We can create bar charts using Matplotlib. A bar chart represents values as vertical bars, where the position of each bar indicates the value it represents. Matplotlib aims to make the transformation of data into bar charts as easy as possible.

Matplotlib Vertical Bar Graph

import matplotlib.pyplot as plt

x = ['Python', 'C++', 'Java', 'Dart', 'C#', 'JavaScript']
y = [51, 62, 63, 54, 65,45]

plt.bar(x, y, align='center', alpha=0.5)
plt.title("Bar Chart", fontdict={'family': 'monospace', 'color': 'red', 'weight': 'bold', 'size': 16}, loc='center')
plt.xlabel('Programming Languages')
plt.ylabel('Scores')
plt.show()

Matplotlib Horizontal Bar Chart

import matplotlib.pyplot as plt

x = ['Python', 'C++', 'Java', 'Dart', 'C#', 'JavaScript']
y = [51, 62, 63, 54, 65, 65]

plt.barh(x, y, align='center', alpha=0.5)
plt.title("Bar Chart", fontdict={'family': 'monospace', 'color': 'red', 'weight': 'bold', 'size': 16}, loc='center')
plt.xlabel('Programming Languages')
plt.ylabel('Scores')
plt.show()

Bar Graph Comparison in Matplotlib

import numpy as np
import matplotlib.pyplot as plt

# Data to plot
n = 4
A_values = [800, 655, 540, 265]
B_values = [950, 562, 454, 620]

# Create plot
fig, ax = plt.subplots()
index = np.arange(n)
bar_width = 0.35
opacity = 0.8

bar_A = plt.bar(index, A_values, bar_width, alpha=opacity, color='r', label='Angola')
bar_B = plt.bar(index + bar_width, B_values, bar_width, alpha=opacity, color='y', label='Brazil')

plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Statistics by Country')
plt.xticks(index + bar_width / 2, ('A', 'B', 'C', 'D'))
plt.legend()
plt.show()

Stacked Bar Graph in Matplotlib

import matplotlib.pyplot as plt

# Data to plot
x = ['A', 'B', 'C', 'D']
y1 = [2100, 1120, 1110, 2130]
y2 = [1120, 1125, 1115, 1125]

# Plot stacked bar graph
plt.bar(x, y1, color='r')
plt.bar(x, y2, bottom=y1, color='y')
plt.title("Stacked Bar Chart")
plt.xlabel("Categories")
plt.ylabel("Values")
plt.show()

This guide provides a clear and structured way to create bar charts using Matplotlib. You can modify these examples to fit your specific data visualization needs.