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Python | Pandas series.cumprod() to find Cumulative product of a Series
  • Last Updated : 20 Nov, 2018

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.

Pandas 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)

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.

Return type: Series



Example #1:

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.




# importing pandas module 
import pandas as pd 
    
# importing numpy module 
import numpy as np 
    
# making list of values 
values = [2, 10, np.nan, 4, 3, 0, 1
    
# making series from list 
series = pd.Series(values) 
    
# calling method 
cumprod = series.cumprod() 
    
# display 
cumprod

Output:

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.




# importing pandas module 
import pandas as pd 
    
# importing numpy module 
import numpy as np 
    
# making list of values 
values = [9, 4, 33, np.nan, 0, 1, 76, 5
    
# making series from list 
series = pd.Series(values) 
    
# calling method 
cumprod = series.cumprod(skipna = False
    
# display 
cumprod 

Output:

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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