Skip to main content
`Ctrl`+`K`

# pandas.Series.squeeze

Series.squeeze(axis=None)

  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.

  Parameters:
    axis{0 or ‘index’, 1 or ‘columns’, None}, default None

      A specific axis to squeeze. By default, all length-1 axes are squeezed. For Series this parameter is unused and defaults to None.

  Returns:
    DataFrame, Series, or scalar

      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()
      np.int64(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()
      np.int64(1)