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 ` |

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**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 ` |

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**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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