AIS Technical Development Workshop 4: Data visualization with Python libraries
- 1. Data Visualization with Python Nhi Nguyen
- 2. Pre-Workshop Checklist ⬡ This is pretty obvious…. but do you have your laptop with you? If you don’t…. Perhaps go grab it? ⬡ Did you download Anaconda? ⬡ Did you have access to TD WS 1 Shared Folder? (on AIS website) ⬡ If you encounter issues → ask our Student Assistants! 2
- 3. “ ● Speaker Series 2 : Abha Kumar ○ 02/17 from 12-1pm ● Phillies Office Visit ○ 02/27 (9:00-12:30) ○ Signups in the Newsletter 3 AIS Upcoming Events
- 5. Hello from our volunteers! Justin 5 Jianjin Luke
- 6. TODAY’S AGENDA 6 PART 1 LINE CHART DIY EXERCISES PART 3 BAR CHART DIY EXERCISES OVERVIEW GENERAL CONCEPT SETTING UP PART 2 PIE CHART DIY EXERCISES PART 4 HISTOGRAM DIY EXERCISES REVIEW RESOURCES SUBMISSION & FEEDBACK
- 7. OVERVIEW General Concept | Setting Up
- 8. Python libraries ▸ pd as alias ▸ data structures and operations ▸ data manipulation and analysis 8 ▸ np as alias ▸ multi-dimensional arrays and matrices ▸ high-level mathematical functions ▸ plt as alias ▸ plotting library ▸ Object-oriented API
- 9. Python libraries 9 MATLAB-style Syntax Object-oriented Syntax ▸ Scripted ▸ Structured ▸ Beginner ▸ More control ▸ Personalization Hybrid
- 10. LET’S GET YOU SET UP!
- 11. PART 1 Simple Line Graph | Multiple Lines DIY Exercise
- 12. Line Graph Used to track changes over short and long periods of time. 12
- 13. Some methods and attributes plot() Take arrays and turn them to line graphs show() Used to invoke the required plot title() Create title for graphs 13 xlabel() and ylabel() Create x label and y label fontsize Adjust the size for the titles, xlabel, ylabel, etc. color or c Change color
- 14. Some methods and attributes linestyle or ls Provide a variety of styles for lines figure() Manipulate and adjust things to the figure figsize Change the size of your figure 14 savefig() Save graphs to your folder legend() Create legend for graphs
- 16. PART 2 Pie Chart | DIY Exercise
- 17. Pie Chart Used to tell a story about the parts-to-whole aspect of a set of data (usually percentage). 17
- 18. Some methods and attributes pie() Create pie charts startangle Adjust slides’ positions explode Cut slides 18
- 20. PART 3 Bar Chart | Horizontal Bar Multiple Bars | DIY Exercise
- 21. Bar Chart Used to compare differences between groups/categories. Also used to track changes over time 21
- 22. Load & Understand Dataset read_excel() Import excel files to Jupyter Notebook dtypes Check the data types in each column of a dataset shape Provide number of rows and columns in a dataset 22 head() and tail() Load first 5 rows Load last 5 rows info Identity data types and null values color or c Change color
- 23. Rename columns & Convert df to numpy arrays rename() Rename one column columns Rename multiple columns 23 loc Load each column’s values values Convert pandas df to numpy arrays
- 24. Some methods and attributes bar() Create vertical bar graphs barh() Create horizontal bar graphs xticks() and yticks() Provide reference for points on a scale for x and y axes 24 np.arange() Create a numpy array
- 26. PART 4 Histogram | DIY Exercise
- 27. Histogram Help you discover, and show, the underlying frequency distribution of a dataset 27
- 28. Some methods and attributes hist() Create histograms np.random.randn() Create random numbers 28
- 30. Additional learning resources ▸ Online Course: Data Visualization for Python ▸ Matplotlib main website ▸ Real Python tutorials ▸ Free data sets 30
- 31. MAKE SURE YOU HAVE ALL YOUR GRAPHS IN THE WORD DOC TO SUBMIT FOR PARTICIPATION!
- 32. 32 Thanks for coming! Suitable Code AIS Attendance http://bit.ly/SAT-TD1 Exit Code: water