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# Pandas DataFrame | squeeze method

schedule Aug 10, 2023
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Pandas `DataFrame.squeeze(~)` method reduces a DataFrame with a single row or column to a Series.

# Parameters

1. `axis`link | `int` or `string` | `optional`

Whether to squeeze the rows or the columns:

Axis

Description

`0` or `"index"`

Squeeze rows.

`1` or `"columns"`

Squeeze columns.

By default, `axis=None`, which means that both rows and columns are considered to see whether any reduction is possible.

# Return Value

A `Series` if reduction is possible. Otherwise, the source `DataFrame` is returned.

# Examples

## Squeezing a single-column DataFrame

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4]})df    A0  31  4 ```

Since our DataFrame only contains a single column, we can reduce it to a `Series` like so:

``` df.squeeze() A 3B 4Name: 0, dtype: int64 ```

## Squeezing a single-row DataFrame

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3],"B":[4]})df    A  B0  3  4 ```

Since our DataFrame only contains a single row, we can reduce it to a `Series` like so:

``` df.squeeze() A 3B 4Name: 0, dtype: int64 ```

## Specifying the axis parameter

By default, both rows and columns are checked to see whether any reduction is possible. We could restrict this check to either just the row or the column by specifying the `axis` parameter.

For instance, consider the following DataFrame:

``` df = pd.DataFrame({"A":[3],"B":[4]})df    A  B0  3  4 ```

If we try to squeeze using the columns, we just get the source DataFrame `df` back:

``` df.squeeze("columns")    A  B0  3  4 ```
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