Bar Graph and Histogram are the two ways to display data in the form of a diagram.

Matplotlib installed using pip. Pip is a management software for installing python packages.

Box plot: The box plot is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum.

Scatter plot: A scatter plot is a type of plot that shows the data as a collection of points. The position of a point depends on its two dimensional value, where each value is a position on either the horizontal or vertical dimension.

Matplotlib is the most popular data visualization library in Python. It allows to create charts in few lines of code.

Dashboard ￫ A dashboard is a collection of resources assembled to create a single unified visual display. Data visualizations and dashboards translate complex ideas and concepts into a simple visual format. Patterns and relationships that are undetectable in text are detectable at a glance using dashboard.

Infographics ￫ An infographic (information graphic) is the representation of information in a graphic format.

plt.plot ([I,2,3,4])	plt.pIot ([I,2,3,4], [1,4,9,16])
1. It refers y values as [1,2, 3,4]	1. It refers x and y values as ([1, 2, 3, 4], [1,4,9, 16])
2. Indirectly it refers x values as [0, 1, 2, 3] (0,1),(1,2), 2,3), (3,4)	2. Directly given in the function (1,1), (2,4), (3,9), (4,16)

We can install Matplotlib using Pip. Pip is a management software for installing Python packages.

(i) Line plot
(ii) Scatter plot
(iii) Histogram
(iv) Box plot
(v) Bar chart and
(vi) Pie chart

(i) Charts
(ii) Tables
(iii) Graphs
(iv) Maps
(v) Infographics
(vi) Dashboards

Data Visualization is the graphical representation of information and data. The objective of Data Visualization is to communicate information visually to users.

(i) Labels ⇾ Specifies labels for the bars.
(ii) Usgae ⇾ Assign values to the labels specified.
(iii) Xticks ⇾ Display the tick marks along the x-axis at the values represented. Then specify the label for each tick mark.
(iv) Range ⇾ Create sequence of numbers.
(v) Show ⇾ Displays the plot

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:

Program:
import matplotlib.pyplot as plt
pIt.plot([1, 2, 3, 4]) ,
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 ( )

S.No	Histogram	Bar graph
i)	Histogram refers to a graphical representation; that displays data by way of bars to show the frequency of numerical data.	A bar graph is a pictorial representation of data that uses bars to compare different categories of data.
ii)	A histogram represents the frequency distribution of continuous variables.	Conversely, a bar graph is a diagrammatic comparison of discrete variables.
iii)	Histogram presents numerical data	Bar graph shows categorical data
iv)	Items of the histogram are numbers, which are categorised together, to represent ranges of data.	As opposed to the bar graph, items are considered as individual entities.
v)	A histogram, this cannot be done, as they are shown in the sequence of classes.	In the case of a bar graph, it is quite common to rearrange the blocks, from highest to lowest.
vi)	The width of rectangular blocks in a histogram mayor may not be same	The width of the bars in a bar graph is always same.

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.

(i) Home Button \(\rightarrow \) The Home Button will help one to begun navigating the chart. If you ever want to return back to the original view, you can click on this.
(ii) Forward/Back buttons \(\rightarrow \) These buttons can be used like the Forward and Back buttons in browser. Click these to move back to the previous point you were at, or forward again.
(iii) Pan Axis \(\rightarrow \) This cross-looking button allows you to click it, and then click and drag graph around.
(iv) Zoom \(\rightarrow \) The Zoom button lets you click on it, then click and drag a square would like to zoom into specifically. Zooming in will require a left click and drag. Zoom out with a right click and drag.
(v) Configure Subplots\(\rightarrow \) This button allows you to configure various spacing options with figure and plot.
(vi) Save Figure \(\rightarrow \)This button will allow you to save figure in various forms

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: