How to Normalize Columns in a Dataframe for Comparison and Analysis?

Normalizing Columns of a Dataframe

In a dataset, it is common for different columns to have varying value ranges. This can make it difficult to compare and analyze the data. Normalizing columns scales them to a common range, usually between 0 and 1, enabling easier comparison and analysis.

One method to normalize columns in Pandas, a popular data analysis library, is mean normalization. It involves subtracting the mean from each value and dividing the result by the standard deviation. This translates the values to a mean of 0 and a standard deviation of 1, as seen in the formula:

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

Alternatively, min-max normalization can be used. This method scales values based on the minimum and maximum values in the column. The formula for min-max normalization is:

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

To apply either method, simply use the provided formulas on the dataframe. Pandas automatically applies the function column-wise, ensuring normalization for each column independently.

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