This document explains how to read files in Python using the open function file objects, reading methods, and best practices for file handling and data extraction.
This document explains how to write to files in Python using the open function, file objects, writing methods, appending, and best practices for file creation and data output.
This document introduces the Pandas library for data analysis, covering its import, usage for reading files, creating DataFrames, and accessing data efficiently. Key concepts include working with CSV and Excel files, DataFrame operations, and indexing methods.
This document explains how to analyze, filter, and save data using Pandas focusing on finding unique values, filtering rows by conditions, and exporting results to CSV and other formats.
This document introduces Numpy for scientific computing, covering array creation, indexing, slicing, vector operations, universal functions, and plotting. Key concepts include speed, memory efficiency, and practical data science applications.
This document explains how to create, index, slice, and perform operations on 2D Numpy arrays, including matrix addition, scalar multiplication, Hadamard product, and matrix multiplication.
This document provides a concise overview of Python file handling, Pandas for data analysis, and Numpy for numerical operations, summarizing their practical applications and key concepts for effective Python data science development.