Description: This repository serves as a handy reference for basic but important Pandas functions commonly used in data analysis tasks. Whether you're a beginner exploring data analysis or an experienced practitioner seeking a quick reminder, this repository covers a range of fundamental Pandas functions essential for working with structured data.
Key Features:
- Dataframe Creation: Methods for creating DataFrames from various data sources including lists, dictionaries, CSV files, and databases.
- Data Cleaning: Techniques for handling missing values, duplicate entries, and outliers in datasets.
- Data Manipulation: Functions for selecting, filtering, sorting, and transforming data within DataFrames.
- Statistical Analysis: Tools for computing descriptive statistics, correlation, and other summary metrics.
- Pandas Exercises: Folder containing exercises from the 'AI Programming with Python Nanodegree' offered by Udacity in collaboration with AWS.
Whether you're analyzing data for insights, building machine learning models, or preparing reports, understanding these Pandas functions is crucial for effective data manipulation and analysis.
Feel free to explore the Pandas functions, work on the exercises, and contribute additional functions or exercises to enhance this repository further! Let's empower the data analysis community with essential Pandas knowledge.