Interactive Data Visualizations in Python: Glossary

Visualization is an important part of both exploratory data analysis and communicating results

Interactivity allows us to visualize more information without overcomplicating a single plot

use conda env create --file environment.yml to create a new environment from a YAML file

see a list of all environments with conda env list

activate the new environment with conda activate <NAME>

see a list of all installed packages with conda list

Import your CSV using pd.read_csv('<FILEPATH>')

Transform your dataframe from wide to long with pd.melt()

Split column values with df['<COLUMN>'].str.split('<DELIM>')

Sort rows using df.sort_values()

Export your dataframe to CSV using df.to_csv('<FILEPATH>')

Before visualizing your dataframe, make sure it only includes the rows you want to visualize. You can use pandas’ query() function to easily accomplish this

To make a line plot with px.line, you need to specify the dataframe, X axis, and Y axis

If you want to have multiple lines, you also need to specify what column determines the line color

In a Jupyter Notebook, you need to call fig.show() to display the chart

The entire streamlit app must be saved in a single python file, typically app.py

To run the app locally, enter the bash command streamlit run app.py

Add a title with st.title('Title'), and other text with st.write('## Markdown can go here')

Make sure your dataframes and figures are stored in variables, typically df for a dataframe and fig for a figure

To display a plotly figure, use st.plotly_chart(fig)

In order to add widgets, we need to refactor our code to make it more flexible.

f-Strings allow you to easily insert variables into a string

Add a widget to select the continent or metric from a list with st.selectbox()

There are two ways to add a widget to the sidebar: st.sidebar.<widget>() and with st.sidebar: st.<widget>()

All Streamlit apps must have a GitHub repo with the code, data, and environment files

You can deploy up to 3 apps for free with Streamlit Cloud