Pandas DataFrame div() method with examples

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The div() method in pandas divides all the values of a dataframe with a specified value. This is similar to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs.

Syntax –

DataFrame.div(other, axis='columns', level=None, fill_value=None)

other – Any single or multiple element data structure, or list-like object.

axis – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

level – Broadcast across a level, matching Index values on the passed MultiIndex level.

fill_value – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Examples –

Let’s create a dataframe to work with.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A':[1, 2, 3, 4, 5, np.nan],
                 'B':[6, 7, 8, 9, 10, np.nan]})

1 . Divide by a value –

Let’s say that you want to divide each values in the dataframe by 10. One way of achieving this is

df / 10

Another way to divide each values in the dataframe by a value is using the div() method.


2 . Filling missing values then divide –

If you look at the dataframe, you can see that it contains some missing values. We can first fill these missing values using the fill_value parameter and then do the division.

df.div(10, fill_value=0)

3 . Diving by a series –

You can also divide the dataframe by a pandas series.

# create a series
s1 = pd.Series([11, 12, 13, 14, 15, np.nan])

# divide the dataframe by this series
df.div(s1, axis=0)

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