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Merge Python 3 DataFrame rows that share a cell value, putting another value into a comma separated array
Filter dataframe rows if value in column is in a set list of valuesUse a list of values to select rows from a pandas dataframeHow to drop rows of Pandas DataFrame whose value in certain columns is NaNSet value for particular cell in pandas DataFrame using indexSelect rows from a DataFrame based on values in a column in pandasDeleting DataFrame row in Pandas based on column valueMerging two DataFrames with conditionals to either update column or append rowComplex Dataframe Merge Python PandasMerge dataframes without duplicating rows in python pandasHow to merge 2 DataFrames on specific rows and columns?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I have a Python DataFrame where emails are duplicated. I'd like to find all the duplicates and merge them so that there are an array of account numbers attached to the email. I'd also like to preserve the 3rd column in the merged column.
AccountID Email Quality_3
1 blue@somedomain.com High
2 red@somedomain.com
3 blue@somedomain.com
4 green@somedomain.com Medium
5 blue@somedomain.com
6 red@somedomain.com
7 blue@somedomain.com
8 green@somedomain.com
AccountID Email Quality_3
1, 3, 5, 7 blue@somedomain.com High
2, 6 red@somedomain.com
4, 8 green@somedomain.com Medium
I am looking at left and right joins, but I can't seem to figure it out.
python python-3.x pandas dataframe merge
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
add a comment |
I have a Python DataFrame where emails are duplicated. I'd like to find all the duplicates and merge them so that there are an array of account numbers attached to the email. I'd also like to preserve the 3rd column in the merged column.
AccountID Email Quality_3
1 blue@somedomain.com High
2 red@somedomain.com
3 blue@somedomain.com
4 green@somedomain.com Medium
5 blue@somedomain.com
6 red@somedomain.com
7 blue@somedomain.com
8 green@somedomain.com
AccountID Email Quality_3
1, 3, 5, 7 blue@somedomain.com High
2, 6 red@somedomain.com
4, 8 green@somedomain.com Medium
I am looking at left and right joins, but I can't seem to figure it out.
python python-3.x pandas dataframe merge
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
add a comment |
I have a Python DataFrame where emails are duplicated. I'd like to find all the duplicates and merge them so that there are an array of account numbers attached to the email. I'd also like to preserve the 3rd column in the merged column.
AccountID Email Quality_3
1 blue@somedomain.com High
2 red@somedomain.com
3 blue@somedomain.com
4 green@somedomain.com Medium
5 blue@somedomain.com
6 red@somedomain.com
7 blue@somedomain.com
8 green@somedomain.com
AccountID Email Quality_3
1, 3, 5, 7 blue@somedomain.com High
2, 6 red@somedomain.com
4, 8 green@somedomain.com Medium
I am looking at left and right joins, but I can't seem to figure it out.
python python-3.x pandas dataframe merge
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
I have a Python DataFrame where emails are duplicated. I'd like to find all the duplicates and merge them so that there are an array of account numbers attached to the email. I'd also like to preserve the 3rd column in the merged column.
AccountID Email Quality_3
1 blue@somedomain.com High
2 red@somedomain.com
3 blue@somedomain.com
4 green@somedomain.com Medium
5 blue@somedomain.com
6 red@somedomain.com
7 blue@somedomain.com
8 green@somedomain.com
AccountID Email Quality_3
1, 3, 5, 7 blue@somedomain.com High
2, 6 red@somedomain.com
4, 8 green@somedomain.com Medium
I am looking at left and right joins, but I can't seem to figure it out.
python python-3.x pandas dataframe merge
python python-3.x pandas dataframe merge
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
edited Mar 23 at 22:13
NothingToSeeHere
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
asked Mar 23 at 6:16
NothingToSeeHereNothingToSeeHere
51921231
51921231
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Try this:
df_new=(df.astype(str).groupby('Email')['AccountID','Quality_3']
.agg('AccountID':lambda x: ','.join(x),'Quality_3':'first').reset_index())
print(df_new)
Email AccountID Quality_3
0 blue@somedomain.com 1,3,5,7 High
1 green@somedomain.com 4,8 Medium
2 red@somedomain.com 2,6 None
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Try this:
df_new=(df.astype(str).groupby('Email')['AccountID','Quality_3']
.agg('AccountID':lambda x: ','.join(x),'Quality_3':'first').reset_index())
print(df_new)
Email AccountID Quality_3
0 blue@somedomain.com 1,3,5,7 High
1 green@somedomain.com 4,8 Medium
2 red@somedomain.com 2,6 None
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
add a comment |
Try this:
df_new=(df.astype(str).groupby('Email')['AccountID','Quality_3']
.agg('AccountID':lambda x: ','.join(x),'Quality_3':'first').reset_index())
print(df_new)
Email AccountID Quality_3
0 blue@somedomain.com 1,3,5,7 High
1 green@somedomain.com 4,8 Medium
2 red@somedomain.com 2,6 None
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
add a comment |
Try this:
df_new=(df.astype(str).groupby('Email')['AccountID','Quality_3']
.agg('AccountID':lambda x: ','.join(x),'Quality_3':'first').reset_index())
print(df_new)
Email AccountID Quality_3
0 blue@somedomain.com 1,3,5,7 High
1 green@somedomain.com 4,8 Medium
2 red@somedomain.com 2,6 None
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
Try this:
df_new=(df.astype(str).groupby('Email')['AccountID','Quality_3']
.agg('AccountID':lambda x: ','.join(x),'Quality_3':'first').reset_index())
print(df_new)
Email AccountID Quality_3
0 blue@somedomain.com 1,3,5,7 High
1 green@somedomain.com 4,8 Medium
2 red@somedomain.com 2,6 None
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
answered Mar 23 at 6:23
anky_91anky_91
12.5k3922
12.5k3922
add a comment |
add a comment |
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