dlab-berkeley / python-data-visualization-legacy

D-Lab's 3 hour introduction to data visualization with Python. Learn how to create histograms, bar plots, box plots, scatter plots, compound figures, and more, using matplotlib and seaborn.

D-Lab's Python Data Visualization Workshop

This repository contains the materials for D-Lab's Python Data Visualization workshop. Prior experience with Python Fundamentals is assumed.

Workshop Goals

In this workshop, we provide an introduction to data visualization with Python. First, we'll cover some basics of visualization theory. Then, we'll explore how to plot data in Python using the matplotlib and seaborn packages. We aim to cover the following types of plots:

• line plots
• bar plots
• scatter plots
• boxplot
• heat maps

We'll also learn how to customize and style plots. Throughout the workshop, we'll discuss the plot types best suited for particular kinds of data.

Basic familiarity with Python is assumed. If you are not familiar with the material in Python Fundamentals, we recommend attending that workshop first.

Installation Instructions

Anaconda is a useful package management software that allows you to run Python and Jupyter notebooks very easily. Installing Anaconda is the easiest way to make sure you have all the necessary software to run the materials for this workshop. Complete the following steps:

1. Download and install Anaconda (Python 3.8 distribution). Click "Download" and then click 64-bit "Graphical Installer" for your current operating system.

2. Download the Python-Data-Visualization workshop materials:

• Click the green "Code" button in the top right of the repository information.
• Click "Download Zip".
• Extract this file to a folder on your computer where you can easily access it (we recommend Desktop).

3. Optional: if you're familiar with git, you can instead clone this repository by opening a terminal and entering git clone [email protected]:dlab-berkeley/Python-Data-Visualization.git.

Run the code

Now that you have all the required software and materials, you need to run the code:

1. Open the Anaconda Navigator application. You should see the green snake logo appear on your screen. Note that this can take a few minutes to load up the first time.

2. Click the "Launch" button under "Jupyter Notebooks" and navigate through your file system to the Python-Data-Visualization folder you downloaded above.

3. Open the lessons folder, and click Python-Data-Visualization.ipynb to begin.

4. Press Shift + Enter (or Ctrl + Enter) to run a cell.

5. By default, the necessary packages for this workshop should already be installed. You can install them within the Jupyter notebook by running the following line in its own cell:

!pip install -r requirements.txt

Note that all of the above steps can be run from the terminal, if you're familiar with how to interact with Anaconda in that fashion. However, using Anaconda Navigator is the easiest way to get started if this is your first time working with Anaconda.

Is Python not working on your laptop?

If you do not have Anaconda installed and the materials loaded on your workshop by the time it starts, we strongly recommend using the UC Berkeley Datahub to run the materials for these lessons. You can access the DataHub by clicking this button:

The DataHub downloads this repository, along with any necessary packages, and allows you to run the materials in a Jupyter notebook that is stored on UC Berkeley's servers. No installation is necessary from your end - you only need an internet browser and a CalNet ID to log in. By using the DataHub, you can save your work and come back to it at any time. When you want to return to your saved work, just go straight to DataHub, sign in, and you click on the Python-Data-Visualization folder.

If you don't have a Berkeley CalNet ID, you can still run these lessons in the cloud, by clicking this button:

By using this button, however, you cannot save your work.

Additional Resources

Check out the following resources to learn more about data visualization:

• Matplotlib tutorials

• Seaborn tutorials

• Go further with the plotly, bokeh, and dash packages.

About the UC Berkeley D-Lab

D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.

Visit the D-Lab homepage to learn more about us. You can view our calendar for upcoming events, learn about how to utilize our consulting and data services, and check out upcoming workshops.

Other D-Lab Python Workshops

Here are other Python workshops offered by the D-Lab:

Basic competency

• Python Fundamentals
• Introduction to Pandas
• Geospatial Fundamentals in Python

Intermediate/advanced competency

• Computational Text Analysis in Python
• Introduction to Machine Learning in Python
• Introduction to Artificial Neural Networks in Python
• Fairness and Bias in Machine Learning

python-data-visualization-legacy's Issues

"!" only works for mac - add windows version

installing pyroot through ! for the terminal only works for mac - add the windows version

Title, xlabel, ylabel commands in the pandas hist function

Pratik suggested a great edit/addition for the title, xlabel, ylabel commands in the pandas hist function under the 'Pandas methods to make plots' section:

gm_latest.hist(column='gdpPercap', bins=3)
plt.title('Distribution of Global Per-Capita GDP in 2007')
plt.xlabel('Per-Capita GDP (Millions of USD)')
plt.ylabel('# of Countries')

Simplifying plot code in Bar Plot section

Currently, we are creating a lot of new variables in order to create a simple bar plot. Some of which are not even used, such as the x variable for ticks.

Instead, we could simplify the plot calls by using the country_counts dataframe columns directly.

Matplotlib plot call
plt.bar(country_counts['continent'], country_counts['n_countries']);

Pandas plot call
country_counts.plot(kind='bar', x='continent', y='n_countries', legend=False);

Curriculum Review: Add plotly lesson

It would be great to add more about plotly here since it’s such a useful tool

PyViz Workshop Notes 11-16-2021

7 participants at start of workshop. Feedback link wasn't working, so checked in with front desk/Aaron/Sev for help. Total of 9 participants at 1:45 pm. 8 as of 2:50 pm. Everything going well in workshop. 9 as of 3:39 pm.

• Typo in this line here just above scatterplot section: "Does Spain or Portugal have a higher life expectantcy across all the years? How does this relate to per-capita GDP? How might we look for a relationship? - expectancy is spelled wrong.
• Typo in this line here: "That looks much better. We can now see there is somehwhat of a linear relationship between the log of GDP and life expectancy. Interesting!" -somewhat is spelled wrong. It's just above the section on Transparency.
• Also, the section head for the Transformations section needs correction - it reads: "Transormations".
• In the section on Color - typo = "plt.ylabel('Life Expectency (Years)');" s/b "Expectancy"? @pssachdeva @bypdlab -add anything I might have missed

Line Plot Challenge

The challenge for the Line Plot section gives the same plot as the example that we use above, so we might want to try something different.

To get started, Pratik suggested we show more customizations (e.g., font size, etc.):

plt.figure(figsize=(10,5))
plt.plot(spain['year'], spain['lifeExp'], label='Spain')
plt.plot(portugal['year'], portugal['lifeExp'], label='Portugal')
plt.title('Life Expectantcy of Portugal & Spain', fontsize=18)
plt.xlabel('Time (Years)', fontsize=15)
plt.ylabel('Life Expectantcy (Years)', fontsize=15)
plt.legend(prop={'size': 13});

Reduce Quotes

Choose the most important key quotes and principles of theory in the first section, and lighten the theory to focus on examples and application.

Solution errors

Solution notebook, "Pandas approach", two cells return errors.

optional challenge question 2 in barplot - pie chart is outdated

optional question in challenge 2 in barplot asks students to make a pie chart; pie charts are outdated, add a different question and remove pie charts altogether

Incorporate Pie Chart vs Bar Chart visualization

Instead of looking at area/bars in an isolated case in the comparisons in the theory section, consider using a real-world example comparing area/length by looking at a pie chart vs bar chart comparison.

Separate out notebooks

Separate out notebooks into sections: matplotlib, seaborn, customization.

Incorporate plot.ly

Another important package for modern data visualization with Python is plot.ly. Consider including at least an example or reference in a 'Next Steps' section, or a larger section introducing the package.

Reformat README into D-Lab Standard - Datahub/binder buttons

In particular, move up the 'DataHub' and 'Binder' buttons to the top of the README

Typos for 'expectancy' in challenge questions for histograms section and scatter plots section

There are a few typos across the notebook for 'expectancy'.

Fix the challenge questions in the histogram section to:

1. Create a histogram of life expectancy in the year 2007 across all 142 countries in the gapminder dataset. Play with the bins= parameter to find the most informative bin number.

2. What can you say about the distribution of life expectancy values in 2007?

Also fix the text in the plots of the scatter plot section.

Typo in README

"competency" is misspelled

Update Challenge Question

Update challenge question format and scaffolding to the D-Lab standard.

Add links to documentation

For each function, add the link to the package documentation