data frame, match two columns and remove repeated value from second columns if that exist in first c-CodePudding

In a data, frame match two-column and if any value from the second column is available in the first column, remove value from the second columns

Output

col1 col2
1
2
3    9
4
5
6

Here, 1 and 2 from col2 are available in col1. So, this repeated data should be removed

CodePudding user response：

Using s.mask to value match and replace, we can do something along the likes of:

df['col2'] = df['col2'].mask(pd.to_numeric(df['col2']).isin(df['col1']), "")

col1    col2
0   1   
1   2   
2   3   9.0
3   4   
4   5   
5   6

CodePudding user response：

import pandas as pd
col1= [1,2,3,4,5,6]
col2= [0,0,9.0,0,0,0]

df = pd.DataFrame({'col1':col1, 'col2':col2})
# add column with no of occurrence of Non None values in the column name starts with 'a'

# iterate over columns
for col in df.columns:
    # remove values that are in previous columns
    for prev_col in df.columns[:df.columns.get_loc(col)]:
        df[col] = df[col].where(~df[col].isin(df[prev_col]), None)

# OUTPUT
#    col1  col2
# 0     1   0.0
# 1     2   0.0
# 2     3   9.0
# 3     4   0.0
# 4     5   0.0
# 5     6   0.0