pyspark.pandas.Series.squeeze¶

Series.squeeze(axis: Union[int, str, None] = None) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, DataFrame, Series]¶

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.

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 = ps.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 = ps.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

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_1a = df.loc[[1], ['a']]
>>> df_1a
   a
1  3

Squeezing the rows produces a single scalar Series:

>>> df_1a.squeeze('rows')
a    3
Name: 1, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_1a.squeeze()
3

pyspark.pandas.Series.repeat

pyspark.pandas.Series.factorize