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Scaling numbers column by column with pandas

Given a pandas dataframe, we have to scale numbers column by column with pandas. By Pranit Sharma Last updated : September 30, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Scale numbers column by column with pandas

Scaling numbers is a practice of pre-processing technique which is a process used in machine learning models to generalize the independent features present in the data in a fixed range. When we apply this process to Python sequence like Pandas Series, it results in a new sequence such that your entire values in a series falls under a range. For example, if the range is (0 ,1 ) our entire data within that column will be in the range 0,1 only.

We are going to have to create a DataFrame, where we will be having two columns A and B and we will set the maximum value of A as 1 and minimum values as 0 whereas we will set the maximum value of B as 0 and minimum values as 1.

Python program to scale numbers column by column with pandas

# Importing pandas package
import pandas as pd

# Importing methods from sklearn
from sklearn.preprocessing import MinMaxScaler

# Creating a dictionary
d = {
    'A':[20,30,32,34,54,56],
    'B':[18,93,28,56,83,39]
}

# Creating DataFrame
df = pd.DataFrame(d)

# Display the DataFrame
print("Original DataFrame:\n",df,"\n")

# Scaling columns
scaler = MinMaxScaler()

scaled_df = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)

# Display result
print("Result:\n",scaled_df)

Output

The output of the above program is:

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