squeeze(self, axis=None)

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don't know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters

axis : {0 or 'index', 1 or 'columns', None}, default None

A specific axis to squeeze. By default, all length-1 axes are squeezed.

Returns

DataFrame, Series, or scalar

The projection after squeezing :None:None:`axis` or all the axes.

Squeeze 1 dimensional axis objects into scalars.

See Also

DataFrame.iloc

Integer-location based indexing for selecting Series.

Series.iloc

Integer-location based indexing for selecting scalars.

Series.to_frame

Inverse of DataFrame.squeeze for a single-column DataFrame.

Examples

This example is valid syntax, but we were not able to check execution

>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

This example is valid syntax, but we were not able to check execution

>>> even_primes = primes[primes % 2 == 0]
... even_primes
0    2
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> even_primes.squeeze()
2

Squeezing objects with more than one value in every axis does nothing:

This example is valid syntax, but we were not able to check execution

>>> odd_primes = primes[primes % 2 == 1]
... odd_primes
1    3
2    5
3    7
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
... df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

This example is valid syntax, but we were not able to check execution

>>> df_a = df[['a']]
... df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

This example is valid syntax, but we were not able to check execution

>>> df_a.squeeze('columns')
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

This example is valid syntax, but we were not able to check execution

>>> df_0a = df.loc[df.index < 1, ['a']]
... df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

This example is valid syntax, but we were not able to check execution

>>> df_0a.squeeze('rows')
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

This example is valid syntax, but we were not able to check execution

>>> df_0a.squeeze()
1

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.generic.NDFrame.squeeze

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