We need to convert three different series to a dataframe in Python 3.
Series 1 (Mean):
e1 40.355
e2 59.556
ec1 17.668
ec2 75.230
Series 2 (SD):
e1 19.815
e2 17.550
ec1 14.704
ec2 54.485
Series 3 (Max):
e1 87.36
e2 80.39
ec1 200.3
ec2 207.5
Above needs to be merged into a dataframe that looks like the following:
Features Mean Standard Deviation Max
e1 40.355 19.815 87.36
e2 59.556 17.550 80.39
ec1 17.668 14.704 200.3
ec2 75.230 54.485 207.5
What we have done so far is:
if __name__ == '__main__':
Mean = df.mean().round(3)
SD = df.std().round(3)
Max = df.max()
df2 = pd.DataFrame(columns=['Features', 'Mean', 'Standard Deviation', 'Max'])
df2['Mean'] = Mean
df2['Standard Deviation'] = SD
df2['Max'] = Max
print(df2)
Above prints/outputs the following:
Features Mean Standard Deviation Max
e1 NaN 40.355 19.815 87.36
e2 NaN 59.556 17.550 80.39
ec1 NaN 17.668 14.704 200.3
ec2 NaN 75.230 54.485 207.5
How can we set the index to features column such that the dataframe looks like the following:
Features Mean Standard Deviation Max
e1 40.355 19.815 87.36
e2 59.556 17.550 80.39
ec1 17.668 14.704 200.3
ec2 75.230 54.485 207.5
>Solution :
Use concat, rename_axis and reset_index:
df2 = (pd.concat([Mean, SD, Max],
keys=["Mean", "SD", "Max"],
axis=1)
.rename_axis(index="Features")
.reset_index())
>>> df2
Features Mean SD Max
0 e1 40.355 19.815 87.36
1 e2 59.556 17.550 80.39
2 ec1 17.668 14.704 200.30
3 ec2 75.230 54.485 207.50