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Split multiple values within a column
Selecting multiple columns in a pandas dataframeRenaming columns in pandasDelete column from pandas DataFrame by column nameSelect rows from a DataFrame based on values in a column in pandasConvert Python dict into a dataframehow to split date and time from same column in csv using python?How to split date and time from string?Split datetime in pandasPandas dealing with offset column names within a single CSV fileImporting excel data with pandas showing date-time despite being date value
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I want to get the values of col1 in 3 different columns with separate headers.
Date/Time col1
0 2019/03/20 10:00:09 212.0/212.0/212.0
so far I tried,
import pandas as pd
data1 = pd.read_csv('file1.csv')
s= pd.Series(data1['col1'])
s.str.split(pat = '/', expand=True)
0 1 2
0 212.0 212.0 212.0
now, how could I put headers and accumulate them into data1.
python-3.x pandas
asked Mar 22 at 18:41
Asef AminAsef Amin
14
add a comment |
I want to get the values of col1 in 3 different columns with separate headers.
Date/Time col1
0 2019/03/20 10:00:09 212.0/212.0/212.0
so far I tried,
import pandas as pd
data1 = pd.read_csv('file1.csv')
s= pd.Series(data1['col1'])
s.str.split(pat = '/', expand=True)
0 1 2
0 212.0 212.0 212.0
now, how could I put headers and accumulate them into data1.
python-3.x pandas
asked Mar 22 at 18:41
Asef AminAsef Amin
14
add a comment |
I want to get the values of col1 in 3 different columns with separate headers.
Date/Time col1
0 2019/03/20 10:00:09 212.0/212.0/212.0
so far I tried,
import pandas as pd
data1 = pd.read_csv('file1.csv')
s= pd.Series(data1['col1'])
s.str.split(pat = '/', expand=True)
0 1 2
0 212.0 212.0 212.0
now, how could I put headers and accumulate them into data1.
python-3.x pandas
asked Mar 22 at 18:41
Asef AminAsef Amin
14
I want to get the values of col1 in 3 different columns with separate headers.
Date/Time col1
0 2019/03/20 10:00:09 212.0/212.0/212.0
so far I tried,
import pandas as pd
data1 = pd.read_csv('file1.csv')
s= pd.Series(data1['col1'])
s.str.split(pat = '/', expand=True)
0 1 2
0 212.0 212.0 212.0
now, how could I put headers and accumulate them into data1.
python-3.x pandas
python-3.x pandas
asked Mar 22 at 18:41
Asef AminAsef Amin
14
asked Mar 22 at 18:41
Asef AminAsef Amin
14
asked Mar 22 at 18:41
Asef AminAsef Amin
14
asked Mar 22 at 18:41
Asef AminAsef Amin
14
asked Mar 22 at 18:41
Asef AminAsef Amin
14
14
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Assuming s is the dataframe shown in your output, you can merge this dataframe into the original data1 and rename the columns using df.rename:
data1.merge(s, left_index=True, right_index=True).rename(0: 'colA', 1: 'colB', 2: 'colC', axis=1)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
or if you have your original dataframe data1 you can do this in a single step:
data1[['colA','colB','colC']] = data1.col1.str.split('/', expand=True)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
I took the second solution, with a small tweak its working fine. p= data1['col_name'] data1[['col1','col2','col3']] =p.str.split('/', expand=True)
– Asef Amin
Mar 22 at 19:26
add a comment |
Your Answer
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Assuming s is the dataframe shown in your output, you can merge this dataframe into the original data1 and rename the columns using df.rename:
data1.merge(s, left_index=True, right_index=True).rename(0: 'colA', 1: 'colB', 2: 'colC', axis=1)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
or if you have your original dataframe data1 you can do this in a single step:
data1[['colA','colB','colC']] = data1.col1.str.split('/', expand=True)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
I took the second solution, with a small tweak its working fine. p= data1['col_name'] data1[['col1','col2','col3']] =p.str.split('/', expand=True)
– Asef Amin
Mar 22 at 19:26
add a comment |
Assuming s is the dataframe shown in your output, you can merge this dataframe into the original data1 and rename the columns using df.rename:
data1.merge(s, left_index=True, right_index=True).rename(0: 'colA', 1: 'colB', 2: 'colC', axis=1)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
or if you have your original dataframe data1 you can do this in a single step:
data1[['colA','colB','colC']] = data1.col1.str.split('/', expand=True)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
I took the second solution, with a small tweak its working fine. p= data1['col_name'] data1[['col1','col2','col3']] =p.str.split('/', expand=True)
– Asef Amin
Mar 22 at 19:26
add a comment |
Assuming s is the dataframe shown in your output, you can merge this dataframe into the original data1 and rename the columns using df.rename:
data1.merge(s, left_index=True, right_index=True).rename(0: 'colA', 1: 'colB', 2: 'colC', axis=1)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
or if you have your original dataframe data1 you can do this in a single step:
data1[['colA','colB','colC']] = data1.col1.str.split('/', expand=True)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
Assuming s is the dataframe shown in your output, you can merge this dataframe into the original data1 and rename the columns using df.rename:
data1.merge(s, left_index=True, right_index=True).rename(0: 'colA', 1: 'colB', 2: 'colC', axis=1)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
or if you have your original dataframe data1 you can do this in a single step:
data1[['colA','colB','colC']] = data1.col1.str.split('/', expand=True)
Datetime col1 colA colB colC
0 2019/03/20 10:00:09 212.0/212.0/212.0 212.0 212.0 212.0
1 2019/03/20 10:30:09 222.0/222.0/222.0 222.0 222.0 222.0
2 2019/03/20 11:00:09 232.0/232.0/232.0 232.0 232.0 232.0
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
answered Mar 22 at 19:01
Ishan SaraswatIshan Saraswat
833
833
I took the second solution, with a small tweak its working fine. p= data1['col_name'] data1[['col1','col2','col3']] =p.str.split('/', expand=True)
– Asef Amin
Mar 22 at 19:26
add a comment |
I took the second solution, with a small tweak its working fine. p= data1['col_name'] data1[['col1','col2','col3']] =p.str.split('/', expand=True)
– Asef Amin
Mar 22 at 19:26
I took the second solution, with a small tweak its working fine. p= data1['col_name'] data1[['col1','col2','col3']] =p.str.split('/', expand=True)
– Asef Amin
Mar 22 at 19:26
I took the second solution, with a small tweak its working fine. p= data1['col_name'] data1[['col1','col2','col3']] =p.str.split('/', expand=True)
– Asef Amin
Mar 22 at 19:26
add a comment |
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