How to normalize a column in Pandas

How to normalize a column in Pandas

To normalize a column in Pandas, you can use the apply method to apply a normalization function to the column. This method allows you to apply a function to each element in the column, and return a new Series object containing the normalized values.

Example

For example, consider the following DataFrame:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [10, 20, 30, 40, 50],
    'C': [100, 200, 300, 400, 500]
})

df

   A   B    C
0  1  10  100
1  2  20  200
2  3  30  300
3  4  40  400
4  5  50  500

This DataFrame has three columns A, B, and C, with five rows of data.

To normalize the values in a specific column, you could do the following:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [10, 20, 30, 40, 50],
    'C': [100, 200, 300, 400, 500]
})

# Define a normalization function
def normalize(x):
    return (x - x.min()) / (x.max() - x.min())

# Apply the normalization function to columns B and C
normalized = df[['B']].apply(normalize)

# Print the resulting DataFrame
print(normalized)

      B
0  0.00
1  0.25
2  0.50
3  0.75
4  1.00

In the code above, a normalization function is defined that takes a Series object as an argument, and returns a new Series object containing the normalized values.

This function calculates the minimum and maximum values for the Series, and then applies the normalization formula (x - x.min()) / (x.max() - x.min()) to each element in the Series.

The normalize function is then applied to the B column of the DataFrame using the apply method. This applies the normalization function to each element in the B column, and returns a new Series object containing the normalized values. In this case, the resulting Series has the values 0.0, 0.25, 0.5, 0.75, and 1.0.

You can also specify multiple columns when using the apply method to normalize data in a Pandas DataFrame.

For example, if you wanted to normalize both columns B and C, you could do the following:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [10, 20, 30, 40, 50],
    'C': [100, 200, 300, 400, 500]
})

# Define a normalization function
def normalize(x):
    return (x - x.min()) / (x.max() - x.min())

# Apply the normalization function to columns B and C
normalized = df[['B', 'C']].apply(normalize)

# Print the resulting DataFrame
print(normalized)

      B     C
0  0.00  0.00
1  0.25  0.25
2  0.50  0.50
3  0.75  0.75
4  1.00  1.00

In the code above, the normalization function is applied to the B and C columns of the DataFrame using the apply method.