Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Series.cumprod() is used to find Cumulative product of a series. In cumulative product, the length of returned series is same as input series and every element is equal to the product of current and all previous values.
Syntax: Series.cumprod(axis=None, skipna=True)
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.
Return type: Series
In this example, a series is created from a Python list. The list also contains a Null value and the
skipna parameter is kept default, that is True.
0 2.0 1 20.0 2 NaN 3 80.0 4 240.0 5 0.0 6 0.0 dtype: float64
Explanation: Cumprod is multiplication of current and all previous values.hence, the first element is always equal to first of caller series.
2 20 (2 x 10) NaN (20 x NaN = NaN, Anything multiplied with NaN returns NaN) 80 (20 x 4) 240 (80 x 3) 0 (240 x 0) 0 (0 x 1)
Example #2: Keeping 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 compared every time on it’s occurrence.
0 9.0 1 36.0 2 1188.0 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN dtype: float64
Explanation: Just like in the above example, product of current and all previous values was returned at every position. Since NaN Multiplied with anything is also NaN, and skipna parameter was kept False, Hence all values after occurrence of NaN are also NaN.
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