In Python, the `pandas` library includes built-in functionalities that allow you to perform different tasks with only a few lines of code. One of these functionalities is *the normalization of all columns in a dataframe*.

To normalize all columns of the dataframe, we first subtract the column mean, and then divide by the standard deviation.

#importing pandas and numpy libraries
import pandas as pd
import numpy as np

#initializing pandas dataframe with random values
df = pd.DataFrame(np.random.randint(1,100, 50).reshape(5, -1))

#normalizing dataframe
result = df.apply(lambda iterator: ((iterator - iterator.mean())/iterator.std()).round(2))

print(result)


Then, we range all columns of the dataframe, such that the min is 0 and the max is 1.

#importing pandas and numpy libraries
import pandas as pd
import numpy as np

#initializing pandas dataframe with random values
df = pd.DataFrame(np.random.randint(1,100, 50).reshape(5, -1))

#normalizing dataframe
result = df.apply(lambda iterator: ((iterator.max() - iterator)/(iterator.max() - iterator.min())).round(2))

print(result)  

How to normalize all columns in a dataframe in pandas

Normalize dataframe columns in pandas by subtracting the column mean, dividing by standard deviation, and scaling between 0 and 1.

How to normalize all columns in a dataframe in pandas

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