Data Visualization with Python

Data visualization is a critical aspect of data science that helps in understanding complex data through graphical representation. Python offers several libraries that facilitate effective data visualization.

Commonly used libraries for data visualization in Python include:

• Matplotlib
• Seaborn
• Plotly
• Bokeh
• Altair

You can install the required libraries using pip. For example:

pip install matplotlib seaborn plotly bokeh altair

4.1 Line Plot

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.title('Sine Wave')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.grid()
plt.show()

4.2 Bar Plot

categories = ['A', 'B', 'C']
values = [3, 7, 5]

plt.bar(categories, values)
plt.title('Bar Plot Example')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.show()

5.1 Heatmap with Seaborn

import seaborn as sns
import pandas as pd

data = pd.DataFrame(np.random.rand(10, 12), columns=[f'Month {i}' for i in range(1, 13)])
sns.heatmap(data, annot=True)
plt.title('Heatmap Example')
plt.show()

To create effective visualizations, consider the following:

• Use appropriate chart types for your data.
• Maintain simplicity and readability.
• Label axes and include legends.
• Choose color schemes wisely for clarity.
• Test visualizations on different audiences.