# dask.dataframe.Series.where

#### Series.where(cond, other=nan)

Replace values where the condition is False.

This docstring was copied from pandas.DataFrame.where.

Some inconsistencies with the Dask version may exist.

This method allows conditional replacement of values. Where the
condition evaluates to True, the original values are retained; where
it evaluates to False, values are replaced with corresponding entries
from `other`.

* **Parameters:**
  **cond**
  : Where cond is True, keep the original value. Where
    False, replace with corresponding value from other.
    If cond is callable, it is computed on the Series/DataFrame and
    should return boolean Series/DataFrame or array. The callable must
    not change input Series/DataFrame (though pandas doesn’t check it).

  **other**
  : Entries where cond is False are replaced with
    corresponding value from other.
    If other is callable, it is computed on the Series/DataFrame and
    should return scalar or Series/DataFrame. The callable must not
    change input Series/DataFrame (though pandas doesn’t check it).
    If not specified, entries will be filled with the corresponding
    NULL value (`np.nan` for numpy dtypes, `pd.NA` for extension
    dtypes).

  **inplace**
  : Whether to perform the operation in place on the data.

  **axis**
  : Alignment axis if needed. For Series this parameter is
    unused and defaults to 0.

  **level**
  : Alignment level if needed.
* **Returns:**
  Series or DataFrame
  : When applied to a Series, the function will return a Series,
    and when applied to a DataFrame, it will return a DataFrame.

#### SEE ALSO
[`DataFrame.mask()`](dask.dataframe.DataFrame.mask.html.md#dask.dataframe.DataFrame.mask)
: Return an object of same shape as caller.

[`Series.mask()`](dask.dataframe.Series.mask.html.md#dask.dataframe.Series.mask)
: Return an object of same shape as caller.

### Notes

The where method is an application of the if-then idiom. For each
element in the caller, if `cond` is `True` the
element is used; otherwise the corresponding element from
`other` is used. If the axis of `other` does not align with axis of
`cond` Series/DataFrame, the values of `cond` on misaligned index positions
will be filled with False.

The signature for [`Series.where()`](#dask.dataframe.Series.where) or
[`DataFrame.where()`](dask.dataframe.DataFrame.where.html.md#dask.dataframe.DataFrame.where) differs from [`numpy.where()`](https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where).
Roughly `df1.where(m, df2)` is equivalent to `np.where(m, df1, df2)`.

For further details and examples see the `where` documentation in
[indexing](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-where-mask).

The dtype of the object takes precedence. The fill value is casted to
the object’s dtype, if this can be done losslessly.

### Examples

```pycon
>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64
```

```pycon
>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)
0    99
1     1
2    99
3    99
4    99
dtype: int64
```

```pycon
>>> s.where(s > 1, 10)
0    10
1    10
2    2
3    3
4    4
dtype: int64
>>> s.mask(s > 1, 10)
0     0
1     1
2    10
3    10
4    10
dtype: int64
```

```pycon
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=["A", "B"])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
```

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