Pandas Introduction

Study Notes

Pandas is a Python library for data manipulation and analysis, offering powerful features for working with structured data.

Data Structures:
- Series: One-dimensional labeled array holding values.
- DataFrame: Two-dimensional labeled data structure with columns of varying types.
Data Manipulation: Pandas provides functions for filtering, sorting, grouping, merging, and reshaping data.
Data Analysis: It integrates well with popular libraries like NumPy, Matplotlib, and Scikit-learn for further analysis.
Data Input/Output: Pandas supports reading and writing data from various file formats like CSV, Excel, JSON, and SQL databases.

A Series is like a column in a spreadsheet, with a label for each value.
Example: s = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e']) creates a Series with values 1 to 5 and labels a to e.

A DataFrame is a table-like structure, similar to a spreadsheet, where each column can have different data types.
Example: df = pd.DataFrame({'Name': ['John', 'Mary', 'David'], 'Age': [25, 31, 42]}) creates a DataFrame with two columns: Name and Age.

Filtering: Selecting rows based on a condition, e.g., df[df['Age'] > 30] filters for rows where Age is greater than 30.
Sorting: Ordering rows according to a specific column, e.g., df.sort_values(by='Age') sorts the DataFrame by the Age column.
Grouping: Combining rows based on a common value, e.g., df.groupby('Name') groups the data by Name.
Merging: Combining data from multiple DataFrames based on a common column, e.g., pd.merge(df1, df2, on='Name') merges two DataFrames based on the Name column.

Data Science: Data cleaning, transformation, and analysis.
Business Intelligence: Reporting, dashboards, and data insights.
Web Scraping: Extracting data from websites and structuring it.
Data Visualization: Creating charts and graphs for presenting data visually.
Machine Learning: Preparing and manipulating data for machine learning models.