Pandas Series.cumprod()
is used to find the cumulative product of a series. In the cumulative product, the length of the returned series is the same as that of the input series, and each element is equal to the product of the 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:
This example creates a series from a Python list. The list also contains a Null value, and the skipna
parameter remains the default, which is True.
# pandas module import import pandas as pd # numpy module import import numpy as np # list values values = [ 2 , 10 , np.nan, 4 , 3 , 0 , 1 ] # create a series from the list series = pd.Series (values) # calling method cumprod = series.cumprod () # display cumprod |
Exit :
0 2.0 1 20.0 2 NaN 3 80.0 4 240.0 5 0.0 6 0.0 dtype: float64
Explanation: Cumprod — it is the multiplication of the current and all previous values. Therefore, the first element is always equal to the first of the callers.
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: saving skipna = False
In this example, a series is created in the same as in the above example. But the skipna
parameter remains False. Therefore, NULL values will not be ignored and they will be compared each time they are found.
# pandas module import import pandas as pd # numpy module import import numpy as np # compiling a list of values values = [ 9 , 4 , 33 , np.nan, 0 , 1 , 76 , 5 ] # create a series from the list series = pd.Series (values) # calling method cumprod = series.cumprod (skipna = False ) # display cumprod |
Exit:
0 9.0 1 36.0 2 1188.0 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN dtype: float64
Explanation: As in the previous example, the product of the current and all previous values was returned at each position. Since the multiplication of NaN by anything is also NaN, and the skipna parameter was stored to False, hence all values after NaN appear are also NaN.
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