Grouping and Summarizing Data with Pandas (Without Pain)

Written by: Marlon Colca
Posted on 14 May 2025 - 3 months ago
python pandas analytics

Learn how to group, aggregate, and pivot your data using Pandas. Master `groupby`, `agg`, and `pivot_table` with real examples.

Grouping and Summarizing Data with Pandas (Without Pain) 📊

In this post, we’ll explore how to group and summarize your data with Pandas using methods like:

• groupby()
• agg()
• pivot_table()

These tools help you answer real questions like:

• What’s the average price per category?
• Which brand has the most products?
• How many products are in stock per category?

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🧱 Let’s load the data

We’ll use the same dataset (prices_sample.csv):

import pandas as pd

df = pd.read_csv("prices_sample.csv")

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🔢 Using groupby() + aggregation

Let’s start simple: what’s the average price per category?

avg_price = df.groupby("category")["price"].mean()
print(avg_price)

You can also get multiple aggregations:

summary = df.groupby("category")["price"].agg(["mean", "min", "max", "count"])
print(summary)

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🧠 Custom aggregations with agg()

You can even pass custom functions or lambdas:

df.groupby("brand")["price"].agg(
    average="mean",
    highest="max",
    products="count"
)

You can rename the output columns with named aggregation like above (average=...).

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📊 Counting products per category

This is a very common task:

product_counts = df["category"].value_counts()
print(product_counts)

Or using groupby:

df.groupby("category")["product_id"].count()

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🔄 Using pivot_table()

Pivot tables give you more flexibility for reshaping data. Let’s say we want the average price per category and availability:

pivot = pd.pivot_table(df, values="price", index="category", columns="in_stock", aggfunc="mean")
print(pivot)

This shows how price changes depending on whether items are in stock or not.

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🔁 Reshaping with .unstack() and .melt()

You can convert groupby results into wide format with .unstack():

group = df.groupby(["category", "in_stock"])["price"].mean()
print(group.unstack())

Or go from wide → long format using .melt():

melted = df.melt(id_vars=["category"], value_vars=["price"])
print(melted.head())

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🧠 When to use each?

Task	Use
Simple summaries	groupby()
Complex reshaping	pivot_table()
Multi-level aggregations	agg()
Format manipulation	melt(), unstack()

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✅ Summary

Grouping and aggregating are essential tools in your data toolkit.

Remember:

• groupby() for grouping rows
• agg() for multiple summaries
• pivot_table() for reshaping with aggregations
• value_counts() for quick category counts

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📌 What’s next?

In Part 6, we’ll work with dates and time in Pandas: parsing, grouping by month/week, and time series tricks ⏳.

Perfect if you want to build charts over time or analyze trends.

See you there!

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🔜 Coming up next