(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: