Pandas Series: squeeze() function

Squeeze 1 dimensional axis objects into scalars

Squeeze 1 dimensional axis Series or DataFrames 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.

Syntax:

Series.squeeze(self, axis=None)

Returns: DataFrame, Series, or scalar - The projection after squeezing axis or all the axes.

Example - Slicing might produce a Series with a single value:

Python-Pandas Code:

import numpy as np
import pandas as pd
primes = pd.Series([3, 4, 5, 7])
even_primes = primes[primes % 2 == 0]
even_primes

Output:

1    4
dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
primes = pd.Series([3, 4, 5, 7])
even_primes = primes[primes % 2 == 0]
even_primes.squeeze()

Output:

4

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

Python-Pandas Code:

import numpy as np
import pandas as pd
primes = pd.Series([3, 4, 5, 7])
odd_primes = primes[primes % 2 == 1]
odd_primes

Output:

0    3
2    5
3    7
dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
primes = pd.Series([3, 4, 5, 7])
odd_primes = primes[primes % 2 == 1]
odd_primes.squeeze()

Output:

0    3
2    5
3    7
dtype: int64

Example - Squeezing is even more effective when used with DataFrames:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [4, 5]], columns=['x', 'y'])
df

Output:

  x	y
0	2	3
1	4	5

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

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [4, 5]], columns=['x', 'y'])
df_a = df[['x']]
df_a

Output:

  x
0	2
1	4

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

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [4, 5]], columns=['x', 'y'])
df_a = df[['x']]
df_a.squeeze('columns')

Output:

0    2
1    4
Name: x, dtype: int64

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

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [4, 5]], columns=['x', 'y'])
df_0a = df.loc[df.index < 1, ['x']]
df_0a

Output:

  x
0	2

Example - Squeezing the rows produces a single scalar Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [4, 5]], columns=['x', 'y'])
df_0a = df.loc[df.index < 1, ['x']]
df_0a.squeeze('rows')

Output:

x    2
Name: 0, dtype: int64

Example - Squeezing all axes will project directly into a scalar:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [4, 5]], columns=['x', 'y'])
df_0a = df.loc[df.index < 1, ['x']]
df_0a.squeeze()

Output:

2

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