I am trying to sum the numeric values of one column depending on the value of another column. For example: whenever in the first column there is a Glovoapp
I would like to sum its corresponding numeric values (of another column) together.
Table example:
Initiator | Price |
---|---|
Glovoapp | 566 |
XXXXX | 545 |
Glovoapp | 899 |
XXXXX | 200 |
montant_init = new_data.loc[new_data['Initiateur'] == 'Glovoapp', 'Price'].sum()
output: 0 (none)
expected output (example): 1465
Use:
montant_init = df[df["Initiator"]=="Glovoapp"]["Price"].sum()
Alternatively you can use groupby if you have multiple Initiators:
price_sum = df.groupby("Initiator")["Price"].sum()
output:
Initiator
Glovoapp 1465
XXXXX 745
Then
price_sum["Glovoapp"]
outputs:
1465