CODEWITHSUNDEEP
pythonpandas
user6708957
user6708957

Reputation: 51

add new column and remove duplicates in that replace null values column wise

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 should add a new column to the dataframe which contains the same as the source column for any unique columns and is null for any duplicate columns.

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

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

You should specify the columns in the subset parameter based on the setting of duplication_type

You should 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

I want to get like this by using in pandas.in below file i have choose 2 column.

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

Please anybody reply to my query.

Upvotes: 4

Views: 426

Answers (1)

Nickil Maveli
Nickil Maveli

Reputation: 29711

You can use DF.duplicated and assign the values of column C where the first occurence of values appears along columns A and B.

You could then fill the Nans produced with empty strings to produce the required dataframe.

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

Another way of doing would be to take a subset of the duplicated columns and replace the concerned column with empty strings. Then, you could use update to modify the dataframe in place with the original, 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

Upvotes: 1

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