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Pandas DataFrame | add method

Programming
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Python
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Pandas
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DataFrame
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Binary Operators
schedule Mar 9, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.add(~) method computes the sum of the values in the source DataFrame and another scalar, sequence Series or DataFrame, that is:

DataFrame + other
NOTE

Unless you use the parameters axis, level and fill_value, add(~) is equivalent to performing addition using the + operator.

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The resulting DataFrame will be the sum of the source DataFrame and 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

This is only relevant if the shape of the source DataFrame does not align with that of other. 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 of the sum. If the sum involves two NaN, then the result would still be NaN. By default, fill_value=None.

Return Value

A new DataFrame computed by the sum of the source DataFrame and other.

Examples

Basic usage

Consider the following DataFrames:

df = pd.DataFrame({"A":[2,3], "B":[4,5]})
df_other = pd.DataFrame({"A":[6,7], "B":[8,9]})
A B | A B
0 2 4 | 0 6 8
1 3 5 | 1 7 9

Computing their sum:

df.add(df_other)
A B
0 8 12
1 10 14

Note that this equivalent to:

df + df_other
A B
0 8 12
1 10 14

Broadcasting

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3], "B":[4,5]})
df
A B
0 2 4
1 3 5

Row-wise addition

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

df.add([10,20]) # axis=1
A B
0 12 24
1 13 25

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

2+10 4+20
3+10 5+20

Column-wise addition

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

df.add([10,20], axis=0)
A B
0 12 14
1 23 25

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

2+10 4+10
3+20 5+20

Specifying fill_value

Consider the following DataFrames with some missing values:

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 NaN | 0 10 NaN
1 NaN 5 | 1 20 NaN

By default, when we take the sum using add(~), any operation with NaN results in NaN:

df.add(df_other)
A B
0 12.0 NaN
1 NaN NaN

We can fill the NaN values before we compute the sum using fill_value:

df.add(df_other, fill_value=100)
A B
0 12.0 NaN
1 120.0 105.0

Here, notice when the addition involves NaN, the resulting sum is still NaN, regardless of fill_value.

robocat
Published by Isshin Inada
Edited by 0 others
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