colum 1, colum2
a,b,c 30
b,c,f 40
a,g,z 50
.
.
.
In this data frame, I would like to construct column4 by adding the value of column2 corresponding to a.
column3, column4
a 80
b 70
c 70
f 40
g 50
z 50
I would like to get the above result, so I wonder if there is a method of numpy and pandas related.
python numpy pandas dataframe
df = pd.DataFrame({'col1': ['a, b, c', 'b, c, f', 'a, g, z'],
'col2': [30, 40, 50]})
df1 = df['col1'].str.split(', ', expand=True)
df1.rename({i: 'new_col%s' % i for i in range(df1.shape[1])}, axis=1, inplace=True)
df1['value'] = df['col2']
df1 = df1.melt(['value'], ['new_col%s' % i for i in range(3)], 'col', 'key')
df1.drop(['col'], axis=1, inplace=True)
df1.groupby('key').sum()
I don't know if you're watching this because the question is old.
If you use Pandas, you can do this.
>>> import pandas as pd
>>> d = {'col1': [1, 2], 'col2': [3, 4], 'col3': [5,6]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2 col3
0 1 3 5
1 2 4 6
>>> df['col4'] = df[['col1','col2']].sum(axis=1)
>>> df
col1 col2 col3 col4
0 1 3 5 4
1 2 4 6 6
df.sum(axis=1)This adds everyone's meaning.
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