Pandas DataFrame mul() method with examples

The mul() method in pandas multiplies each value in the 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.mul(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 creates a dataframe.

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]})
df

1 . Multiplying with a value –

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

df * 10

Another way to multiply each values in the dataframe is using the mul() method.

df.mul(10)

2 . Fill missing values then multiply –

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 multiply with a value.

df.mul(10, fill_value=1)

3 . Multiply with a series –

You can also multiply with a pandas series.

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

# multiply the series with the dataframe
df.mul(s1, axis=0)