Program:
For example, to plot x versus y, you can issue the command:
import matplotlib. pyplot as plt
pIt.plot([1,2,3,4], [1,4,9,16])
plt.show ( )
Output:

Plot:
import matplotlib.pyplot as plt
sizes =  [105, 30, 30, 195]
label= ["sleeping", "eating", "working", "playing"]
plt.pie (sizes, labels = labels, autopct = "%.2f")
plt, axes ( ). set_aspect ("equal")
plt.show ( )

(a) Check if PIP is Installed:
To check if PIP is already installed in our system, navigate our command line to the location of Python's script directory.
Command:
Python m pip install - U pip.
(b) To check PIP version:
To check the version of PIP in our system, type the command as:
C: \Users\Your Name\App Data\Local\Programs\Python\Python 36-32\Scripts> Pip--version.
(c) List packages:
To view the list of installed packages on our system, use the list command:
C:\Users\Your Name\App Data\Local\Programs\Python\Python 36-32\scripts> Pip list

(i) Data Visualization help users to analyze and interpret the data easily.
(ii) It makes complex data understandable and usable.
(iii) Various Charts in Data Visualization helps to show relationship in the data for one or more variables.

Program:
import matplotlib.pyplot as plt
x = [1,2,3]
Y = [5,7,4]
x2 = [1,2,3]
y2 = [10,14,12]
plt.plot(x, y, Iabel='Line 1')
plt.plot(x2, y2, Iabel='Line 2')
pIt.xlabel('X -Axis')
pIt.yIabel('Y- Axis')
pIt.title('LINE GRAPH')
plt.legend ( )
plt.show ( )

Program:
import matplotlib.pyplot as plt
sizes = [89, 80, 90, 100, 75]
labels = ["Tamil", "English", "Maths", "Science", "Social"]
plt.pie (sizes, labels = labels, autopct = "%.2f")
plt.axes( ).set aspect ("equal")
pIt.show ( )

(a) plt.xlabel:
plt.xlabel() \(\rightarrow \) specifies label for X - axis
(b) plt.ylabel:
plt.ylabel() \(\rightarrow \) specifies label for Y - axis
(c) plt.title:
plt.title() \(\rightarrow \) specifies title to the graph
(d) plt.show():
Display a figure. When running in Python with its Pylab mode, display all figures and return to the Python prompt.
(e) plt.legend():
Calling legend() with no arguments automatically fetches the legend handles and their associated labels.

Line Chart:
(i) 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.
(ii) A Line Chart is often used to visualize a trend in data over intervals of time - a time series - thus the line is often drawn chronologically.
Example:
import matplotlib.pyplot as plt
years = [2014, 2015, 2016, 2017, 2018]
total_populations = [8939007, 8954518, 8960387, 8956741, 8943721]
plt. plot ("years, total_populations)
pit. title ("Year vs Population in India")
plt.xlabel ("Year")
pIt.ylabel ("Total Population")
plt.showt (1)
In this program,
Plt.titlet( ) \(\rightarrow\) specifies title to the graph
Plt.xlabelt) \(\rightarrow\) specifies label for X-axis
Plt.ylabel() \(\rightarrow\) specifies label for Y-axis
Output:

Bar Chart:
(i) A Bar Plot (or BarChart) is one of the most common type of plot. It shows the relationship between a numerical variable and a categorical variable
(ii) Bar chart represents categorical data with rectangular bars. Each bar has a height corresponds to the value it represents. The bars can be plotted vertically or horizontally.
(iii) It's useful when we want to compare a given numeric value on different categories. To make a bar chart with Matplotlib, we can use the plt.bar() function.
Example:
import matplotlib.pyplot as plt
# Our data
labels = ["TAMIL", "ENGLISH", "MATHS", "PHYSICS", "CHEMISTRY", "CS"]
usage = [79.8,67.3,77.8,68.4,70.2,88.5]
# Generating the y positions. 
y_positions = range (len(labels))
# Creating our bar plot
plt.bar (y_positions, usage)
plt.xticks (y_positions, labels)
plt.ylabel ("RANGE")
plt.title ("MARKS")
plt.show( )
Output:

Labels → specifies labels for the bars.
Usage → Assign values to the labels specified.
Xticks → Display the tick marks along the x - axis at the values represented. Then specify the ladel for each tick mark.
Range → Create sequence of numbers.
Pie Chart:
(i) Pie Chart is probably one of the most common type of chart. It is a circular graphic which is divided into slices to illustrate numerical proportion.
(ii) The point of a pie chart is to show the relationship of parts out of a whole. To make a Pie Chart with Matplotlib, we can use the plt.pief) function.
(iii) The autopct parameter allows us to display the percentage value using the Python string formatting.
Example:
import matplotlib.pyplot as plt
sizes = [89, 80, 90, 100, 75]
labels = ["Tamil", "English", "Maths", "Science", "Social"]
plt.pie (sizes, labels = labels, autopct = "%.2f")
plt.axes().set aspect ("equal")
plt.show( )
Output: