Series.
squeeze
Squeeze 1 dimensional axis objects into scalars.
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.
A specific axis to squeeze. By default, all length-1 axes are squeezed.
The projection after squeezing axis or all the axes.
See also
Series.iloc
Integer-location based indexing for selecting scalars.
DataFrame.iloc
Integer-location based indexing for selecting Series.
Series.to_frame
Inverse of DataFrame.squeeze for a single-column DataFrame.
Examples
>>> primes = pd.Series([2, 3, 5, 7])
Slicing might produce a Series with a single value:
>>> even_primes = primes[primes % 2 == 0] >>> even_primes 0 2 dtype: int64
>>> even_primes.squeeze() 2
Squeezing objects with more than one value in every axis does nothing:
>>> odd_primes = primes[primes % 2 == 1] >>> odd_primes 1 3 2 5 3 7 dtype: int64
>>> odd_primes.squeeze() 1 3 2 5 3 7 dtype: int64
Squeezing is even more effective when used with DataFrames.
>>> 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:
>>> df_a = df[['a']] >>> df_a a 0 1 1 3
So the columns can be squeezed down, resulting in a Series:
>>> 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:
>>> df_0a = df.loc[df.index < 1, ['a']] >>> df_0a a 0 1
Squeezing the rows produces a single scalar Series:
>>> df_0a.squeeze('rows') a 1 Name: 0, dtype: int64
Squeezing all axes will project directly into a scalar:
>>> df_0a.squeeze() 1