Finding the Product of Columns in a Pandas DataFrame
This tutorial demonstrates how to find the product of multiple columns in a Pandas DataFrame in Python.
A DataFrame is a data structure, somewhat similar to a table with labeled rows and columns, that can be accessed, created, and manipulated using the Pandas module.
product()Find the product of several columns in a Pandas DataFrame using the function in Python
product()The function directly returns the product of the specified columns sorted by the axis required by the programmer.
For ease of understanding, product()the syntax of the function is shown below.
DataFrame.product(
axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs
)
product()All the function parameters are explained in detail below.
axis: As the name suggests, it defines the axis, 0 represents the index and 1 represents the column.skipna: It accepts a boolean value. By default, the value is treated asNone. If the result isTrue, then all NA/null values are excluded while calculating the result.level: The default isNone. It just represents the hierarchy of the index.numeric_only: It accepts a boolean value. By default, the value is treated asNone. If treated asTrue, only int, float, and boolean columns are included in this parameter.min_count: Usually an int value, defaulting to 0. It specifies the number of valid values required to perform a given operation.**kwargs: Any additional keywords that need to be passed are passed through this.
The following code uses product()the function to find the product of several columns in a Pandas DataFrame in Python.
example:
import pandas as pd
df1 = pd.DataFrame({"A": [8, 4], "B": [6, 2], "C": [1, 9]})
print(df1)
print(df1[["A", "B"]].product(axis=1))
Output:
A B C
0 8 6 1
1 4 2 9
0 48
1 8
dtype: int64
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