def squeeze(self, axis=None):
axis = (self._AXIS_NAMES if axis is None else<EOL>(self._get_axis_number(axis),))<EOL>try:<EOL><INDENT>return self.iloc[<EOL>tuple(<NUM_LIT:0> if i in axis and len(a) == <NUM_LIT:1> else slice(None)<EOL>for i, a in enumerate(self.axes))]<EOL><DEDENT>except Exception:<EOL><INDENT>return self<EOL><DEDENT>
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.

    .. versionadded:: 0.20.0

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()
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 wil project directly into a scalar:

>>> df_0a.squeeze()
1