Pandas DataFrame - product() function

The Pandas DataFrame product() function returns the product of the values over the specified axis. The syntax for using this function is mentioned below:

Syntax

DataFrame.product(axis=None, skipna=None, level=None, 
                  numeric_only=None, min_count=0)

Parameters

`axis`	`Optional.` Specify {0 or 'index', 1 or 'columns'}. If 0 or 'index', products are generated for each column. If 1 or 'columns', products are generated for each row. Default: 0
`skipna`	`Optional.` Specify True to exclude NA/null values when computing the result. Default is True.
`level`	`Optional.` Specify level (int or str). If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series. A str specifies the level name.
`numeric_only`	`Optional.` Specify True to include only float, int or boolean data. Default: False
`min_count`	`Optional.` Specify required number of valid values to perform the operation. If the count of non-NA values is less than the min_count, the result will be NA.

Return Value

Returns product of Series or DataFrame if a level is specified.

Example: using product() column-wise on whole DataFrame

In the example below, a DataFrame df is created. The product() function is used to get the product of each column.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Sample1": [1, 2, 3, 4, 5],
  "Sample2": [11, 12, 13, 14, 15]},
  index= ["x1", "x2", "x3", "x4", "x5"]
)

print("The DataFrame is:")
print(df)

#Product of all entries column-wise
print("\ndf.product() returns:")
print(df.product())

The output of the above code will be:

The DataFrame is:
    Sample1  Sample2
x1        1       11
x2        2       12
x3        3       13
x4        4       14
x5        5       15

df.product() returns:
Sample1       120
Sample2    360360
dtype: int64

Example: using product() row-wise on whole DataFrame

To get the row-wise product, the axis parameter can be set to 1.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Sample1": [1, 2, 3, 4, 5],
  "Sample2": [11, 12, 13, 14, 15]},
  index= ["x1", "x2", "x3", "x4", "x5"]
)

print("The DataFrame is:")
print(df)

#Product of all entries row-wise
print("\ndf.product(axis=1) returns:")
print(df.product(axis=1))

The output of the above code will be:

The DataFrame is:
    Sample1  Sample2
x1        1       11
x2        2       12
x3        3       13
x4        4       14
x5        5       15

df.product(axis=1) returns:
x1    11
x2    24
x3    39
x4    56
x5    75
dtype: int64

Example: using product() on selected column

Instead of whole DataFrame, the product() function can be applied on selected columns. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Sample1": [1, 2, 3, 4, 5],
  "Sample2": [11, 12, 13, 14, 15],
  "Sample3": [9, 8, 7, 6, 5]},
  index= ["x1", "x2", "x3", "x4", "x5"]
)

print("The DataFrame is:")
print(df)

#product of single column
print("\ndf['Sample1'].product() returns:")
print(df["Sample1"].product())

#product of multiple columns
print("\ndf[['Sample1', 'Sample3']].product() returns:")
print(df[["Sample1", "Sample3"]].product())

The output of the above code will be:

The DataFrame is:
    Sample1  Sample2  Sample3
x1        1       11        9
x2        2       12        8
x3        3       13        7
x4        4       14        6
x5        5       15        5

df['Sample1'].product() returns:
120

df[['Sample1', 'Sample3']].product() returns:
Sample1      120
Sample3    15120
dtype: int64

❮ Pandas DataFrame - Functions