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# Pandas DataFrame | cumsum method

Pandas
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
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Pandas `DataFrame.cumsum(~)` method computes the cumulative sum along the row or column of the source DataFrame.

# Parameters

1. `axis`link | `int` or `string` | `optional`

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

Axis

Description

Compute the cumulative sum of each column.

`0` or `"index"`

Compute the cumulative sum of each row.

`1` or `"columns"`

By default, `axis=0`.

2. `skipna`link | `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  B0  3  51  4  6 ```

## Cumulative sum of each column

To compute the cumulative sum for each column:

``` df.cumsum()    A  B0  3  51  7  11 ```

## Cumulative sum of each row

To compute the cumulative sum for each row:

``` df.cumsum(axis=1)    A  B0  3  81  4  10 ```

## Dealing with missing values

Consider the following DataFrame with a missing value:

``` df = pd.DataFrame({"A":[3,pd.np.nan,5]})df A0 3.01 NaN2 5.0 ```

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

``` df.cumsum()   # skipna=True A0 3.01 NaN2 8.0 ```

To take into account the missing values:

``` df.cumsum(skipna=False) A0 3.01 NaN2 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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