Bivariate visualizations

Introduction to Data Visualization with Plotly in Python

Alex Scriven

Data Scientist

What are bivariate visualizations?

Bivariate plots are those which display (and can therefore compare) two variables.

Common bivariate plots include:

scatterplots
Correlation plots
Line charts

scatterplot

A scatterplot is a plot consisting of:

A y-axis representing one variable
An x-axis representing a different variable
Each point is a dot on the graph, e.g., (68, 472)

scatterplot

scatterplot with plotly.express

Visualizing Flipper Length and Body Mass with plotly.express:

import plotly.express as px

fig = px.scatter(
        data_frame=penguins, 
        x="Body Mass (g)", 
        y="Flipper Length (mm)")
fig.show()

Penguin Scatter

More plotly.express arguments

Useful plotly.express scatterplot arguments:

trendline: Add different types of trend lines
symbol: Set different symbols for different categories

Check the documentation for more!

Line charts in plotly.express

A line chart is used to plot some variable (y-axis) over time (x-axis).

Let's visualize Microsoft's stock price.

fig = px.line(
  data_frame=msft_stock,  
  x='Date', 
  y='Open', 
  title='MSFT Stock Price (5Y)')
fig.show()

Here is our simple line chart:

Simple line chart of stock prices

scatterplots and line plots with graph_objects

For more customization, graph_objects uses go.Scatter() for both scatter and line plots.

Here is the code for our penguins scatterplot using graph_objects

Here is the code for our line chart with graph_objects

Remember to set 'mode'
- And use DataFrame subsets, not column names

import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
  x=penguins['Body Mass (g)'],
  y=penguins['Flipper Length (mm)'],
  mode='markers'))

fig = go.Figure(go.Scatter(
  x=msft_stock['Date'], 
  y=msft_stock['Opening Stock Price'],
  mode='lines'))

graph_objects vs. plotly.express?

When should we use plotly.express or graph_objects? Largely, it is about customization - graph_objects has many more options!

`graph_objects`	`express`

Correlation plot

A correlation plot is a way to visualize correlations between variables.

The Pearson Correlation Coefficient summarizes this relationship

Has a value -1 to 1
1 is totally positively correlated
- As x increases, y increases
0 is not at all correlated
- No relationship between x and y
-1 is totally negatively correlated
- As x increases, y decreases

Correlation plot setup

df contains data on bike sharing rental numbers in Korea with various weather variables

pandas provides a method to create the data needed:

cr = df.corr(method='pearson')
print(cr)

Our Pearson correlation table:

Correlation table

Correlation plot with Plotly

Let's build a correlation plot:

import plotly.graph_objects as go

fig = go.Figure(go.Heatmap(

        x=cr.columns,
        y=cr.columns,

        z=cr.values.tolist(),

        colorscale='rdylgn', zmin=-1, zmax=1))

fig.show()

Our correlation plot

Voila!

Heatmap example

Let's practice!

Introduction to Data Visualization with Plotly in Python