Module 6 Lesson 4: Pandas Basics: DataFrames and Series

If NumPy is the "engine" of data science, Pandas is the "user interface." It allows you to work with data in a way that feels like a powerful, programmable version of Excel. In this lesson, we’ll meet the two most important objects in Pandas: Series and DataFrames.

Lesson Overview

In this lesson, we will cover:

• What is Pandas?: Why it’s the king of data analysis.
• Series: One-dimensional labeled arrays.
• DataFrames: Two-dimensional tables.
• Accessing Data: Head, Tail, and Columns.

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1. What is a Series?

A Series is like a column in a spreadsheet. It’s a list of values, but every value has a label called an "index."

import pandas as pd # Standard way to import pandas

grades = pd.Series([85, 90, 78], index=["Alex", "Sara", "Tom"])
print(grades)
print(grades["Sara"]) # Output: 90

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2. What is a DataFrame?

A DataFrame is a full table. It’s essentially a collection of Series objects that share the same index.

data = {
    "Name": ["Alex", "Sara", "Tom"],
    "Age": [15, 16, 15],
    "Score": [85, 90, 78]
}

df = pd.DataFrame(data)
print(df)

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3. Navigating Large Data

When you have 10,000 rows, you don't want to print them all. Pandas provides tools to "peek" at your data.

# See the first 5 rows
print(df.head())

# See the last 5 rows
print(df.tail())

# Get summary statistics for all numerical columns
print(df.describe())

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4. Selecting Data

You can select a single column from a DataFrame just like a key in a dictionary.

# Get just the 'Name' column (this returns a Series!)
names = df["Name"]

# Get multiple columns
subset = df[["Name", "Score"]]

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Practice Exercise: The Employee Table

1. Create a dictionary with three keys: Employee, Department, and Salary.
2. Add at least 5 rows of data.
3. Convert the dictionary into a Pandas DataFrame.
4. Print the first 3 rows using head().
5. Print the average salary using df["Salary"].mean().

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Quick Knowledge Check

1. What is the difference between a Series and a DataFrame?
2. How do you import pandas according to industry convention?
3. What does the describe() method do?
4. How do you select only the "Price" column from a DataFrame named df?

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Key Takeaways

• Pandas is built for tabular data (rows and columns).
• A Series is a labeled column; a DataFrame is a table.
• DataFrames allow for complex selection and quick statistical summaries.
• Pandas is built on top of NumPy, meaning it’s incredibly fast.

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What’s Next?

Manually creating a dictionary for every table is exhausting. In Lesson 5, we’ll learn how to Load Data directly from CSV and Excel files into your Python code with just one line!