function to replace null values with mean - pandas

Question

I have an unemployment data for the 30 countries and there are some missing values but in the excel sheet these all numbers are all strings so I first convert them to floats and then if row is empty then I want to replace row with its columns mean value. Function works well doesnt return any error but when I print the data still I have the Null values
data=pd.read_excel(r'C:\Users\OĞUZ\Desktop\employment.xlsx')
data=data.set_index('Unnamed: 0')
for column in data:
for row in column:
if len(row)>5:
row=float(row)
if row.isnull():
row=column.mean()
print(data['Argentina'].head())
This is what I get after print.
Unnamed: 0
1990 NaN
1991 NaN
1992 NaN
1993 NaN
1994 NaN
Name: Argentina, dtype: float64

Answer 1

You can either iterate over the columns, or use DataFrame.transform or DataFrame.apply.
Whichever approach you use, you'll want to:
Convert column values from strings to floats
Calculate the mean of the column
Use Series.fillna to fill the NaN values with the previously calcualted value
Create Data
import pandas as pd
import numpy as np
rng = np.random.default_rng(0)
df = pd.DataFrame({
"a": rng.integers(5, size=10),
"b": rng.integers(5, 10, size=10),
"c": rng.integers(10, 15, size=10)
}).astype(str)
df.loc[2:5, :] = np.nan
# note all the numbers you see are actually strings
print(df)
a b c
0 4 8 11
1 3 9 14
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
6 0 8 12
7 0 7 10
8 0 7 13
9 4 9 13
Solution - DataFrame transform
def clean_column(series):
series = pd.to_numeric(series, downcast="float")
avg = series.mean()
return series.fillna(avg)
new_df = df.transform(clean_column)
print(new_df)
0 4.000000 8.0 11.000000
1 3.000000 9.0 14.000000
2 1.833333 8.0 12.166667
3 1.833333 8.0 12.166667
4 1.833333 8.0 12.166667
5 1.833333 8.0 12.166667
6 0.000000 8.0 12.000000
7 0.000000 7.0 10.000000
8 0.000000 7.0 13.000000
9 4.000000 9.0 13.000000

Answer 2

To fill NaNs use df.fillna(value). For the mean use df.mean(). If your column is named Argentina this could look like below:
df.Argentina.fillna(df.Argentina.mean(), inplace=True)
The inplace=True is for the reassignment. The line is equivalent to
df.Argentina = df.Argentina.fillna(df.Argentina.mean())
Example
df = pd.DataFrame({'Argentina':[1,np.nan,2,4]}, index=[1990, 1991, 1992, 1993])
>>> df
Argentina
1990 1.0
1991 NaN
1992 2.0
1993 4.0
df.Argentina.fillna(df.Argentina.mean(), inplace=True)
>>> df
Argentina
1990 1.000000
1991 2.333333
1992 2.000000
1993 4.000000
If you have many columns, and you want to fill the NaNs with values depending by the column, you can loop over the column names like below:
for name in df.columns:
df[name].fillna(df[name].mean(), inplace=True)