NumPy and Pandas for Data Analysis and Visualization
This project demonstrates the use of NumPy and Pandas libraries in Python for data analysis and visualization.
NumPy: The project covers creating and manipulating NumPy arrays, including array creation, data types, dimensions, and array operations. It also demonstrates the use of functions like arange, concatenate, ndmin, and nditer for array manipulation and iteration. Additional features like copying arrays, changing values, sorting, and searching are explored.
Pandas: The project uses Pandas to import and work with datasets, showcasing functions such as head, tail, and info for dataset exploration. Data cleaning techniques are demonstrated, including handling missing values using dropna and fillna, and removing duplicates with drop_duplicates. Data manipulation is illustrated by replacing values in a dataset using the replace function.
Matplotlib: The project incorporates Matplotlib for data visualization. It shows how to create plots, add markers, labels, titles, and customize plot elements using functions such as plot, xlabel, ylabel, title, and more. Examples of line plots and bar plots are provided, along with customizations like line style and width.
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