Contents

## ### MATPLOTLIB:-

it is a python library which provides many interfaces and functionality for 2D graphics like MATLAB.

### pyplot:-

it is a collection of method within matplotlib library of python. which allow user to construct 2D plot easily and intellectually.

### data visualization:-

it refers to graphical representation of information and data using visual elements like charts, graphs and maps.

1.line chart
2.bar chart
3. pychart

### Line Chart:-

The line chart is represented by a series of data points connected
with a straight line.
line charts are used to display trends over time.
A line chart or line graph can be created
using the plot()
function available in pyplot library.

### Python Program to Draw a Line Chart

import matplotlib.pyplot as plt
import numpy as np
x = [‘Delhi’, ‘Banglore’, ‘Chennai’, ‘Pune’]
y = [250, 300, 260, 400]
plt.xlabel(‘City’)
plt.ylabel(‘Sales in Million’)
plt.title(‘Sales Recorded in Major Cities’)
plt.plot(x, y)
plt.show()

### Bar Chart

A bar chart/bar graph, is a very common two-dimensional data visualization made up of rectangular bars,
each for a specific category and it’s length represents the value of that category.
Python Program for bar graphs
import matplotlib.pyplot as plt
x = [‘Delhi’, ‘Banglore’, ‘Chennai’, ‘Pune’]
y = [250, 300, 260, 400]
plt.xlabel(‘City’)
plt.ylabel(‘Sales in Million’)
plt.title(‘Sales Recorded in Major Cities’)
plt.bar(x, y)
plt.show()

### Pie Chart:-

A pie graph/pie chart is a specialized graph used in statistics.
The independent variable is plotted around a
circle.
Pie Charts shows proportions and percentages between categories, by dividing a circle into proportional
segments/parts.
Each arc length represents a proportion of each category, while the full circle represents the total sum of all the data, equal to 100%.

### Program to draw Pie-chart

import matplotlib.pyplot as plt
import numpy as np
y = [250,300,260,400,599,320]
plt.pie(y,labels=x,autopct=’%1.2f’,startangle=90,explode=(0,0.1,0,0,0.2,0))
plt.show()

### numpy:-

numpy is the core library for scientific computing in python.
It provides a high performance,
multidimensional array object and tools for working with
these arrays.

#### module to be imported:-

import numpy as np

### example

1. np.arrange(11)                output:- [0 1 2 3 4 5 6 7 8 9 10]
2.np.arrange(1,10,2)          output:-  [1 3 5 7 9]
3.np.arrange(-10,15,3)       output:-  [-10 -7 -4 -1 2 5 8 11 14]
4.np.zeros(5)                         output:-  [0 0 0 0 0]