Data Visualization

This page has information about data visualization, containing types of plots, use cases, and how to implement using various python libraries.

Overview

PyPlot
- fast
- easy
- only on figure
Object Oriented
- better customization
- friendly for multiple charts
- more coding
Seaborn
- based on matplotlib
- integrated with pandas
- speed
- easy to use
- customizable

Implementation

Importing libraries using the standard of the industry

Code

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

PyPlot (matplotlib)

Code

fig, ax = plt.subplots() # Create a figure containing a single axes
ax.plot([1,2,3,4], [1,4,2,3]); # plot some data on the axes

Code

# linspace
# Return evenly spaced numbers over a specified interval.
# Returns num evenly spaced samples, calculated over the interval [start, stop].

#The endpoint of the interval can optionally be excluded.

x = np.linspace(0,5,11)
y = x**2
print(x)
print(y)

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

[0.  0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5. ]
[ 0.    0.25  1.    2.25  4.    6.25  9.   12.25 16.   20.25 25.  ]

Code

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

Code

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

Code

plt.plot(x,y,'yD:')
plt.show()

Code

plt.hist(x)
plt.show()

Code

plt.pie(x)
plt.show()

Code

plt.scatter(x,y)
plt.show()

Code

plt.boxplot(x)
plt.show()

Subplots

Code

plt.subplot(1,2,1) # Chart 1
plt.plot(x,y,'r--')

plt.subplot(1,2,2)  # Chart 2
plt.pie(y)
plt.show()

Code

plt.subplot(1,2,1)       # 1 row, 2 columns, Ref 1
plt.plot(x,y,'r--')
plt.plot(y,x,'b:')       # Keep sama reference, adding another line
plt.subplot(1,2,2)       # Ref to plot #2
plt.pie(y)
plt.show()

Code

plt.subplot(2,1,1)
plt.plot(x,y,'r--')
plt.plot(y,x,'b:')
plt.subplot(2,1,2)
plt.pie(y)
plt.show()

Object-Oriented Method

Code

x1= np.linspace(0, 2*np.pi,400)
y1 = np.sin(x1**2)

Code

fig, ax1 = plt.subplots()
ax1.plot(x1,y1)
fig.show()

C:\Users\carlj\AppData\Local\Temp\ipykernel_42696\2806661265.py:3: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Code

fig = plt.figure()   # Empty canvas (figure)
axes = fig.add_axes([0,0,0.5,0.5])   #  Add axes
axes.plot(x,y,'b')
fig.show()

C:\Users\carlj\AppData\Local\Temp\ipykernel_42696\3333502070.py:4: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Code

fig = plt.figure()

axes = fig.add_axes([0.1,0.1,0.8,0.9]) # eje principal
axes2 = fig.add_axes([0.2,0.55,0.4,0.3]) # eje interior
# principal fig axes
axes.plot(x,y,'b')
# secondary fig axes2
axes2.plot(y,x,'r')
axes2.set_facecolor('lightgray')

fig.show()

C:\Users\carlj\AppData\Local\Temp\ipykernel_42696\111565761.py:11: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Code

fig, (ax1,ax2) = plt.subplots(1,2)

Code

fig, ax1 = plt.subplots()

Code

fig = plt.figure()
axes = fig.add_axes([0,0,0.5,0.5])

axes2 = fig.add_axes ([0.6,0,0.5,0.5])
axes3 = fig.add_axes ([0,-0.6,0.5,0.5])
axes4 = fig.add_axes ([0.6,-0.6,0.5,0.5])

axes.set_facecolor('green')
axes2.set_facecolor('blue')
axes3.set_facecolor('yellow')
axes4.set_facecolor('red')

Code

fig, axs = plt.subplots(2,2)
axs[0,0].set_facecolor('green')
axs[0,1].set_facecolor('yellow')
axs[1,0].set_facecolor('red')
axs[1,1].set_facecolor('purple')
fig.set_facecolor('gray')

Code

x=np.linspace(0,10,200)
y=np.sin(x)

Code

fig, axes = plt.subplots()
axes.plot(x,y,'r')
fig.show()

C:\Users\carlj\AppData\Local\Temp\ipykernel_42696\4205225829.py:3: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Code

fig, axes = plt.subplots(nrows=1, ncols=2)
axes[0].plot(x,y,'r')
axes[1].plot(y,x,'b')
fig.show()

C:\Users\carlj\AppData\Local\Temp\ipykernel_42696\518181344.py:4: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Code

fig, axes = plt.subplots(nrows=2, ncols=4)
axes[0,0].plot(x,np.cos(x),'r')
axes[0,1].plot(x,np.sin(x),'g')
axes[0,2].plot(x,np.tan(x),'y')
axes[0,3].plot(x,np.cos(x)**2,'b')
fig.tight_layout()
fig.show()

C:\Users\carlj\AppData\Local\Temp\ipykernel_42696\4260253020.py:7: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Code

fig, axes = plt.subplots(1,2, figsize=(8,5))
axes[0].plot(x,y)
axes[0].set_title('Relation X and Y')
axes[0].set_xlabel('X')
axes[0].set_ylabel('Y')

axes[1].plot(y,x)
axes[1].set_title('Relation Y and X')
axes[1].set_xlabel('Y')
axes[1].set_ylabel('X')

fig.show()

C:\Users\carlj\AppData\Local\Temp\ipykernel_42696\256813773.py:12: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Code

plt.plot(x,y,label='sin(x)')
plt.title('This is a title')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend() # plt.legend(loc='lower right')
plt.show()

Color and Style

Code

x=np.linspace(0,10,10)
y=np.sin(x)

Code

print(plt.style.available)

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']

Code

plt.style.use('dark_background')

Code

fig, ax = plt.subplots()
ax.plot(x,x+1,'r--')
ax.plot(x,x+2,'b-')
ax.plot(x,x+3,'y.-')
ax.plot(x,x+4,'g:')

Code

fig, ax = plt.subplots()
ax.plot(x,x+1,color='red',linewidth=1, linestyle ='--')
ax.plot(x,x+2,color='blue', linewidth=2, marker ='8')
ax.plot(x,x+3,color='#FFCC22', marker='d')
ax.plot(x,x+4,color='#00FF00', alpha=0.5, linewidth=5, linestyle =':')

Categorical Variables

Code

plt.style.use('classic')
data = np.random.randint(1, 50, 100)

Code

country = ['USA', 'INDIA', 'JAPAN', 'CHILE', 'MEXICO']
population = [350, 1408, 125, 20, 126]

Code

#plt.bar(country,population)
plt.bar(country,population, width=0.5, color=['red','blue'])
plt.xticks (np.arange(5),('USA','India', 'Japan','Chile','Mexico'), rotation = 45)
plt.show()

Code

plt.barh(country,population)
plt.show()

More Charts

Code

plt.hist(data,bins=20) # bins = 10 (default)
plt.show()

Code

plt.hist(data,bins=20, histtype='step')
plt.show()

Code

# Adding an outlier
data=np.append(data,200)
plt.boxplot(data, vert=False, patch_artist=True, notch=True, showfliers=True)
plt.show()

Scatter

Code

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
area = (30* np.random.rand(N))**2
colors = np.random.rand(N)

plt.scatter(x,y, s=area, c=colors, marker='p', alpha=0.5)
plt.show()

Seaborn

Code

import seaborn as sns

Code

sns.barplot(x=['A','B','C'], y=[1,2,3])
plt.show()

Code

sns.set(style='dark', palette='deep')
sns.barplot(x=['A','B','C'], y=[1,2,3])
plt.show()

Code

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

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Distributions

Code

sns.displot(data=tips, x='total_bill')
plt.show()

Code

sns.displot(data=tips, x='total_bill', y='tip')
plt.show()

Code

sns.displot(data=tips, x='total_bill', y='tip', hue = 'sex')
plt.show()

Code

sns.displot(data=tips, x='total_bill', hue = 'sex')
plt.show()

Code

sns.displot(data=tips, x='total_bill', hue = 'sex', kind='kde')
plt.show()

Code

sns.histplot(data=tips, x='tip', bins =15, cumulative = False, hue='sex',
    stat='probability', multiple='dodge') # multiple: stack, dodge, fill
plt.show()

Code

sns.kdeplot(data=tips, x='tip',hue='sex')
plt.show()

Code

sns.kdeplot(data=tips, x='tip',hue='sex', cumulative = True)
plt.show()

Code

sns.kdeplot(data=tips, x='tip',hue='sex', cumulative = False, fill=True)
plt.show()

Code

sns.kdeplot(data=tips, x='tip',hue='sex', cumulative = False, fill=True)
plt.show()

Categorical Variables

Code

sns.countplot(data=tips, x='day',hue='sex')
plt.show()

Code

sns.countplot(data=tips, y='day',hue='sex')
plt.show()

Code

sns.stripplot(data=tips, x='day', y='total_bill', hue='sex')
plt.show()

Code

sns.stripplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True)
plt.show()

Swarmplot

Code

sns.swarmplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True)
plt.show()

Code

sns.boxplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True)
plt.show()

Code

sns.swarmplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True , color="0")
sns.boxplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True)
plt.show()

Violinplot

Code

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

Code

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

Code

sns.catplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True,
            kind='box', col='time')
plt.show()

Relation Charts

Code

sns.scatterplot(data=tips,x='total_bill', y='tip', hue='day')
plt.show()

Code

sns.scatterplot(data=tips,x='total_bill', y='tip', hue='day', style='time')
plt.show()

Code

sns.scatterplot(data=tips,x='total_bill', y='tip', hue='day', style='time', size='size')
plt.legend(loc='center', bbox_to_anchor=(1.12,0.5))
plt.show()

Code

plt.figure(figsize=(8,8))
markers={"Lunch":"D","Dinner":"s"}
sns.scatterplot(data=tips,x='total_bill', y='tip', hue='day',
  style='time', size='size', markers=markers)
plt.legend(loc='center', bbox_to_anchor=(1.12,0.5))
plt.show()

Code

sns.relplot(data=tips,x='total_bill', y='tip', hue='day',
  style='time', size='size', col='time')
plt.show()

Jointplot and Pairplot

Code

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

Code

sns.jointplot(data=tips, x='total_bill',y='tip', hue='sex')
plt.show()

Code

sns.jointplot(data=tips, x='total_bill',y='tip', hue='sex',
              kind='hist')
plt.show()

Code

sns.pairplot(data=tips)
plt.show()

Code

sns.pairplot(data=tips, hue='sex')
plt.show()

Heatmap

Code

tips.corr()

	total_bill	tip	size
total_bill	1.000000	0.675734	0.598315
tip	0.675734	1.000000	0.489299
size	0.598315	0.489299	1.000000

Code

sns.heatmap(tips.corr())
plt.show()

Code

sns.heatmap(tips.corr(), annot=True, cmap='coolwarm', linewidth=5, linecolor='black',
            vmin=0.5, vmax=1, cbar=True)
plt.show()

Note: can use corr(numeric_only=True) if using a later version of pandas.