How do I sum all values in one column based on values from range of another column

How do I sum all values in last column (time in minutes) based on values from the Combi column (from 300244871 – to 300244871) Total time for Combi should be 142,03 for 300244871.

I have tried with below code.

df['time in minutes'] = pd.to_numeric(df['time in minutes'].str.replace(',', '.'))
start_value = df['Combi'].min()
end_value = df['Combi'].max()
filtered_df = df[(df['Combi'] >= start_value) & (df['Combi'] <= end_value)]
sum_result = filtered_df['time in minutes'].sum()
print(f"Sum of values for {start_value} to {end_value} in 'time in minutes': {sum_result}")

>Solution :

You can use a custom groupby.agg. Assuming NaNs in the empty cell, create the grouper with ffill:

df['time in minutes'] = pd.to_numeric(df['time in minutes'].str.replace(',', '.'))
out = (df.groupby(df['Combi'].ffill())
         .agg(**{'start': ('time', 'min'),
                 'end': ('time', 'max'),
                 'sum': ('time in minutes', 'sum')
                })
      )

NB. If the empty cells are empty strings, use df['Combi'].replace('', np.nan).ffill() as grouper.

Output:

                start       end     sum
Combi                                     
300244871.0  05:12:30  07:34:32  142.03
300244881.0  05:12:30  07:34:32   83.03

Using a loop:

for k, g in df.groupby(df['Combi'].ffill()):
    start_value = g['time'].min()
    end_value = g['time'].max()
    sum_result = g['time in minutes'].sum()
    print(f"Sum of values for {start_value} to {end_value} in 'time in minutes': {sum_result}")

Output:

Sum of values for 05:12:30 to 07:34:32 in 'time in minutes': 142.03
Sum of values for 05:12:30 to 07:34:32 in 'time in minutes': 83.03