Matplotlib and Seaborn for Data Visualization - Python

Examples to learn Matplotlib and Seaborn for Data Visualization.

Install Numpy, Matplotlib, and Seaborn with the following commands on Terminal/Command Prompt

• pip install numpy OR conda install numpy
• pip install matplotlib OR conda install matplotlib
• pip install seaborn OR conda install seaborn 

Run the following in Jupyter Notebook

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
x = np.random.rand(50)
x.sort()
print(x)

y = x**2
print(y)

Plot with red line

plt.plot(x, y, 'r-')
plt.show()

Plot with dotted red line

plt.plot(x, y,'r--')
plt.show()

Plot with dots

plt.plot(x, y,'r.')
plt.show()

Plot with '+' sign

plt.plot(x, y,'r+')
plt.show()

Plot with labels of x and y and title

plt.plot(x, y, 'r.')
plt.xlabel('Number')
plt.ylabel('Square')
plt.title('y = x**2')

Plot two or more using subplot

plt.subplot(1,2,1)
plt.plot(x,y,'r,')
plt.subplot(1,2,2)
plt.plot(y,x,'b,')

Plot using 'figure'

fig = plt.figure()
a = fig.add_axes([0,0,1,1])
a.plot(x,y,'r.')
a.set_xlabel('X Label')
a.set_ylabel('Y Label')
a.set_title('Title')

Plot second inside first using 'figure'

fig = plt.figure()
axes1 = fig.add_axes([0,0,1,1])
axes2 = fig.add_axes([0.1,0.6,0.4,0.3])
axes1.plot(x,y)
axes1.set_title('Larger Plot')
axes2.plot(x,y**2)
axes2.set_title('Smaller Plot')

Plot side by side

fig, axes = plt.subplots(nrows=1,ncols=2)
axes[0].plot(x,y)
axes[0].set_title('Title 1')
axes[1].plot(y,x)
axes[1].set_title('Title 2')
plt.tight_layout()

Set custom size of chart

fig = plt.figure(figsize=(8,2))
ax = fig.add_axes([0,0,1,1])
ax.plot(x,y)

Multiple charts with custom sizes

fig, ax = plt.subplots(nrows=2,ncols=1,figsize=(8,4))
ax[0].plot(x,y)
ax[1].plot(x,y**2)
plt.tight_layout()

Save chart in image file

fig.savefig('my_picture.png', dpi=300)

Two lines in one chart

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(x, y, 'r--',label="x=y")
ax.plot(x, y**2, 'g-.', label="x=y**2")
ax.legend(loc=(0.02,0.85))

A part of the chart

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(x,y,color="purple", linewidth=3, alpha=0.5, linestyle='steps', marker="s", markersize=20, markerfacecolor="yellow", markeredgewidth=3, markeredgecolor="green")
ax.set_xlim([0.3,0.7])
ax.set_ylim([0.3,0.7])

Scattered Chart

plt.scatter(x,y)

Histogram Chart

plt.hist(x)

Box Chart

plt.boxplot([x,y],vert=True, patch_artist=True)

SEABORN

import seaborn as sns

Create Dataset using sample provided by Seaborn

tips = sns.load_dataset('tips')
tips.head()

View structure of Dataset

tips.info()

Dist Plot without KDE

sns.distplot(tips['total_bill'], kde=False, bins=30)

Joint Plot

sns.jointplot(x='total_bill',y='tip',data=tips)

Joint Plot Hexagonal

sns.jointplot(x='total_bill',y='tip',data=tips,kind='hex')

Joint Plot Regression

sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg')

Joint Plot KDE

sns.jointplot(x='total_bill',y='tip',data=tips,kind='kde')

Pair Plot

sns.pairplot(tips)

Pair Plot with hue

sns.pairplot(tips, hue='sex', palette='cool')

Rug Plot

sns.rugplot(tips['total_bill'])

Dist Plot

sns.distplot(tips['total_bill'])

Create new Dataset

from scipy import stats
dataset = np.random.randn(1000)
sns.rugplot(dataset)

Setup for calculating KDE

x_min = dataset.min() - 2
x_max = dataset.max() + 2
x_axis = np.linspace(x_min,x_max,100)
bandwidth = ((4*dataset.std()**5)/(3*len(dataset)))**.2
kernel_list = []
for datapoint in dataset:
    kernel = stats.norm(datapoint, bandwidth).pdf(x_axis)
    kernel_list.append(kernel)
    
    kernel = kernel/kernel.max()
    kernel = kernel*0.4
    plt.plot(x_axis, kernel, color="grey", alpha=0.5)
    
plt.ylim(0,1)

Calculating KDE

sum_of_kde = np.sum(kernel_list, axis=0)
fig = plt.plot(x_axis, sum_of_kde, color="indianred")
sns.rugplot(dataset, c="indianred")
plt.yticks([])
plt.suptitle("Sum of the Basis Functions")

Dist Plot

sns.distplot(dataset)

Bar Plot

sns.barplot(x='sex', y='total_bill', data=tips, estimator=np.std)

Count Plot

sns.countplot(x='sex', data=tips)

Box Plot

sns.boxplot(x='day', y='total_bill', data=tips)

Box Plot with hue

sns.boxplot(x='day', y='total_bill', data=tips, hue='smoker')

Violin Plot

sns.violinplot(x='day', y='total_bill', data=tips)

Violin Plot with hue and Split

sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', split=True)

Strip Plot

sns.stripplot(x='day', y='total_bill', data=tips, jitter=True, hue='sex', split=True)

Swarm Plot

sns.swarmplot(x='day', y='total_bill', data=tips)

Violing Plot with Swarm Plot

sns.violinplot(x='day', y='total_bill', data=tips)
sns.swarmplot(x='day', y='total_bill', data=tips, color='black')

Factor Plot - Bar

sns.factorplot(x='day', y='total_bill', data=tips, kind='bar')

Factor Plot - Violin

sns.factorplot(x='day', y='total_bill', data=tips, kind='violin')

Use 'flights' sample dataset provided by Seaborn

flights = sns.load_dataset('flights')

View tips dataset

tips.head()

View flights dataset

flights.head()

Create Correlation of 'tips' dataset

tc = tips.corr()

tc

Heat Map

sns.heatmap(tc, annot=True, cmap='coolwarm')

Create Pivot Table of 'flights' dataset

fp = flights.pivot_table(index='month', columns='year', values='passengers')

fp

Heat Map with line arguments

sns.heatmap(fp, cmap='magma', linecolor='white', linewidth=0.1)

Cluster Map

sns.clustermap(fp, cmap='coolwarm')

Cluster Map with Standard Scale

sns.clustermap(fp, cmap='coolwarm', standard_scale=1)

Load New Sample Dataset named 'iris'

iris = sns.load_dataset('iris')

iris.head()

Get unique list of species from iris dataset

iris['species'].unique()

Pair Plot Iris

sns.pairplot(iris)

Pair Grid Iris

g = sns.PairGrid(iris)

g.map(plt.scatter)

Pair Grid with Upper and Lower

g = sns.PairGrid(iris)

g.map_diag(sns.distplot)

g.map_upper(plt.scatter)

g.map_lower(sns.kdeplot)

FacetGrid - Dist Plot

g = sns.FacetGrid(data=tips, col='time', row='smoker')

g.map(sns.distplot, 'total_bill')

FacetGrid - Scatter Plot

g = sns.FacetGrid(data=tips, col='time', row='smoker')

g.map(plt.scatter, 'total_bill', 'tip')

Seaborn LM Plot

sns.lmplot(x='total_bill', y='tip', data=tips)

Seaborn LM Plot with hue

sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex')

Seaborn LM Plot with markers and sizing the markers

sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex', markers=['o','v'], scatter_kws={'s':100})

Seaborn LM Plot with Column

sns.lmplot(x='total_bill', y='tip', data=tips, col='sex')

Seaborn LM Plot with Column and Rows

sns.lmplot(x='total_bill', y='tip', data=tips, col='sex', row='time')

Seaborn LM Plot with Column, Row, and Hue

sns.lmplot(x='total_bill', y='tip', data=tips, col='day', row='time', hue='sex')

]

Seaborn LM Plot with Column and Hue

sns.lmplot(x='total_bill', y='tip', data=tips, col='day', hue='sex')

Seaborn LM Plot with Aspect and Size

sns.lmplot(x='total_bill', y='tip', data=tips, col='day', hue='sex', aspect=0.6, size=8)

Seaborn - Set Style

sns.set_style('whitegrid')

sns.countplot(x='sex', data=tips)

Seaborn Despine

sns.countplot(x='sex', data=tips)

sns.despine(left=True, bottom=True)

Seaborn Context

sns.set_context('poster')

sns.countplot(x='sex', data=tips)

Seaborn - Context with Font Scale

sns.set_context('notebook', font_scale=2)

sns.countplot(x='sex', data=tips)

Seaborn - Palette

sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex', palette='magma')