groupby two columns take highest sum (Pandas)

Question

Looking to see which client has the highest price sum for the month of February. 2/1/2022

Data

client  box Price   date
charles AA  5,000   2/1/2022
charles AA  5,050   2/1/2022
charles AA  5,075   2/1/2022
cara    BB  25,116  2/1/2022
cara    BB  5,154   2/1/2022
lu      CC  0       2/1/2022
max     DD  10,000  3/1/2022

Desired

   client   box Price   date
   cara     BB  30,270  2/1/2022

Doing

df.groupby(['client','date']) 
.agg({'Price':'sum'}).reset_index()

Any suggestion is helpful.

Asked By: Lynn

||

Source

Answer 1

idxmax will return the index of the maximum value—which you can then use to look up the row you want. Be aware that in the case that two customers are tied for the highest price, it will return only the first occurence.

Example:

(
    df
    # Fist filter to only Februrary
    .loc[lambda df_: df_["date"].dt.month == 2]
    .groupby(["client", "box"])["Price"]
    .sum()
    .reset_index()
    # Then select where price is max
    .loc[lambda df_: df_["Price"].idxmax()]
)

Answered By: Paulo Costa

Answer 2

import pandas as pd

df = pd.DataFrame({"client": ["charles", "charles", "charles", "cara", "cara", "lu", "max"],
                    "box": ["AA", "AA", "AA", "BB", "BB", "CC", "DD"],
                    "price": [5000, 5050, 5075, 25116, 5154, 0, 10000],
                    "date": ["2/1/2022", "2/1/2022", "2/1/2022", "2/1/2022", "2/1/2022", "2/1/2022", "3/1/2022"]})
# print(df)

print(df.groupby(by= ["client", "box", "date"]).price.aggregate('sum'))

Answered By: Soumen Ghosh

Answer 3

Based on the OP’s desired output, here is a suggestion:

gdf = df.groupby(['client','box','date']).agg({'Price':'sum'}).reset_index()
gdf.loc[gdf.Price.idxmax()]

Answered By: Heelara

Answer 4

If box is always the same per client.

df = df.groupby(["client", "date"]).agg({"box": "first", "Price": "sum"})
df = df[df["Price"].eq(df["Price"].max())].reset_index()

If box could be different per client.

df = df.groupby(["client", "date", "box"]).agg({"Price": "sum"})
df = df[df["Price"].eq(df["Price"].max())].reset_index()

Answered By: Jason Baker

groupby two columns take highest sum (Pandas)

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