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Pandas DataFrame | cumsum method

schedule Aug 12, 2023
Last updated
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Pandas DataFrame.cumsum(~) method computes the cumulative sum along the row or column of the source DataFrame.

Parameters

1. axislink | int or string | optional

Whether to compute the cumulative sum along the row or the column:

Axis

Description

0 or "index"

Compute the cumulative sum of each column.

1 or "columns"

Compute the cumulative sum of each row.

By default, axis=0.

2. skipnalink | boolean | optional

Whether or not to ignore NaN. By default, skipna=True.

Return Value

A DataFrame holding the cumulative sum of the row or columns values.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4],"B":[5,6]})
df
   A  B
0  3  5
1  4  6

Cumulative sum of each column

To compute the cumulative sum for each column:

df.cumsum()
   A  B
0  3  5
1  7  11

Cumulative sum of each row

To compute the cumulative sum for each row:

df.cumsum(axis=1)
   A  B
0  3  8
1  4  10

Dealing with missing values

Consider the following DataFrame with a missing value:

df = pd.DataFrame({"A":[3,pd.np.nan,5]})
df
A
0 3.0
1 NaN
2 5.0

By default, skipna=True, which means that missing values are skipped and do not mutate the sum:

df.cumsum()   # skipna=True
A
0 3.0
1 NaN
2 8.0

To take into account the missing values:

df.cumsum(skipna=False)
A
0 3.0
1 NaN
2 NaN

Here, notice how we end up with a NaN after the first NaN. This is because the sum of a scalar and a NaN in Pandas is a NaN, that is:

5 + pd.np.NaN
nan
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Published by Isshin Inada
Edited by 0 others
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