pandas Essential Training

3h 11mIntermediate2024-05-24

Authors

Jonathan Fernandes

Consultant focusing on data science, AI, and big data

Course details

pandas is an open-source data analysis library that provides high-performance, easy-to-use data structures, and data analysis tools for Python. In this intermediate-level, hands-on course, learn how to use the pandas library and tools for data analysis and data structuring with instructor Jonathan Fernandes. Take a deep dive into topics such as DataFrames, basic plotting, indexing, and groupby. To help you learn how to work with data more effectively, Jonathan guides you through a series of practical coding exercises that are based on the same large, public dataset.

Note: A basic working knowledge of Python is a prerequisite of this course.

Skills covered

pandasPythonData AnalysisEssential TrainingData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen Source

Concepts

0. Introduction

01 - Welcome to pandas

1. Technical Setup

02 - Using Google Colab
03 - What is pandas
04 - Using pandas
05 - Reading tabular data into pandas

2. Fundamentals of Working with pandas

06 - Get an overview of the data and displaying it
07 - Select a Series (column)
08 - Challenge - Fundamentals
09 - Solution - Fundamentals
10 - Python lists and dictionaries
11 - Rename a Series (or column)
12 - Remove a Series (column) or row
13 - Filtering rows for a single condition
14 - Filter rows for multiple conditions
15 - Using String methods
16 - Sorting a DataFrame or Series

3. Intermediate pandas Techniques

17 - Working with data types (dtype)
18 - Memory usage of dtypes
19 - Defining dtypes when you read in a file
20 - Python functions
21 - Working with indexes
22 - Being productive in pandas - My best practices
23 - Creating Series and DataFrames
24 - Working with dates
25 - Combining DataFrames
26 - Combining datasets
27 - Working with missing data
28 - Removing missing data
29 - Working with duplicates
30 - Validating data
31 - Updating the dtypes
32 - Combine the datasets

4. Visualizations

33 - Plotting data
34 - Working with colormaps and seaborn
35 - Working with groupby
36 - Reshaping data - Stacking, unstacking, and MultiIndex
37 - Challenge - Visualizations
38 - Solution - Visualizations
39 - Creating your own colormaps

5. Learning Recap

40 - Final challenge - Recap
41 - Solution - Recap

Conclusion

42 - Your next steps in pandas