pandas Essential Training (2017)

2h 15mIntermediate2017-11-03

Authors

Jonathan Fernandes

Consultant focusing on data science, AI, and big data

Course details

pandas is an open-source 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. Instructor Jonathan Fernandes dives into topics such as DataFrames, basic plotting, indexing, and groupby. To help you learn how to work with data more effectively, Jonathan takes you through a series of exercises that are based on the same large, public data set: the Olympic medal winners from 1896 to 2008.

Learning objectives
DataFrames
Working with plots
Boolean indexing
String handling
Indexing
Grouping data
Reshaping
Creating your own colormaps

Skills covered

pandasData AnalysisEssential TrainingData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen Source

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Exercise files

1. Technical Setup

04 - Installing Anaconda
05 - Downloading the data set
06 - Using the Jupyter notebook
07 - Using Pandas

2. Series and DataFrames

08 - DataFrames
09 - Series
10 - Challenge
11 - Solution

3. Data Input and Validation

12 - Using read csv()
13 - Using shape
14 - Using head() and tail()
15 - Using info()

4. Basic Analysis

16 - Using value counts()
17 - Using sort values()
18 - Boolean indexing
19 - String handling
20 - Challenge
21 - Solution

5. Basic Plotting

22 - Basic plotting
23 - Plot types
24 - Colors
25 - Figsize
26 - Colormaps
27 - Seaborn basic plotting
28 - Challenge
29 - Solution

6. Indexing

30 - Index
31 - Using set index()
32 - Using reset index()
33 - Using sort index()
34 - Using loc
35 - Using iloc
36 - Challenge
37 - Solution

7. Groupby

38 - Groupby
39 - Iterate through a group
40 - Groupby computations
41 - Challenge
42 - Solution

8. Reshaping

43 - Reshaping
44 - Using stack()
45 - Using unstack()
46 - Challenge
47 - Solution

9. Data Visualizations

48 - Learning heatmaps
49 - Creating your own colormaps

10. Challenge

50 - Final challenge
51 - Final solution

Conclusion

52 - Next steps