Easy Notes of Data Analytics unit- 5 @Computer Diploma

Description

5.1 Matplotlib Overview & Setup

Matplotlib is the “grandfather” library of Python visualization, built to resemble MATLAB’s plotting capabilities.

• Pyplot: The core module (import matplotlib.pyplot as plt) providing a state-machine interface for plotting.

• Backend: The engine that renders the plots (e.g., Inline for Jupyter, GUI for desktop).

• Installation: Handled via package managers using pip install matplotlib or conda install matplotlib.

• Figure & Axes: The “Figure” is the entire window/page; the “Axes” is the specific area where the data is plotted.

5.2 Basic Plotting & Customization

Creating the primary chart types and adding essential metadata.

• Line Plot: plt.plot() — Used for trends over time.

• Scatter Plot: plt.scatter() — Used to show correlation between two variables.

• Bar Chart: plt.bar() (vertical) or plt.barh() (horizontal) — Used for categorical comparisons.

• Histogram: plt.hist() — Used to show frequency distribution; requires “bins.”

• Metadata:

  • plt.title(): The main heading.

  • plt.xlabel() / plt.ylabel(): Identifying the X and Y axes.

  • plt.legend(): A key to identify different data series (requires a label argument in the plot function).

5.3 Figure Control & Axis Customization

Fine-tuning the layout and scale of the visualization.

• Figure Size: plt.figure(figsize=(w, h)) — Defining the width and height in inches.

• DPI: Dots Per Inch; controls the “sharpness” or resolution of the figure.

• Axis Limits: plt.xlim() and plt.ylim() — Setting the minimum and maximum range of the axes.

• Ticks: plt.xticks() and plt.yticks() — Customizing the markers/numbers on the axes (e.g., rotating text or changing intervals).

5.4 Exporting & Interactivity

Saving your work and making it dynamic.

• Saving Plots: plt.savefig('filename.ext').

• File Formats: * Raster: PNG, JPG (good for web).

  • Vector: PDF, SVG (scalable without losing quality).

• Quality Control: Using the dpi parameter within savefig() for high-resolution print output.

• Matplotlib Widgets: Tools like Sliders and Buttons that allow users to interact with the data (e.g., changing a frequency or filtering data points) without rerunning code.

Core Syntax Summary Table