Pandas: Tabular Data Analysis

Pandas is built on top of NumPy. While NumPy is excellent for matrices of numbers, real-world data is messy, tabular (like Excel), and contains mixed data types (text, dates, numbers). Pandas provides the DataFrame object to handle this complexity.

Core Concepts

1. DataFrames & Series

A Series is a single column of data. A DataFrame is a collection of Series sharing an index (like a database table).

2. Handling Missing Data (Imputation)

Real datasets are rarely complete. Pandas provides methods like .isna(), .fillna(), and .dropna() to clean datasets before feeding them to ML models (which cannot process NaN values).

3. GroupBy & Aggregation

To engineer new features (e.g., getting a customer's total historical spend), we use the split-apply-combine strategy via .groupby(). We split the data by a key (CustomerID), apply a mathematical function (sum), and combine the results back into a new DataFrame.

4. Joining & Merging

Machine learning datasets are often assembled from multiple sources. Pandas provides SQL-like .merge() capabilities (Inner, Left, Right, Outer joins) to stitch relational data together via common keys.

How to execute the examples:

Go to the Examples/ folder and run the scripts using Python: python Pandas_DataCleaning.py python Pandas_Groupby.py python Pandas_Joins.py