PANDAS

Why Pandas is Important
in Data Science and AI

Pandas provides fast, flexible, and expressive data structures designed to work with structured (tabular, multidimensional) data seamlessly.

It acts as the foundational tool for data manipulation, which is crucial for preprocessing before applying any AI or ML models. Data cleaning and preparation consume up to 80% of a data scientist’s time—Pandas simplifies these tasks.

Its DataFrame structure mimics spreadsheets and SQL tables, making it intuitive and powerful.
Pandas integrates well with NumPy, Matplotlib, Scikit-learn, and other libraries in the data science ecosystem.
Built-in handling for missing data, date/time processing, and categorical data gives it an edge over other tools.
Pandas makes it easy to reshape, filter, merge, and aggregate data using concise syntax.
It supports input/output with a wide range of formats including CSV, Excel, SQL, JSON, and even big data formats like Parquet.
The ability to perform vectorized operations leads to performance gains over traditional loops in Python.
Mastery of Pandas leads to efficient EDA (Exploratory Data Analysis), an essential step in building AI models.

Module 1: Introduction to Pandas

What is Pandas?
Installing Pandas
Pandas vs Excel vs SQL
Overview of Series and DataFrame
Pandas Data Types

Module 2: Working with Series

Creating Series from lists, dictionaries, arrays
Indexing and slicing Series
Vectorized operations with Series
Applying functions to Series (apply, map)
Handling missing data in Series

Module 3: Working with DataFrames

Creating DataFrames from lists, dictionaries, arrays, Series
Reading and writing data (CSV, Excel, JSON, SQL, etc.)
DataFrame indexing, selecting, and filtering:
1. loc, iloc, at, iat
2. Boolean indexing
3. Conditional filtering
Adding and deleting columns
Changing column names and index

Module 4: Data Exploration and Summarization

Descriptive statistics (mean, std, count, etc.)
DataFrame shape and structure
Summary functions (info(), describe())
Value counts and unique values
Correlation and covariance

Module 5: Data Cleaning

Detecting and handling missing data:
- isnull(), notnull()
- fillna(), dropna()
Handling duplicates
String operations (str accessor)
Data type conversion (astype)
Renaming and replacing values.

Module 6: Data Transformation

apply(), map(), applymap()
Lambda functions
Binning and discretization
Using cut() and qcut()
Feature engineering basics using Pandas

Module 7: Merging, Joining, and Concatenating

Concatenation using concat()
Append operations
Merging with merge():
- Inner, Outer, Left, Right joins
Joining DataFrames with join()
Handling key conflicts and suffixes

Module 8: Grouping and Aggregation

Grouping data with groupby()
Aggregation functions (sum(), mean(), agg(), etc.)
Multi-level grouping
Transforming group data
Pivot tables and cross-tabulations

Module 9: Working with Time Series

Date-time basics and datetime objects
Converting columns to datetime
Date indexing and slicing
Resampling and frequency conversion
Rolling, expanding, and EWMA

Module 10: Advanced Data Handling

Hierarchical indexing (MultiIndex)
Reshaping with stack(), unstack(), and melt()
Working with categorical data
Window functions
Efficient Pandas code: performance tips

Module 11: Input and Output

Reading from and writing to:

1. 1. CSV
  2. Excel
  3. JSON
  4. SQL databases
  5. Parquet and HDF5
Reading APIs and web data into Pandas

Module 12: Pandas in Data Science Projects

EDA using Pandas
Preprocessing pipeline using Pandas
Data transformation before ML model
Integration with Scikit-learn
Mini projects:
1. Titanic dataset analysis
2. Sales data analytics
3. Stock price analysis