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Archive for the ‘Python Library Matplotlib’ Category

It’s fun that Math formula can visualize ‘LOVE’ word.
Type the code below in your python IDE and run it.

import matplotlib.pyplot as plt
import numpy as np
L=np.arange(0,6,0.1)
O=np.arange(-3,3,0.1)
V=np.arange(-2,3,1)
E=np.arange(-3,3,0.1)
fig,(ax1,ax2,ax3,ax4)=plt.subplots(ncols=4)
ax1.set_title(r'$ y=\frac{1}{x}$') #display Math formula
ax1.plot(L,1/L)                    #print L
ax2.set_title(r'$ x^2+y^2=9$')     #display Math formula
ax2.plot(O,(9-O**2)**0.5)          #print O
ax2.plot((9-O**2)**0.5,O)
ax2.plot(O,-(9-O**2)**0.5)
ax3.set_title(r'$ y=|-2x| $')      #display Math formula
ax3.plot(V,(abs(-2*V)))            #print V
ax4.set_title(r'$ -3|sin y| $')    #display Math formula
ax4.plot(-3*abs(np.sin(E)),E)      #print E
plt.show()

love-code
love

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Pie is very useful to visualize relative proportions of a data set and easily to be understood. The size of the circle will calculated based on the total quantity it represents.
pie01
For example:
There are 3 color in a cirlce, Grey 25, Blue 25 and Green 50.
So total circle size is: 25+25+50=100
The size for the color will be:

Grey  → 25/100 = 0.25 x 100% = 25%
Blue  → 25/100 = 0.25 x 100% = 25%
Green → 50/100 = 0.25 x 100% = 50%

Let’s say the size total is not 100, Grey 30, Blue20 and Green 40.
The total size is: 30+20+40=90.
What the percentage will be?

Grey → 30/90 = 0.3333 x 100% = 33.33%
Blue → 20/90 = 0.2222 x 100% = 22.22%
Green → 40/90 = 0.4444 x 100% = 44,44%

pie02
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Histogram is also a bar type graph chart. The main different between Bar Chart and Histogram Chart are:
Bar Chart for compare numeric data among Categories.
Histogram Chart for compare numeric data in a Category which is distributed into ‘bin’ or ‘bucket’. The term of ‘bin’ in here is a group data.

For example:
you have 24 population in a town with age below:

5,6,7,5,6,4,10,15,14,13,30,35,23,36,45,49,40,51,55,53,60,65,66,70

and you want to visualize it into a graph.

Let’s try.
Open your python IDE and type the code below.

import matplotlib.pyplot as plt
age=[5,6,7,5,6,4,10,15,14,13,30,35,23,36,45,49,40,51,55,53,60,65,66,70]
plt.hist(age)
plt.show()

hist01
hist02

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In this tutorial, I will show you how to create Bar graph. Many people use Bar graph because it’s easy to presenting a comparison between 1,2 or 3 value. More than that it’s not recommended because it will be difficult to show the comparison.

Bar Chart can Vertical or Horizontal, depend on what you need.

Single Bar Vertical
Let’s try with Single bar vertical. Type the code below and run in your python text editor.

import matplotlib.pyplot as plt
month=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
revenue=[100,110,120,100,90,115,70,90,140,100,110,120]
plt.bar(month,revenue) 
plt.title('Simple Bar Graph') 
plt.xlabel('Month') 
plt.ylabel('Revenue (K)USD')
plt.grid(linestyle='dotted',axis='y')
plt.legend() 
plt.show()

bar01
bar02
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Matplotlib is a plotting library for python. Matplotlib was originally written by John D Hunter during his post-doctoral research in neurobiology to visualize electrocorticography (ECoG) data of epilepsy patients. Then Michael Droettboom lead the development project after John Hunter passed away in August 2012.
Matplotlib version 2.0.x support python version 2.7 – 3.6.
Starting 2020, Matplotlib does not support python 2 anymore.

Using Matplotlib with python is easy.

I will show you how to visualize a simple graph.
In this tutorial, I use IDLE as python editor.
If you don’t have it, you can install using command below from your Linux Terminal.
$ sudo apt-get install idle

Run the idle.
plot00

From menu, select File>New File to display the Editor.
Type the code below and press F5 to run the code.

import matplotlib.pyplot as plt
x=[1,2,3,4]
y=[5,6,7,8]
plt.plot(x,y)
plt.show()

plot01plot02
You have your first graph with Matplotlib.
It’s easy, right?
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