Pandas DataFrame floordiv() method with examples

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The floordiv() method divides each value in the DataFrame with a specified value, and returns the integer (removes any decimals). This is equivalent to dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs.

Syntax –

DataFrame.floordiv(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.

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 . Integer division by a value –

Let’s say you want to do integer division and divide each values in the dataframe by 2. One way of doing this is

df // 2

Another way is using the floordiv() method.


2 . Fill missing values then do integer division –

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 integer division.

df.floordiv(2, fill_value=5)

3 . Dividing by a series –

You can also divide the dataframe by a pandas series.

# create a series
s1 = pd.Series([1, 2, 3, 4, 5, np.nan])

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

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