Pandas and MatPlotLib

matplotlib.pyplot, often shortened to plt, is a submodule within the matplotlib library for Python. It provides a state-based, MATLAB-like interface for creating various data visualizations.

Think of it as a toolset that allows you to take your numerical data and turn it into informative and visually appealing charts, graphs, and plots.

Here are some key features of matplotlib.pyplot:

• Simple and easy to use: pyplot offers a concise and intuitive syntax, making it accessible even for beginners in data visualization.

• Extensive plotting capabilities: It supports a wide range of plot types, including line plots, bar charts, scatter plots, histograms, pie charts, and many more.

• Customization options: You can fine-tune the visual elements of your plots, including colors, fonts, axes labels, and legends, to create professional-looking visualizations.

• Object-oriented approach: While pyplot is state-based, it also offers object-oriented functionalities for more advanced customization and control over your plots.

Sample Data

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
Dept1	10	11	12	13	14	15	16	17	18	19	20	21
Dept2	20	21	22	23	24	25	26	27	28	29	30	31
Dept3	30	31	32	33	34	35	36	37	38	39	40	41
Dept4	40	40	40	40	50	50	50	50	60	60	60	60
Dept5	50	51	52	53	54	55	56	57	58	59	60	61

Lecture Code

# -*- coding: utf-8 -*-
"""
Created on Fri Nov 6 11:27:09 2020

@author: jgoudy

This script demonstrates various ways to analyze and visualize data
stored in a pandas DataFrame.

"""

import numpy as np
import pandas as pd
import sys

import matplotlib.pyplot as plt


def Example1():
    """
    This function analyzes and visualizes data in a pandas DataFrame.

    """

    # Create a NumPy array with sample data.
    arr = np.array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
                   [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
                   [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41],
                   [40, 40, 40, 40, 50, 50, 50, 50, 60, 60, 60, 60],
                   [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]])

    # Create a pandas DataFrame from the NumPy array.
    df = pd.DataFrame(arr,
                      index=['Dept1', 'Dept2', 'Dept3', 'Dept4', 'Dept5'],
                      columns=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 
                               'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])

    # Print descriptive statistics of the DataFrame.
    print("*** df.describe ***")
    print(df.describe())
    print()

    # Print the entire DataFrame.
    print("*** df ***")
    print(df)
    print()

    # Print the first 2 and last 2 rows of the DataFrame.
    print("*** df.head(2) ***")
    print(df.head(2))
    print()
    print("*** df.tail(2) ***")
    print(df.tail(2))
    print()

    # Access specific columns.
    print("*** df['Mar'] ***")
    print(df['Mar'])
    print()

    # Access specific columns and rows.
    print("*** df.loc[:,['Jan','Feb','Mar']] ***")
    print(df.loc[:, ['Jan', 'Feb', 'Mar']])
    print()

    # Access specific row and columns.
    print(df.loc['Dept1', ['Jan', 'Feb', 'Mar']])
    print()
    print((df.loc['Dept3']))
    print()

    # Access multiple rows.
    print((df.loc[['Dept1', 'Dept3']]))
    print()

    # Access specific element by position.
    print("*** select by position ***")
    print((df.iloc[1][1]))
    print()

    # Modify element by position.
    df.iloc[1][1] = 19

    # Get the number of rows and columns.
    print(df.index.size)
    print(df.columns.size)
    print()

    # Iterate through all elements and print them.
    for r in range(df.index.size):
        for c in range(df.columns.size):
            sys.stdout.write(str(df.iloc[r][c]) + " ")
        print()

    # Print the DataFrame transposed.
    print("*** Transposed DataFrame ***")
    print(df.T)
    print()

    # Verify the original DataFrame is unchanged.
    print("*** Original DataFrame ***")
    print(df)
    print()

    # Print the row and column indexes.
    print("*** Row indexes ***")
    print(df.index)
    print()
    print("*** Column indexes ***")
    print(df.columns)
    print()
    
    # Start a new figure for plotting.
    # plt.figure()

    # Create a transposed DataFrame for easier bar graph plotting.
    dft = df.T

    # Plot the bar graph from the transposed DataFrame.
    dft.plot(kind='bar')

    # Add labels and title to the graph.
    plt.xlabel('Months')
    plt.ylabel('Units')
    plt.title(label='Graph 1', loc='center')

    # Display the first graph.
    plt.show()

    # Extract data for Department 1 from the transposed DataFrame.
    dft1 = dft['Dept1']

    # Plot a bar graph for Department 1 data.
    dft1.plot()

    # Add labels and title to the graph.
    plt.xlabel('Months')
    plt.ylabel('Units')
    plt.title(label='Graph 2', loc='center')

    # Display the second graph.
    plt.show()

    # Extract data for Departments 1 and 2 from the transposed DataFrame.
    dft1 = dft['Dept1']
    dft2 = dft['Dept2']

    # Plot a bar graph for both Department 1 and 2 data.
    dft1.plot()
    dft2.plot()

    # Add labels and title to the graph.
    plt.xlabel('Months')
    plt.ylabel('Units')
    plt.title(label='Graph 3', loc='center')

    # Display the third graph.
    plt.show()

    # Note: The following graphs have similar patterns with different data combinations.

    # Extract data for Departments 1 and 3.
    dft1 = dft['Dept1']
    dft3 = dft.Dept3

    # Plot a bar graph for Department 1 with stacking and yellow color.
    dft1.plot(kind='bar', stacked=True, color="yellow")

    # Plot Department 2 without stacking.
    dft2.plot()

    # Plot Department 3 with stacking and green color.
    dft3.plot(kind='bar', stacked=True, color="green")

    # Add labels and title to the graph.
    plt.xlabel('Months')
    plt.ylabel('Units')
    plt.title(label='Graph 4', loc='center')

    # Display the sixth graph.
    plt.show()

    # Additional graphs can be created following the same pattern 
    # with different data combinations and plot options.

    # Close all open figures to avoid clutter.
    plt.close('all')
    
def main():

    # This function serves as the entry point for the script.
    # It calls the `Example1` function to analyze and visualize the data.


    # Clear the terminal using platform-specific methods (may not work everywhere).
    # _ = os.system('cls')
    # ipx.get_ipython().magic('clear')
    # ipx.get_ipython().magic('reset -f')

    # Run the analysis and visualization function.
    Example1()

    # end of program
    print("\nbye\n\n")

if __name__ == "__main__":
    # Only run the `main` function if the script is executed directly.
    # This prevents it from running when imported as a module.
    main()

    

Graphs