Group duplicate columns and sum the corresponding column values using pandas [duplicate]

问题

This question already has answers here:

Pandas group-by and sum (6 answers)

Closed last year.

I am preprocessing apache server log data. I have 3 columns ID, TIME, and BYTES. Example:

ID &nbsp &nbsp TIME &nbsp &nbsp BYTES

1 &nbsp &nbsp 13:00 &nbsp &nbsp 10

2 &nbsp &nbsp 13:02 &nbsp &nbsp 30

3 &nbsp &nbsp 13:03 &nbsp &nbsp 40

4 &nbsp &nbsp 13:02 &nbsp &nbsp 50

5 &nbsp &nbsp 13:03 &nbsp &nbsp 70

I want to achieve something like this:

ID &nbsp &nbsp TIME &nbsp &nbsp BYTES

1 &nbsp &nbsp 13:00 &nbsp &nbsp 10

2 &nbsp &nbsp 13:02 &nbsp &nbsp 80

3 &nbsp &nbsp 13:03 &nbsp &nbsp 110

回答1:

Let's try:

df['TIME'] = pd.to_datetime(df['TIME'])
ax = df.groupby('TIME')['BYTES'].sum().plot()
ax.set_xlim('13:00:00','13:03:00')

Output:

来源：https://stackoverflow.com/questions/48013007/group-duplicate-columns-and-sum-the-corresponding-column-values-using-pandas