Data Manipulation with Pandas & Polars – Complete Guide & Best Practices 2026
Welcome to the complete Data Manipulation learning hub. Master fast, clean, and production-ready data wrangling with Pandas, Polars, datetime handling, groupby, pivot tables, missing values, and real-world analysis in 2026.
Data Manipulation Learning Roadmap
Foundation
- Python Counter – Practical Patterns & Polars
- most_common() – collections Counter
- OrderedDict Power Features
- namedtuple – Powerful Tool for Data Manipulation
Date & Time Handling
- From String to datetime
- Datetime Components
- Timezone in Action
- TimeDelta – Time Travel with timedelta
- Parsing Time with Pendulum
Pandas Basics & DataFrames
- Data Manipulation with Pandas – Master Guide
- Creating DataFrames with Pandas
- Creating DataFrames with Dictionaries
- Reading DataFrame from CSV Files
- Fast CSV Processing (Polars vs pandas)
Summary Statistics & Aggregation
- Summary Statistics in Pandas
- The .agg() Method
- Summaries on Multiple Columns
- Summaries by Group
- Multiple Grouped Summaries
Pivot Tables & Reshaping
Indexing, Slicing & Advanced Selection
Handling Missing Values & Data Cleaning
Visualization & Real-World Analysis
- Visualizing Data in Pandas
- Avocado Prices Analysis
- Data Manipulation with Pandas & Polars – Complete Guide
Use this page as your central hub for all data manipulation techniques in Python 2026.