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Plot correlation matrix using pandas

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How do you plot a correlation matrix in pandas?

You can plot correlation between two columns of pandas dataframe using sns. regplot(x=df['column_1'], y=df['column_2']) snippet. What is this? You can see the correlation of the two columns of the dataframe as a scatterplot.

How do you make a correlation matrix plot in Python?

Method 1: Creating a correlation matrix using Numpy libraryNumpy library make use of corrcoef() function that returns a matrix of 2×2. The matrix consists of correlations of x with x (0,0), x with y (0,1), y with x (1,0) and y with y (1,1).


You can use pyplot.matshow() from matplotlib:

import matplotlib.pyplot as plt

plt.matshow(dataframe.corr())
plt.show()

Edit:

In the comments was a request for how to change the axis tick labels. Here's a deluxe version that is drawn on a bigger figure size, has axis labels to match the dataframe, and a colorbar legend to interpret the color scale.

I'm including how to adjust the size and rotation of the labels, and I'm using a figure ratio that makes the colorbar and the main figure come out the same height.


EDIT 2: As the df.corr() method ignores non-numerical columns, .select_dtypes(['number']) should be used when defining the x and y labels to avoid an unwanted shift of the labels (included in the code below).

f = plt.figure(figsize=(19, 15))
plt.matshow(df.corr(), fignum=f.number)
plt.xticks(range(df.select_dtypes(['number']).shape[1]), df.select_dtypes(['number']).columns, fontsize=14, rotation=45)
plt.yticks(range(df.select_dtypes(['number']).shape[1]), df.select_dtypes(['number']).columns, fontsize=14)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
plt.title('Correlation Matrix', fontsize=16);

correlation plot example


If your main goal is to visualize the correlation matrix, rather than creating a plot per se, the convenient pandas styling options is a viable built-in solution:

import pandas as pd
import numpy as np

rs = np.random.RandomState(0)
df = pd.DataFrame(rs.rand(10, 10))
corr = df.corr()
corr.style.background_gradient(cmap='coolwarm')
# 'RdBu_r', 'BrBG_r', & PuOr_r are other good diverging colormaps

enter image description here

Note that this needs to be in a backend that supports rendering HTML, such as the JupyterLab Notebook.


Styling

You can easily limit the digit precision:

corr.style.background_gradient(cmap='coolwarm').set_precision(2)

enter image description here

Or get rid of the digits altogether if you prefer the matrix without annotations:

corr.style.background_gradient(cmap='coolwarm').set_properties(**{'font-size': '0pt'})

enter image description here

The styling documentation also includes instructions of more advanced styles, such as how to change the display of the cell the mouse pointer is hovering over.


Time comparison

In my testing, style.background_gradient() was 4x faster than plt.matshow() and 120x faster than sns.heatmap() with a 10x10 matrix. Unfortunately it doesn't scale as well as plt.matshow(): the two take about the same time for a 100x100 matrix, and plt.matshow() is 10x faster for a 1000x1000 matrix.


Saving

There are a few possible ways to save the stylized dataframe:

  • Return the HTML by appending the render() method and then write the output to a file.
  • Save as an .xslx file with conditional formatting by appending the to_excel() method.
  • Combine with imgkit to save a bitmap
  • Take a screenshot (like I have done here).

Normalize colors across the entire matrix (pandas >= 0.24)

By setting axis=None, it is now possible to compute the colors based on the entire matrix rather than per column or per row:

corr.style.background_gradient(cmap='coolwarm', axis=None)

enter image description here


Single corner heatmap

Since many people are reading this answer I thought I would add a tip for how to only show one corner of the correlation matrix. I find this easier to read myself, since it removes the redundant information.

# Fill diagonal and upper half with NaNs
mask = np.zeros_like(corr, dtype=bool)
mask[np.triu_indices_from(mask)] = True
corr[mask] = np.nan
(corr
 .style
 .background_gradient(cmap='coolwarm', axis=None, vmin=-1, vmax=1)
 .highlight_null(null_color='#f1f1f1')  # Color NaNs grey
 .set_precision(2))

enter image description here


Seaborn's heatmap version:

import seaborn as sns
corr = dataframe.corr()
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)

Try this function, which also displays variable names for the correlation matrix:

def plot_corr(df,size=10):
    """Function plots a graphical correlation matrix for each pair of columns in the dataframe.

    Input:
        df: pandas DataFrame
        size: vertical and horizontal size of the plot
    """

    corr = df.corr()
    fig, ax = plt.subplots(figsize=(size, size))
    ax.matshow(corr)
    plt.xticks(range(len(corr.columns)), corr.columns)
    plt.yticks(range(len(corr.columns)), corr.columns)

You can observe the relation between features either by drawing a heat map from seaborn or scatter matrix from pandas.

Scatter Matrix:

pd.scatter_matrix(dataframe, alpha = 0.3, figsize = (14,8), diagonal = 'kde');

If you want to visualize each feature's skewness as well - use seaborn pairplots.

sns.pairplot(dataframe)

Sns Heatmap:

import seaborn as sns

f, ax = pl.subplots(figsize=(10, 8))
corr = dataframe.corr()
sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),
            square=True, ax=ax)

The output will be a correlation map of the features. i.e. see the below example.

enter image description here

The correlation between grocery and detergents is high. Similarly:

Pdoducts With High Correlation:
  1. Grocery and Detergents.
Products With Medium Correlation:
  1. Milk and Grocery
  2. Milk and Detergents_Paper
Products With Low Correlation:
  1. Milk and Deli
  2. Frozen and Fresh.
  3. Frozen and Deli.

From Pairplots: You can observe same set of relations from pairplots or scatter matrix. But from these we can say that whether the data is normally distributed or not.

enter image description here

Note: The above is same graph taken from the data, which is used to draw heatmap.


For completeness, the simplest solution i know with seaborn as of late 2019, if one is using Jupyter:

import seaborn as sns
sns.heatmap(dataframe.corr())