Correlation Plot

Correlation plots are an easy way to visualize the linear correlation between variable in dataframe. In this example we implement a correlation plot overlayed as a heat map to show how variables are correlated.

We will need to import the following packages

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

In this notebook, I use a subset of housing data for the state of seattle.

housing_df = pd.read_csv('housing.csv')
housing_data.head()

	lot_area	firstfloor_sqft	living_area	bath	garage_area	price
0	8450	856	1710	2	548	208500
1	9600	1262	1262	2	460	181500
2	11250	920	1786	2	608	223500
3	9550	961	1717	1	642	140000
4	14260	1145	2198	2	836	250000

We have a few features about the house and the corresponding price.

Correlation Matrix

To build our correlation plot, we first call the correlation method on the dataframe and pass the results to a seaborn heat map. Let's generate the correlation matrix for all the variables

housing_data.corr()

	lot_area	firstfloor_sqft	living_area	bath	garage_area	price
lot_area	1.000000	0.299475	0.263116	0.126031	0.180403	0.263843
firstfloor_sqft	0.299475	1.000000	0.566024	0.380637	0.489782	0.605852
living_area	0.263116	0.566024	1.000000	0.630012	0.468997	0.708624
bath	0.126031	0.380637	0.630012	1.000000	0.405656	0.560664
garage_area	0.180403	0.489782	0.468997	0.405656	1.000000	0.623431
price	0.263843	0.605852	0.708624	0.560664	0.623431	1.000000

Correlation Heat Map

We can then use the correlation matrix above to plot our correlation heat map. We use seaborn heatmap method to plot the correlation

plt.figure(figsize=(10,8))
sns.heatmap(housing_data.corr(), annot=True, square=True, cmap="YlGnBu")
plt.title('Housing Data Correlation Plot')

Heatmap Color Scheme

There are multiple possible coloring schemes for the heat map. Here are a few to try:

cmap="YlGnBu"
cmap="Blues"
cmap="BuPu"
cmap="YlGnBu"
cmap="Greens"