np.where( ) not changing all the values

Numpy will convert the arrays to strings on the first call, meaning you no longer have 1 in the column, but '1' instead.

This code would work:

df['view'] = np.where(df['view']==0, 'No view', df['view'])
df['view'] = np.where(df['view']=='1', 'Mediocore', df['view'])
df['view'] = np.where(df['view']=='2', 'Average', df['view'])
df['view'] = np.where(df['view']=='3', 'Good', df['view'])
df['view'] = np.where(df['view']=='4', 'Very good', df['view'])

I know you asked how to get numpy's "where" to work, which I think the above does. But it's worth mentioning that pandas apply might work well here too , mapping with dictionaries as laid out it the other answer would be even better.

Try mapping values instead to avoid dtype troubles:

mappings = {0: 'No view', 1: 'Mediocre', 2: 'Average', 3: 'Good', 4: 'Very good'}

df['view'] = df['view'].map(mappings)
print(df)

# Output
        view
0  Very good
1    No view
2   Mediocre
3   Mediocre
4    No view
5    No view
6    Average
7    No view
8    No view
9   Mediocre

Setup I used:

import pandas as pd
import numpy as np

np.random.seed(2022)
df = pd.DataFrame({'view': np.random.randint(0, 5, 10)})
print(df)

# Output
   view
0     4
1     0
2     1
3     1
4     0
5     0
6     2
7     0
8     0
9     1

Update

After the first np.where, your data looks like:

>>> np.where(df['view']==0, 'No view', df['view'])
array(['4', 'No view', '1', '1', 'No view', 'No view', '2', 'No view',
       'No view', '1'], dtype='<U21')

The remain values 1, 2, 3, 4 (integer) became '1', '2', '3', '4' (strings). So, you can't check integer values now. That's why, it's important to process your data in a single pass.