Module 3 Data Analysis – Visualization Using Python

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Data analysis and visualization are important steps in the data science workflow. Python provides several libraries that are widely used for data analysis and visualization, including:


Pandas: Pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames that allow you to easily handle and analyze tabular data. You can read data from various sources, clean and preprocess the data, perform aggregations, and more.


Matplotlib: Matplotlib is a popular plotting library in Python. It provides a wide range of customizable plots, including line plots, scatter plots, bar plots, histograms, and more. Matplotlib allows you to create publication-quality visualizations and offers fine-grained control over plot elements.


Seaborn: Seaborn is a statistical data visualization library that is built on top of Matplotlib. It provides a higher-level interface for creating attractive and informative statistical graphics. Seaborn simplifies the process of creating complex visualizations and provides options for visualizing relationships in data, such as scatter plots, box plots, violin plots, and heatmaps.


Plotly: Plotly is a library that offers interactive and web-based visualizations. It provides a wide range of chart types and allows you to create interactive plots that can be embedded in web applications or shared online. Plotly supports offline plotting as well as integration with Jupyter Notebook.


Bokeh: Bokeh is another library for creating interactive visualizations. It focuses on providing interactive and interactive plots for modern web browsers. Bokeh supports a wide range of visualizations, including line plots, scatter plots, bar plots, and geographical maps.