Python | Pandas dataframe.prod()
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 dataframe.prod()
function return the value of the product for the requested axis. It multiplies all the element together on the requested axis. By default the index axis is selected.
Syntax: DataFrame.prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)
Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
min_count : The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
Returns : prod : Series or DataFrame (if level specified)
Example #1: Use prod()
function to find product of all the elements over the column axis in the dataframe.
import pandas as pd
df = pd.DataFrame({ "A" :[ 1 , 5 , 3 , 4 , 2 ],
"B" :[ 3 , 2 , 4 , 3 , 4 ],
"C" :[ 2 , 2 , 7 , 3 , 4 ],
"D" :[ 4 , 3 , 6 , 12 , 7 ]})
df
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Let’s use the dataframe.prod()
function to find the product of each element in the dataframe over the column axis.
Output :
Example #2: Use prod()
function to find the product of any axis in the dataframe. The dataframe contains NaN
values.
import pandas as pd
df = pd.DataFrame({ "A" :[ 1 , 5 , 3 , 4 , 2 ],
"B" :[ 3 , None , 4 , 3 , 4 ],
"C" :[ 2 , 2 , 7 , None , 4 ],
"D" :[ None , 3 , 6 , 12 , 7 ]})
df.prod(axis = 1 , skipna = True )
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Output :
Last Updated :
22 Nov, 2018
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