Pandas Tutorial

Master data analysis in Python with Pandas — 56 interactive lessons from your first Series to scaling up with Dask, Polars, Spark and GeoPandas.

Learn Pandas from scratch — DataFrames, Series, filtering, grouping, joining and cleaning real data — with free, interactive, beginner-to-advanced lessons.

👋 New to Pandas? Some Python basics are all you need.

Start with Lesson 1 and build real data-analysis skills step by step — at your own pace.

Continue where you left off

Lessons in this course

• Introduction to Pandas — What Pandas is, why it powers data analysis, and how to install and import it
• The Series: Pandas' 1-D Building Block — Create and work with a Series — a labelled one-dimensional array
• The DataFrame: Rows and Columns — Understand the DataFrame — Pandas' core 2-D table of rows and columns
• Creating DataFrames — Build DataFrames from dicts, lists, and NumPy arrays
• Reading & Writing Data (CSV, Excel, JSON) — Load and save data with read_csv, read_excel, read_json and to_* methods
• Inspecting Data: head, tail, info, describe — Get a fast overview of any dataset with the core inspection methods
• Selecting Columns and Rows — Pick out the exact columns and rows you need from a DataFrame
• Filtering with Boolean Masks — Keep only the rows that match your conditions using boolean masks
• loc vs iloc: Label vs Position — Master the two indexers — label-based loc and position-based iloc
• SettingWithCopyWarning: Explained & Fixed — Why edits silently vanish, and the one-line .loc fix that stops it
• Adding, Modifying & Dropping Columns — Create new columns, transform existing ones, and remove what you don't need
• Handling Missing Data (NaN, fillna, dropna) — Detect, fill, and drop missing values to clean real-world datasets
• Sorting and Ranking — Order data by values or index and assign ranks within columns
• idxmax & idxmin: Find the Row Behind the Max — Get the whole record holding the largest value, not just the number
• apply, map & applymap — Run custom functions across Series and DataFrames element by element
• String Operations with .str — Clean and transform text columns with vectorised string methods
• Working with Dates & Times — Parse, extract, and resample datetime data with the .dt accessor
• GroupBy: Split-Apply-Combine — Group rows and compute summaries with the split-apply-combine pattern
• Aggregation & Transformation — Apply multiple aggregations and group-aware transforms in one step
• Merging & Joining DataFrames — Combine datasets on keys with inner, left, right, and outer joins
• Concatenating & Appending — Stack DataFrames vertically and horizontally with pd.concat
• Pivot Tables & Crosstab — Reshape and summarise data into spreadsheet-style pivot tables
• Reshaping: melt, stack, unstack — Move between wide and long formats with melt, stack, and unstack
• MultiIndex (Hierarchical Indexing) — Work with multiple index levels for richer, hierarchical data
• Performance: vectorization & memory — Write fast, memory-efficient Pandas by vectorising and downcasting
• The Index: set_index, reset_index & reindex — Work with the Index — set a meaningful index, reset it, and reindex for fast lookups
• Data Types & astype — Inspect dtypes and convert columns with astype and to_numeric for speed and correctness
• Categorical Data — Save memory and add order with the category dtype and the .cat accessor
• Dropping & Deduplicating Rows — Remove rows and duplicates with drop, drop_duplicates, and duplicated
• Renaming & Relabelling — Rename columns and index labels cleanly with rename, add_prefix, and add_suffix
• Counting & Uniques — Summarise categories fast with value_counts, unique, and nunique
• Filtering with query() & eval() — Write readable filters and computed columns with query() and eval()
• Replacing Values: replace, where & mask — Swap and conditionally overwrite values with replace, where, mask, and clip
• Checkpoint: Cleaning & Wrangling — Combine indexing, dtypes, dropping, renaming and replacing in a build — then a quiz
• Binning Continuous Data: cut & qcut — Group numbers into ranges with cut for fixed edges and qcut for quantiles
• One-Hot Encoding with get_dummies — Turn category columns into model-ready 0/1 columns with get_dummies
• Rolling & Expanding Windows — Compute moving averages and running totals with rolling, expanding, and ewm
• GroupBy transform & filter — Add group-aware columns with transform and keep/drop whole groups with filter
• Time Series Ops: shift, diff & pct_change — Lag, difference, and compute returns over time with shift, diff, and pct_change
• Plotting with DataFrame.plot() — Make quick line, bar, histogram, and scatter charts straight from a DataFrame
• Method Chaining & .pipe() — Write clean, readable pipelines with chaining, assign, and pipe
• Styling & Formatting Output — Control display options and format tables with round, to_markdown, and .style
• Checkpoint: Analysis & Aggregation — Combine binning, windows, transforms and time-series ops in a build — then a quiz
• Time Series Resampling (resample, asfreq) — Downsample and upsample time series with resample and asfreq
• Interpolation & Filling Gaps — Fill missing values with linear, time, and polynomial interpolation
• Exploding Lists into Rows (explode) — Turn a column of lists into one row per element with explode
• Cross-Tabulation (crosstab) — Count and summarise category combinations with pd.crosstab
• Reading & Writing SQL — Move data between pandas and databases with read_sql and to_sql
• Efficient Formats: Parquet, Feather & Pickle — Store data faster and smaller than CSV with columnar formats
• Ranking & Top-N (rank, nlargest) — Rank rows, find the top N, and rank within groups
• Checkpoint: Time Series & I/O — Combine SQL loading, resampling and ranking in a build — then a quiz
• Capstone: End-to-End Data Cleaning — Combine every skill into a complete real-world data-cleaning pipeline
• Scaling with Dask — Go beyond memory with Dask's partitioned, parallel, pandas-like DataFrame
• Polars: A Faster DataFrame — Rust-backed speed with lazy evaluation, expressions, and Arrow memory
• Pandas & Spark — Process big data across a cluster with PySpark and the pandas API on Spark
• Geospatial Data with GeoPandas — Work with geometries, CRS, spatial joins, and maps using GeoDataFrames

More for this course

• Pandas cheat sheet — Quick syntax reference you can scan while you code
• Data Preprocessing for Machine Learning — The essential guide to cleaning, transforming, and preparing data for AI models. Learn data cleaning, scaling, encoding, feature engineering, and more.