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Pandas Tutorial for Data Analysis (with examples)

Free, hands-on Pandas tutorial - DataFrame fundamentals, reading and writing CSVs, selecting, filtering, joining, grouping, reshaping, and visualizing data. 30+ examples tested on Python 3.11 + pandas 2.x.

  • 38 parts
  • ~377 min total
  • Beginner to Intermediate
  • Updated May 2026
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Pandas Tutorial for Data Analysis (with examples)
By Last updated

Pandas is the de-facto standard for working with tabular data in Python - and once you internalize its model (DataFrame = labeled 2D table, Series = labeled 1D array), 90% of your "wait, how do I do X in Pandas?" questions disappear. This tutorial gives you that mental model first, then walks through every operation you will actually hit in real data work.

We cover reading and writing data (CSV, SQL, Excel), selecting rows and columns, cleaning and converting types, combining DataFrames (concat / merge / join), grouping and reshaping (groupby / pivot / melt), and visualization. Every example uses real-looking data instead of toy [1, 2, 3] rows, and every snippet is tested on pandas 2.x.

Click Start the course to begin with the Pandas concepts chapter, or jump to the chapter you need - Selecting Rows and Columns and Combining DataFrames are the most-bookmarked.

What you'll learn

  • Create, inspect, and manipulate Pandas DataFrames and Series
  • Read and write CSV, JSON, Excel, and SQL data
  • Select, filter, and transform columns with loc / iloc / boolean masks
  • Combine DataFrames with concat, merge, join, and append
  • Aggregate with groupby, pivot, melt, and reshape data for analysis
  • Handle missing values, datetimes, and produce simple visualizations

Prerequisites

  • Python 3.10+ installed locally
  • Basic Python syntax (variables, lists, dicts, functions, list comprehensions)
  • pandas 2.x and numpy installed (pip install pandas numpy)

Syllabus

11 chapters · 38 lessons · ~377 min of reading

  1. 1 Pandas Concepts (start here if new) 2 lessons
    1. Part 1 Introduction to Python Pandas 11 min read
    2. Part 2 The Pandas DataFrame explained 16 min read
  2. 2 Reading and Writing Data 4 lessons
    1. Part 3 Read CSV files with pandas 13 min read
    2. Part 4 Write a DataFrame to CSV 14 min read
    3. Part 5 Export a DataFrame to SQL 10 min read
    4. Part 6 Print the entire DataFrame (no truncation) 13 min read
  3. 3 Inspecting and Sizing DataFrames 3 lessons
    1. Part 7 Count rows in a DataFrame 7 min read
    2. Part 8 Get the size and shape of a DataFrame 9 min read
    3. Part 9 Get unique values from a column 4 min read
  4. 4 Selecting Rows and Columns 5 lessons
    1. Part 10 loc vs iloc vs at vs iat - the difference 6 min read
    2. Part 11 Select by integer position with iloc 12 min read
    3. Part 12 Select a single column 7 min read
    4. Part 13 Select multiple columns 7 min read
    5. Part 14 Filter rows by column value 11 min read
  5. 5 Adding and Modifying Columns / Rows 5 lessons
    1. Part 15 Add a new column to a DataFrame 6 min read
    2. Part 16 Add an empty column 13 min read
    3. Part 17 Add a row to a DataFrame 7 min read
    4. Part 18 Rename one or more columns 16 min read
    5. Part 19 Change the order of columns 7 min read
  6. 6 Cleaning and Type Conversion 6 lessons
    1. Part 20 Convert a column to int 6 min read
    2. Part 21 Convert a column to float 12 min read
    3. Part 22 Convert strings to datetime 15 min read
    4. Part 23 Drop missing values with dropna() 14 min read
    5. Part 24 Drop rows by condition 8 min read
    6. Part 25 Drop columns from a DataFrame 5 min read
  7. 7 Indexing 2 lessons
    1. Part 26 Set a column as the index 9 min read
    2. Part 27 Reset the DataFrame index 9 min read
  8. 8 Combining DataFrames 3 lessons
    1. Part 28 Concatenate DataFrames with concat() 18 min read
    2. Part 29 merge vs concat vs append vs join 13 min read
    3. Part 30 Build a DataFrame from a list of dictionaries 6 min read
  9. 9 Aggregation and Reshaping 5 lessons
    1. Part 31 Group and aggregate with groupby() 17 min read
    2. Part 32 Pivot tables in pandas 15 min read
    3. Part 33 Reshape with melt() 4 min read
    4. Part 34 Resample time-series data 11 min read
    5. Part 35 Rolling window calculations 7 min read
  10. 10 Iteration and Mapping 2 lessons
    1. Part 36 Iterate over DataFrame rows 9 min read
    2. Part 37 Apply a function with Series.map() 4 min read
  11. 11 Visualization 1 lesson
    1. Part 38 Create histograms from a DataFrame 6 min read
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