Python Pandas Basics

Study Notes

Pandas is a popular Python library used for data manipulation and analysis.
It provides data structures and functions to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables.

Series: A one-dimensional labeled array of values, similar to a column in a spreadsheet.
- Can be thought of as a single column of a DataFrame.
DataFrame: A two-dimensional labeled data structure with columns of potentially different types.
- Can be thought of as a spreadsheet or a table in a relational database.

Loading and Saving Data: Pandas can read and write data in various formats, including CSV, Excel, and SQL databases.
Data Cleaning and Preprocessing: Pandas provides functions for handling missing data, data normalization, and data transformation.
Data Filtering and Sorting: Pandas allows for easy filtering and sorting of data based on various conditions.
Data Grouping and Aggregation: Pandas provides functions for grouping data and performing aggregation operations, such as sum, mean, and count.

Data Merging and Joining: Pandas allows for merging and joining data from different DataFrames based on common columns.
Data Reshaping and Pivot Tables: Pandas provides functions for reshaping data and creating pivot tables.
Data Visualization: Pandas integrates well with data visualization libraries, such as Matplotlib and Seaborn, for data visualization.

Multi-Indexing and Hierarchical Indexing: Pandas allows for creating and manipulating complex index structures.
Rolling and Expanding Windows: Pandas provides functions for performing rolling and expanding window calculations.
Resampling and Time Series Analysis: Pandas provides functions for resampling and performing time series analysis.

Use Vectorized Operations: Pandas provides optimized vectorized operations for performance.
Use Appropriate Data Types: Using appropriate data types can improve performance and reduce memory usage.
Use Efficient Data Structures: Using efficient data structures, such as DataFrames, can improve performance and scalability.

Pandas is a widely-used Python library for data manipulation and analysis.
It handles structured data efficiently, catering to formats like spreadsheets and SQL tables.

Series: A one-dimensional labeled array, analogous to a single column in a DataFrame.
DataFrame: A two-dimensional labeled structure, comparable to a spreadsheet or a SQL database table, with columns of various data types.

Loading and Saving Data: Capable of reading and writing datasets in formats such as CSV, Excel, and SQL.
Data Cleaning and Preprocessing: Offers functions for managing missing values, normalizing data, and transforming datasets.
Data Filtering and Sorting: Facilitates easy data filtration and sorting based on specified conditions.
Data Grouping and Aggregation: Supports grouping datasets and performing aggregation functions like sum, mean, and count.

Data Merging and Joining: Enables integration of DataFrames based on shared columns.
Data Reshaping and Pivot Tables: Provides functionality for reshaping data and generating pivot tables.
Data Visualization: Seamlessly integrates with libraries like Matplotlib and Seaborn for effective data visualization.

Multi-Indexing and Hierarchical Indexing: Accommodates the creation and management of complex index structures.
Rolling and Expanding Windows: Includes functions for conducting rolling and expanding window calculations.
Resampling and Time Series Analysis: Offers tools for data resampling and executing time series analyses.

Use Vectorized Operations: Optimizes performance through the use of vectorized operations.
Use Appropriate Data Types: Enhances performance and minimizes memory usage with suitable data types.
Use Efficient Data Structures: Improves performance and scalability by employing efficient structures like DataFrames.