# 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
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
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, 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, 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, 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, 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
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¶

A total of 1 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Tom Augspurger