English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 199 lectures (20h 13m) | 8.49 GB
Pandas, Matplotlib, Seaborn, & More! Analyze Dozens of Datasets & Create Stunning Visualizations
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.
What you’ll learn
- 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
Table of Contents
Introduction
1 Course Welcome & Curriculum Walkthrough
2 What Do You Need To Know To Take This Course
3 Downloading The Course Materials IMPORTANT!!
4 How The Exercises Work
Setup & Installation
5 Introducing Jupyter Notebook!
6 Mac Installation Walkthrough
7 Windows Installation Walkthrough
8 Installing Pandas & Matplotlib (Mac & Windows)
Working With Jupyter Notebook
9 Creating Notebooks & Running Cells
10 Shutting Down The Notebook Server
11 How Cell Output Works
12 Command Mode Shortcuts
13 Cell Types Markdown Time!
14 Restarting The Kernel
15 Viewing The Docs Inside A Notebook
16 EXERCISE Jupyter Notebook
17 SOLUTION Jupyter Notebook
Dataframes & Datasets
18 Datasets & CSV
19 pd.read csv & DataFrames
20 Inspecting DataFrames head(), tail(), etc
21 DataTypes and info()
22 The House Sales Dataset Walkthrough
23 The Titanic Passenger Dataset Walkthrough
24 Non-comma Separators Netflix Dataset
25 Overriding Headers Country Population Dataset
26 EXERCISE DataFrames & Datasets
27 SOLUTION DataFrames & Datasets
Basic DataFrame Methods & Computations
28 Min & Max
29 Sum & Count
30 Mean, Median, & Mode
31 Describe With Numeric Values
32 Describe With Objects (Text) Values
33 EXERCISE Basic DataFrame Methods
34 SOLUTION Basic DataFrame Methods
Series & Columns
35 Selecting A Single Column
36 A Closer Look At Series
37 Important Series Methods
38 unique & nunique
39 nlargest & nsmallest
40 Selecting Multiple Columns
41 The powerful value counts() method
42 Using plot() to visualize!
43 EXERCISE Series & Plotting
44 SOLUTION Series & Plotting
Indexing & Sorting
45 Set Index Basics
46 set index The World Happiness Index Dataset
47 setting index with read csv
48 sort values intro
49 sorting by multiple columns
50 sorting text columns
51 sort index
52 Sorting and Plotting!
53 loc
54 iloc
55 loc & iloc with Series
56 EXERCISE Indexes & Sorting
57 SOLUTION Indexes & Sorting
Filtering DataFrames
58 Filtering DataFrames With A Boolean Series
59 Filtering With Comparison Operators
60 The Between Method
61 The isin() Method
62 Combining Conditions Using AND (&)
63 Combining Conditions Using OR ()
64 Bitwise Negation
65 isna() and notna() Methods
66 Filtering + Plotting Examples
67 EXERCISE Filtering
68 SOLUTION Filtering Exercise
Adding & Removing Columns
69 Dropping Columns
70 Dropping Rows
71 Adding Static Columns
72 Creating New Dynamic Columns
73 Finding The Highest pricesqft homes
74 Finding Largest Bitcoin Price Changes
75 EXERCISE AddingRemoving Columns & Rows
76 SOLUTION AddingRemoving Columns & Rows
Updating Values
77 Renaming Columns and Index Labels
78 The replace() method
79 Updating Values Using loc[]
80 Updating Multiple Values Using loc[]
81 Making Updates With loc[] and Boolean Masks
82 EXERCISE Updating Values
83 SOLUTION Updating Values Exercise
Working With Types and NA Values
84 Casting Types With astype()
85 Introducing the Category Type
86 Casting With pd.to numeric()
87 dropna() and isna()
88 fillna()
89 EXERCISE Dealing With NA Values
90 SOLUTION Dealing With NA Values
Working With Dates & Times
91 Why Dates Matter
92 Converting With pd.to datetime()
93 Specifying Fancy Formats With pd.to datetime()
94 Dates and DataFrames
95 The Useful dt Properties
96 Comparing Dates
97 Finding StarLink Flybys In UFO Dataset
98 Date Math & TimeDeltas
99 Billboard Charts Dataset Exploration
100 EXERCISE Dates & Times
101 SOLUTION Dates & Times
Matplotlib
102 Intro to Matplotlib
103 Our First Matplotlib Plots!
104 Do We Need plt.show()
105 Anatomy of Plots
106 Figsize & Plot Dimensions
107 Changing Matplotlib Stylesheets
108 Line Styles, Colors, Widths, and More!
109 Plot Labels & Titles
110 Changing X & Y Ticks
111 Adding Legends To Plots
112 EXERCISE Matplotlib Challenge #1
113 Creating Bar Plots
114 Creating Histograms
115 EXERCISE Matplotlib Challenge #2
116 Creating Scatter Plots
117 Creating Pie Charts
118 EXERCISE Matplotlib Challenge #3
119 Working With Subplots
120 Putting It All Together
121 EXERCISE Matplotlib Challenge #4
Revisiting Pandas Plotting
122 A Pandas Plotting Recap
123 Changing Pandas Plot Styles
124 Adding Labels and Titles to Pandas Plots
125 Using rename() When Plotting
126 Closer Look at Pandas Bar Plots
127 EXERCISE Pandas Plotting Challenge #1
128 Pandas Histograms
129 Box Plots
130 Pandas Line Plots
131 EXERCISE Pandas Plotting Challenge #2
132 Pandas Scatter Plots
133 Multiple Plots On The Same Axes
134 UFOS Plotting Challenge!
135 EXERCISE Pandas Plotting Challenge #3
136 Pandas Automatic Subplots
137 Manual Subplots With Pandas
138 EXERCISE Pandas Plotting Challenge #4
139 EXERCISE Pandas Plotting Challenge #5
140 Exporting Figures With savefig()
Grouping & Aggregating
141 Introducing Groupby
142 Exploring Groups
143 Split-Apply-Combine
144 Using The Agg Method
145 Agg with Custom Functions
146 Named Aggregation
Hierarchical Indexing
147 Groupby With Multiple Columns
148 Creating a MultiIndex With set index
149 Sorting A MultiIndex
150 Using .loc[] With A MultiIndex
151 Cross Sections With The XS Method
152 get level values()
153 Hierarchical Columns
154 Stack() and Unstack()
155 Plotting With Unstack()
156 Grouping By Index
Working With Text
157 The String Datatype Vs. Object Datatype
158 Upper(), Lower(), and Capitalize()
159 Indexing String Series With []
160 Stripping Whitespace With Strip()
161 Splitting Text Values With Split()
162 Replacing Portions of Strings With Replace()
163 Testing Strings With Contains()
Apply, Map, & Applymap
164 Applying Functions To Series
165 Apply() With Lambdas & Arguments
166 Apply() w DataFrames Columns
167 Apply() w DataFrames Rows
168 The Series Map() Method
169 The ApplyMap() Method
Combining Series & DataFrames
170 Concatenating Series
171 Concatenating Series By Index
172 Inner vs. Outer Joins
173 Concatenating DataFrames By Columns
174 Concatenating DataFrames By Index
175 The DataFrame Merge() Method
176 Merge() w Left, Right, Inner, & Outer Joins
177 Merge() On and Suffixes Arguments
Seaborn
178 Intro to Seaborn
179 The Helpful load dataset() method
180 Seaborn Scatterplots
181 Seaborn Lineplots
182 The relplot() Method
183 Resizing Seaborn Plots Aspect & Height
184 Seaborn Histograms
185 KDE Plots
186 Bivariate Distribution Plots
187 Rugplots
188 The Amazing displot() Method
Seaborn Categorical Plots
189 Countplot
190 Strip & Swarm Plots
191 Boxplots
192 Boxenplots
193 Violinplots
194 Barplots
195 The Big Boy Catplot Method
Controlling Seaborn Aesthetics
196 Changing Seaborn Themes
197 Customizing Styles with set style()
198 Altering Spines With despine()
199 Changing Color Palettes
Resolve the captcha to access the links!