Pandas - Plotting

Certainly! Pandas, a popular Python library for data manipulation and analysis, provides convenient ways to plot data directly from DataFrames and Series. Here's a basic guide on how to use Pandas for plotting:

1. Import Necessary Libraries: Import pandas and matplotlib, which pandas uses for plotting.

          import pandas as pd
          import matplotlib.pyplot as plt
        



2. Create or Load Data: Load your data into a Pandas DataFrame.

          # Example: Creating a DataFrame
          data = {
              'Year': [2010, 2011, 2012, 2013, 2014],
              'Sales': [100, 150, 200, 250, 300]
          }
          df = pd.DataFrame(data)
         



3. Basic Plotting: Use the plot() method directly on the DataFrame or Series object.

           

          # Plotting Sales over the Years
          df.plot(x='Year', y='Sales', kind='line', title='Sales Over Years')
          plt.show()
         



4. Customizing Plots: You can customize your plots by using additional parameters.

          # Customizing the Plot
          df.plot(x='Year', y='Sales', kind='bar', color='green', title='Sales Over Years', legend=False)
          plt.xlabel('Year')
          plt.ylabel('Sales (in units)')
          plt.xticks(rotation=45)
          plt.grid(axis='y', linestyle='--', alpha=0.7)
          plt.show()
        



5. Plotting Multiple Columns: You can plot multiple columns in the same plot.

          # Plotting Multiple Columns
          df.plot(x='Year', y=['Sales', 'Expenses'], kind='line', title='Sales and Expenses Over Years')
          plt.show()
        




These are just some basic examples of what you can do with Pandas plotting. You can explore further by checking out the Pandas documentation for more options and functionalities!