I have this DataFrame and want only the records whose "Total" column is not NaN ,and records when A~E has more than two NaN:
A B C D E Total
1 1 3 5 5 8
1 4 3 5 5 NaN
3 6 NaN NaN NaN 6
2 2 5 9 NaN 8
..ie something like df.dropna(....) to get this resulting dataframe:
A B C D E Total
1 1 3 5 5 8
2 2 5 9 NaN 8
Here's my code
import pandas as pd
dfInputData = pd.read_csv(path)
dfInputData = dfInputData.dropna(axis=1,how = 'any')
RowCnt = dfInputData.shape[0]
But it looks like no modification has been made even error
Please help!! Thanks
Use boolean indexing
with count all columns without Total
for number of missing values and not misisng values in Total
:
df = df[df.drop('Total', axis=1).isna().sum(axis=1).le(2) & df['Total'].notna()]
print (df)
A B C D E Total
0 1 1 3.0 5.0 5.0 8.0
3 2 2 5.0 9.0 NaN 8.0
Or filter columns between A:E
:
df = df[df.loc[:, 'A':'E'].isna().sum(axis=1).le(2) & df['Total'].notna()]
print (df)
A B C D E Total
0 1 1 3.0 5.0 5.0 8.0
3 2 2 5.0 9.0 NaN 8.0
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