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
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
Introduction
- Welcome
- What you should know
- Exercise files
Technical Setup
- Installing Anaconda
- Downloading the data set
- Using the Jupyter notebook
- Using Pandas
Series and DataFrames
- DataFrames
- Series
- Challenge
- Solution
Data Input and Validation
- Using read csv()
- Using shape
- Using head() and tail()
- Using info()
Basic Analysis
- Using value counts()
- Using sort values()
- Boolean indexing
- String handling
- Challenge
- Solution
Basic Plotting
- Basic plotting
- Plot types
- Colors
- Figsize
- Colormaps
- Seaborn basic plotting
- Challenge
- Solution
Indexing
- Index
- Using set index()
- Using reset index()
- Using sort index()
- Using loc
- Using iloc
- Challenge
- Solution
Groupby
- Groupby
- Iterate through a group
- Groupby computations
- Challenge
- Solution
Reshaping
- Reshaping
- Using stack()
- Using unstack()
- Challenge
- Solution
Data Visualizations
- Learning heatmaps
- Creating your own colormaps
Challenge
- Final challenge
- Final solution
Conclusion
- Next steps