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# Pandas Series | map method

schedule Aug 12, 2023
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Pandas `Series.map(~)` method applies a mapping to each value of the Series. The mapping is not applied inplace, that is, a new Series is returned.

# Parameters

1. `arg` | `function` or `dict` or `Series`

The mapping to apply to each value of the Series. Check out the examples below for clarification.

2. `na_action`link | `None` or `string` | `optional`

• If `None`, then the mapping is also applied to `NaN` values.

• If `"ignore"`, then no mapping is applied to `NaN` values.

By default, `na_action=None`.

# Return Value

A `Series` with the mapping applied.

# Examples

## Applying a function

To apply a function to a Series:

``` s = pd.Series([2,3])s.map(lambda x: x+5) 0 71 8dtype: int64 ```

Here, a new Series is returned, so the original `s` is kept intact.

## Applying a mapping

We can pass a `dict` or `Series` to map each value of the source Series to another value:

``` s = pd.Series(["a",3])s.map({"a":"A"}) 0 A1 NaNdtype: object ```

Notice how since `3` is not present as a a key in our `dict`, we get a `NaN` for that entry.

## Specifying na_action

By default, `na_action=None`, which means that `NaN` values are also passed into the mapping function:

``` s = pd.Series([2,None,3])s.map(lambda x: 5 if pd.isna(x) else 10) # na_action=None 0 101 52 10dtype: int64 ```

Setting to `na_action="ignore"` means that no mapping is applied to `NaN` values:

``` s = pd.Series([2,None,3])s.map(lambda x: 5 if pd.isna(x) else 10, na_action="ignore") 0 10.01 NaN2 10.0dtype: float64 ```
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