Master Pandas for Data Handling [2026]

Name: Master Pandas for Data Handling [2026]
Rating: 4.6 (503 reviews)

Learn to Master the worlds most powerful software for Advanced Data Handling

Highest Rated

Created byHenrik Johansson

Last updated 6/2026

English

What you'll learn

Master the Pandas library for advanced Data Handling
Advanced Data Preparation with Pandas, including model-based imputation of missing data
Make Data Visualizations with Pandas, Matplotlib, and Seaborn
File handling with the Pandas library
Use the .concat(), .join(), and .merge() functions/methods to combine Pandas DataFrame objects
Scale and Standardize data
The fundamental concepts and language of the Pandas DataFrame object
Make advanced Data Descriptions with Pandas, including cross-tabulations, groupings, and descriptive statistics
All aspects of changing, modifying and selecting Data from a Pandas DataFrame
Cloud Computing - use Anaconda Cloud Notebook (Jupyter Notebook). Learn to use Cloud Computing resources
Optional: use Anaconda Distribution's Jupyter Notebook and Conda package management system

Course content

2 sections • 39 lectures • 16h 53m total length

Introduction to Master Pandas for Data Handling10:32
Introduction to Master Pandas for Data Handling
Setup of the Anaconda Cloud Notebook20:47
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address

Note: Anaconda often updates its resources and user interface plus utilizes anti-drone technology. This may cause minor deviations from graphics and procedures in the video
Download and installation of the Anaconda Distribution (optional)20:39
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional

Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
The Conda Package Management System (optional)46:52
This video describes the Conda Package Management System
Conda requires internet access
Video is optional

Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures

Master Pandas for Data Handling: Overview14:32
This video provides and introduction and overview of this section of the video course. "Master Pandas for Data Handling" is updated to current Pandas 2.2 and all known new changes in the future Pandas 3 version
Pandas Theory and Terminology15:30
Learn the concepts and language of the Pandas DataFrame, the Pandas Series, and the data or object content of a DataFrame/Series object
Creating a DataFrame from scratch46:00
Learn to create Pandas DataFrame from scratch using Python and Pandas. You will learn how to create Pandas DataFrames using Python Dictionaries, Lists, and lots more
Pandas File Handling: Overview5:16
This video contains an overview of the Pandas File Handing part of this section
Pandas File Handling: The .csv file format23:30
Learn to load and save files from/to Pandas DataFrames from .csv files
Pandas File Handling: Combine CSV files32:00
Learn to combine .csv files with Pandas and to handle and combine various common, uneven and non-uniform data structures into useful Pandas DataFrames
Pandas File Handling: The .xlsx file format23:20
Learn to load and save files from/to Pandas DataFrames from .xlsx files and hierarchical .xlsx files
Pandas File Handling: SQL-database files and Pandas DataFrame15:08
Learn to load and save files from/to Pandas Dataframes from a SQL-database file
Pandas Operations & Techniques: Overview5:34
This video contains an overview of the Pandas Operations and Techniques part of this section
Pandas Operations & Techniques: Object Inspection19:34
Learn to inspect Pandas Dataframes and Dataframe content with Pandas .info() method, Python's .type() method, and more
Pandas Operations & Techniques: DataFrame Inspection18:53
Learn to inspect the contents of large-sized Pandas DataFrames. Learn to use the .head, .tail, and other general methods to inspect the contents of a DataFrame
Pandas Operations & Techniques: Column Selections21:04
Learn to select subsets of Columns from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
Pandas Operations & Techniques: Row Selections21:11
Learn to select subsets of Rows from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
Pandas Operations & Techniques: Conditional Selections21:27
Learn to make conditional selections of subsets from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
Pandas Operations & Techniques: Scalers and Standardization23:08
Learn about Scalers, Normalization, and Standardization. Learn to use mean-correction, normalization, and zero-one unity-based normalization
Pandas Operations & Techniques: Concatenate DataFrames29:21
Learn to Concatenate Pandas DataFrames. Learn to use Pandas .concat() function to add DataFrames together horizontally and vertically. Learn to use the .concat() function with Inner and Outer joins
Pandas Operations & Techniques: Joining DataFrames19:30
Learn to join Pandas DataFrames. Learn to use Pandas DataFrames .join() method. Learn to use "left joins", "right joins", "inner joins", "outer joins", and "cross joins"
Pandas Operations & Techniques: Merging DataFrames30:48
Learn to merge Pandas DataFrames. Learn to use Pandas DataFrames .merge() method. Learn to use "left joins", "right joins", "inner joins", and "outer joins" to merge different DataFrames on column variables
Pandas Operations & Techniques: Transpose & Pivot Functions34:31
Learn to Transpose and Pivot Pandas DataFrames. Learn to use the transpose, pivot, pivot_table, and melt functions
Pandas Data Preparation: Overview & workflow8:15
This video has an overview of the Data Preparation part of the course and includes a workflow for Data preparation or so-called data cleaning
Pandas Data Preparation II: Edit DataFrame labels20:16
Learn to edit Pandas DataFrame column names, index, and index labels
Pandas Data Preparation III: Duplicates22:23
Learn about Duplicates. Duplicate rows or observations may impact the quality of data products. Learn how to properly handle Duplicates with Pandas functionality
Pandas Data Preparation IV: Missing Data & Imputation54:35
Learn to handle Missing data and Missing values with Pandas functionality. Learn Imputation and to augment Pandas with scikit-learn to use advanced model-based imputation of missing data
Pandas Data Preparation V: Data Binning [Extra video]46:32
Learn Data Binning with Pandas. Learn to use Administrative Data Binning, Algorithmic Data Binning, and subjective Data Binning. Learn to use Pandas .qcut() and .cut() functions.
Pandas Data Preparation VI: Indicator Features [Extra Video]33:01
Learn to create Indicator Features or Dummy Variables with Pandas.
Pandas Data Description: Overview2:43
This video provides an overview of the part of this section about Pandas Data Description
Pandas Data Description II: Sorting and Ranking26:51
Learn to use Pandas functions for Sorting and Ranking data
Pandas Data Description III: Descriptive Statistics31:40
Learn to create useful descriptive statistics with Pandas .agg() and .describe() functions. Learn to augment Pandas functions with the powerful .apply() and .value_counts() functions
Pandas Data Description IV: Crosstabulations & Groupings30:06
Learn to create crosstabulations with Pandas .crosstab() function and to use the powerful Pandas .groupby() operation. Learn to augment these functions with a selection of Pandas functionality
Pandas Data Visualization: Overview6:30
This video provides an overview of the part of this section about Pandas Data Visualization
Pandas Data Visualization II: Histograms42:34
Learn to make Histograms with Pandas, Matplotlib, and Seaborn. You will learn to make simple Histograms, advanced Histograms, multi-dimensional Histograms, and advanced Jointgrid Histograms
Pandas Data Visualization III: Boxplots33:00
Learn to make traditional and modern Boxplots with Pandas, Matplotlib, and Seaborn. You will learn to make Boxplots, Boxenplots, Violinplots, Swarmplots and to create graphs consisting of many types of boxplots
Pandas Data Visualization IV: Scatterplots40:00
Learn to make scatterplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple scatterplots, advanced scatterplots, advanced multi-scatterplots, and advanced pairplots of scatterplots
Pandas Data Visualization V: Pie Charts45:40
Learn to make Pie Charts with Pandas, Matplotlib, and support from Seaborn. You will learn to make Pie Charts, detailed Pie Charts, multiple Pie Charts, and how to properly use Pie Charts for effect
Pandas Data Visualization VI: Line plots50:24
Learn to make Lineplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple Lineplots, advanced Lineplots, advanced Line-area plots, and advanced multidimensional Line-area plots

Requirements

Everyday experience using a computer with Windows, MacOS, Ios, Android, ChromeOS, or Linux is recommended
Basic Python knowledge is recommended
The four ways of counting (+-*/)
Access to a computer with an internet connection
The course only uses costless software
Walk-you-through installation and setup videos for Windows 10/11 is included

Description

This video course will teach you to master Pandas, the most powerful, efficient, and useful Data Handling library in existence. You will learn to master the Pandas library and to use powerful Data Handling techniques with the intention of making you able to use the powerful Pandas library for Data Science and Machine Learning Data Handling tasks.

With the Pandas library you get a fast, powerful, flexible and easy to use, open-source data analysis and data manipulation tool, directly usable with the Python programming language and able to use any data source with the incredibly powerful Pandas DataFrame object.

This video course is updated to Pandas 2 and the announced upcoming Pandas 3 version.

You will learn:

Master the Pandas library for advanced Data Handling
The fundamental concepts and language of the Pandas DataFrame object
All aspects of changing, modifying and selecting Data from a Pandas DataFrame
File handling with the Pandas library
Use the .concat(), .join(), and .merge() functions/methods to combine Pandas DataFrame objects
Scale and Standardize data
Advanced Data Preparation with Pandas, including model-based imputation of missing data
Make advanced Data Descriptions with Pandas, including cross-tabulations, groupings, and descriptive statistics
Make Data Visualizations with Pandas, Matplotlib, and Seaborn
Cloud Computing: To use the web browser-based Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud Computing resources in this course.
Option: To use the Anaconda Distribution (Windows, Mac, Linux, and more)
Option: Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work life.
And much more…

This course is an excellent way to learn to Master Pandas and Data Handling! Data Handling is the process of making data useful and usable for data analysis.

Most Data Scientists and Machine Learners spends about 80% of their working efforts and time on Data Handling tasks. Being good at Data Handling and Pandas is extremely useful and time-saving skills that functions as a force multiplier for productivity.

This course is designed for anyone who wants to

Anyone who knows the basics of Python programming and want to learn the Pandas library!
Anyone who is a new student at the University level and want to learn Data Handling skills that they will have use for in their entire data science, engineering or academic careers!
Anyone who knows Python and wants to extend your knowledge of the Pandas library and Data Handling!
Anyone who knows about Data Science or Machine Learning and want to learn Data Handling skills that work as a force multiplier with the skills you already know!
Anyone who wants to learn advanced Data Handling and improve their capabilities and productivity

Requirements:

Everyday experience using a computer with Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Basic Python knowledge is recommended
The four ways of counting (+-*/)
Access to a computer with an internet connection
The course only uses costless software
Walk-you-through installation and setup videos for Windows 10/11 is included

This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Pandas and Data Handling.

Enroll now to receive 12+ hours of detailed video tutorials with manually edited English captions, and a certificate of completion after completing the course!

Who this course is for:

Anyone who knows the basics of Python programming and want to learn the Pandas library
Anyone who is a new student at the University level and want to learn Data Handling skills that they will have use for in their entire data science, engineering or academic careers
Anyone who knows Python and wants to extend your knowledge of the Pandas library and Data Handling
Anyone who knows about Data Science or Machine Learning and want to learn Data Handling skills that work as a force multiplier with the skills you already know
Anyone who wants to learn advanced Data Handling and improve their capabilities and productivity

Master Pandas for Data Handling [2026]

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 1hr 39min

Master Pandas for Data Handling35 lectures • 15hr 15min

Requirements

Description

Who this course is for: