Hi I have a dataset like the one below:
data = [['ALFA', 351740.00, 0.31, 0.22, 0.44, 0.19, 0.05],
['ALFA', 401740.00, 0.43, 0.26, 0.23, 0.16, 0.09],
['Bravo', 511830.00, 0.52, 0.16, 0.08, 0.26, 0],
['Charlie', 590030.00, 0.75, 0.2, 0.14, 0.37, 0.06],
['Charlie', 590030.00, 0.75, 0.2, 0.27, 0.2, 0.01],
['Delta', 590030.00, 0.75, 0.2, 0.34, 0.3, 0],
['Delta', 590030.00, 0.75, 0.2, 0, 0.28, 0],
['Echo', 590030.00, 0.75, 0.2, 0.08, 0.26, 0],
['Echo', 590030.00, 0.75, 0.2, 0.14, 0.37, 0.06]]
df = pd.DataFrame(data, columns= ['Name', 'Column1', 'Column2', 'Column3', 'Column4', 'Column5', 'Column6'])
df
I grouped by 'Names' to get the mean of the 'Names' and saved it as a new dataframe using the code below:
df1 = df.groupby('Name').mean()
df1
However, I can no longer access the 'Names' column (as would a normal dataframe column) after the last 'groupby' statement. I tried main things including df1['cluster_id'] = df1['Name'] but ended up with errors.
Is there a way to create another column 'Cluster_id' with same data as the 'Names' column so the output looks like the df shown below and I can use it for further analysis?
Name Column1 Column2 Column3 Column4 Column5 Column6 Cluster_id
ALFA 376740.0 0.37 0.24 0.335 0.175 0.070 ALFA
Bravo 511830.0 0.52 0.16 0.080 0.260 0.000 Bravo
Charlie 590030.0 0.75 0.20 0.205 0.285 0.035 Charlie
Delta 590030.0 0.75 0.20 0.170 0.290 0.000 Delta
Echo 590030.0 0.75 0.20 0.110 0.315 0.030 Echo