preserve index and column names whenever possible (GH35847). In the case where all inputs share a common name, this name will be assigned to the result. When the input names do not all agree, the result will be unnamed. Here is an example where the index name is preserved:

In [18]: idx = pd.Index(range(5), name='abc')

In [19]: ser = pd.Series(range(5, 10), index=idx)

In [20]: pd.concat({'x': ser[1:], 'y': ser[:-1]}, axis=1)
       x    y
1    6.0  6.0
2    7.0  7.0
3    8.0  8.0
4    9.0  NaN
0    NaN  5.0

[5 rows x 2 columns]

The same is true for MultiIndex, but the logic is applied separately on a level-by-level basis.

GroupBy supports EWM operations directly#

DataFrameGroupBy now supports exponentially weighted window operations directly (GH16037).

In [21]: df = pd.DataFrame({'A': ['a', 'b', 'a', 'b'], 'B': range(4)})

In [22]: df
   A  B
0  a  0
1  b  1
2  a  2
3  b  3

[4 rows x 2 columns]

In [23]: df.groupby('A').ewm(com=1.0).mean()
a 0  0.000000
  2  1.333333
b 1  1.000000
  3  2.333333

[4 rows x 1 columns]

Additionally mean supports execution via Numba with the engine and engine_kwargs arguments. Numba must be installed as an optional dependency to use this feature.

Other enhancements#

Notable bug fixes#

These are bug fixes that might have notable behavior changes.

Consistency of DataFrame Reductions#

DataFrame.any() and DataFrame.all() with bool_only=True now determines whether to exclude object-dtype columns on a column-by-column basis, instead of checking if all object-dtype columns can be considered boolean.

This prevents pathological behavior where applying the reduction on a subset of columns could result in a larger Series result. See (GH37799).

In [24]: df = pd.DataFrame({"A": ["foo", "bar"], "B": [True, False]}, dtype=object)

In [25]: df["C"] = pd.Series([True, True])

Previous behavior:

In [5]: df.all(bool_only=True)
C    True
dtype: bool

In [6]: df[["B", "C"]].all(bool_only=True)
B    False
C    True
dtype: bool

New behavior:

In [26]: In [5]: df.all(bool_only=True)
C    True
Length: 1, dtype: bool

In [27]: In [6]: df[["B", "C"]].all(bool_only=True)
C    True
Length: 1, dtype: bool

Other DataFrame reductions with numeric_only=None will also avoid this pathological behavior (GH37827):

In [28]: df = pd.DataFrame({"A": [0, 1, 2], "B": ["a", "b", "c"]}, dtype=object)

Previous behavior:

In [3]: df.mean()
Out[3]: Series([], dtype: float64)

In [4]: df[["A"]].mean()
A    1.0
dtype: float64

New behavior:

In [3]: df.mean()
A    1.0
dtype: float64

In [4]: df[["A"]].mean()
A    1.0
dtype: float64

Moreover, DataFrame reductions with numeric_only=None will now be consistent with their Series counterparts. In particular, for reductions where the Series method raises TypeError, the DataFrame reduction will now consider that column non-numeric instead of casting to a NumPy array which may have different semantics (GH36076, GH28949, GH21020).

In [29]: ser = pd.Series([0, 1], dtype="category", name="A")

In [30]: df = ser.to_frame()

Previous behavior:

In [5]: df.any()
A    True
dtype: bool

New behavior:

In [5]: df.any()
Out[5]: Series([], dtype: bool)

Increased minimum version for Python#

pandas 1.2.0 supports Python 3.7.1 and higher (GH35214).

Increased minimum versions for dependencies#

Some minimum supported versions of dependencies were updated (GH35214). If installed, we now require:


Minimum Version



















pytest (dev)



mypy (dev)



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.


Minimum Version



















































See Dependencies and Optional dependencies for more.

Other API changes#

  • Sorting in descending order is now stable for Series.sort_values() and Index.sort_values() for Datetime-like Index subclasses. This will affect sort order when sorting a DataFrame on multiple columns, sorting with a key function that produces duplicates, or requesting the sorting index when using Index.sort_values(). When using Series.value_counts(), the count of missing values is no longer necessarily last in the list of duplicate counts. Instead, its position corresponds to the position in the original Series. When using Index.sort_values() for Datetime-like Index subclasses, NaTs ignored the na_position argument and were sorted to the beginning. Now they respect na_position, the default being last, same as other Index subclasses (GH35992)

  • Passing an invalid fill_value to Categorical.take(), DatetimeArray.take(), TimedeltaArray.take(), or PeriodArray.take() now raises a TypeError instead of a ValueError (GH37733)

  • Passing an invalid fill_value to Series.shift() with a CategoricalDtype now raises a TypeError instead of a ValueError (GH37733)

  • Passing an invalid value to IntervalIndex.insert() or CategoricalIndex.insert() now raises a TypeError instead of a ValueError (GH37733)

  • Attempting to reindex a Series with a CategoricalIndex with an invalid fill_value now raises a TypeError instead of a ValueError (GH37733)

  • CategoricalIndex.append() with an index that contains non-category values will now cast instead of raising TypeError (GH38098)


  • Deprecated parameter inplace in MultiIndex.set_codes() and MultiIndex.set_levels() (GH35626)

  • Deprecated parameter dtype of method copy() for all Index subclasses. Use the astype() method instead for changing dtype (GH35853)

  • Deprecated parameters levels and codes in MultiIndex.copy(). Use the set_levels() and set_codes() methods instead (GH36685)

  • Date parser functions parse_date_time(), parse_date_fields(), parse_all_fields() and generic_parser() from pandas.io.date_converters are deprecated and will be removed in a future version; use to_datetime() instead (GH35741)

  • DataFrame.lookup() is deprecated and will be removed in a future version, use DataFrame.melt() and DataFrame.loc() instead (GH35224)

  • The method Index.to_native_types() is deprecated. Use .astype(str) instead (GH28867)

  • Deprecated indexing DataFrame rows with a single datetime-like string as df[string] (given the ambiguity whether it is indexing the rows or selecting a column), use df.loc[string] instead (GH36179)

  • Deprecated Index.is_all_dates() (GH27744)

  • The default value of regex for Series.str.replace() will change from True to False in a future release. In addition, single character regular expressions will not be treated as literal strings when regex=True is set (GH24804)

  • Deprecated automatic alignment on comparison operations between DataFrame and Series, do frame, ser = frame.align(ser, axis=1, copy=False) before e.g. frame == ser (GH28759)

  • Rolling.count() with min_periods=None will default to the size of the window in a future version (GH31302)

  • Using “outer” ufuncs on DataFrames to return 4d ndarray is now deprecated. Convert to an ndarray first (GH23743)

  • Deprecated slice-indexing on tz-aware DatetimeIndex with naive datetime objects, to match scalar indexing behavior (GH36148)

  • Index.ravel() returning a np.ndarray is deprecated, in the future this will return a view on the same index (GH19956)

  • Deprecate use of strings denoting units with ‘M’, ‘Y’ or ‘y’ in to_timedelta() (GH36666)

  • Index methods &, |, and ^ behaving as the set operations Index.intersection(), Index.union(), and Index.symmetric_difference(), respectively, are deprecated and in the future will behave as pointwise boolean operations matching Series behavior. Use the named set methods instead (GH36758)

  • Categorical.is_dtype_equal() and CategoricalIndex.is_dtype_equal() are deprecated, will be removed in a future version (GH37545)

  • Series.slice_shift() and DataFrame.slice_shift() are deprecated, use Series.shift() or DataFrame.shift() instead (GH37601)

  • Partial slicing on unordered DatetimeIndex objects with keys that are not in the index is deprecated and will be removed in a future version (GH18531)

  • The how keyword in PeriodIndex.astype() is deprecated and will be removed in a future version, use index.to_timestamp(how=how) instead (GH37982)

  • Deprecated Index.asi8() for Index subclasses other than DatetimeIndex, TimedeltaIndex, and PeriodIndex (GH37877)

  • The inplace parameter of Categorical.remove_unused_categories() is deprecated and will be removed in a future version (GH37643)

  • The null_counts parameter of DataFrame.info() is deprecated and replaced by show_counts. It will be removed in a future version (GH37999)

Calling NumPy ufuncs on non-aligned DataFrames

Calling NumPy ufuncs on non-aligned DataFrames changed behaviour in pandas 1.2.0 (to align the inputs before calling the ufunc), but this change is reverted in pandas 1.2.1. The behaviour to not align is now deprecated instead, see the the 1.2.1 release notes for more details.

Performance improvements#

Bug fixes#


  • Categorical.fillna() will always return a copy, validate a passed fill value regardless of whether there are any NAs to fill, and disallow an NaT as a fill value for numeric categories (GH36530)

  • Bug in Categorical.__setitem__() that incorrectly raised when trying to set a tuple value (GH20439)

  • Bug in CategoricalIndex.equals() incorrectly casting non-category entries to np.nan (GH37667)

  • Bug in CategoricalIndex.where() incorrectly setting non-category entries to np.nan instead of raising TypeError (GH37977)

  • Bug in Categorical.to_numpy() and np.array(categorical) with tz-aware datetime64 categories incorrectly dropping the time zone information instead of casting to object dtype (GH38136)


  • Bug in DataFrame.combine_first() that would convert datetime-like column on other DataFrame to integer when the column is not present in original DataFrame (GH28481)

  • Bug in DatetimeArray.date where a ValueError would be raised with a read-only backing array (GH33530)

  • Bug in NaT comparisons failing to raise TypeError on invalid inequality comparisons (GH35046)

  • Bug in DateOffset where attributes reconstructed from pickle files differ from original objects when input values exceed normal ranges (e.g. months=12) (GH34511)

  • Bug in DatetimeIndex.get_slice_bound() where datetime.date objects were not accepted or naive Timestamp with a tz-aware DatetimeIndex (GH35690)

  • Bug in DatetimeIndex.slice_locs() where datetime.date objects were not accepted (GH34077)

  • Bug in DatetimeIndex.searchsorted(), TimedeltaIndex.searchsorted(), PeriodIndex.searchsorted(), and Series.searchsorted() with datetime64, timedelta64 or Period dtype placement of NaT values being inconsistent with NumPy (GH36176, GH36254)

  • Inconsistency in DatetimeArray, TimedeltaArray, and PeriodArray method __setitem__ casting arrays of strings to datetime-like scalars but not scalar strings (GH36261)

  • Bug in DatetimeArray.take() incorrectly allowing fill_value with a mismatched time zone (GH37356)

  • Bug in DatetimeIndex.shift incorrectly raising when shifting empty indexes (GH14811)

  • Timestamp and DatetimeIndex comparisons between tz-aware and tz-naive objects now follow the standard library datetime behavior, returning True/False for !=/== and raising for inequality comparisons (GH28507)

  • Bug in DatetimeIndex.equals() and TimedeltaIndex.equals() incorrectly considering int64 indexes as equal (GH36744)

  • Series.to_json(), DataFrame.to_json(), and read_json() now implement time zone parsing when orient structure is table (GH35973)

  • astype() now attempts to convert to datetime64[ns, tz] directly from object with inferred time zone from string (GH35973)

  • Bug in TimedeltaIndex.sum() and Series.sum() with timedelta64 dtype on an empty index or series returning NaT instead of Timedelta(0) (GH31751)

  • Bug in DatetimeArray.shift() incorrectly allowing fill_value with a mismatched time zone (GH37299)

  • Bug in adding a BusinessDay with nonzero offset to a non-scalar other (GH37457)

  • Bug in to_datetime() with a read-only array incorrectly raising (GH34857)

  • Bug in Series.isin() with datetime64[ns] dtype and DatetimeIndex.isin() incorrectly casting integers to datetimes (GH36621)

  • Bug in Series.isin() with datetime64[ns] dtype and DatetimeIndex.isin() failing to consider tz-aware and tz-naive datetimes as always different (GH35728)

  • Bug in Series.isin() with PeriodDtype dtype and PeriodIndex.isin() failing to consider arguments with different PeriodDtype as always different (GH37528)

  • Bug in Period constructor now correctly handles nanoseconds in the value argument (GH34621 and GH17053)














  • Bug in Styler.render() HTML was generated incorrectly because of formatting error in rowspan attribute, it now matches with w3 syntax (GH38234)






A total of 257 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

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