How to fill missing values in a column based on another column values in a Pandas dataframe?-CodePudding

I am currently working with the following data.

import pandas as pd
import io

csv_data = '''
gender,age,Cr,eGFR
1,76,0.56,60.7
1,50,0.76, 70.6
2,64,0.62,55.9
1,62,0.45,Nan
1,68,0.88,80.2
2,69,0.65,Nan
1,70,0.64,62.8
2,65,0.39,60.2
'''
df = pd.read_csv(io.StringIO(csv_data))

gender = 1 is male and 2 is female. This time, there are two missing values.

If it is a male：

eGFR = 194 × Cr - 1.094 × age - 0.287

If female:

eGFR = 194 × Cr - 1.094 × age - 0.287 × 0.739

I want to fill in the missing values as indicated above.

CodePudding user response：

Use

CodePudding user response：

One option is with case_when from pyjanitor, which emulates a case_when in SQL, or ifelse/multiple ifelse in python.

Do note that this is just an option; since the task is filling nulls, the idiomatic route would be a fillna and should be faster.

# pip install pyjanitor
import pandas as pd
import janitor


df = df.transform(pd.to_numeric, errors = 'coerce')

df.case_when(
       # condition, result
       df.eGFR.isna() & df.gender.eq(1), 194 * df.Cr - 1.094 * df.age - 0.287,
       df.eGFR.isna() & df.gender.eq(2), 194 * df.Cr - 1.094 * df.age - 0.287 * 0.739,
       df.eGFR, # default
       column_name = 'eGFR')

   gender  age    Cr       eGFR
0       1   76  0.56  60.700000
1       1   50  0.76  70.600000
2       2   64  0.62  55.900000
3       1   62  0.45  19.185000
4       1   68  0.88  80.200000
5       2   69  0.65  50.401907
6       1   70  0.64  62.800000
7       2   65  0.39  60.200000

You can use strings for the conditions section, as long as it can be evaluated by pd.eval. Note that pd.eval might be slower than your regular bracket access option:

df.case_when(
    # condition, result
    'eGFR.isna() and gender==1',  194 * df.Cr - 1.094 * df.age - 0.287,
    'eGFR.isna() and gender==2', 194 * df.Cr - 1.094 * df.age - 0.287 * 0.739,
    df.eGFR, # default
    column_name = 'eGFR')
   gender  age    Cr       eGFR
0       1   76  0.56  60.700000
1       1   50  0.76  70.600000
2       2   64  0.62  55.900000
3       1   62  0.45  19.185000
4       1   68  0.88  80.200000
5       2   69  0.65  50.401907
6       1   70  0.64  62.800000
7       2   65  0.39  60.200000