Mô tả

Welcome to (what I think is) the web's best course on Pandas, Matplotlib, Seaborn, and more! This course will level up your data skills to help you grow your career in Data Science, Machine Learning, Finance, Web Development, or any tech-adjacent field.

This is a tightly structured course that covers a ton, but it's all broken down into human-sized pieces rather than an overwhelming reference manual that throws everything at you at once. After each and every new topic, you'll have the chance to practice what you're learning and challenge yourself with exercises and projects. We work with dozens of fun and real-world datasets including Amazon bestsellers, Rivian stock prices, Presidential Tweets, Bitcoin historic data, and UFO sightings.

If you're still reading, let me tell you a little about the curriculum.. In the course, you'll learn how to:

  • Work with Jupyter Notebooks

  • Use Pandas to read and manipulate datasets

  • Work with DataFrames and Series objects

  • Organize, filter, clean, aggregate, and analyze DataFrames

  • Extract and manipulate date, time, and textual information from data

  • Master Hierarchical Indexing

  • Merge datasets together in Pandas

  • Create complex visualizations with Matplotlib

  • Use Seaborn to craft stunning and meaningful visualizations

  • Create line, bar, box, scatter, pie, violin, rug, swarm, strip, and other plots!

What makes this course different from other courses on the same topics?  First and foremost, this course integrates visualizations as soon as possible rather than tacking it on at the end, as many other courses do.  You'll be creating your first plots within the first couple of sections!  Additionally, we start using real datasets from the get go, unlike most other courses which spend hours working with dull, fake data (colors, animals, etc) before you ever see your first real dataset.  With all of that said, I feel bad trash talking my competitors, as there are quite a few great courses on the platform :) 

I think that about wraps it up! The topics in this courses are extremely visual and immediate, which makes them a joy to teach (and hopefully for you to learn).   If you have even a passing interest in these topics, you'll likely enjoy the course and tear through it quickly.  This stuff might seem intimidating, but it's actually really approachable and fun! I'm not kidding when I say this is my favorite course I've ever made. I hope you enjoy it too.

Bạn sẽ học được gì

Master Pandas Dataframes and Series

Create beautiful visualizations with Seaborn

Analyze dozens of real-world datasets

Practice with tons of exercises and challenges

Learn the ins and outs of Matplotlib

Organize, filter, clean, aggregate, and analyze DataFrames

Master Hierarchical Indexing

Merge datasets together in Pandas

Create line, bar, box, scatter, pie, violin, rug, swarm, strip, and other plots!

Work with Jupyter Notebooks

Yêu cầu

  • Basic Python Knowledge (variables, conditionals, etc)

Nội dung khoá học

22 sections

Introduction

5 lectures
Course Welcome & Curriculum Walkthrough
08:23
Join The Community!
00:25
What Do You Need To Know To Take This Course?
01:49
Downloading The Course Materials IMPORTANT!!
02:38
How The Exercises Work
02:14

Setup & Installation

4 lectures
Introducing Jupyter Notebook!
05:31
Mac Installation Walkthrough
06:20
Windows Installation Walkthrough
06:38
"Installing" Pandas & Matplotlib (Mac & Windows)
04:07

Working With Jupyter Notebook

9 lectures
Creating Notebooks & Running Cells
06:38
Shutting Down The Notebook Server
05:08
How Cell Output Works
02:31
Command Mode Shortcuts
06:20
Cell Types: Markdown Time!
04:56
Restarting The Kernel
06:47
Viewing The Docs Inside A Notebook
02:46
EXERCISE: Jupyter Notebook
02:42
SOLUTION: Jupyter Notebook
06:02

Dataframes & Datasets

10 lectures
Datasets & CSV
05:31
pd.read_csv & DataFrames
06:42
Inspecting DataFrames: head(), tail(), etc.
07:16
DataTypes and info()
04:47
The House Sales Dataset Walkthrough
05:13
The Titanic Passenger Dataset Walkthrough
08:27
Non-comma Separators: Netflix Dataset
08:17
Overriding Headers: Country Population Dataset
04:18
EXERCISE: DataFrames & Datasets
03:10
SOLUTION: DataFrames & Datasets
08:48

Basic DataFrame Methods & Computations

7 lectures
Min & Max
05:24
Sum & Count
09:00
Mean, Median, & Mode
05:35
Describe With Numeric Values
04:23
Describe With Objects (Text) Values
07:47
EXERCISE: Basic DataFrame Methods
01:45
SOLUTION: Basic DataFrame Methods
04:35

Series & Columns

10 lectures
Selecting A Single Column
07:21
A Closer Look At Series
08:31
Important Series Methods
05:10
unique & nunique
05:15
nlargest & nsmallest
07:15
Selecting Multiple Columns
03:42
The powerful value_counts() method
08:13
Using plot() to visualize!
10:50
EXERCISE: Series & Plotting
02:56
SOLUTION: Series & Plotting
08:49

Indexing & Sorting

13 lectures
Set_Index Basics
09:33
set_index: The World Happiness Index Dataset
05:06
setting index with read_csv
02:38
sort_values intro
03:54
sorting by multiple columns
03:05
sorting text columns
03:37
sort_index
02:21
Sorting and Plotting!
05:01
loc
07:50
iloc
04:18
loc & iloc with Series
05:51
EXERCISE: Indexes & Sorting
04:21
SOLUTION: Indexes & Sorting
09:55

Filtering DataFrames

11 lectures
Filtering DataFrames With A Boolean Series
08:48
Filtering With Comparison Operators
08:15
The Between Method
03:05
The isin() Method
04:07
Combining Conditions Using AND (&)
11:52
Combining Conditions Using OR (|)
11:08
Bitwise Negation
06:56
isna() and notna() Methods
03:36
Filtering + Plotting Examples
06:01
EXERCISE: Filtering
01:43
SOLUTION: Filtering Exercise
10:37

Adding & Removing Columns

8 lectures
Dropping Columns
06:02
Dropping Rows
06:25
Adding Static Columns
05:59
Creating New "Dynamic" Columns
06:54
Finding The Highest price/sqft homes
04:01
Finding Largest Bitcoin Price Changes
05:13
EXERCISE: Adding/Removing Columns & Rows
03:18
SOLUTION: Adding/Removing Columns & Rows
05:09

Updating Values

7 lectures
Renaming Columns and Index Labels
04:50
The replace() method
07:31
Updating Values Using loc[]
07:59
Updating Multiple Values Using loc[]
04:11
Making Updates With loc[] and Boolean Masks
07:54
EXERCISE: Updating Values
02:20
SOLUTION: Updating Values Exercise
08:21

Working With Types and NA Values

7 lectures
Casting Types With astype()
07:14
Introducing the Category Type
04:45
Casting With pd.to_numeric()
04:43
dropna() and isna()
08:38
fillna()
05:37
EXERCISE: Dealing With NA Values
01:21
SOLUTION: Dealing With NA Values
05:08

Working With Dates & Times

11 lectures
Why Dates Matter
03:42
Converting With pd.to_datetime()
08:06
Specifying Fancy Formats With pd.to_datetime()
09:05
Dates and DataFrames
07:07
The Useful dt Properties
08:49
Comparing Dates
06:14
Finding StarLink Flybys In UFO Dataset
08:43
Date Math & TimeDeltas
08:47
Billboard Charts Dataset Exploration
11:29
EXERCISE: Dates & Times
04:51
SOLUTION: Dates & Times
15:16

Matplotlib

20 lectures
Intro to Matplotlib
04:24
Our First Matplotlib Plots!
07:04
Do We Need plt.show() ?
02:33
Anatomy of Plots
09:06
Figsize & Plot Dimensions
04:25
Changing Matplotlib Stylesheets
04:11
Line Styles, Colors, Widths, and More!
07:09
Plot Labels & Titles
06:00
Changing X & Y Ticks
07:06
Adding Legends To Plots
05:10
EXERCISE: Matplotlib Challenge #1
04:45
Creating Bar Plots
09:39
Creating Histograms
10:27
EXERCISE: Matplotlib Challenge #2
04:06
Creating Scatter Plots
04:41
Creating Pie Charts
05:42
EXERCISE: Matplotlib Challenge #3
04:27
Working With Subplots
10:53
Putting It All Together
05:54
EXERCISE: Matplotlib Challenge #4
09:36

Revisiting Pandas Plotting

19 lectures
A Pandas Plotting Recap
05:13
Changing Pandas Plot Styles
02:29
Adding Labels and Titles to Pandas Plots
07:46
Using rename() When Plotting
03:11
Closer Look at Pandas Bar Plots
07:29
EXERCISE: Pandas Plotting Challenge #1
08:01
Pandas Histograms
03:11
Box Plots
05:08
Pandas Line Plots
05:33
EXERCISE: Pandas Plotting Challenge #2
04:05
Pandas Scatter Plots
02:59
Multiple Plots On The Same Axes
05:11
UFOS Plotting Challenge!
07:13
EXERCISE: Pandas Plotting Challenge #3
03:56
Pandas Automatic Subplots
07:38
Manual Subplots With Pandas
06:26
EXERCISE: Pandas Plotting Challenge #4
11:22
EXERCISE: Pandas Plotting Challenge #5
10:34
Exporting Figures With savefig()
02:37

Grouping & Aggregating

8 lectures
Introducing Groupby
05:41
Exploring Groups
09:41
Split-Apply-Combine
09:35
Using The Agg Method
07:41
Agg with Custom Functions
05:28
Named Aggregation
04:25
EXERCISE: Groupby
03:57
SOLUTION: Groupby
11:14

Hierarchical Indexing

10 lectures
Groupby With Multiple Columns
07:12
Creating a MultiIndex With set_index
06:02
Sorting A MultiIndex
08:28
Using .loc[] With A MultiIndex
10:12
Cross Sections With The XS Method
02:30
get_level_values()
08:10
Hierarchical Columns
05:05
Stack() and Unstack()
03:48
Plotting With Unstack()
07:58
Grouping By Index
05:07

Working With Text

7 lectures
The String Datatype Vs. Object Datatype
07:03
Upper(), Lower(), and Capitalize()
04:09
Indexing String Series With []
05:53
Stripping Whitespace With Strip()
03:58
Splitting Text Values With Split()
06:57
Replacing Portions of Strings With Replace()
07:00
Testing Strings With Contains()
03:59

Apply, Map, & Applymap

6 lectures
Applying Functions To Series
07:55
Apply() With Lambdas & Arguments
04:52
Apply() w/ DataFrames: Columns
04:12
Apply() w/ DataFrames: Rows
06:46
The Series Map() Method
02:56
The ApplyMap() Method
03:51

Combining Series & DataFrames

8 lectures
Concatenating Series
05:19
Concatenating Series By Index
04:09
Inner vs. Outer Joins
03:45
Concatenating DataFrames By Columns
04:47
Concatenating DataFrames By Index
03:03
The DataFrame Merge() Method
04:34
Merge() w/ Left, Right, Inner, & Outer Joins
06:11
Merge() On and Suffixes Arguments
09:41

Seaborn

11 lectures
Intro to Seaborn
08:14
The Helpful load_dataset() method
04:18
Seaborn Scatterplots
10:17
Seaborn Lineplots
12:26
The relplot() Method
09:18
Resizing Seaborn Plots: Aspect & Height
07:04
Seaborn Histograms
06:18
KDE Plots
02:44
Bivariate Distribution Plots
05:43
Rugplots
05:55
The Amazing displot() Method
06:58

Seaborn Categorical Plots

7 lectures
Countplot
04:00
Strip & Swarm Plots
09:16
Boxplots
09:29
Boxenplots
02:23
Violinplots
04:47
Barplots
08:55
The Big Boy Catplot Method
08:29

Controlling Seaborn Aesthetics

4 lectures
Changing Seaborn Themes
04:27
Customizing Styles with set_style()
05:44
Altering Spines With despine()
02:52
Changing Color Palettes
09:14

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