Learn PANDAS with Real Code Examples

Pandas enables efficient handling, cleaning, transformation, and analysis of datasets.

It provides flexible data structures like DataFrame and Series for tabular data manipulation.

Pandas integrates seamlessly with NumPy, Matplotlib, and other data science and machine learning libraries.

DataFrame: 2D labeled data structure

Series: 1D labeled array

Indexing, slicing, filtering, and selection

Aggregation, grouping, and pivoting

Merging, joining, and concatenation

Series: labeled 1D array

DataFrame: labeled 2D table with columns and rows

Index: row and column labels

NaN: missing data placeholder

GroupBy: aggregation and splitting of datasets

main.py / notebook.ipynb - main scripts or notebooks

data/ - raw and processed datasets

utils/ - helper functions for data cleaning

plots/ - saved visualizations

models/ - ML preprocessing or trained models

Load data from CSV, Excel, SQL, or JSON

Inspect and clean data (missing values, duplicates)

Filter, slice, and transform columns or rows

Aggregate or summarize data

Visualize or export processed data for analysis

Beginner: loading, inspecting, and simple filtering

Intermediate: grouping, pivoting, aggregations

Advanced: time-series operations, joins, multi-indexing

Expert: custom transformations, efficient pipelines

Enterprise: large-scale ETL and analytics workflows

Pandas vs NumPy: high-level tabular vs array operations

Pandas vs SQL: in-memory analytics vs database queries

Pandas vs Dask: single-machine vs distributed datasets

Pandas vs Excel: programmatic vs GUI-driven data analysis

Pandas vs R data.frame: Python vs R ecosystem

2008 – Pandas created by Wes McKinney

2010 – Pandas 0.1 released

2012 – Pandas 0.10 with DataFrame enhancements

2015 – Pandas 0.17 with improved time-series support

2023 – Pandas 2.x with performance improvements and nullable types

Series: 1D labeled array

DataFrame: 2D labeled table

Index: labels for rows/columns

NaN: missing data placeholder

GroupBy: splitting, applying, and combining data