Python programming Basics for Data analysis and visualization

Python Programming Basics

Setup Environment

00:00
Variables in Python

00:00
Numbers in Python

00:00
String in Python

00:00
Lists in Python

00:00
if Condition

00:00
For Loop

00:00
Functions

00:00
Dictionary and Tuple

00:00
Module and Pip

00:00
File handling

00:00
Classes and Object

00:00
Inheritance

00:00
Exception Handling

00:00

DataFrame and Dataset

Getting Started with Datasets & CSV Files

00:00
Reading Data with pd.read_csv & Exploring DataFrames

00:00
Quick Insights: Using head(), tail(), and More

00:00
Understanding Data Types with .info()

00:00
Case Study: House Sales Dataset Walkthrough

00:00
Case Study: Titanic Passenger Dataset Walkthrough

00:00
Working Beyond Commas: Netflix Dataset with Other Separators

00:00
Customizing Headers: Country Population Dataset

00:00
Hands-On Challenge: DataFrames & Datasets
Step-by-Step Solution: DataFrames & Datasets

Essential DataFrame Methods & Computations

Mastering Series & Columns in Python: From Selection to Visualization

Selecting a Single Column
Deep Dive: Understanding Series
Essential Series Methods
Discovering Uniqueness: unique() & unique()
Finding Extremes: nlargest() & nsmallest()
Working with Multiple Columns
The Powerful value_counts() Method
From Numbers to Insights: Visualizing with plot()
Hands-On Challenge: Series & Plotting
Step-by-Step Solution: Series & Plotting

Indexing & Sorting in Python: Organize, Access, and Unlock Your Data

Indexing Made Simple: Set_Index Basics
Case Study: World Happiness Index Dataset
Seamless Importing: Setting Index with read_csv
Sorting Essentials: Introduction to sort_values
Sorting by Multiple Columns
Sorting Text Columns
Organize by Index: Using sort_index
Sorting Meets Visualization
Accessing Data with loc
Accessing Data with iloc
Mix & Match: Using loc and iloc with Series
Hands-On Challenge: Indexing & Sorting
Step-by-Step Solution: Indexing & Sorting

Filtering DataFrames in Python

Boolean Magic: Filtering DataFrames with a Boolean Series
Powerful Comparisons: Filtering with Operators
Range Made Simple: The between() Method
Filtering by Lists: The isin() Method
Combining Conditions with AND (&)
Combining Conditions with OR (|)
Excluding Data: Bitwise Negation (~)
Handling Missing Data: isna() and notna()
From Filtering to Storytelling: Plotting Examples
Hands-On Challenge: Filtering DataFrames
Hands-On Challenge: Filtering DataFrames

Adding & Removing Columns in Python

Updating Values in Python: Clean, Correct, and Control Your Data

Working with Data Types & Handling Missing Values in Python

Working with Dates & Times in Python: Unlock Time-Based Insights

Why Dates Matter in Data Analysis
Converting Strings into Dates with pd.to_datetime()
Handling Complex Formats: Advanced pd.to_datetime()
Integrating Dates into DataFrames
Unlocking Insights with .dt Properties
Comparing Dates with Ease
Real-World Project: Finding Starlink Flybys in UFO Reports
Date Math & TimeDeltas
Real-World Project: Billboard Charts Dataset Exploration
Hands-On Challenge: Dates & Times
Step-by-Step Solution: Dates & Times

Data Visualization with Matplotlib: Transform Numbers into Stories

Introduction to Matplotlib
Your First Matplotlib Plots
Do We Really Need plt.show()?
The Anatomy of Plots
Customizing Size: Figsize & Plot Dimensions
Styling Made Simple: Changing Matplotlib Stylesheets
Line Styles, Colors, Widths & More
Adding Context: Plot Labels & Titles
Fine-Tuning: Customizing X & Y Ticks
Highlighting Data: Adding Legends
Hands-On Challenge #1: Basic Plotting
Beyond Lines: Creating Bar Plots
Exploring Distributions: Creating Histograms
Hands-On Challenge #2: Bars & Histograms
Relationships at a Glance: Creating Scatter Plots
Breaking Down Proportions: Creating Pie Charts
Hands-On Challenge #3: Scatter & Pie Plots
Working with Subplots
Putting It All Together
Hands-On Challenge #4: Full Visualization Project

Revisiting Pandas Plotting

A Pandas Plotting Recap
Styling Your Visuals: Changing Pandas Plot Styles
Adding Clarity: Labels & Titles in Pandas Plots
Smarter Naming: Using rename() in Plots
A Deeper Dive: Pandas Bar Plots
Hands-On Challenge #1: Bar Plots in Action
Exploring Distributions: Pandas Histograms
Understanding Spread: Box Plots
Trend Tracking: Pandas Line Plots
Hands-On Challenge #2: Histograms & Line Plots
Spotting Relationships: Pandas Scatter Plots
Layering Insights: Multiple Plots on the Same Axes
Case Study: The UFO Sightings Plotting Challenge!
Hands-On Challenge #3: Scatter & Multi-Axes Plots
Quick Dashboards: Pandas Automatic Subplots
Full Control: Manual Subplots with Pandas
Hands-On Challenge #4: Subplots Mastery
Hands-On Challenge #5: Advanced Plotting Project
Sharing Your Work: Exporting Figures with savefig()

Grouping & Aggregating

Hierarchical Indexing

Smarter Grouping: Groupby With Multiple Columns
Organizing Data: Creating a MultiIndex With set_index()
Bringing Order: Sorting A MultiIndex
Precision Access: Using .loc[] With A MultiIndex
Zooming In: Cross Sections With The xs() Method
Digging Into Levels: get_level_values()
Beyond Rows: Hierarchical Columns
Reshaping Data: stack() and unstack()
Visualization Boost: Plotting With unstack()
Index-Based Insights: Grouping By Index

Working with Text

Apply, Map & Applymap

Combining Series & DataFrames

Seaborn

Getting Started: Intro to Seaborn
Instant Data Access: The Helpful load_dataset() Method
Exploring Relationships: Seaborn Scatterplots
Tracking Trends: Seaborn Lineplots
The Versatile relplot() Method
Custom Sizing: Resizing Seaborn Plots (Aspect & Height)
Exploring Distributions: Seaborn Histograms
Smooth Insights: KDE (Kernel Density Estimation) Plots
Two-Dimensional Views: Bivariate Distribution Plots
Adding Detail: Rugplots
The All-in-One Tool: The Amazing displot() Method

Course Content

Python Programming Basics

Setup Environment

Variables in Python

Numbers in Python

String in Python

Lists in Python

if Condition

For Loop

Functions

Dictionary and Tuple

Module and Pip

File handling

Classes and Object

Inheritance

Exception Handling

DataFrame and Dataset

Getting Started with Datasets & CSV Files

Reading Data with pd.read_csv & Exploring DataFrames

Quick Insights: Using head(), tail(), and More

Understanding Data Types with .info()

Case Study: House Sales Dataset Walkthrough

Case Study: Titanic Passenger Dataset Walkthrough

Working Beyond Commas: Netflix Dataset with Other Separators

Customizing Headers: Country Population Dataset

Hands-On Challenge: DataFrames & Datasets

Step-by-Step Solution: DataFrames & Datasets

Essential DataFrame Methods & Computations

Finding Extremes: Min & Max Values

Totals & Counts: Summing Up Data

Central Tendencies: Mean, Median, & Mode

Quick Stats: Descriptive Analysis with Numeric Values

Analyzing Text Data: Describe With Objects

Hands-On Challenge: Essential DataFrame Methods

Step-by-Step Solution: Essential DataFrame Methods

Mastering Series & Columns in Python: From Selection to Visualization

Selecting a Single Column

Deep Dive: Understanding Series

Essential Series Methods

Discovering Uniqueness: unique() & unique()

Finding Extremes: nlargest() & nsmallest()

Working with Multiple Columns

The Powerful value_counts() Method

From Numbers to Insights: Visualizing with plot()

Hands-On Challenge: Series & Plotting

Step-by-Step Solution: Series & Plotting

Indexing & Sorting in Python: Organize, Access, and Unlock Your Data

Indexing Made Simple: Set_Index Basics

Case Study: World Happiness Index Dataset

Seamless Importing: Setting Index with read_csv

Sorting Essentials: Introduction to sort_values

Sorting by Multiple Columns

Sorting Text Columns

Organize by Index: Using sort_index

Sorting Meets Visualization

Accessing Data with loc

Accessing Data with iloc

Mix & Match: Using loc and iloc with Series

Hands-On Challenge: Indexing & Sorting

Step-by-Step Solution: Indexing & Sorting

Filtering DataFrames in Python

Boolean Magic: Filtering DataFrames with a Boolean Series

Powerful Comparisons: Filtering with Operators

Range Made Simple: The between() Method

Filtering by Lists: The isin() Method

Combining Conditions with AND (&)

Combining Conditions with OR (|)

Excluding Data: Bitwise Negation (~)

Handling Missing Data: isna() and notna()

From Filtering to Storytelling: Plotting Examples

Hands-On Challenge: Filtering DataFrames

Hands-On Challenge: Filtering DataFrames

Adding & Removing Columns in Python

Dropping Columns You Don’t Need

Removing Rows for Cleaner Data

Adding Static Columns

Generating Dynamic Columns

Real-World Example: Highest Price per Square Foot Homes

Real-World Example: Largest Bitcoin Price Changes

Hands-On Challenge: Adding & Removing Columns/Rows