Iterating over rows and columns in Pandas - pandas

Question

I am trying to fill mean values of columns for all NaNs values in the column.
import numpy as np
import pandas as pd
table = pd.DataFrame({'A':[1,2,np.nan],
'B':[3,np.nan, np.nan],
'C':[4,5,6]})
def impute_missing_values(table):
for column in table:
for value in column:
if value == 'NaN':
value = column.mean(skipna=True)
else:
value = value
impute_missing_values(table)
table
Why I am getting an error for this code?

Answer 1

IIUC:
table.fillna(table.mean())
Output:
A B C
0 1.0 3.0 4
1 2.0 3.0 5
2 1.5 3.0 6

Answer 2

Okay, I am adding this as another answer because this isn't something I recommend at all. Using pandas methods vectorizes operations for better performance.
Using loops is not recommended when possible to avoid.
However, here is a quick fix to your code:
import pandas as pd
import numpy as np
import math
table = pd.DataFrame({'A':[1,2,np.nan],
'B':[3,np.nan, np.nan],
'C':[4,5,6]})
def impute_missing_values(df):
for column in df:
for idx, value in df[column].iteritems():
if math.isnan(value):
df.loc[idx,column] = df[column].mean(skipna=True)
else:
pass
return df
impute_missing_values(table)
table
Output:
A B C
0 1.0 3.0 4
1 2.0 3.0 5
2 1.5 3.0 6

Answer 3

You can try the SimpleImputer from scikit learn (https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer) using the mean option.
import pandas as pd
from sklearn.impute import SimpleImputer
table = pd.DataFrame({'A':[1,2,np.nan],
'B':[3,np.nan, np.nan],
'C':[4,5,6]})
print(table, '\n')
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
table_means = pd.DataFrame(imp.fit_transform(table), columns = {'C','B','A'})
print(table_means)
The print commands results in:
A B C
0 1.0 3.0 4
1 2.0 NaN 5
2 NaN NaN 6
A C B
0 1.0 3.0 4.0
1 2.0 3.0 5.0
2 1.5 3.0 6.0
To correct your code (as per my comment below):
def impute_missing_values(table):
for column in table:
table.loc[:,column] = np.where(table[column].isna(), table[column].mean(), table[column])
return table