pythonpandasgroup-byduplicates

# Pandas - Duplicate Row based on condition

I'm trying to create a duplicate row if the row meets a condition. In the table below, I created a cumulative count based on a groupby, then another calculation for the MAX of the groupby.

``````df['PathID'] = df.groupby(DateCompleted).cumcount() + 1
df['MaxPathID'] = df.groupby(DateCompleted)['PathID'].transform(max)

Date Completed    PathID    MaxPathID
1/31/17           1         3
1/31/17           2         3
1/31/17           3         3
2/1/17            1         1
2/2/17            1         2
2/2/17            2         2
``````

In this case, I want to duplicate only the record for 2/1/17 since there is only one instance for that date (i.e. where the MaxPathID == 1).

Desired Output:

``````Date Completed    PathID    MaxPathID
1/31/17           1         3
1/31/17           2         3
1/31/17           3         3
2/1/17            1         1
2/1/17            1         1
2/2/17            1         2
2/2/17            2         2
``````

Solution

• I think you need get `unique` rows by `Date Completed` and then `concat` rows to original:

``````df1 = df.loc[~df['Date Completed'].duplicated(keep=False), ['Date Completed']]
print (df1)
Date Completed
3         2/1/17

df = pd.concat([df,df1], ignore_index=True).sort_values('Date Completed')
df['PathID'] = df.groupby('Date Completed').cumcount() + 1
df['MaxPathID'] = df.groupby('Date Completed')['PathID'].transform(max)
print (df)
Date Completed  PathID  MaxPathID
0        1/31/17       1          3
1        1/31/17       2          3
2        1/31/17       3          3
3         2/1/17       1          2
6         2/1/17       2          2
4         2/2/17       1          2
5         2/2/17       2          2
``````

EDIT:

``````print (df)
Date Completed  a  b
0        1/31/17  4  5
1        1/31/17  3  5
2        1/31/17  6  3
3         2/1/17  7  9
4         2/2/17  2  0
5         2/2/17  6  7

df1 = df[~df['Date Completed'].duplicated(keep=False)]
#alternative - boolean indexing by numpy array
#df1 = df[~df['Date Completed'].duplicated(keep=False).values]
print (df1)
Date Completed  a  b
3         2/1/17  7  9

df = pd.concat([df,df1], ignore_index=True).sort_values('Date Completed')
print (df)
Date Completed  a  b
0        1/31/17  4  5
1        1/31/17  3  5
2        1/31/17  6  3
3         2/1/17  7  9
6         2/1/17  7  9
4         2/2/17  2  0
5         2/2/17  6  7
``````