I have a DataFrame with columns ['A', 'B', 'C']. I am trying to normalize each of the column using my function.
The problem is that it works when I do normalization(df['A'])
, but doesn't work when I pass a list to the function:
def normalization(x):
x = (x - np.min(x)) / (np.max(x) - np.min(x))
for column in df.columns:
normalization(df[column])
How to deal with it in this case?
I did read answers with .map
and .apply
but that didn't work in my case for some reason. I am new to Python, hope my question makes sense.
CodePudding user response:
The problem is your normalization function. it should return the value of the normalization:
def normalization(x):
return (x - np.min(x)) / (np.max(x) - np.min(x))
When you don't return the value the value None is returned causing the values in map\apply to be None.
Example of working code:
import pandas as pd
import numpy as np
def normalization(x):
return (x - np.min(x)) / (np.max(x) - np.min(x))
data = {'A': [1, 2, 3], 'B': [3, 4, 5], 'C': [4,5,6]}
df = pd.DataFrame(data=data)
df = df.apply(normalization)