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I am having following dataframe
df1 = pd.DataFrame({'Name': ['A0', 'A1', 'A2', 'A3', 'A4'],
'Buy': [True, True, False, False, False],
'Sell': [False, False, True, False, True]
},
index=[0, 1, 2, 3, 4])
df1
Name Buy Sell
0 A0 True False
1 A1 True False
2 A2 False True
3 A3 False False
4 A4 False True
I want to merge Buy and Sell columns on a condition that if "Buy" is having True value then "Buyer" if "Sell" has True value then "Seller" and if both "Buy" and "Sell" has False value then it should have "NA"
sample required output
Name Type
0 A0 Buyer
1 A1 Buyer
2 A2 Seller
3 A3 NA
4 A4 Seller
443
By use + operator simply you can combine/merge two or multiple text/string columns in pandas DataFrame. Note that when you apply + operator on numeric columns it actually does addition instead of concatenation.
To start, you may use this template to concatenate your column values (for strings only): df['New Column Name'] = df['1st Column Name'] + df['2nd Column Name'] + ... Notice that the plus symbol ('+') is used to perform the concatenation.
merge() for combining data on common columns or indices. . join() for combining data on a key column or an index. concat() for combining DataFrames across rows or columns.
np.selecta = np.select([df1.Buy, df1.Sell], ['Buyer', 'Seller'], 'NA')
pd.DataFrame({'Name': df1.Name, 'Type': a})
Name Type
0 A0 Buyer
1 A1 Buyer
2 A2 Seller
3 A3 NA
4 A4 Seller
df1.assign(Type=np.select([df1.Buy, df1.Sell], ['Buyer', 'Seller'], 'NA'))
Name Buy Sell Type
0 A0 True False Buyer
1 A1 True False Buyer
2 A2 False True Seller
3 A3 False False NA
4 A4 False True Seller
You can do:
s = df[['Buy','Sell']]
df['Type'] = ([email protected]).add('er').replace('er', np.nan)
# or
# df['Type'] = np.where(s.any(1), s.idxmax(1).add('er'), np.nan)
Output:
Name Buy Sell Type
0 A0 True False Buyer
1 A1 True False Buyer
2 A2 False True Seller
3 A3 False False NaN
4 A4 False True Seller
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