Python | Pandas dataframe.product()

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.product() 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.product(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 product() function to find product of all the elements over the column axis in the dataframe.

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
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]})

# Print the dataframe
df

Let's use the dataframe.product() function to find the product of each element in the dataframe over the column axis.

# find the product over the column axis
df.product(axis = 1)

Output :   Example #2: Use product() function to find the product of any axis in the dataframe. The dataframe contains NaN values.

# importing pandas as pd
import pandas as pd

# Creating the first dataframe 
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]})

# using prod() function to raise each element
# in df1 to the power of corresponding element in df2
df.product(axis = 1, skipna = True)

Output :