Reputation: 3677
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
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
Reputation: 863801
Create dictionaries from key
s, 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