GroupBy and Aggregation are powerful features in pandas used to summarize and analyze data by categories.<\/p>\n\n\n\n
They help you answer questions like:<\/p>\n\n\n\n
What is the total sales per department?
What is the average salary by job role?
How many employees are in each city?<\/p>\n\n\n\n
First, import pandas:<\/p>\n\n\n\n
import pandas as pd<\/pre>\n\n\n\nCreate a sample dataset:<\/p>\n\n\n\n
data = {
\"Department\": [\"IT\", \"HR\", \"IT\", \"Finance\", \"HR\", \"Finance\"],
\"Employee\": [\"Ali\", \"Sara\", \"Ahmed\", \"John\", \"Ayesha\", \"David\"],
\"Salary\": [60000, 50000, 65000, 70000, 52000, 75000]
}df = pd.DataFrame(data)<\/pre>\n\n\n\nWhat is GroupBy?<\/h2>\n\n\n\n
GroupBy splits the data into groups based on one or more columns.<\/p>\n\n\n\n
Syntax:<\/p>\n\n\n\n
df.groupby(\"ColumnName\")<\/pre>\n\n\n\nExample:<\/p>\n\n\n\n
df.groupby(\"Department\")<\/pre>\n\n\n\nThis groups data by Department.<\/p>\n\n\n\n
Aggregation Functions<\/h2>\n\n\n\n
Aggregation means performing calculations on grouped data.<\/p>\n\n\n\n
Common aggregation functions:<\/p>\n\n\n\n
sum()
mean()
count()
min()
max()
std()<\/p>\n\n\n\nExample 1: Total Salary by Department<\/h2>\n\n\n\n
df.groupby(\"Department\")[\"Salary\"].sum()<\/pre>\n\n\n\nExample 2: Average Salary by Department<\/h2>\n\n\n\n
df.groupby(\"Department\")[\"Salary\"].mean()<\/pre>\n\n\n\nExample 3: Count Employees per Department<\/h2>\n\n\n\n
df.groupby(\"Department\")[\"Employee\"].count()<\/pre>\n\n\n\nMultiple Aggregations<\/h2>\n\n\n\n
You can apply multiple functions at once:<\/p>\n\n\n\n
df.groupby(\"Department\")[\"Salary\"].agg([\"sum\", \"mean\", \"max\"])<\/pre>\n\n\n\nGroupBy with Multiple Columns<\/h2>\n\n\n\n
df.groupby([\"Department\", \"Employee\"])[\"Salary\"].sum()<\/pre>\n\n\n\nResetting Index<\/h2>\n\n\n\n
Grouped results often create a new index.
To convert it back to a normal DataFrame:<\/p>\n\n\n\ndf.groupby(\"Department\")[\"Salary\"].sum().reset_index()<\/pre>\n\n\n\nPractical Example in Data Analytics<\/h2>\n\n\n\n
GroupBy is used to:<\/p>\n\n\n\n
Analyze sales by region
Calculate revenue by product
Find average marks by class
Summarize expenses by category
Measure performance by department<\/p>\n\n\n\nKey Takeaway<\/h2>\n\n\n\n
GroupBy splits data into categories, and Aggregation performs calculations on those groups.<\/p>\n\n\n\n
Together, they are essential tools for summarizing and analyzing structured data in real-world analytics projects.<\/p>\n\n\n\n
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