Python Matplotlib Tutorial | Matplotlib Tutorial | Python Tutorial | Python Training | Edureka

Jul 19, 201726 likes5,027 views

This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial: 1. Why Data Visualization? 2. What Is Data Visualization? 3. Various Types Of Plots 4. What Is Matplotlib? 6. How To Use Matplotlib?

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Python Matplotlib

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Agenda
➢ Why Data Visualization?
➢ What Is Data Visualization?
➢ What Is Matplotlib?
➢ Types Of Plots
➢ Getting Started

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Why Data Visualization?

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Why Data Visualization?
Human brain can process information easily when it is in pictorial or graphical form

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Why Data Visualization?
Data visualization allows us to quickly interpret the data and adjust different variables to see their effect

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What Is Data Visualization?

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What Is Data Visualization?
Data visualization is the presentation of data in a pictorial or graphical format.
Visualize
Analyse
Document
Insight
Transform
Data set
Finding
Insights In
Data

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What Is Matplotlib?

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What is Matplolib?
Matplotlib is a Python package used for 2D graphics

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Types Of Plots

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Types of Plots
Bar graph Histograms Scatter Plot
Pie Plot Hexagonal Bin Plot Area Plot

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Getting Started

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Getting Started
Here's some basic code to generate one of the most simple graph.
from matplotlib import pyplot as plt
#Plotting to our canvas
plt.plot([1,2,3],[4,5,1])
#Showing what we plotted
plt.show()

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Getting Started
Lets add title and labels to our graph
from matplotlib import pyplot as plt
x = [5,8,10]
y = [12,16,6]
plt.plot(x,y)
plt.title('Info')
plt.ylabel('Y axis')
plt.xlabel('X axis')
plt.show()
Title
Labels

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Adding Style To Our Graph
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
x = [5,8,10]
y = [12,16,6]
x2 = [6,9,11]
y2 = [6,15,7]
plt.plot(x,y,'g',label='line one', linewidth=5)
plt.plot(x2,y2,'c',label='line two',linewidth=5)
plt.title('Epic Info')
plt.ylabel('Y axis')
plt.xlabel('X axis')
plt.legend()
plt.grid(True,color='k')
plt.show()

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Bar Graph
import matplotlib.pyplot as plt
plt.bar([1,3,5,7,9],[5,2,7,8,2], label="Example one")
plt.bar([2,4,6,8,10],[8,6,2,5,6], label="Example two", color='g')
plt.legend()
plt.xlabel('bar number')
plt.ylabel('bar height')
plt.title('Info')
plt.show()

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Histogram
import matplotlib.pyplot as plt
population_ages =
[22,55,62,45,21,22,34,42,42,4,99,102,110,120,121,122,130,111,115,112,80,75,6
5,54,44,43,42,48]
bins = [0,10,20,30,40,50,60,70,80,90,100,110,120,130]
plt.hist(population_ages, bins, histtype='bar', rwidth=0.8)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Histogram')
plt.legend()
plt.show()

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Scatter Plot
import matplotlib.pyplot as plt
x = [1,2,3,4,5,6,7,8]
y = [5,2,4,2,1,4,5,2]
plt.scatter(x,y, label='skitscat', color='k)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Scatter Plot')
plt.legend()
plt.show()

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Stack Plot
import matplotlib.pyplot as plt
days = [1,2,3,4,5]
sleeping = [7,8,6,11,7]
eating = [2,3,4,3,2]
working = [7,8,7,2,2]
playing = [8,5,7,8,13]
plt.plot([],[],color='m', label='Sleeping', linewidth=5)
plt.plot([],[],color='c', label='Eating', linewidth=5)
plt.plot([],[],color='r', label='Working', linewidth=5)
plt.plot([],[],color='k', label='Playing', linewidth=5)
plt.stackplot(days, sleeping,eating,working,playing, colors=['m','c','r','k'])
plt.xlabel('x')
plt.ylabel('y')
plt.title('Stck Plot')
plt.legend()
plt.show()

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Pie Chart
import matplotlib.pyplot as plt
slices = [7,2,2,13]
activities = ['sleeping','eating','working','playing']
cols = ['c','m','r','b']
plt.pie(slices,
labels=activities,
colors=cols,
startangle=90,
shadow= True,
explode=(0,0.1,0,0),
autopct='%1.1f%%')
plt.title('Pie Plot')
plt.show()

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Working With Multiple Plots
import numpy as np
import matplotlib.pyplot as plt
def f(t):
return np.exp(-t) * np.cos(2*np.pi*t)
t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)
plt.subplot(211)
plt.plot(t1, f(t1), 'bo', t2, f(t2))
plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2))
plt.show()

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Session In A Minute
Why Data Visualization? What Is Data Visualization?
Types Of Plots Getting Started
What Is Matplotlib?
Working With Multiple Plots

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1. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Python Matplotlib

2. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Agenda ➢ Why Data Visualization? ➢ What Is Data Visualization? ➢ What Is Matplotlib? ➢ Types Of Plots ➢ Getting Started

3. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Why Data Visualization?

4. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Why Data Visualization? Human brain can process information easily when it is in pictorial or graphical form

5. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Why Data Visualization? Data visualization allows us to quickly interpret the data and adjust different variables to see their effect

6. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What Is Data Visualization?

7. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What Is Data Visualization? Data visualization is the presentation of data in a pictorial or graphical format. Visualize Analyse Document Insight Transform Data set Finding Insights In Data

8. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What Is Matplotlib?

9. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What is Matplolib? Matplotlib is a Python package used for 2D graphics

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11. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Types of Plots Bar graph Histograms Scatter Plot Pie Plot Hexagonal Bin Plot Area Plot

12. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Getting Started

13. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Getting Started Here's some basic code to generate one of the most simple graph. from matplotlib import pyplot as plt #Plotting to our canvas plt.plot([1,2,3],[4,5,1]) #Showing what we plotted plt.show()

14. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Getting Started Lets add title and labels to our graph from matplotlib import pyplot as plt x = [5,8,10] y = [12,16,6] plt.plot(x,y) plt.title('Info') plt.ylabel('Y axis') plt.xlabel('X axis') plt.show() Title Labels

15. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Adding Style To Our Graph from matplotlib import pyplot as plt from matplotlib import style style.use('ggplot') x = [5,8,10] y = [12,16,6] x2 = [6,9,11] y2 = [6,15,7] plt.plot(x,y,'g',label='line one', linewidth=5) plt.plot(x2,y2,'c',label='line two',linewidth=5) plt.title('Epic Info') plt.ylabel('Y axis') plt.xlabel('X axis') plt.legend() plt.grid(True,color='k') plt.show()

16. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Bar Graph import matplotlib.pyplot as plt plt.bar([1,3,5,7,9],[5,2,7,8,2], label="Example one") plt.bar([2,4,6,8,10],[8,6,2,5,6], label="Example two", color='g') plt.legend() plt.xlabel('bar number') plt.ylabel('bar height') plt.title('Info') plt.show()

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18. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Scatter Plot import matplotlib.pyplot as plt x = [1,2,3,4,5,6,7,8] y = [5,2,4,2,1,4,5,2] plt.scatter(x,y, label='skitscat', color='k) plt.xlabel('x') plt.ylabel('y') plt.title('Scatter Plot') plt.legend() plt.show()

19. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Stack Plot import matplotlib.pyplot as plt days = [1,2,3,4,5] sleeping = [7,8,6,11,7] eating = [2,3,4,3,2] working = [7,8,7,2,2] playing = [8,5,7,8,13] plt.plot([],[],color='m', label='Sleeping', linewidth=5) plt.plot([],[],color='c', label='Eating', linewidth=5) plt.plot([],[],color='r', label='Working', linewidth=5) plt.plot([],[],color='k', label='Playing', linewidth=5) plt.stackplot(days, sleeping,eating,working,playing, colors=['m','c','r','k']) plt.xlabel('x') plt.ylabel('y') plt.title('Stck Plot') plt.legend() plt.show()

20. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Pie Chart import matplotlib.pyplot as plt slices = [7,2,2,13] activities = ['sleeping','eating','working','playing'] cols = ['c','m','r','b'] plt.pie(slices, labels=activities, colors=cols, startangle=90, shadow= True, explode=(0,0.1,0,0), autopct='%1.1f%%') plt.title('Pie Plot') plt.show()

21. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Working With Multiple Plots import numpy as np import matplotlib.pyplot as plt def f(t): return np.exp(-t) * np.cos(2*np.pi*t) t1 = np.arange(0.0, 5.0, 0.1) t2 = np.arange(0.0, 5.0, 0.02) plt.subplot(211) plt.plot(t1, f(t1), 'bo', t2, f(t2)) plt.subplot(212) plt.plot(t2, np.cos(2*np.pi*t2)) plt.show()

22. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Session In A Minute Why Data Visualization? What Is Data Visualization? Types Of Plots Getting Started What Is Matplotlib? Working With Multiple Plots

23. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Thank You … Questions/Queries/Feedback

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