Research Guides: Bite-Sized Library Workshop Materials: The Python 101 Series

Introduction

The Python 101 Series

Join Bella Ratmelia, Data Services Librarian, in this Python 101 series where you will learn the basics of Python programming, various data types, and how to use DataFrame to work with data! You are welcome regardless of your experience with Python, but please do note that this series is aimed for Python novices.

Part I: The Basics

Learn the basics of Python such as data types, variable assignment, arithmetic operation, if-else statements, and loops in this hands-on workshop. No prior coding experience is required to attend this workshop.

Part II: Tinkering with Pandas' DataFrame

Learn more about using Dataframe to work with data (including time series) in this hands-on session. A basic understanding of coding (e.g. loops, if-else statements) is required to attend this workshop.

Part III: Visualise your data in Python

Learn how to create and customise interesting data visualisations using two popular visualisation libraries in Python: Matplotlib and Seaborn. A basic understanding of coding (e.g. loops, if-else statements) is required to attend this workshop.

This workshop series was designed by Bella Ratmelia, Senior Librarian, Research & Data Services. If you have any questions, please contact Bella at bellar@smu.edu.sg.

Workshop Materials

Link to Binder that will launch the virtual environment used during the workshops: https://bit.ly/python101-2023. Please go to the respective folders to access the .ipynb files.

Below are some additional resources:

Part I: The Basics

If you would like more practice questions, you can try the exercises by W3C: https://www.w3schools.com/python/python_exercises.asp.

Part II: Tinkering with Pandas' DataFrame

General Pandas documentation: https://pandas.pydata.org/docs/reference/index.html.
Handling missing values in time series using backfill, forward fill, interpolate: https://www.geeksforgeeks.org/how-to-deal-with-missing-values-in-a-timeseries-in-python/.
More on upsampling and downsampling in Time series: https://www.w3resource.com/pandas/series/series-resample.php.
- Upsampling: increase the amount of sample in time series e.g., from daily data to hourly data.
- Downsampling: decrease the amount of data in time series e.g., calculating daily average from hourly data.

Part III: Visualise your data in Python

All the graphs available in Seaborn and their function: https://seaborn.pydata.org/api.html#distribution-api.
Handy code snippets: https://www.python-graph-gallery.com/.
More visualization libraries mentioned:
- Plotly – especially for its candlestick chart! Also offers interactive visualizations (https://plotly.com/python/candlestick-charts/)
- Bokeh – Interactive visualizations (https://bokeh.org/)
- Folium – geospatial visualizations (https://python-visualization.github.io/folium/)

Workshop Recordings

To watch the video, please login using your SMU account and password.