# Python | Pandas Series.cumsum() to find cumulative sum of a Series

Pandas `Series.cumsum()` is used to find Cumulative sum of a series. In cumulative sum, the length of returned series is same as input and every element is equal to sum of all previous elements.

Syntax: Series.cumsum(axis=None, skipna=True)

Parameters:
axis: 0 or ‘index’ for row wise operation and 1 or ‘columns’ for column wise operation
skipna: Skips NaN addition for elements after the very next one if True.

Result type: Series

Example #1:
In this example, a series is created from a Python list using Pandas .Series() method. The list also contains a Null value and the skipna parameter is kept default, that is True.

 `# importing pandas module ` `import` `pandas as pd ` ` `  `# importing numpy module ` `import` `numpy as np ` ` `  `# making list of values ` `values ``=` `[``3``, ``4``, np.nan, ``7``, ``2``, ``0``] ` ` `  `# making series from list ` `series ``=` `pd.Series(values) ` ` `  `# calling method ` `cumsum ``=` `series.cumsum() ` ` `  `# display ` `cumsum `

Output:

```3
7
NaN
14
16
16
dtype: float64```

Explanation
Cumulative sum is sum of current and all previous values. As shown in above output, the addition was done as follows

```3
3+4 = 7
7+NaN = NaN
7+7 = 14
14+2 = 16
16+0 = 16```

Example #2: skipna=False
In this example, a series is created just like in the above example. But the `skipna `parameter is kept False. Hence NULL values won’t be ignored and it would be added every time after it’s occurrence.

 `# importing pandas module ` `import` `pandas as pd ` ` `  `# importing numpy module ` `import` `numpy as np ` ` `  `# making list of values ` `values ``=` `[``1``, ``20``, ``13``, np.nan, ``0``, ``1``, ``5``, ``23``] ` ` `  `# making series from list ` `series ``=` `pd.Series(values) ` ` `  `# calling method ` `cumsum ``=` `series.cumsum(skipna ``=` `False``) ` ` `  `# display ` `cumsum `

Output:

```0     1.0
1    21.0
2    34.0
3     NaN
4     NaN
5     NaN
6     NaN
7     NaN
dtype: float64```

Explanation: As it can be seen in output, all the values after first occurrence of NaN are also NaN since any number + NaN is also NaN.

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