How To replace multiple unknown values with default value except nan in python dataframe column-CodePudding

I have a dataframe. How can I replace multiple unknown values with default value except nan in python dataframe column.

df = S.No.  Columns_A

      1      python
      2       java
      3        NAN
      4       C  
      5       python , java

How to get updated data frame

df_updated = S.No.  Columns_A
               1      Good
               2      Good
               3      NAN
               4      Good
               5      Good

CodePudding user response：

How about this:

import pandas as pd
import numpy as np

df = pd.DataFrame(data={
    'S.No.':[
        1,
        2,
        3,
        4,
        5,
    ],
    'ColumnA':[
        'python',
        'java',
        np.nan,
        'C  ',
        'python , java',
    ]
})
df['ColumnA'] = df.apply(lambda row: np.nan if pd.isna(row['ColumnA']) else 'Good', axis=1)

result:
    S.No.   ColumnA
0    1       Good
1    2       Good
2    3       NaN
3    4       Good
4    5       Good

CodePudding user response：

You easily need to select not Na values by .loc and make it 'Good' like this:

df.loc[~ df.ColumnA.isna()] = 'Good'

import pandas as pd

df = pd.DataFrame(data={
    'ColumnA':[
        'python',
        'java',
        None,
        'C  ',
        'python , java',
    ]
})

df.loc[~ df.ColumnA.isna()] = 'Good'
df

    ColumnA
0   Good
1   Good
2   NaN
3   Good
4   Good

CodePudding user response：

Use df.where: This should be faster than other solutions

In [1443]: df['Columns_A'] = df['Columns_A'].where(df['Columns_A'].isna(), 'Good')

In [1444]: df
Out[1444]: 
   S.No. Columns_A
0      1      Good
1      2      Good
2      3       NaN
3      4      Good
4      5      Good