Introduction to Pandas

What is Pandas?

Pandas is a Python library for working with structured data.

It gives you two core data structures:

• Series: a 1‑dimensional labeled array (like a single column)
• DataFrame: a 2‑dimensional labeled table (like a spreadsheet / SQL table)

Pandas is built on top of NumPy, so many NumPy concepts (like arrays, vectorization, and missing values) show up again here. As Wes McKinney (the creator of Pandas) puts it, NumPy is best for homogeneous numeric arrays, while Pandas is built specifically for tabular, heterogeneous data — the messy, real-world tables you get from spreadsheets, databases, and APIs.

From the book

McKinney notes that a DataFrame can be thought of as a dictionary of Series that all share the same index. That mental model explains a lot of Pandas behavior at once — why df["col"] returns a Series, why columns can have different dtypes, and why row alignment “just works” across columns.

Why Pandas is so popular in Data Analytics

In real data work, you spend a lot of time:

• Reading data from CSV/Excel/JSON/APIs
• Cleaning messy values
• Handling missing data
• Filtering and transforming rows/columns
• Aggregating data into summaries
• Preparing datasets for visualization and machine learning

Pandas is designed for exactly this.

When (not) to use Pandas

Pandas is great for:

• Small-to-medium datasets that fit in memory
• Exploratory analysis
• Data cleaning and feature engineering

Pandas may not be ideal for:

• Huge datasets that don’t fit in memory (use DuckDB/Polars/Spark)
• Highly-parallel compute workloads

Installing Pandas

If you already installed libraries in Phase 1, you probably have it.

Install with pip

Install pandas with pip

pip install pandas

Install with conda

Install pandas with conda

conda install pandas

Your first Pandas import

Import pandas

import pandas as pd
 
print(pd.__version__)

Quick mental model: DataFrame thinking

A DataFrame is basically:

• Rows (observations / records)
• Columns (features / fields)

Common questions you’ll ask:

• What columns do I have?
• How many rows?
• Are there missing values?
• How do I filter rows?
• How do I compute group summaries?

We’ll answer all of these in this phase.

Visualize it

diagram From raw data to insight mermaid

  flowchart LR
  A["CSV / Excel / JSON / API"] --> B["pandas.DataFrame"]
  B --> C["Clean & transform"]
  C --> D["Group & aggregate"]
  D --> E["Visualize / Model"]

Pandas sits between messy raw sources and the analysis you actually want to do.

🧪 Try It Yourself

Exercise 1 – Import Pandas and Check the Version

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Exercise 2 – Create Your First Series

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Exercise 3 – Build a DataFrame From a Dict

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Next

Now that you know what Pandas is and why it matters, move on to Series and DataFrames to build and inspect these structures in more depth.