Pandas Series: sum() function

Last update on September 15 2022 12:55:38 (UTC/GMT +8 hours)

Sum of the values for the requested axis in Pandas

The sum() function is used to getg the sum of the values for the requested axis.

This is equivalent to the method numpy.sum.

Syntax:

Series.sum(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
axis	Axis for the function to be applied on.	{index (0)}	Required
skipna	Exclude NA/null values when computing the result.	bool Default Value: True	Required
level	If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.	int or level name Default Value: None	Required
numeric_only	Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.	bool Default Value: None	Required
min_count	The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.	int Default Value: 0	Required
**kwargs	Additional keyword arguments to be passed to the function.		Required

Returns: scalar or Series (if level specified)

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['fox', 'cat', 'snake', 'spider']],
    names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
s

Output:

  blooded  animal
warm     fox       4
         cat       4
cold     snake     0
         spider    8
Name: legs, dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['fox', 'cat', 'snake', 'spider']],
    names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
s.sum()

Output:

Example - Sum using level names, as well as indices:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['fox', 'cat', 'snake', 'spider']],
    names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
s.sum(level='blooded')

Output:

  blooded
warm    8
cold    8
Name: legs, dtype: int64

Example - By default, the sum of an empty or all-NA Series is 0:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['fox', 'cat', 'snake', 'spider']],
    names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
pd.Series([]).sum()  # min_count=0 is the default

Output:

0.0

Example - This can be controlled with the min_count parameter. For example, if you’d like the sum of an empty series to be NaN, pass min_count=1:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['fox', 'cat', 'snake', 'spider']],
    names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
pd.Series([]).sum(min_count=1)

Output:

nan

Example - Thanks to the skipna parameter, min_count handles all-NA and empty series identically:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['fox', 'cat', 'snake', 'spider']],
    names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
pd.Series([np.nan]).sum()

Output:

0.0

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['fox', 'cat', 'snake', 'spider']],
    names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
pd.Series([np.nan]).sum(min_count=1)

Output:

nan

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