Pandas: use map function to LOOKUP a value in another df
0
I'd like to use the map function to update values in df1 based on the looked up value in df2The lookup column is ISIN_CUSIP_CODE
df1 = [('ISIN_CUSIP_CODE', ['US68323ABL70', '9128284D9', '912828W89', 'CA135087J470','CA135087J470','912796QP7','US20030NCM11','US912810SD19','XS1851277969',]),
('Product', ['GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', '', '', '',]),
]
df1 = pd.DataFrame.from_items(df1)
print(df1)
df2 = [('ISIN_CUSIP_CODE', ['US20030NCM11', 'US912810SD19', 'XS1851277969', 'XS1391086987', 'CA064151BL66', 'CA13595ZZ661', ]),
('Product_MRD', ['CORP', 'GOVT', 'CORP', 'CORP','CORP','CORP',]),
]
df2 = pd.DataFrame.from_items(df2)
print(df2)
df1
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
df2
ISIN_CUSIP_CODE Product_MRD
0 US20030NCM11 CORP
1 US912810SD19 GOVT
2 XS1851277969 CORP
3 XS1391086987 CORP
4 CA064151BL66 CORP
5 CA13595ZZ661 CORP
My map function is not returning the looked up values in df2
df1['Product'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])
print(df1)
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
python pandas dataframe
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
|
show 1 more comment
0
I'd like to use the map function to update values in df1 based on the looked up value in df2The lookup column is ISIN_CUSIP_CODE
df1 = [('ISIN_CUSIP_CODE', ['US68323ABL70', '9128284D9', '912828W89', 'CA135087J470','CA135087J470','912796QP7','US20030NCM11','US912810SD19','XS1851277969',]),
('Product', ['GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', '', '', '',]),
]
df1 = pd.DataFrame.from_items(df1)
print(df1)
df2 = [('ISIN_CUSIP_CODE', ['US20030NCM11', 'US912810SD19', 'XS1851277969', 'XS1391086987', 'CA064151BL66', 'CA13595ZZ661', ]),
('Product_MRD', ['CORP', 'GOVT', 'CORP', 'CORP','CORP','CORP',]),
]
df2 = pd.DataFrame.from_items(df2)
print(df2)
df1
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
df2
ISIN_CUSIP_CODE Product_MRD
0 US20030NCM11 CORP
1 US912810SD19 GOVT
2 XS1851277969 CORP
3 XS1391086987 CORP
4 CA064151BL66 CORP
5 CA13595ZZ661 CORP
My map function is not returning the looked up values in df2
df1['Product'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])
print(df1)
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
python pandas dataframe
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
Typo: usedf1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])
– Sandeep Kadapa
Nov 16 '18 at 7:52
Usenp.where(df1['Product'].replace('',np.nan).isnull(),df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD']),df1['Product'])
– Sandeep Kadapa
Nov 16 '18 at 7:55
Getting NameError: name 'np' is not defined. Does np need to be prefixed/
– Peter Lucas
Nov 16 '18 at 7:57
import numpy as np
– Sandeep Kadapa
Nov 16 '18 at 7:58
Sorry same result
– Peter Lucas
Nov 16 '18 at 8:01
|
show 1 more comment
0
0
0
I'd like to use the map function to update values in df1 based on the looked up value in df2The lookup column is ISIN_CUSIP_CODE
df1 = [('ISIN_CUSIP_CODE', ['US68323ABL70', '9128284D9', '912828W89', 'CA135087J470','CA135087J470','912796QP7','US20030NCM11','US912810SD19','XS1851277969',]),
('Product', ['GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', '', '', '',]),
]
df1 = pd.DataFrame.from_items(df1)
print(df1)
df2 = [('ISIN_CUSIP_CODE', ['US20030NCM11', 'US912810SD19', 'XS1851277969', 'XS1391086987', 'CA064151BL66', 'CA13595ZZ661', ]),
('Product_MRD', ['CORP', 'GOVT', 'CORP', 'CORP','CORP','CORP',]),
]
df2 = pd.DataFrame.from_items(df2)
print(df2)
df1
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
df2
ISIN_CUSIP_CODE Product_MRD
0 US20030NCM11 CORP
1 US912810SD19 GOVT
2 XS1851277969 CORP
3 XS1391086987 CORP
4 CA064151BL66 CORP
5 CA13595ZZ661 CORP
My map function is not returning the looked up values in df2
df1['Product'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])
print(df1)
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
python pandas dataframe
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
I'd like to use the map function to update values in df1 based on the looked up value in df2The lookup column is ISIN_CUSIP_CODE
df1 = [('ISIN_CUSIP_CODE', ['US68323ABL70', '9128284D9', '912828W89', 'CA135087J470','CA135087J470','912796QP7','US20030NCM11','US912810SD19','XS1851277969',]),
('Product', ['GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', '', '', '',]),
]
df1 = pd.DataFrame.from_items(df1)
print(df1)
df2 = [('ISIN_CUSIP_CODE', ['US20030NCM11', 'US912810SD19', 'XS1851277969', 'XS1391086987', 'CA064151BL66', 'CA13595ZZ661', ]),
('Product_MRD', ['CORP', 'GOVT', 'CORP', 'CORP','CORP','CORP',]),
]
df2 = pd.DataFrame.from_items(df2)
print(df2)
df1
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
df2
ISIN_CUSIP_CODE Product_MRD
0 US20030NCM11 CORP
1 US912810SD19 GOVT
2 XS1851277969 CORP
3 XS1391086987 CORP
4 CA064151BL66 CORP
5 CA13595ZZ661 CORP
My map function is not returning the looked up values in df2
df1['Product'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])
print(df1)
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
python pandas dataframe
python pandas dataframe
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
asked Nov 16 '18 at 7:43
Peter LucasPeter Lucas
413312
413312
Typo: usedf1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])
– Sandeep Kadapa
Nov 16 '18 at 7:52
Usenp.where(df1['Product'].replace('',np.nan).isnull(),df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD']),df1['Product'])
– Sandeep Kadapa
Nov 16 '18 at 7:55
Getting NameError: name 'np' is not defined. Does np need to be prefixed/
– Peter Lucas
Nov 16 '18 at 7:57
import numpy as np
– Sandeep Kadapa
Nov 16 '18 at 7:58
Sorry same result
– Peter Lucas
Nov 16 '18 at 8:01
|
show 1 more comment
Typo: usedf1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])
– Sandeep Kadapa
Nov 16 '18 at 7:52
Usenp.where(df1['Product'].replace('',np.nan).isnull(),df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD']),df1['Product'])
– Sandeep Kadapa
Nov 16 '18 at 7:55
Getting NameError: name 'np' is not defined. Does np need to be prefixed/
– Peter Lucas
Nov 16 '18 at 7:57
import numpy as np
– Sandeep Kadapa
Nov 16 '18 at 7:58
Sorry same result
– Peter Lucas
Nov 16 '18 at 8:01
Typo: use
df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])– Sandeep Kadapa
Nov 16 '18 at 7:52
Typo: use
df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])– Sandeep Kadapa
Nov 16 '18 at 7:52
Use
np.where(df1['Product'].replace('',np.nan).isnull(),df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD']),df1['Product'])– Sandeep Kadapa
Nov 16 '18 at 7:55
Use
np.where(df1['Product'].replace('',np.nan).isnull(),df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD']),df1['Product'])– Sandeep Kadapa
Nov 16 '18 at 7:55
Getting NameError: name 'np' is not defined. Does np need to be prefixed/
– Peter Lucas
Nov 16 '18 at 7:57
Getting NameError: name 'np' is not defined. Does np need to be prefixed/
– Peter Lucas
Nov 16 '18 at 7:57
import numpy as np– Sandeep Kadapa
Nov 16 '18 at 7:58
import numpy as np– Sandeep Kadapa
Nov 16 '18 at 7:58
Sorry same result
– Peter Lucas
Nov 16 '18 at 8:01
Sorry same result
– Peter Lucas
Nov 16 '18 at 8:01
|
show 1 more comment
2 Answers
2
active
oldest
votes
1
This is a simple solution using partial.
from functools import partial
def lookup(row, lookup_df):
try:
return lookup_df[lookup_df.ISIN_CUSIP_CODE == row['ISIN_CUSIP_CODE']].Product_MRD.values[0]
except:
return row['Product']
df1['ProductLooked'] = df1.apply(partial(lookup, lookup_df=df2), axis=1)
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
Thanks @Federico Pucci. Was not aware of this library at all. Cheers!
– Peter Lucas
Nov 16 '18 at 8:33
You're welcome! It's quite useful as well in case you need to reuse functions by just changing args.
– Federico Pucci
Nov 16 '18 at 8:34
add a comment |
2
A purely pandas solution:
pd.concat([df1,df2.rename(columns = 'Product_MRD':'Product')]).drop_duplicates(['ISIN_CUSIP_CODE'],keep='last').sort_values('ISIN_CUSIP_CODE')
No extra libraries required
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
Very nice one liner!
– Datanovice
Nov 16 '18 at 8:39
@Mark Warburton Great!!
– Peter Lucas
Nov 16 '18 at 8:52
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
This is a simple solution using partial.
from functools import partial
def lookup(row, lookup_df):
try:
return lookup_df[lookup_df.ISIN_CUSIP_CODE == row['ISIN_CUSIP_CODE']].Product_MRD.values[0]
except:
return row['Product']
df1['ProductLooked'] = df1.apply(partial(lookup, lookup_df=df2), axis=1)
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
Thanks @Federico Pucci. Was not aware of this library at all. Cheers!
– Peter Lucas
Nov 16 '18 at 8:33
You're welcome! It's quite useful as well in case you need to reuse functions by just changing args.
– Federico Pucci
Nov 16 '18 at 8:34
add a comment |
1
This is a simple solution using partial.
from functools import partial
def lookup(row, lookup_df):
try:
return lookup_df[lookup_df.ISIN_CUSIP_CODE == row['ISIN_CUSIP_CODE']].Product_MRD.values[0]
except:
return row['Product']
df1['ProductLooked'] = df1.apply(partial(lookup, lookup_df=df2), axis=1)
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
Thanks @Federico Pucci. Was not aware of this library at all. Cheers!
– Peter Lucas
Nov 16 '18 at 8:33
You're welcome! It's quite useful as well in case you need to reuse functions by just changing args.
– Federico Pucci
Nov 16 '18 at 8:34
add a comment |
1
1
1
This is a simple solution using partial.
from functools import partial
def lookup(row, lookup_df):
try:
return lookup_df[lookup_df.ISIN_CUSIP_CODE == row['ISIN_CUSIP_CODE']].Product_MRD.values[0]
except:
return row['Product']
df1['ProductLooked'] = df1.apply(partial(lookup, lookup_df=df2), axis=1)
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
This is a simple solution using partial.
from functools import partial
def lookup(row, lookup_df):
try:
return lookup_df[lookup_df.ISIN_CUSIP_CODE == row['ISIN_CUSIP_CODE']].Product_MRD.values[0]
except:
return row['Product']
df1['ProductLooked'] = df1.apply(partial(lookup, lookup_df=df2), axis=1)
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
answered Nov 16 '18 at 8:28
Federico PucciFederico Pucci
513
513
Thanks @Federico Pucci. Was not aware of this library at all. Cheers!
– Peter Lucas
Nov 16 '18 at 8:33
You're welcome! It's quite useful as well in case you need to reuse functions by just changing args.
– Federico Pucci
Nov 16 '18 at 8:34
add a comment |
Thanks @Federico Pucci. Was not aware of this library at all. Cheers!
– Peter Lucas
Nov 16 '18 at 8:33
You're welcome! It's quite useful as well in case you need to reuse functions by just changing args.
– Federico Pucci
Nov 16 '18 at 8:34
Thanks @Federico Pucci. Was not aware of this library at all. Cheers!
– Peter Lucas
Nov 16 '18 at 8:33
Thanks @Federico Pucci. Was not aware of this library at all. Cheers!
– Peter Lucas
Nov 16 '18 at 8:33
You're welcome! It's quite useful as well in case you need to reuse functions by just changing args.
– Federico Pucci
Nov 16 '18 at 8:34
You're welcome! It's quite useful as well in case you need to reuse functions by just changing args.
– Federico Pucci
Nov 16 '18 at 8:34
add a comment |
2
A purely pandas solution:
pd.concat([df1,df2.rename(columns = 'Product_MRD':'Product')]).drop_duplicates(['ISIN_CUSIP_CODE'],keep='last').sort_values('ISIN_CUSIP_CODE')
No extra libraries required
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
Very nice one liner!
– Datanovice
Nov 16 '18 at 8:39
@Mark Warburton Great!!
– Peter Lucas
Nov 16 '18 at 8:52
add a comment |
2
A purely pandas solution:
pd.concat([df1,df2.rename(columns = 'Product_MRD':'Product')]).drop_duplicates(['ISIN_CUSIP_CODE'],keep='last').sort_values('ISIN_CUSIP_CODE')
No extra libraries required
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
Very nice one liner!
– Datanovice
Nov 16 '18 at 8:39
@Mark Warburton Great!!
– Peter Lucas
Nov 16 '18 at 8:52
add a comment |
2
2
2
A purely pandas solution:
pd.concat([df1,df2.rename(columns = 'Product_MRD':'Product')]).drop_duplicates(['ISIN_CUSIP_CODE'],keep='last').sort_values('ISIN_CUSIP_CODE')
No extra libraries required
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
A purely pandas solution:
pd.concat([df1,df2.rename(columns = 'Product_MRD':'Product')]).drop_duplicates(['ISIN_CUSIP_CODE'],keep='last').sort_values('ISIN_CUSIP_CODE')
No extra libraries required
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
edited Nov 16 '18 at 8:39
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
answered Nov 16 '18 at 8:37
Mark WarburtonMark Warburton
9217
9217
Very nice one liner!
– Datanovice
Nov 16 '18 at 8:39
@Mark Warburton Great!!
– Peter Lucas
Nov 16 '18 at 8:52
add a comment |
Very nice one liner!
– Datanovice
Nov 16 '18 at 8:39
@Mark Warburton Great!!
– Peter Lucas
Nov 16 '18 at 8:52
Very nice one liner!
– Datanovice
Nov 16 '18 at 8:39
Very nice one liner!
– Datanovice
Nov 16 '18 at 8:39
@Mark Warburton Great!!
– Peter Lucas
Nov 16 '18 at 8:52
@Mark Warburton Great!!
– Peter Lucas
Nov 16 '18 at 8:52
add a comment |
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Typo: use
df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD'])– Sandeep Kadapa
Nov 16 '18 at 7:52
Use
np.where(df1['Product'].replace('',np.nan).isnull(),df1['ISIN_CUSIP_CODE'].map(df2.set_index('ISIN_CUSIP_CODE')['Product_MRD']),df1['Product'])– Sandeep Kadapa
Nov 16 '18 at 7:55
Getting NameError: name 'np' is not defined. Does np need to be prefixed/
– Peter Lucas
Nov 16 '18 at 7:57
import numpy as np– Sandeep Kadapa
Nov 16 '18 at 7:58
Sorry same result
– Peter Lucas
Nov 16 '18 at 8:01