reading-notes

Matplotlib:

• Matplotlib is a versatile library for creating static, animated, and interactive visualizations in Python.
• It provides a low-level interface and allows fine-grained control over the appearance of the plots.
• Matplotlib is a good choice for creating basic plots, such as line plots, scatter plots, bar plots, histograms, etc.
• Example: Creating a line plot to visualize the trend of a time series data.

  Seaborn:

• Seaborn is a higher-level library built on top of Matplotlib that simplifies the process of creating attractive statistical visualizations.
• It provides a wide range of built-in themes and color palettes to enhance the aesthetics of the plots.
• Seaborn is particularly useful for statistical data exploration and visualization.
• Example: Creating a box plot to compare the distribution of a variable across different categories.

  Bokeh:

• Bokeh is a library for creating interactive visualizations that are suitable for web browsers.
• It focuses on producing interactive and interactive-ready visualizations for web-based applications.
• Bokeh allows the creation of interactive plots, dashboards, and applications with features like zooming, panning, and tooltips.
• Example: Creating an interactive scatter plot with tooltips

to explore the relationship between two variables. In Seaborn, the main functions to create different types of plots are:

1. Relational plots:

• Functions like scatterplot(), lineplot(), and relplot() are used to create relational plots.
• Relational plots show the relationship between two variables and are often used to explore correlations and patterns.
• Example use case: Creating a scatter plot to visualize the relationship between the sepal length and width in the Iris dataset.

  2. Categorical plots:

• Functions like barplot(), boxplot(), violinplot(), and countplot() are used to create categorical plots.
• Categorical plots are used to display the distribution of categorical variables or to compare different categories.
• Example use case: Creating a bar plot to compare the average sales of different products in a store.

  3. Distribution plots:

• Functions like histplot(), kdeplot(), and distplot() are used to create distribution plots.
• Distribution plots show the distribution of a single variable or the comparison between distributions.
• Example use case: Creating a histogram to visualize the distribution of student test scores.
• The Seaborn Cheat Sheet is a quick reference guide that provides a summary of Seaborn’s functionalities and syntax. It can be a helpful resource for Python developers working with Seaborn.

The cheat sheet includes key sections such as:

• Importing Seaborn and data loading.
• Setting aesthetics: Changing default themes, color palettes, and figure styles.
• Plotting with categorical data: Examples and syntax for various categorical plots.
• Plotting with relational data: Examples and syntax for various relational plots.
• Plotting with distribution data: Examples and syntax for various distribution plots.
• Controlling plot aesthetics: Customizing colors, markers, and other plot attributes.
• Plotting with linear regression models: Visualizing linear relationships between variables.
• Controlling figure aesthetics: Modifying axes, titles, legends, and other figure elements.