Notes on Data Visualization with Matplotlib

Purpose of Matplotlib Library

• Matplotlib: A Python library for creating 2D graphics, primarily used to generate graphs and charts.
• Modules: Contains different modules, the most popular being Pyplot.

Common Chart Functions

Chart Types and Corresponding Functions

• Line Chart: plt.plot()
• Bar Chart: plt.bar()
• Horizontal Bar Chart: plt.barh()

Example: Bar Chart of Students

• Sample Data Structures:
  • Create lists for classes and corresponding number of students.
  • Example:

    import matplotlib.pyplot as plt
    classes = [6, 7, 8, 9, 10]  # X-axis
    strengths = [30, 25, 20, 35, 40]  # Y-axis
    plt.bar(classes, strengths)
    plt.show()
    

Adding Titles

• To add a title to a chart: plt.title('Your Title Here')

Histogram and Data Counting

• Histogram: Used to count how many values fall within each interval. Ideal for continuous data.

Customization of Charts

• Common customization methods: plt.xlabel(), plt.ylabel(), plt.title(), plt.legend().
• Misconception: No method named color for customization.

Example: Line Chart for Temperature

• For depicting changing temperatures over weeks:

  import matplotlib.pyplot as plt
  weeks = [1, 2, 3, 4]
  avg_temp = [30, 35, 33, 31]
  plt.plot(weeks, avg_temp)
  plt.show()
  

Legend Placement

• The default position of the legend is the upper right corner. Specify location using: plt.legend(loc='upper left') or by using numbers.

General Overview of Python’s Data Visualization

• Python's most popular library for data visualization is Matplotlib.
• Use of libraries facilitates effective data representation.

Example: Code Representation of a Graph

• When constructing specific graphs, remember to align x and y values properly.

Common Issues in Code Implementation

• Error reason: Shapes of data lists (x and y) must match for plotting. Mismatched lengths cause errors.

Using Legends in Charts

• Legends help identify different data series in a graph.

Example of Data Comparison with Column Chart

• E.g., comparing students' scores in a class can be effectively shown using a column chart.

Final Programming Task Example (Cricket Team Scores)

• Create a horizontal bar chart using scores from matches.

  import matplotlib.pyplot as plt
  matches = ['Match 1', 'Match 2', 'Match 3', 'Match 4']
  scores = [270, 230, 150, 190]
  plt.barh(matches, scores)
  plt.xlabel('Runs')
  plt.legend(loc='4')
  plt.show()
  

Conclusion and Tips

• Best Practices: Always include plt.show() after plotting to display the chart.
• The command to install Matplotlib: pip install matplotlib.
• If a filename extension is not provided when saving a figure, it defaults to .jpg.

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Essential to keep practicing and learning concepts related to data visualization.