## Visualizations in Python

Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends

Python is blessed with some good libraries for visualizations.

Open Jupyter notebook or any other IDE of your preference.

Library to use – There are

So

Importing the library and giving it the standard alias as

Following are the two important functions which will come
handy in this book:-

To display a chart you should use – **plt.show()**

To save the chart as an image, use the code – **plt.savefig(“Filename.png”)
**Popular plotting libraries in Python are:-

**1. Matplotlib –**Best to start with. It provides easy implementation and gives a lot of freedom

**2. Seaborn –**It has a high level interface and great default styles

**3. Plotly –**To create interactive plots

**4. Pandas Visualization –**Easy interface, built on Matplotlib

**Line Chart ** A line chart or line graph is a type of chart which displays information as a series of data points called ‘markers’ connected by straight line segments.

So, a line plot is a very basic plot which is used to show observations collected after a regular interval. The x-axis represents the interval and the y-axis represents the values.

Lets plot our first graph

*import matplotlib.pyplot as plt*

x = [1,2,3,4,5,6]

y = [10,12,20,21,30,35]

plt.plot(x,y)

Here is what you will get

x = [1,2,3,4,5,6]

y = [10,12,20,21,30,35]

plt.plot(x,y)

**Graph 1 – **Basic Line Chart

**Plot a sin graph using line plot **

*import matplotlib.pyplot as*plt

fromnumpy import cos

x = [x*0.01 for x in range(100)]

y = cos(x)

plt.plot(x,y)

plt.show()

from

x = [x*0.01 for x in range(100)]

y = cos(x)

plt.plot(x,y)

plt.show()

Here is what you get as a cos graph

**Graph 2 –** Cos graph using line plot

You know how to plot a line graph, but there is one important thing missing in the graph i.e. the x and y-axis, and the plot title. Let’s create another line plot for

c = [1,2,3,4,5,6]

student = [40,52,50,61,70,78]

Following commands are used to put x-axis label, y-axis label, and chart title

plt.xlabel(“Label”)

plt.ylabel(“Label”)

plt.title(“Title”)

The code is given below*c = [1,2,3,4,5,6] student = [40,52,50,61,70,78] plt.xlabel(“Class”) plt.ylabel(“Number of Students”) plt.title(“Class vs Number of students”) plt.plot(c, student)*

**Graph 3 –** Class vs Number of Students chart with proper labels and plot title

Do you want to change the color of the line?

Try the following code instead to make the line green in color*plt.plot(c ,student,color=’g’) *

**Graph 4 –** Adding color to the same graph * *

You can also add multiple plots in the same graph. Let’s try to put a couple of new lines in the graph i.e. number of teachers and average marks

**Graph 5 –** Adding multiple lines to a graph

To add a legend, you have to give

The
code is self explanatory and is given below:-*c = [1,2,3,4,5,6]
student = [40,52,50,61,70,78]
avg_marks = [34,43,54,44,50,55]
num_of_teachers = [10,12,13,10,15,10]
plt.xlabel(“Class”)
#plt.ylabel(“Number of Students”)
plt.title(“Class vs Number of students”)
plt.plot(c,student,color=’orange’,label=’Student’)
plt.plot(c,avg_marks,color=’red’,label=’Marks’)
plt.plot(c,num_of_teachers,color=’green’,label=’Teachers’)
plt.legend(loc=”upper left”)*

**Bar Chart **

*“A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally.”*

After the line chart, the second basic but

To create a bar chart – plt.bar(x,y)

We will plot

*import matplotlib.pyplot as plt*

a = [‘Apple’,’Mango’,’Pineapple’]

b = [40,60,50]

plt.bar(a,b)

a = [‘Apple’,’Mango’,’Pineapple’]

b = [40,60,50]

plt.bar(a,b)

**Graph 6 –** A simple bar chart

Use random values between 1 and 100 to create the same graph.*import matplotlib.pyplot as plt from random import seed from random import randint seed(123) x = [‘Apple’,’Mango’,’Pineapple’] y = [randint(0,100),randint(0,100),randint(0,100)] plt.bar(x,y) *

**Graph 7 – **Bar chart with random values * *

Adding color, labels, and title to the random values bar chart

**Stacked 100% bar chart with sub component **When you have to show components of components like the graph below

Example of 100% bar chart

*x =
[“a”,”b”,”c”,”d”]
y1 = np.array([3,8,6,4])
y2 = np.array([10,2,4,3])
y3 = np.array([5,6,2,5])
snum = y1+y2+y3
# normalization
y1 = y1/snum*100.
y2 = y2/snum*100.
y3 = y3/snum*100.
plt.figure(figsize=(4,3))
# stack bars
plt.bar(x, y1, label=’y1′)
plt.bar(x, y2 ,bottom=y1,label=’y2′)
plt.bar(x, y3 ,bottom=y1+y2,label=’y3′)*

**Graph 8 –** A 100% stacked bar chart

**Histogram **Histograms are density estimates. A density estimate gives a good impression of the distribution of the data. The idea is to locally represent the data density by counting the number of observations in a sequence of consecutive intervals (bins).

To plot a histogram use this code –

A simple histogram plot

*q = [1,2,34,5,44,66,66,90,33,45,2,1,2,3,4]*

plt.hist(q,bins = 3,color=’green’)

plt.hist(q,bins = 3,color=’green’)

**Graph 9 –** A simple histogram

Create a list using random variables and plot it in 4 bins

import random

my_rand = random.sample(range(1,30),20)

print(my_rand)

print(type(my_rand))

plt.hist(my_rand,bins=4,color=’orange’)

**Graph 10 –** A histogram made with random variables

In Histogram also you can add more than one data points to make parallel bars.

*import random my_rand = random.sample(range(1,30),20) my_rand2 = random.sample(range(1,25),20) print(my_rand) print(type(my_rand)) plt.hist([my_rand,my_rand2],bins=4,color=[‘green’,’red’]) legend = [‘Rand1′,’Rand2’] plt.legend(legend) plt.xlabel(“Bins”) plt.ylabel(“Random Number”) plt.title(“Random Variable distribution”)*

**Graph 11 – **Parallel histogram

**Horizontal Histogram **

*import numpy as np*

import matplotlib.pyplot as plt

name = [‘Nitin’,’Saurabh’,’Rahul’,’Gaurav’,’Amit’]

run = [200,70,130,120,100]

plt.barh(name,run,color=’orange’)

plt.xlabel(“Runs Scored”)

plt.ylabel(“Cricketer”)

plt.title(“Runs scored by cricketers”)

plt.show()

import matplotlib.pyplot as plt

name = [‘Nitin’,’Saurabh’,’Rahul’,’Gaurav’,’Amit’]

run = [200,70,130,120,100]

plt.barh(name,run,color=’orange’)

plt.xlabel(“Runs Scored”)

plt.ylabel(“Cricketer”)

plt.title(“Runs scored by cricketers”)

plt.show()

**Graph 12 – **A horizontal histogram

Keep making irrelevant and unnecessary graphs.

Keep practicing 🙂

XtraMous

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