In this lesson, we'll explore data aggregation, a powerful tool in data analysis. Aggregation helps you summarize and simplify large sets of data to gain insights quickly. By the end, you'll know how to use data aggregation techniques to find specific information about groups in your dataset.

Data aggregation involves combining, summarizing, or consolidating data points into a single representation. Imagine you have a large set of student test scores. Instead of looking at every individual score, you might want to know the average score for each class. This simplifies your data and helps you see the bigger picture.

Common functions used in data aggregation include:

• Maximum (max): Finds the highest value in a group.
• Mean (mean): Calculates the average value of a group.
• Sum (sum): Calculates the total sum of a group.
• Standard Deviation (std): Calculates the dispersion or spread of a group.

Let's start with a simple example:

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1scores = [89, 76, 92, 54, 88]
2print("Maximum score:", max(scores))  # Maximum score: 92
3print("Average score:", sum(scores) / len(scores))  # Average score: 79.8

Here, max gives us the highest score, and calculating the average (mean) summarizes the test scores.

Pandas offers an easy way to perform data aggregation using groupby and agg methods. Here’s how you can use them:

• groupby: Splits the data into groups based on some criteria.
• agg: Applies one or more aggregation functions to these groups.

Let’s see this in action.

We'll use an example dataset containing information about different products sold in various stores. Here's a small sample:

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1import pandas as pd
2
3data = {
4    'store': ['Store A', 'Store A', 'Store B', 'Store B', 'Store C'],
5    'product': ['Apples', 'Bananas', 'Apples', 'Bananas', 'Apples'],
6    'units_sold': [30, 50, 40, 35, 90],
7    'price': [1.20, 0.50, 1.00, 0.50, 1.30]
8}
9
10df = pd.DataFrame(data)
11print(df)

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1     store  product  units_sold  price
20  Store A   Apples          30   1.20
31  Store A  Bananas          50   0.50
42  Store B   Apples          40   1.00
53  Store B  Bananas          35   0.50
64  Store C   Apples          90   1.30

Let’s find the maximum units sold and the average price of products by store using Pandas.

1. Create the Aggregation Dictionary:

• This maps column names to their aggregation functions.

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1agg_funcs = {'units_sold': 'max', 'price': 'mean'}

1. Group Data by Store and Apply the Aggregation Functions:

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1result = df.groupby('store').agg(agg_funcs)
2print(result)

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1         units_sold  price
2store                      
3Store A          50   0.85
4Store B          40   0.75
5Store C          90   1.30

Breaking this down:

• groupby('store') groups rows by the store column.
• agg(agg_funcs) applies the specified functions (max for units_sold, mean for price) to each group.

This tells us that in Store A, the most units sold for any product was 50, and the average price of products in that store is $0.85.

In this lesson, we've covered:

• What data aggregation is and why it's useful.
• Common aggregation functions like max and mean.
• How to use Python's Pandas library to aggregate data effectively.

By understanding and applying these concepts, you're now better equipped to summarize and analyze large datasets.

Now it's your turn to practice data aggregation. You'll get a chance to apply these techniques on different datasets to strengthen your understanding and skills. Good luck!