# Version 0.22.0 (December 29, 2017)

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

## Backwards incompatible API changes

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

- The sum of an empty or all-NA `Series` is now `0`

- The product of an empty or all-NA `Series` is now `1`

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

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

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

### Arithmetic operations

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

pandas 0.21.x

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

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

pandas 0.22.0

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

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

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

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

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

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

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

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

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

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

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

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

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

### Grouping by a Categorical

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

pandas 0.21.x

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

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

pandas 0.22

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

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

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

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

### Resample

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

pandas 0.21.x

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

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

pandas 0.22.0

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

    In [12]: s.resample("2d").sum()
    Out[12]:
    2017-01-01    2.0
    2017-01-03    0.0
    Freq: 2D, Length: 2, dtype: float64

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

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

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

pandas 0.21.x

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

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

pandas 0.22.0

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

    In [15]: pd.Series([1, 2], index=idx).resample("12H").sum()
    Out[15]:
    2017-01-01 00:00:00    1
    2017-01-01 12:00:00    0
    2017-01-02 00:00:00    2
    Freq: 12H, Length: 3, dtype: int64

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

    In [16]: pd.Series([1, 2], index=idx).resample("12H").sum(min_count=1)
    Out[16]:
    2017-01-01 00:00:00    1.0
    2017-01-01 12:00:00    NaN
    2017-01-02 00:00:00    2.0
    Freq: 12H, Length: 3, dtype: float64

### Rolling and expanding

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

pandas 0.21.1

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

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

pandas 0.22.0

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

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

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

## Compatibility

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

With setuptools, in your `setup.py` use:

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

With conda, use

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

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

## Contributors

For contributors, please see /usr/share/doc/contributors_list.txt or https://github.com/pandas-dev/pandas/graphs/contributors