CODEWITHSUNDEEP
pythonpython-3.xpandasdictionaryseries
RustyShackleford
RustyShackleford

Reputation: 3677

How to map key to multiple values to dataframe column?

I have a df column that looks like this: 

col1
Non Profit
Other-501c3
501c3
Sole Proprietor

How can I create a dictionary object or mapping layer(open to all suggestions) where I can pass any value if it matches criteria and changes to the key value?

For example if the value is Other-501c3 then change it to non-profit.

Examples (everything after the equal sign needs to change to the value before equal sign):

1. non-profit = (Non Profit, Other-501c3, 501c3,NON-Profit, Not-for-profit).

2. Sole Proprietor = (Sole Proprietor,Sole Proprietorship)

The solution should be scalable I can add in more 'key value' pairs

Thank you in advance.

Upvotes: 7

Views: 8335

Answers (2)

jpp
jpp

Reputation: 164843

Similar solution to @jezrael's, but instead of creating a new dictionary you can use collections.ChainMap:

from collections import ChainMap

# dataframe setup
df = pd.DataFrame({'col1': ['Non Profit', 'Other-501c3', '501c3', 'Sole Proprietor']})

# create ChainMap
L1 = ['Non Profit', 'Other-501c3', '501c3','NON-Profit', 'Not-for-profit']
L2 = ['Sole Proprietor','Sole Proprietorship']
d = ChainMap(dict.fromkeys(L1, 'non-profit'), dict.fromkeys(L2, 'Sole Proprietor'))

# map values
df['new'] = df['col1'].map(d.get)

print(df)

              col1              new
0       Non Profit       non-profit
1      Other-501c3       non-profit
2            501c3       non-profit
3  Sole Proprietor  Sole Proprietor

Upvotes: 4

jezrael
jezrael

Reputation: 863801

Create dictionaries from keys, merge them and map:

L1 = ['Non Profit', 'Other-501c3', '501c3','NON-Profit', 'Not-for-profit']
d1 = dict.fromkeys(L1, 'non-profit')

L2 = ['Sole Proprietor','Sole Proprietorship']
d2 = dict.fromkeys(L2, 'Sole Proprietor')

d = {**d1, **d2}
print (d)
{'Non Profit': 'non-profit', 
 'Other-501c3': 'non-profit', 
 '501c3': 'non-profit',
 'NON-Profit': 'non-profit', 
 'Not-for-profit': 'non-profit', 
 'Sole Proprietor': 'Sole Proprietor',
 'Sole Proprietorship': 'Sole Proprietor'}

df['new'] = df['col1'].map(d)
print (df)
              col1              new
0       Non Profit       non-profit
1      Other-501c3       non-profit
2            501c3       non-profit
3  Sole Proprietor  Sole Proprietor

Upvotes: 15

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