Python pandas Can I calculate the delta of column values by the ID of another column in minute increments?

Question

I have a Csv file that looks something like this

Time	Count	Operation
10:01:00	2	Up
10:01:00	5	Down
10:01:00	1	Down
10:01:00	2	Up
10:01:00	1	Up
10:02:00	3	Down
10:02:00	2	Up
10:02:00	5	Down

I want to add up the values of the count column for each minute by the id of the operations column and then for the same minute subtract the up and down sums from each other which should give me something like this

Sum():

Time	Count	Operation
10:01:00	5	Up
10:01:00	6	Down
10:02:00	2	Up
10:02:00	8	Down

Diff():

Time	Delta
10:01:00	1
10:02:00	6

To do this, I try something like

def Delta_Volume():
    df = pd.read_csv(Ex_Csv, usecols=['Time','Count','Operation'], parse_dates=[0])
    df['Time'] = df['Time'].dt.floor("T", 0).dt.time
    df1 = df.groupby('Operation').sum('Count')
    df2 = df.groupby('Operation').diff('Count')
    #df['Delt_of_row'] = df.loc[1 : 3,['Count' , 'Operation']].sum(axis = 1)
    #df['Delt_of_row'] = df.loc[1 : 3,['Count' , 'Operation']].diff(axis = 1)
    print(df1)

But it doesn’t work the way I need unfortunately

Asked By: Show_man

||

Source

Answer 1

You can start with a classical GroupBy.sum, then use the MultiIndex to compute the difference:

df2 = df.groupby(['Operation', 'Time']).sum()
print(df2)

out = df2.loc['Down']-df2.loc['Up']
print(out)

output:

# groupby.sum
                    Count
Operation Time           
Down      10:01:00      6
          10:02:00      8
Up        10:01:00      5
          10:02:00      2

# difference
          Count
Time           
10:01:00      1
10:02:00      6

Answered By: mozway

Python pandas Can I calculate the delta of column values by the ID of another column in minute increments?

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