Data Visualization with Matplotlib, Seaborn & Pandas – Cheat Sheet

Introduction

Matplotlib is the omnipresent plotting library for data science with Python. Seaborn is another Python data visualization tool, created on top of Matplotlib. In this cheat sheet I will use them along with Pandas’s plotting capabilities. Pandas integrates with Matplotlib to make plotting even easier.

Data used in the examples:

df.head()

 

df.describe()

 

Importing libraries and Loading the data

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

%matplotlib inline #use this to display inline plots on Jupyter notebooks

df = pd.read_csv('./path/data.csv')

 

Histograms

Are used to get insights about data distribution. Too few bins can oversimplify reality and won’t show you the details, conversely too many bins tend to overcomplicate reality and won’t show the details.

 

Using Pandas’s integration with Matplotlib

df.hist(bins=20, figsize=(24, 22))
# df['insulin'].hist(bins=20, figsize=(22, 20)) # This would print only one series
df.plot()

Pie Chart with Matplotlib

This kind of chart can be used to check class distribution on a dataset.

counts = df['diabetes'].value_counts()
labels = counts.index.values # array([0, 1])
values = counts.values # array([500, 268])

def make_custom_autopct(values):
    def custom_autopct(pct):
        total = sum(values)
        val = int(round(pct*total/100.0))
        return '{p:.2f}% ({v:d})'.format(p=pct,v=val)
    return custom_autopct

fig1, ax1 = plt.subplots()
plt.title("Class Distribution")
ax1.pie(values, labels=labels, autopct=make_custom_autopct(values), startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()

 

 

 

 

 

 

 

 

 

Bar Chart with Matplotlib

import numpy as np
%matplotlib inline

df = pd.read_csv('./data/pima-data-orig.csv')

# print(df.head())
counts = df['diabetes'].value_counts()
labels = counts.index.values # array([0, 1])
values = counts.values # array([500, 268])

# Dividing into groups of ranges
groups = int(df['num_preg'].max() / 4)

labels = []
for i in range(groups):
    start = i * groups
    end = start + (groups - 1)
    labels.append(str(start)+'-'+str(end))
#     labels.append(str(start)+'-'+str(end)+':P')

print(labels) #['0-3', '4-7', '8-11', '12-15']
    
diabetes_0 = [] 
diabetes_1 = []
for i in range(groups):
    # TODO deal with the last range so it gets all greater than
    start = i * groups
    end = start + (groups - 1)
    df_filtered = df[(df['num_preg'] >= start) & (df['num_preg'] <= end ) & (df['diabetes'] == 0 )] 
    df_filtered2 = df[(df['num_preg'] >= start) & (df['num_preg'] <= end ) & (df['diabetes'] == 1 )]
    diabetes_0.append(len(df_filtered['num_preg']))
    diabetes_1.append(len(df_filtered2['num_preg']))
    
print(diabetes_0) # [311, 135, 44, 10]
print(diabetes_1) # [113, 85, 57, 12]


x = np.arange(len(labels))  # the label locations
width = 0.35  # the width of the bars

fig, axes = plt.subplots()

rects1 = axes.bar(x - width/2, diabetes_0, width, label='diabetes_0')
rects2 = axes.bar(x + width/2, diabetes_1, width, label='diabetes_1')

# Add some text for labels, title and custom x-axis tick labels, etc.
axes.set_ylabel('Observations')
axes.set_xlabel('Number of Pregnancies Ranges')
axes.set_title('Number of Diabetes by number of Pregnancies')
axes.set_xticks(x)
axes.set_xticklabels(labels)
axes.legend()

def autolabel(rects):
    for rect in rects:
        height = rect.get_height()
        axes.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')

autolabel(rects1)
autolabel(rects2)

fig.tight_layout()

plt.show()

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Heatmap with Seaborn – Correlation Matrix

correlation = df.corr()

plt.figure(figsize=(18,8))
sns.heatmap(correlation, annot = True)
plt.show()

References

https://seaborn.pydata.org/