Add a new column and remove duplicates in which to replace columns with zero values

Question

Duplication type:
Check this column only (default)
Check other columns only
Check all columns

Use Last Value:
True - retain the last duplicate value
False - retain the first of the duplicates (default)

This rule is to add a new column to the data framework that contains the same as the original column for any unique columns, and is zero for any repeating columns.

the base code is df.loc [df.duplicated (), get_unique_column_name (df, "clean")] = df [get_column_name (df, column)] with parameters for duplicated () specified based on the duplication type

See the link for this function above: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.duplicated.html

You must specify the columns in the subset parameter based on the duplication_type parameter

You must specify use_last_value based on use_last_value above

This is my file.

Jason   Miller  42  4   25
Tina    Ali     36  31  57
Jake    Milner  24  2   62
Jason   Miller  42  4   25
Jake    Milner  24  2   62
Amy     Cooze   73  3   70
Jason   Miller  42  4   25
Jason   Miller  42  4   25
Jake    Milner  24  2   62
Jake    Miller  42  4   25

, pandas.in . 2 .

Jason   Miller  42  4   25
Jake    Ali     36  31  57
Jake    Milner  24  2   62
Jason   Miller      4   25
Jake    Milner      2   62
Jake    Cooze   73  3   70
Jason   Miller      4   25
Jason   Miller      4   25
Jake    Milner      2   62
Jake    Miller      4   25

, .

Answer 1

DF.duplicated C, A B.

Nans, , .

df = pd.read_csv(data, delim_whitespace=True, header=None, names=['A','B','C','D','E'])
df.loc[~df.duplicated(), "C'"] = df['C']
df.fillna('', inplace=True)
df = df[["A","B", "C'","D","E"]]
print(df)

       A       B  C'   D   E
0  Jason  Miller  42   4  25
1   Tina     Ali  36  31  57
2   Jake  Milner  24   2  62
3  Jason  Miller       4  25
4   Jake  Milner       2  62
5    Amy   Cooze  73   3  70
6  Jason  Miller       4  25
7  Jason  Miller       4  25
8   Jake  Milner       2  62
9   Jake  Miller  42   4  25

- . update, df.

In [2]: duplicated_cols = df[df.duplicated(subset=['C', 'D', 'E'])]

In [3]: duplicated_cols
Out[3]: 
       A       B   C  D   E
3  Jason  Miller  42  4  25
4   Jake  Milner  24  2  62
6  Jason  Miller  42  4  25
7  Jason  Miller  42  4  25
8   Jake  Milner  24  2  62
9   Jake  Miller  42  4  25

In [4]: duplicated_cols.loc[:,'C'] = ''

In [5]: df.update(duplicated_cols)

In [6]: df
Out[6]: 
       A       B   C     D     E
0  Jason  Miller  42   4.0  25.0
1   Tina     Ali  36  31.0  57.0
2   Jake  Milner  24   2.0  62.0
3  Jason  Miller       4.0  25.0
4   Jake  Milner       2.0  62.0
5    Amy   Cooze  73   3.0  70.0
6  Jason  Miller       4.0  25.0
7  Jason  Miller       4.0  25.0
8   Jake  Milner       2.0  62.0
9   Jake  Miller       4.0  25.0