How Can You Normalize DataFrame Columns to Achieve Data Consistency?

Normalizing DataFrame Columns for Consistency

In data analysis, it's often necessary to normalize columns of a dataframe to ensure consistency in data ranges. This is especially important when dealing with data from diverse sources or when values are on different scales.

Problem Statement

Consider a dataframe with columns that have varying value ranges:

df:
    A     B   C
1000  10  0.5
765   5   0.35
800   7   0.09

The objective is to normalize the columns of this dataframe so that each value falls between 0 and 1.

Solution

Mean Normalization

Using Pandas, mean normalization can be implemented as follows:

normalized_df = (df - df.mean()) / df.std()

This method subtracts the mean of each column from the original values and then divides them by the standard deviation.

Min-Max Normalization

For min-max normalization:

normalized_df = (df - df.min()) / (df.max() - df.min())

This approach calculates the minimum and maximum values of each column and uses them to scale the original values to the range [0, 1].

Result

Both normalization methods will produce a dataframe with columns where each value is between 0 and 1. For the given example dataframe, the expected output is:

A     B    C
1     1    1
0.765 0.5  0.7
0.8   0.7  0.18

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