Python Commands for Data Visualization

Python provides several powerful libraries for data visualization. Here are some commonly used Python libraries along with example commands to perform data visualization:

1. Matplotlib: Matplotlib is a versatile plotting library that provides a wide range of visualization options.

   import matplotlib.pyplot as plt

   # Line plot

   x = [1, 2, 3, 4, 5]

   y = [1, 4, 9, 16, 25]

   plt.plot(x, y)

   plt.xlabel('X-axis')

   plt.ylabel('Y-axis')

   plt.title('Line Plot')

   plt.show()

 

  # Bar plot

   labels = ['A', 'B', 'C']

   values = [10, 15, 7]

   plt.bar(labels, values)

   plt.xlabel('Categories')

   plt.ylabel('Values')

   plt.title('Bar Plot')

   plt.show()

  2. Seaborn: Seaborn is a statistical data visualization library built on top of Matplotlib. It provides a high-level interface for creating attractive and informative visualizations.

   import seaborn as sns

   # Scatter plot

   tips = sns.load_dataset('tips')

   sns.scatterplot(data=tips, x='total_bill', y='tip', hue='smoker')

   plt.xlabel('Total Bill')

   plt.ylabel('Tip')

   plt.title('Scatter Plot')

   plt.show()

  

 

 # Box plot

   sns.boxplot(data=tips, x='day', y='total_bill')

   plt.xlabel('Day')

   plt.ylabel('Total Bill')

   plt.title('Box Plot')

   plt.show()

3. Plotly: Plotly is an interactive plotting library that allows you to create interactive and dynamic visualizations.

   import plotly.graph_objects as go

   # Scatter plot

   fig = go.Figure(data=go.Scatter(x=[1, 2, 3, 4, 5], y=[1, 4, 9, 16, 25]))

   fig.update_layout(title='Scatter Plot', xaxis_title='X-axis', yaxis_title='Y-axis')

   fig.show()

 # Heatmap

   z = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

   fig = go.Figure(data=go.Heatmap(z=z))

   fig.update_layout(title='Heatmap')

   fig.show()

 

4. Pandas: Pandas is a powerful data analysis library that includes built-in visualization capabilities.

   import pandas as pd

   # Line plot

   df = pd.DataFrame({'x': [1, 2, 3, 4, 5], 'y': [1, 4, 9, 16, 25]})

   df.plot(x='x', y='y', kind='line')

   plt.xlabel('X-axis')

   plt.ylabel('Y-axis')

   plt.title('Line Plot')

   plt.show()

   

# Histogram

   df.plot(kind='hist');

   plt.xlabel('Values')

   plt.ylabel('Frequency')

   plt.title('Histogram')

   plt.show()

These are just a few examples of the vast possibilities for data visualization in Python. Each library offers a wide range of customization options, so you can tailor your visualizations to your specific needs.