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Pandas DataFrame | squeeze method

Programming
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Python
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Pandas
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DataFrame
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Sorting and Restructuring DataFrames
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.squeeze(~) method reduces a DataFrame with a single row or column to a Series.

Parameters

1. axislink | int or string | optional

Whether to squeeze the rows or the columns:

Axis

Description

Squeeze rows.

0 or "index"

Squeeze columns.

1 or "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
   A
0  3
1  4

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

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

Squeezing a single-row DataFrame

Consider the following DataFrame:

df = pd.DataFrame({"A":[3],"B":[4]})
df
   A  B
0  3  4

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

df.squeeze()
A 3
B 4
Name: 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  B
0  3  4

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

df.squeeze("columns")
   A  B
0  3  4
robocat
Published by Isshin Inada
Edited by 0 others
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