Python libraries

 #Here's a simple Python program that uses the `matplotlib` library to create a pie chart for the sales distribution of different fruits in a store:

import matplotlib.pyplot as plt


# Sample data

fruits = ['Apples', 'Bananas', 'Cherries', 'Oranges', 'Strawberries']

sales = [30, 20, 25, 15, 10]


# Create a pie chart

plt.figure(figsize=(8, 8))  # Set the figure size

plt.pie(sales, labels=fruits, autopct='%1.1f%%')

plt.title('Sales Distribution of Different Fruits in a Store')

 

# Show the pie chart

plt.show()

2. Histogram 

#The `hist()` function is used to create a histogram, which is a graphical representation of the distribution of numerical data. It groups the data into bins and counts the number of observations in each bin.


#### Example Code for `hist()`:  


import matplotlib.pyplot as plt

import numpy as np


# Generate random data

data = np.random.randn(1000) # 1000 random numbers from a normal distribution


# Create a histogram

plt.hist(data, bins=30, color='green', alpha=0.7)


# Adding labels and title

plt.xlabel('Value')

plt.ylabel('Frequency')

plt.title('Histogram Example')


# Show the histogram

plt.show()

3. Bar chart

#The `bar()` function is used to create a bar chart, which is a way of representing categorical data with rectangular bars. The height of each bar represents the value of the corresponding category.


#### Example Code for `bar()`:  


import matplotlib.pyplot as plt


# Data for the bar chart

categories = ['A', 'B', 'C', 'D']

values = [3, 7, 5, 2]


# Create a bar chart

plt.bar(categories, values, color='blue')


# Adding labels and title

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Bar Chart Example')


# Show the chart

plt.show()

4. Scatter plot 

#Scatter plots display individual data points and are commonly used to observe relationships or trends between two variables.

#plt.scatter(x, y, color='optional_color', marker='optional_marker')

#• x and y: Coordinates of the points.

#• color: Optional; specifies the color of the points.

#• marker: Optional; defines the style of the markers (e.g., 'o', 'x', '^').

import matplotlib.pyplot as plt 

x = [1, 2, 3, 4, 5]

y = [5, 4, 3, 2, 1]

plt.scatter(x, y, color='green', marker='o')

plt.title("Scatter Plot")

plt.xlabel("X-axis")

plt.ylabel("Y-axis")

plt.show()

5. Line plot 

#Here's a simple Python program that uses Matplotlib to draw a line graph with the specified data and axis labels

import matplotlib.pyplot as plt


# Data

x = [1, 2, 3, 4]

y = [1, 4, 9, 16]


# Create a line graph

plt.plot(x, y, marker='o')


# Adding labels

plt.xlabel('X-axis')

plt.ylabel('Y-axis')


# Adding a title

plt.title('Line Graph Example')


# Show the graph

plt.grid()

plt.show()



### Explanation:

'''- The program uses the `matplotlib.pyplot` library to create a line graph.

- It defines the x and y data points, then plots them using `plt.plot()`.

- The `xlabel()` and `ylabel()` functions are used to label the axes.

- Finally, it displays the graph using `plt.show()`. '

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