How to split in Pandas cells with multiple data
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1
I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):
0 22
2014 {'tag': 'operatingrevenue', 'value': 182795000}
[1 rows x 23 columns]
How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?
| operatingrevenue
2014 | 182795000
I specify that i'm working with pandas and python 3.7
Thanks
python python-3.x pandas
asked Nov 16 '18 at 11:20
GiangiGiangi
84
add a comment |
1
I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):
0 22
2014 {'tag': 'operatingrevenue', 'value': 182795000}
[1 rows x 23 columns]
How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?
| operatingrevenue
2014 | 182795000
I specify that i'm working with pandas and python 3.7
Thanks
python python-3.x pandas
asked Nov 16 '18 at 11:20
GiangiGiangi
84
1
what's your expected output?
– AkshayNevrekar
Nov 16 '18 at 11:24
1
Could you update your question with an example dataframe, so that we can help.
– Philip
Nov 16 '18 at 11:24
Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.
– Giangi
Nov 16 '18 at 11:40
That original sample data isnt very clear. can you update?
– MEdwin
Nov 16 '18 at 11:42
It should be clearer now. I have 23 columns but i will work on those with a loop.
– Giangi
Nov 16 '18 at 11:51
add a comment |
1
1
1
I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):
0 22
2014 {'tag': 'operatingrevenue', 'value': 182795000}
[1 rows x 23 columns]
How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?
| operatingrevenue
2014 | 182795000
I specify that i'm working with pandas and python 3.7
Thanks
python python-3.x pandas
asked Nov 16 '18 at 11:20
GiangiGiangi
84
I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):
0 22
2014 {'tag': 'operatingrevenue', 'value': 182795000}
[1 rows x 23 columns]
How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?
| operatingrevenue
2014 | 182795000
I specify that i'm working with pandas and python 3.7
Thanks
python python-3.x pandas
python python-3.x pandas
asked Nov 16 '18 at 11:20
GiangiGiangi
84
asked Nov 16 '18 at 11:20
GiangiGiangi
84
edited Nov 16 '18 at 11:50
Giangi
asked Nov 16 '18 at 11:20
GiangiGiangi
84
asked Nov 16 '18 at 11:20
GiangiGiangi
84
asked Nov 16 '18 at 11:20
GiangiGiangi
84
84
1
what's your expected output?
– AkshayNevrekar
Nov 16 '18 at 11:24
1
Could you update your question with an example dataframe, so that we can help.
– Philip
Nov 16 '18 at 11:24
Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.
– Giangi
Nov 16 '18 at 11:40
That original sample data isnt very clear. can you update?
– MEdwin
Nov 16 '18 at 11:42
It should be clearer now. I have 23 columns but i will work on those with a loop.
– Giangi
Nov 16 '18 at 11:51
add a comment |
1
what's your expected output?
– AkshayNevrekar
Nov 16 '18 at 11:24
1
Could you update your question with an example dataframe, so that we can help.
– Philip
Nov 16 '18 at 11:24
Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.
– Giangi
Nov 16 '18 at 11:40
That original sample data isnt very clear. can you update?
– MEdwin
Nov 16 '18 at 11:42
It should be clearer now. I have 23 columns but i will work on those with a loop.
– Giangi
Nov 16 '18 at 11:51
1
1
what's your expected output?
– AkshayNevrekar
Nov 16 '18 at 11:24
what's your expected output?
– AkshayNevrekar
Nov 16 '18 at 11:24
1
1
Could you update your question with an example dataframe, so that we can help.
– Philip
Nov 16 '18 at 11:24
Could you update your question with an example dataframe, so that we can help.
– Philip
Nov 16 '18 at 11:24
Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.
– Giangi
Nov 16 '18 at 11:40
Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.
– Giangi
Nov 16 '18 at 11:40
That original sample data isnt very clear. can you update?
– MEdwin
Nov 16 '18 at 11:42
That original sample data isnt very clear. can you update?
– MEdwin
Nov 16 '18 at 11:42
It should be clearer now. I have 23 columns but i will work on those with a loop.
– Giangi
Nov 16 '18 at 11:51
It should be clearer now. I have 23 columns but i will work on those with a loop.
– Giangi
Nov 16 '18 at 11:51
add a comment |
1 Answer
1
active
oldest
votes
0
If this data is representative of your case, it should work:
import pandas as pd
df = pd.DataFrame({0: [{'tag': 'operatingrevenue', 'value': 182795000},
{'tag': 'operatingrevenue', 'value': 182796000}],
1: [{'tag': 'cashdividendspershare', 'value': 1.82},
{'tag': 'cashdividendspershare', 'value': 1.92}]},
index=[2014, 2015])
df.columns = [i['tag'] for i in df.iloc[0].values]
df = df.applymap(lambda x: x['value'])
df
operatingrevenue cashdividendspershare
2014 182795000 1.82
2015 182796000 1.92
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
amazing, thanks a lot. It works super well
– Giangi
Nov 16 '18 at 12:04
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
0
If this data is representative of your case, it should work:
import pandas as pd
df = pd.DataFrame({0: [{'tag': 'operatingrevenue', 'value': 182795000},
{'tag': 'operatingrevenue', 'value': 182796000}],
1: [{'tag': 'cashdividendspershare', 'value': 1.82},
{'tag': 'cashdividendspershare', 'value': 1.92}]},
index=[2014, 2015])
df.columns = [i['tag'] for i in df.iloc[0].values]
df = df.applymap(lambda x: x['value'])
df
operatingrevenue cashdividendspershare
2014 182795000 1.82
2015 182796000 1.92
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
amazing, thanks a lot. It works super well
– Giangi
Nov 16 '18 at 12:04
add a comment |
0
If this data is representative of your case, it should work:
import pandas as pd
df = pd.DataFrame({0: [{'tag': 'operatingrevenue', 'value': 182795000},
{'tag': 'operatingrevenue', 'value': 182796000}],
1: [{'tag': 'cashdividendspershare', 'value': 1.82},
{'tag': 'cashdividendspershare', 'value': 1.92}]},
index=[2014, 2015])
df.columns = [i['tag'] for i in df.iloc[0].values]
df = df.applymap(lambda x: x['value'])
df
operatingrevenue cashdividendspershare
2014 182795000 1.82
2015 182796000 1.92
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
amazing, thanks a lot. It works super well
– Giangi
Nov 16 '18 at 12:04
add a comment |
0
0
0
If this data is representative of your case, it should work:
import pandas as pd
df = pd.DataFrame({0: [{'tag': 'operatingrevenue', 'value': 182795000},
{'tag': 'operatingrevenue', 'value': 182796000}],
1: [{'tag': 'cashdividendspershare', 'value': 1.82},
{'tag': 'cashdividendspershare', 'value': 1.92}]},
index=[2014, 2015])
df.columns = [i['tag'] for i in df.iloc[0].values]
df = df.applymap(lambda x: x['value'])
df
operatingrevenue cashdividendspershare
2014 182795000 1.82
2015 182796000 1.92
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
If this data is representative of your case, it should work:
import pandas as pd
df = pd.DataFrame({0: [{'tag': 'operatingrevenue', 'value': 182795000},
{'tag': 'operatingrevenue', 'value': 182796000}],
1: [{'tag': 'cashdividendspershare', 'value': 1.82},
{'tag': 'cashdividendspershare', 'value': 1.92}]},
index=[2014, 2015])
df.columns = [i['tag'] for i in df.iloc[0].values]
df = df.applymap(lambda x: x['value'])
df
operatingrevenue cashdividendspershare
2014 182795000 1.82
2015 182796000 1.92
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
answered Nov 16 '18 at 11:58
zipazipa
16.3k31738
16.3k31738
amazing, thanks a lot. It works super well
– Giangi
Nov 16 '18 at 12:04
add a comment |
amazing, thanks a lot. It works super well
– Giangi
Nov 16 '18 at 12:04
amazing, thanks a lot. It works super well
– Giangi
Nov 16 '18 at 12:04
amazing, thanks a lot. It works super well
– Giangi
Nov 16 '18 at 12:04
add a comment |
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1
what's your expected output?
– AkshayNevrekar
Nov 16 '18 at 11:24
1
Could you update your question with an example dataframe, so that we can help.
– Philip
Nov 16 '18 at 11:24
Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.
– Giangi
Nov 16 '18 at 11:40
That original sample data isnt very clear. can you update?
– MEdwin
Nov 16 '18 at 11:42
It should be clearer now. I have 23 columns but i will work on those with a loop.
– Giangi
Nov 16 '18 at 11:51