6-4 Visualizations

• plotly express

• Programming Challenge

Plotly Express

Plotly Express is a high-level interface for creating beautiful and meaningful visualizations. Plotly Express is easy to use and allows you to create a wide variety of visualizations with just a few lines of code.

https://plotly.com/python/plotly-express/

Difference between seaborn and plotly express:

• Unlike seaborn, plotly express does not aggregate data. It is up to you to aggregate the data before passing it to plotly express.

• Plotly express is built on top of plotly.js, a JavaScript library for creating interactive visualizations. This means that plotly express visualizations are interactive by default.

• Plotly express can draw maps, too.

# Here's the seaborn code from 6-1
import seaborn as sns
pengo = sns.load_dataset("penguins")
pengo['count'] = 1

sns.barplot(data=pengo, x="species", y="count", hue="species", estimator="sum")

<Axes: xlabel='species', ylabel='count'>

# here's the plotly express equivalent
import plotly.express as px
fig = px.bar(pengo, x="species", y="count", color="species", barmode="group")
fig

Notice the difference

• You can hover, zoom and take a screenshot of the plotly express visualizations.

• barmode=group is used to group the bars in a bar chart. so when you hover over the bars, you can see the values of each bar.

Plotly Express In Streamlit

• use st.plotly_chart() to display plotly express visualizations in streamlit.

• pass in the figure as the first argument to st.plotly_chart(fig).

6-4-st-plotly-express.py

import streamlit as st
import plotly.express as px
import pandas as pd
import seaborn as sns
pengo = sns.load_dataset("penguins")
pengo['count'] = 1

st.title('Plotly Express Example')
fig = px.bar(pengo, x="species", y="count", color="species", barmode="group")
st.plotly_chart(fig) # this displays the plotly express visualization in streamlit

Summarizing Data for Plotly Express

Depending on your data, you may need to summarize it prior to plotting it with plotly express. For example, if you have a dataset with multiple rows for each observation, you may need to aggregate the data before passing it to plotly express.

this can be accomplished with a df.groupby() or pd.pivot_table()

6-4-st-plotly-express-summary.py

Lots of examples

The rest is up to your! Some examples to follow

Here: https://plotly.com/python/plotly-express/

import seaborn as sns
tips = sns.load_dataset("tips")

# text_auto=True shows the count of observations in each bin
fig = px.density_heatmap(tips, x="total_bill", y="tip", text_auto=True)
fig.show()

import pandas as pd
import random
stlucia_df = pd.read_csv("https://raw.githubusercontent.com/mafudge/datasets/refs/heads/master/st-lucia/parishes.csv")
stlucia_df['Amount'] = stlucia_df.apply(lambda row: random.randint(50,500), axis=1)

# Saint Lucia Parishes - Scatter
fig = px.scatter_mapbox(stlucia_df,  lat="Lat", lon="Lng", zoom=10, color = 'Parish', hover_name = 'Parish', size = 'Amount',  mapbox_style="open-street-map")
fig.show()

# Saint Lucia Parishes - Density
fig = px.density_mapbox(stlucia_df, lat='Lat', lon='Lng', z='Amount', radius=10, center=dict(lat=13.9, lon=-60.97), zoom=10, mapbox_style="open-street-map")
fig.show()