Skip to content
Visualize Interdependencies with a Correlation Plot
Visualize interdependencies with a correlation plot
This template helps you visualize the correlation between numeric columns of your dataset as an interactive correlation plot. Correlation plots display the dependence between multiple numeric variables at the same time. They highlight the most strongly correlated variables in your dataset. The higher or lower the correlation value the stronger the color indication.
# Load packages
import pandas as pd
import plotly.graph_objects as go# Upload your data as CSV and load as data frame
df = pd.read_csv('mpg.csv')
df.head()# Visualize correlation with a heatmap
df_corr = df.corr(method='pearson') # Choose correlation method
fig = go.Figure(
go.Heatmap(
z = df_corr.values.tolist(), # Set z values (list of numbers)
x = df_corr.columns, # Set x values (list of strings)
y = df_corr.columns, # Set y values (list of strings)
colorscale = 'rdylgn', # Set the color scale
zmin = -1, # Set min value
zmax = 1 # Set max value
),
)
fig.update_layout(
title = "Interactive Correlation Plot", # Set title of plot
#xaxis_title = "X Axis Title", # Set title of x axis
#yaxis_title = "Y Axis Title", # Set title of y axis
legend_title = "Legend Title", # Set title of legend
font = dict(
family = "sans-serif, sans-serif", # Set font and default font
size = 18, # Set font size
color = "RebeccaPurple" # Set font color
),
margin = dict(l=0, r=0, b=0)
)
fig.show(config = {"displayModeBar": False})Appendix
| Topic | Resource |
|---|---|
| Correlation Method | pandas documentation |
| Heatmap Graph Object | plotly documentation |