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Pandas DataFrame | div method

Programming
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Python
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Pandas
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Binary Operators
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.div(~) method divides the values in the source DataFrame by a scalar, sequence, Series or DataFrame, that is:

DataFrame / other
NOTE

Unless you use the parameters axis, level and fill_value, div(~) is equivalent to performing division using the / operator.

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The resulting DataFrame will be the source DataFrame divided by other.

2. axislink | int or string | optional

Whether to broadcast other for each column or row of the source DataFrame:

Axis

Description

other is broadcasted for each column.

"index" or 0

other is broadcasted for each row.

"columns" or 1

axis is only relevant if the shape of the source DataFrame and that of other does not align. By default, axis=1.

3. level | int or string | optional

The name or the integer index of the level to consider. This is relevant only if your DataFrame is Multi-index.

4. fill_valuelink | float or None | optional

The value to replace NaN before the computation. Division between two NaN will still result in NaN. By default, fill_value=None.

Return Value

A new DataFrame resulting from the division.

Examples

Basic usage

Consider the following DataFrames:

df = pd.DataFrame({"A":[20,30], "B":[40,50]})
df_other = pd.DataFrame({"A":[5,6], "B":[4,25]})
A B | A B
0 20 40 | 0 5 4
1 30 50 | 1 6 25

Performing division yields:

df.div(df_other)
A B
0 4.0 10.0
1 5.0 2.0

Note that this is equivalent to:

df / df_other
A B
0 4.0 10.0
1 5.0 2.0

Broadcasting

Consider the following DataFrame:

df = pd.DataFrame({"A":[20,30], "B":[40,50]})
df
A B
0 20 40
1 30 50

Row-wise division

By default, axis=1, which means that other will be broadcasted for each row in df:

df.div([10,100]) # axis=1
A B
0 2.0 0.4
1 3.0 0.5

Here, we're doing the following element-wise division:

20/10 40/100
30/10 50/100

Column-wise division

To broadcast other for each column in df, set axis=0 like so:

df.div([10,100], axis=0)
A B
0 2.0 4.0
1 0.3 0.5

Here, we're doing the following element-wise division:

20/10 40/10
30/100 50/100

Specifying fill_value

Consider the following DataFrame:

df = pd.DataFrame({"A": [2,np.NaN], "B":[np.NaN,5]})
df_other = pd.DataFrame({"A":[10,20],"B":[np.NaN,np.NaN]})
A B | A B
0 2.0 NaN | 0 10 NaN
1 NaN 5.0 | 1 20 NaN

By default, when we compute the division using div(~), any operation with NaN results in NaN:

df.div(df_other)
A B
0 0.2 NaN
1 NaN NaN

We can fill the NaN values before we perform division by using the fill_value parameter like so:

df.div(df_other, fill_value=100)
A B
0 0.2 NaN
1 5.0 0.05

Notice when the operation is between two NaN, its result would still be NaN, regardless of fill_value.

robocat
Published by Isshin Inada
Edited by 0 others
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