How to Get the Aggregate of Pandas Group-By and Sum

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Asad Riaz Feb 02, 2024
Pandas Pandas DataFrame
  1. Cumulative Sum With groupby
  2. pivot() to Rearrange the Data in a Nice Table
  3. Apply function to groupby in Pandas
  4. agg() to Get Aggregate Sum of the Column
How to Get the Aggregate of Pandas Group-By and Sum

We will demonstrate how to get the aggregate in Pandas by using groupby and sum. We will also look at the pivot functionality to arrange the data in a nice table and define our custom function and run it on the DataFrame. We will also get the aggregate sum by using agg().

Cumulative Sum With groupby

We can get the cumulative sum by using groupby method. Consider the following Dataframe with Date, Fruit and Sale columns:

import pandas as pd

df = pd.DataFrame(
    {
        "Date": ["08/09/2018", "10/09/2018", "08/09/2018", "10/09/2018"],
        "Fruit": [
            "Apple",
            "Apple",
            "Banana",
            "Banana",
        ],
        "Sale": [34, 12, 22, 27],
    }
)

If we want to calculate the cumulative sum of Sale per Fruit and for every date, we can do:

import pandas as pd

df = pd.DataFrame(
    {
        "Date": ["08/09/2018", "10/09/2018", "08/09/2018", "10/09/2018"],
        "Fruit": [
            "Apple",
            "Apple",
            "Banana",
            "Banana",
        ],
        "Sale": [34, 12, 22, 27],
    }
)
print(df.groupby(by=["Fruit", "Date"]).sum().groupby(level=[0]).cumsum())

Output:

Fruit  Date         Sale
Apple  08/09/2018    34
       10/09/2018    46
Banana 08/09/2018    22
       10/09/2018    49

pivot() to Rearrange the Data in a Nice Table

pivot() method could set row and column attribute of the table. Let’s change the above code and apply pivot() method to rearrange the data in a nice table:

import pandas as pd

df = pd.DataFrame(
    {
        "Date": ["08/09/2018", "10/09/2018", "08/09/2018", "10/09/2018"],
        "Fruit": [
            "Apple",
            "Apple",
            "Banana",
            "Banana",
        ],
        "Sale": [34, 12, 22, 27],
    }
)
print(
    df.groupby(["Fruit", "Date"], as_index=False).sum().pivot("Fruit", "Date").fillna(0)
)

Output:

             Sale           
Date   08/09/2018 10/09/2018
Fruit                       
Apple          34         12
Banana         22         27

Apply function to groupby in Pandas

We will create a simple method to get count of values in series or 1d array and use groupby to get aggregate count of each value:

from pandas import *

d = {"series": Series(["1", "2", "1", "1", "4", "4", "5"])}
df = DataFrame(d)


def get_count(values):
    return len(values)


grouped_count = df.groupby("series").series.agg(get_count)
print(grouped_count)

After running the code, we will get the following output, which provides each value’s occurrence in the series.

Output:

series
1    3
2    1
4    2
5    1
Name: series, dtype: int64

agg() to Get Aggregate Sum of the Column

We can use agg() to apply sum operation on column. Example Codes:

import pandas as pd

df = pd.DataFrame(
    {
        "Date": ["08/09/2018", "10/09/2018", "08/09/2018", "10/09/2018"],
        "Fruit": [
            "Apple",
            "Apple",
            "Banana",
            "Banana",
        ],
        "Sale": [34, 12, 22, 27],
    }
)
print(df.groupby(["Fruit"])["Sale"].agg("sum"))

Output:

Fruit
Apple     46
Banana    49
Name: Sale, dtype: int64

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