The 2024 Pandas Bootcamp: Advanced Data Analysis with Python

سرفصل های دوره

Master Pandas and Python with real-world datasets and 200+ hands-on exercises! Go from beginner to expert Data Analyst!

1 - Introduction

1 -Welcome to the Course!

2 - IMPORTANT Example Files (and Exercise Solutions!).html

2 - Python Crash Course

1 -What Is Programming

1 - Note to Students PLEASE READ.html

2 -The Programming Environment

3 -Values and Types

3 - The Programming Environment - Exercises.html

4 -Functions

5 -Expressions

6 -Expressions in Colab

7 -Variables

7 - Expressions in Colab - Exercises.html

8 -Naming Variables

8 - Variables - Exercises.html

9 -Errors

10 -Comments

11 -Text Cells

12 -Colab Tips and Pitfalls

12 - Text Cells - Exercises.html

13 -Objects, Attributes, and Methods

14 -Modules and Libraries

14 -STEM Salaries.csv

15 -Lists

16 -Tuples

17 -Dictionaries

18 - Data Structures - Exercises.html

3 - Working with DataFrames

1 -Introducing DataFrames

1 - IMPORTANT DOWNLOAD EXAMPLE DATASETS.html

2 -Introducing the Example Datasets

2 - Introducing DataFrames - Exercises.html

3 -DataFrames and the read csv Method - Part I

4 -DataFrames and the read csv Method - Part II

5 -Providing DataFrame Column Names

5 - DataFrames and the read csv Method - Exercises.html

6 -Inspecting DataFrames

6 - Providing DataFrame Column Names - Exercises.html

7 -Data Types and the info Method

7 - Inspecting DataFrames - Exercises.html

8 -Renaming Columns

8 - Data Types and the info Method - Exercises.html

9 -Dropping Columns

9 - Renaming Columns - Exercises.html

10 -Selecting Columns

10 - Dropping Columns - Exercises.html

11 - Selecting Columns - Exercises.html

4 - Working with Series

1 -Series 101

2 -Converting Series with to numeric

2 - Series 101 - Exercises.html

3 -Converting Series with to datetime

3 - Converting Series with to numeric - Exercises.html

4 -Adding Columns (Series) to DataFrames

4 - Converting Series with to datetime - Exercises.html

5 -Creating Derived Columns

5 - Adding Columns (Series) to DataFrames - Exercises.html

6 -The assign Method

6 - Creating Derived Columns - Exercises.html

7 - The assign Method - Exercises.html

5 - Basic Data Analysis with Pandas

1 -The sum Method

2 -The count Method

2 - The sum Method - Exercises.html

3 -Mean and Median

3 - The count Method - Exercises.html

4 -Standard Deviation and the describe Method

4 - Mean and Median - Exercises.html

5 -Using describe on Non-Numeric Fields

5 - Standard Deviation and the describe Method - Exercises.html

6 -The unique and nunique Methods

6 - Using describe on Non-Numeric Fields - Exercises.html

7 -The value counts Method

7 - The unique and nunique Methods - Exercises.html

8 - The value counts Method - Exercises.html

6 - Indexing and Sorting

1 -The iloc Method

2 -Indexing Basics

2 - The iloc Method - Exercises.html

3 -The loc Method

3 - Indexing Basics - Exercises.html

4 -Sorting by Index

4 - The loc Method - Exercises.html

5 -Sorting By Columns

5 - Sorting by Index - Exercises.html

6 -Dropping Rows By Index

6 - Sorting By Columns - Exercises.html

7 - Dropping Rows By Index - Exercises.html

7 - Selecting Data with Criteria

1 -Filtering DataFrames with a Boolean Series

2 -Applying Other Logical Conditions

2 - Filtering DataFrames with a Boolean Series - Exercises.html

3 -The between and isin Methods

3 - Applying Other Logical Conditions - Exercises.html

4 -Combining Conditions Using the & Operator

4 - The between and isin Methods - Exercises.html

5 -Combining Conditions Using the Operator

5 - Combining Conditions Using the & Operator - Exercises.html

6 -Combining And & Or Logic

6 - Combining Conditions Using the Operator - Exercises.html

7 -Negation

7 - Combining And & Or Logic - Exercises.html

8 -The isna Method

8 - Negation - Exercises.html

9 - The isna Method - Exercises.html

8 - Updating DataFrames

1 -Updating DataFrame Values with loc

2 -Replacing DataFrame Values

2 - Updating DataFrame Values with loc - Exercises.html

3 -Updating Values with Boolean Masks

3 - Replacing DataFrame Values - Exercises.html

4 -Removing Null Values

4 - Updating Values with Boolean Masks - Exercises.html

5 -Replacing Null Values

5 - Removing Null Values - Exercises.html

6 -Identifying Duplicate Data

6 - Replacing Null Values - Exercises.html

7 -Removing Duplicate Data

8 - Identifying and Removing Duplicate Data - Exercises.html

9 - Combining Datasets

1 -Stacking Datasets Vertically I

2 -Orders 2021.csv

2 -Orders 2022.csv

2 -Orders 2023.csv

2 -Stacking Datasets Vertically II

3 -Fetching Excel Data Into Pandas

3 -Orders.xlsx

3 -Order Details 2021.csv

3 -Order Details 2022.csv

3 -Order Details 2023.csv

3 - Stacking Datasets Vertically - Exercises.html

4 -Customers.csv

4 -Joining DataFrames Horizontally I

4 -Orders 2021.csv

4 -Order Details.xlsx

4 - Fetching Excel Data Into Pandas - Exercises.html

5 -Joining DataFrames Horizontally II

6 -Customers.csv

6 -Left and Right Joins

6 -Orders 2021.csv

6 -Order Details 2021.csv

6 -Products.csv

6 - Joining DataFrames Horizontally - Exercises.html

7 -Full Outer Joins

8 -Combining More Than Two Tables

8 -Customers.csv

8 -Orders 2021.csv

8 -Order Details 2021.csv

8 -Order Details 2022.csv

8 -Order Details 2023.csv

8 -Products.csv

8 - Outer Joins - Exercises.html

9 -Customers.csv

9 -Orders 2022.csv

9 -Order Details 2022.csv

9 -Products.csv

9 - Combining More Than Two Tables - Exercises.html

10 - Grouping and Aggregation

1 -Grouping and Aggregation 101

2 -Applying Multiple Aggregations

2 - Grouping and Aggregation 101 - Exercises.html

3 -Grouping By Multiple Columns

3 - Applying Multiple Aggregations - Exercises.html

4 -The transform Method

4 - Grouping By Multiple Columns - Exercises.html

5 -Pythonic Pivot Tables

5 - The transform Method - Exercises.html

6 - Pythonic Pivot Tables - Exercises.html

11 - Working with String Data

1 -upper, lower, and capitalize

2 -The len Method

2 - upper, lower, and capitalize - Exercises.html

3 -Regular Expressions 101

3 - The len Method - Exercises.html

4 -Matching Digits with Regular Expressions

4 - Regular Expressions 101 - Exercise.html

5 -The contains Method

5 - Matching Digits with Regular Expressions - Exercises.html

6 -The replace Method I

6 - The contains Method - Exercises.html

7 -The replace Method II

8 - The replace Method - Exercises.html

12 - Working with Datetime Data

1 -Using Datetime Values as Criteria

2 -The datetime Module I

2 - Using Datetime Values as Criteria - Exercises.html

3 -The datetime Module II

4 -Date Math in Pandas

4 - The datetime Module - Exercises.html

5 -The shift Method I

5 - Date Math in Pandas - Exercises.html

6 -The shift Method II

7 -Calculating rolling Averages

7 - The shift Method - Exercises.html

8 - Calculating rolling Averages - Exercises.html

13 - Data Visualization with Pandas

1 -Data Visualization 101.1

2 -Data Visualization 101.2

3 -Bar Plots

3 - Data Visualization - Exercises.html

4 -Scatter Plots

4 - Bar Plots - Exercises.html

5 -Customizing Plot Appearance

5 - Scatter Plots - Exercises.html

6 -Customizing Plot Axes

7 - Customizing Plots - Exercises.html

14 - Functional Programming in Python

1 -Apply-ing Functions to Data Analysis

2 -If Statements in Python

3 -Incorporating Multiple Logical Conditions

4 -Incorporating And and Or Logic

5 -Functions in Python

5 - If Statements - Exercises.html

6 -Returning Values From Functions I

7 -Returning Values From Functions II

8 - Functions - Exercises.html

15 - Leveraging the map and apply Methods

1 -The map Method

2 -Using map with Custom Functions I

2 - The map Method - Exercises.html

3 -Using map with Custom Functions II

4 -The apply Method

4 - Using map with Custom Functions - Exercises.html

5 -Applying apply to Multiple Columns

5 - The apply Method - Exercises.html

6 - Applying apply to Multiple Columns - Exercises.html

16 - BONUS LESSON

1 - BONUS LESSON.html

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تولید کننده: Udemy