Informatics Practices: Class 12 — Chapter 4: Plotting Data using Matplotlib

Install Matplotlib via pip:

  
  pip install matplotlib

Import the pyplot module with an alias:

  
  import matplotlib.pyplot as plt

To plot $x$ versus $y$, use plt.plot(x, y) . To display the plot, use plt.show() .

  
    import matplotlib.pyplot as plt
    date = ["25/12", "26/12", "27/12"]
    temp = [8.5, 10.5, 6.8]
    plt.plot(date, temp)
    plt.show()
  

Table 4.1: List of Pyplot functions to plot different charts

Plots can be styled using markers, custom colors, linewidths, and linestyles.

Table 4.3: Some of the Matplotlib Markers

Table 4.4: Colour Abbreviations

Linewidth and Linestyle: Linewidth is specified in pixels (default is 1). Linestyle can take values like "solid" , "dotted" , "dashed" , or "dashdot" .

Line charts are used to show continuous datasets and visualise growth or decline over time.

  
    import pandas as pd
    import matplotlib.pyplot as plt
    df = pd.read_csv("MelaSales.csv")
    df.plot(kind='line', color=['red', 'blue', 'brown'])
    plt.title('Mela Sales Report')
    plt.xlabel('Days')
    plt.ylabel('Sales in Rs')
    plt.show()
  

Bar charts are preferred for comparing discrete categories. Unlike line plots, bar charts can plot strings directly on the x-axis.

  
    df.plot(kind='bar', x='Day', title='Mela Sales Report')
    plt.ylabel('Sales in Rs')
    plt.show()
  

Customising bar charts with colors, edgecolors, linewidths, and linestyles:

  
  df.plot(kind='bar', x='Day', title='Mela Sales Report', color=['red', 'yellow', 'purple'], edgecolor='Green', linewidth=2, linestyle='--')

Histograms represent the frequency distribution of continuous numerical data by grouping values into intervals called bins .

  
    df.plot(kind='hist')
    plt.show()
  

Customising histograms with custom bins, edgecolors, hatch patterns, and fill properties:

  
  df.plot(kind='hist', edgecolor='Green', linewidth=2, linestyle=':', fill=False, hatch='o')

Scatter charts use dots to represent values for two different variables to show their relationship or correlation.

  
  plt.scatter(x=discount, y=saleInRs, s=size, color='red', linewidth=3, marker='*', edgecolor='blue')

A box plot visually represents the statistical summary of a dataset: Minimum, Quartile 1 (Q1), Median (Q2), Quartile 3 (Q3), and Maximum. It also highlights outliers (data points numerically distant from the rest of the data).

  
    df.plot(kind='box')
    plt.title('Performance Analysis')
    plt.xlabel('Subjects')
    plt.ylabel('Marks')
    plt.show()
  

To plot a horizontal box plot, set vert=False :

  
  df.plot(kind='box', title='Compare Resorts', color='red', vert=False)

A pie chart represents numerical data proportionally as sectors of a circle. To plot a pie chart, either the column label y or subplots=True must be specified.

  
  df.plot(kind='pie', y='ForestCover', title='Forest cover of North Eastern states', legend=False, explode=exp, autopct="%.2f", colors=c)