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Pandas Series: div() function

Floating division of series in Pandas

The div() function is used to get floating division of series and other, element-wise (binary operator truediv).

Equivalent to series / other, but with support to substitute a fill_value for missing data in one of the inputs.

Syntax:

Series.div(self, other, level=None, fill_value=None, axis=0)
Pandas Series division() function

Parameters:

Name Description Type/Default Value Required / Optional
other   Series or scalar value Required
fill_value Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result will be missing. None or float value
Default Value: None (NaN)
Required
level Broadcast across a level, matching Index values on the passed MultiIndex level. int or name Required

Returns: Series
The result of the operation.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
x = pd.Series([2, 2, 2, np.nan], index=['p', 'q', 'r', 's'])
x

Output:

p    2.0
q    2.0
r    2.0
s    NaN
dtype: float64
Pandas Series division() function

Python-Pandas Code:

import numpy as np
import pandas as pd
y = pd.Series([2, np.nan, 1, np.nan], index=['p', 'q', 's', 't'])
y

Output:

p    2.0
q    NaN
s    1.0
t    NaN
dtype: float64
Pandas Series division() function

Python-Pandas Code:

import numpy as np
import pandas as pd
x = pd.Series([2, 2, 2, np.nan], index=['p', 'q', 'r', 's'])
y = pd.Series([2, np.nan, 1, np.nan], index=['p', 'q', 's', 't'])
x.divide(y, fill_value=0)

Output:

p    1.0
q    inf
r    inf
s    0.0
t    NaN
dtype: float64

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