Lecture# 21 Managing Charts of Line, Bars, and Pie using Python Matplotlib with NumPy in Urdu/ Hindi
Managing Charts of Line, Bars, and Pie using Python Matplotlib with NumPy
Python's Matplotlib library, combined with NumPy, provides powerful tools for creating and managing various types of charts. Here's a short description of how to manage line, bar, and pie charts using these libraries:
Line Charts:
Line charts are used to display data points connected by straight lines, making them ideal for visualizing trends over time. Using Matplotlib, you can create line charts by plotting NumPy arrays. This involves setting up your figure, plotting the data with the plot() function, and customizing the appearance with titles, labels, and legends.
Bar Charts:
Bar charts represent categorical data with rectangular bars. Matplotlib's bar() function allows you to create both vertical and horizontal bar charts using data from NumPy arrays. You can adjust bar width, color, and add labels to make the chart more informative.
Pie Charts:
Pie charts are used to show proportions of a whole. With Matplotlib, the pie() function helps you create pie charts from NumPy arrays. You can customize the chart by adding labels, setting explode values to emphasize certain slices, and adjusting colors.
Example:
python
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import matplotlib.pyplot as plt
import numpy as np
Data for plotting
x = np.arange(1, 11)
y = np.random.randint(1, 10, size=10)
sizes = np.array([15, 30, 45, 10])
Line Chart
plt.figure(figsize=(12, 4))
plt.subplot(1, 3, 1)
plt.plot(x, y, marker='o')
plt.title('Line Chart')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
Bar Chart
plt.subplot(1, 3, 2)
plt.bar(x, y, color='orange')
plt.title('Bar Chart')
plt.xlabel('Categories')
plt.ylabel('Values')
Pie Chart
plt.subplot(1, 3, 3)
plt.pie(sizes, labels=['A', 'B', 'C', 'D'], autopct='%1.1f%%', startangle=140)
plt.title('Pie Chart')
plt.tight_layout()
plt.show()
This example demonstrates how to create and manage line, bar, and pie charts using Matplotlib and NumPy, allowing you to visualize data effectively.