Pie chart:
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. 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.pie() function. 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")
pIt.show ( )
Output:

1. Histogram refers to a frequency of graphical representation; that displays data numerical by way of bars to show the data. A bar graph is a pictorial representation of data that uses bars to compare different categories of data.
2. A histogram represents the frequency distribution of continuous variables. Conversely, a bar graph is a diagrammatic comparison of discrete variables.
3. Histogram presents numerical data whereas bar graph shows categorical data.
4. The histogram is drawn in such a way that there is no gap between the bars. On the other hand, there is proper spacing between bars in a bar graph that indicates discontinuity.
5. Items of the histogram are numbers, which a categorize together, to represent ranges of data. As opposed to the bar graph, items are considered as individual entities.
6. In the case of a graph, it is quite common to rearrange the blocks, from highest to lowest. But with histogram, this cannot be done, as they are shown in the sequence of classes.

Bar Chart:
A Bar plot is one of the most common type of plot. It shows the relationship between a numerical variable and a categorical variable.
Bar chart represents categorical data with rectangler bars. Each bar has a height corresponds to the value it represents. The bars can be plotted vertically. 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.
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( )
Ouptut:

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 label for each tick mark.
Range → Create sequence of numbers.

Program:
To plot two lines, use the following code:
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 ( )
Output:
With plt.xlabel and plt.ylabel, we can assign labels to that respective axis, Next, we can assign the plot's title with plt.title, and then we can invoke the default legend with plt.legend( ).

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 ( )