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Welcome to the best resource online for learning and mastering data analysis with pandas and python.

Over 32 hours, 10+ datasets, and 50+ skill challenges, you will gain hands-on mastery of, not only pandas 1.x, but also tens of computer science, statistics, and programming concepts.

We will break down, understand, and practice hundreds of methods, attributes, and techniques in pandas and python that will fundamentally change the way you work with data.

In The Ultimate Pandas Bootcamp (2022) you won’t be working with outdated versions of pandas, writing repetitive commands on the same boring dataset. Instead, you’ll learn pandorable and pythonic solutions to interesting, real-world data problems, while working with many diverse datasets that range from wine servings, video game sales, and SAT scores to stock prices, college salaries and more!

Data analysis is an applied science, which is why in each section, you’ll stop and practice what you learn in dedicated skill challenges, followed by detailed solutions where we often consider and compare alternative solutions.

Data analysis is one of the most in-demand skill across all industries and an increasing number of roles. And python is increasingly the language of choice.

Pandas is the wonderful open-source library that is the embodiment of those trends: based on the python programming language, pandas is the de facto data analysis library in the python data science community.


––––– Structure & Curriculum –––––

Over more than 31 hours, we'll cover everything that pandas has to offer, from manipulating series and dataframes, to merging datasets, handling time series, aggregations, filtering, sorting and much more!

The first four sections of the bootcamp constitute the core curriculum. You'll get acquainted with series and dataframes and develop an in-depth understanding of pandas data structures.

· Series at a Glance

· Series Methods and Handling

· Introducing DataFrames

· DataFrames More In Depth

In the next eight sections, you will dive into more advanced topics and take your pandas skills to another level, learning how to work with multiple datasets, manipulate time series, visualize data, write custom functions to transform data and much more.

· Working With Multiple DataFrames

· Going MultiDimensional

· GroupBy And Aggregates

· Reshaping With Pivots

· Working With Dates And Time

· Regular Expressions And Text Manipulation

· Visualizing Data

· Data Formats And I/O

Pandas and python go hand-in-hand which is why this bootcamp also includes a full-length introduction to the python programming language, to get you up and running writing pythonic code in no time.

This is the ultimate course on one of the most-valuable skills today. I hope you commit to mastering data analysis with pandas.

See you inside!

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15 sections

Introduction

8 lectures
Course Structure
01:25
Pandas Is Not Single
01:58
Anaconda
03:20
Jupyter Notebooks
11:36
Cloud vs Local
05:40
Hello, Python
04:16
NumPy
11:42
All Course Resources
00:13

Series At A Glance

24 lectures
Section Intro
01:06
What Is A Series?
03:57
Parameters vs Arguments
02:48
What’s In The Data?
05:48
The .dtype Attribute
02:15
BONUS: What Is dtype('o'), Really?
03:23
Index And RangeIndex
07:10
Series And Index Names
05:20
Skill Challenge
02:03
Solution
04:13
Another Solution
03:02
The head() And tail() Methods
05:43
Extracting By Index Position
07:34
Accessing Elements By Label
06:59
BONUS: The add_prefix() And add_suffix() Methods
03:30
Using Dot Notation
03:45
Boolean Masks And The .loc Indexer
08:16
Extracting By Position With .iloc
03:58
BONUS: Using Callables With .loc And .iloc
09:53
Selecting With .get()
05:19
Selection Recap
05:33
Skill Challenge
01:50
Solution
05:22
Section Recap Notebook
00:04

Series Methods And Handling

33 lectures
Section Intro
01:49
Reading In Data With read_csv()
08:58
Series Sizing With .size, .shape, And len()
04:35
Unique Values And Series Monotonicity
05:20
The count() Method
02:16
Accessing And Counting NAs
09:26
BONUS: Another Approach
05:16
The Other Side: notnull() And notna()
03:01
BONUS: Booleans Are Literally Numbers In Python
03:19
Skill Challenge
01:20
Solution
02:52
Dropping And Filling NAs
04:34
Descriptive Statistics
08:23
The describe() Method
02:31
mode() And value_counts()
07:21
idxmax() And idxmin()
05:49
Sorting With sort_values()
05:16
nlargest() And nsmallest()
02:49
Sorting With sort_index()
03:41
Skill Challenge
01:02
Solution
02:09
Series Arithmetics And fill_value()
08:26
BONUS: Calculating Variance And Standard Deviation
04:33
Cumulative Operations
05:02
Pairwise Differences With diff()
03:42
Series Iteration
04:11
Filtering: filter(), where(), And mask()
11:41
Transforming With update(), apply() And map()
13:26
Skill Challenge
02:24
Solution I - Reading Data
02:07
Solution II - Mean, Median, And Standard Deviation
03:19
Solution III - Z-scores
08:04
Section Recap Notebook
00:04

Working With DataFrames

37 lectures
Section Intro
01:40
What Is A DataFrame
10:31
Creating A DataFrame
04:40
BONUS - Four More Ways To Build DataFrames
16:08
The info() Method
04:29
Reading In Nutrition Data
03:55
Some Cleanup: Removing The Duplicated Index
05:38
The sample() Method
04:14
BONUS - Sampling With Replacement Or Weights
07:37
BONUS - How Are Random Numbers Generated?
05:40
DataFrame Axes
04:29
Changing The Index
07:41
Extracting From DataFrames By Label
07:22
DataFrame Extraction by Position
08:37
Single Value Access With .at And .iat
05:24
BONUS - The get_loc() Method
06:28
Skill Challenge
01:17
Solution
07:49
More Cleanup: Going Numeric
03:25
The astype() Method
05:56
DataFrame replace() + A Glimpse At Regex
10:18
Part I: Collecting The Units
12:59
The rename() Method
07:51
DataFrame dropna()
10:53
BONUS - dropna() With Subset
07:55
Part II: Merging Units With Column Names
11:32
Part III: Removing Units From Values
06:35
Filtering in 2D
09:15
DataFrame Sorting
07:59
Using Series between() With DataFrames
06:09
BONUS - Min, Max and Idx[MinMax], And Good Foods
09:15
DataFrame nlargest() And nsmallest()
05:49
Skill Challenge
01:26
Solution
05:53
Another Skill Challenge
02:00
Solution
06:48
Section Recap Notebook
00:04

DataFrames In Depth

44 lectures
Section Intro
02:23
Introducing A New Dataset
03:56
Quick Review: Indexing With Boolean Masks
03:42
More Approaches To Boolean Masking
10:27
Binary Operators With Booleans
10:29
BONUS - XOR and Complement Binary Ops
12:57
Combining Conditions
07:52
Conditions As Variables
04:44
Skill Challenge
01:07
Solution
06:58
2d Indexing
10:00
Fancy Indexing With lookup()
08:30
Sorting By Index Or Column
06:59
Sorting vs. Reordering
12:29
BONUS - Another Way
02:13
15. BONUS - Please Avoid Sorting Like This
03:37
Skill Challenge
01:17
Solution
04:03
Identifying Dupes
10:40
Removing Duplicates
06:13
Removing DataFrame Rows
02:58
BONUS - Removing Columns
03:02
BONUS - Another Way: pop()
04:13
BONUS - A Sophisticated Alternative
05:12
Null Values In DataFrames
07:13
Dropping And Filling DataFrame NAs
07:49
BONUS - Methods And Axes With fillna()
10:05
Skill Challenge
01:37
Solution
07:00
Calculating Aggregates With agg()
09:23
Same-shape Transforms
14:43
More Flexibility With apply()
13:14
Element-wise Operations With applymap()
13:35
Skill Challenge
02:07
Solution
04:56
Setting DataFrame Values
06:54
The SettingWithCopy Warning
07:15
View vs Copy
09:00
Adding DataFrame Columns
08:02
Adding Rows To DataFrames
09:58
BONUS - How Are DataFrames Stored In Memory
04:07
Skill Challenge
01:25
Solution
05:53
Section Recap Notebook
00:05

Working With Multiple DataFrames

22 lectures
Section Intro
01:09
Introducing (Five?) New Datasets
05:41
Concatenating DataFrames
07:39
The Duplicated Index Issue
07:54
Enforcing Unique Indices
07:36
BONUS - Creating Multiple Indices With concat()
04:30
Column Axis Concatenation
04:17
The append() Method: A Special Case Of concat()
02:32
Concat On Different Columns
04:48
Skill Challenge
01:42
Solution
10:29
The merge() Method
06:12
The left_on And right_on Params
04:44
Inner vs Outer Joins
05:37
Left vs Right Joins
03:58
One-to-One and One-to-Many Joins
09:32
Many-to-Many Joins
08:34
Merging By Index
05:38
The join() Method
03:05
Skill Challenge
01:11
Solution
06:36
Section Recap Notebook
00:04

Going MultiDimensional

25 lectures
Section Intro
01:51
Introducing New Data
04:49
Index And RangeIndex
04:29
Creating A MultiIndex
03:45
MultiIndex From read_csv()
03:53
Indexing Hierarchical DataFrames
08:06
Indexing Ranges And Slices
11:56
BONUS - Use : With pd.IndexSlice!
04:13
Cross Sections With xs()
05:30
Skill Challenge
01:13
Solution
07:23
The Anatomy Of A MultiIndex Object
07:52
Adding Another Level
05:58
Shuffling Levels
04:29
Removing MultiIndex Levels
06:02
MultiIndex sort_index()
06:11
More MultiIndex Methods
08:06
Reshaping With stack()
05:51
The Flipside: unstack()
06:35
BONUS: Creating MultiLevel Columns Manually
10:59
An Easier Way: transpose()
02:53
BONUS - What About Panels?
03:28
Skill Challenge
01:31
Solution
07:47
Section Recap Notebook
00:04

GroupBy And Aggregates

22 lectures
Section Intro
01:15
New Data: Game Sales
03:04
Simple Aggregations Review
05:13
Conditional Aggregates
05:29
The Split-Apply-Combine Pattern
04:43
The groupby() Method
04:34
The DataFrameGroupBy Object
04:06
Customizing Index To Group Mappings
04:32
BONUS - Series groupby()
04:48
Skill Challenge
01:01
Solution
05:42
Iterating Through Groups
03:15
Handpicking Subgroups
04:52
MultiIndex Grouping
05:46
Fine-tuned Aggregates
08:59
Named Aggregations
07:08
The filter() Method
05:35
GroupBy Transformations
08:19
BONUS - There's Also apply()
07:48
Skill Challenge
01:11
Solution
04:53
Section Recap Notebook
00:04

Reshaping With Pivots

14 lectures
Section Intro
01:12
New Data: New York City SAT Scores
04:39
Pivoting Data
07:19
Undoing Pivots
06:01
What About Aggregates?
05:47
The pivot_table()
06:42
BONUS: The Problem With Average Percentage
07:57
Replicating Pivot Tables With GroupBy
02:51
Adding Margins
05:06
MultiIndex Pivot Tables
03:13
Applying Multiple Functions
04:13
Skill Challenge
01:25
Solution
06:02
Section Recap Notebook
00:04

Handling Date And Time

24 lectures
Section Intro
01:13
The Python datetime Module
09:39
Parsing Dates From Text
10:19
Even Better: dateutil
04:54
From Datetime To String
05:31
Performant Datetimes With Numpy
08:59
The Pandas Timestamp
05:10
Our Dataset: Brent Prices
04:29
Date Parsing And DatetimeIndex
05:54
A Cool Shorcut: read_csv() With parse_dates
04:01
Indexing Dates
05:39
Skill Challenge
01:06
Solution
03:52
DateTimeIndex Attribute Accessors
08:54
Creating Date Ranges
06:33
Shifting Dates With pd.DateOffset
07:27
BONUS: Timedeltas And Absolute Time
06:55
Resampling Timeseries
08:12
Upsampling And Interpolation
10:20
What About asfreq()?
09:21
BONUS: Rolling Windows
11:26
Skill Challenge
01:24
Solution
05:18
Section Recap Notebook
00:04

Regex And Text Manipulation

24 lectures
Section Intro
01:48
Our Data: Boston Marathon Runners
03:08
String Methods In Python
07:04
Vectorized String Operations In Pandas
03:36
Case Operations
02:51
Finding Characters And Words
06:22
Strips And Whitespace
07:02
String Splitting And Concatenation
09:32
More Split Parameters
08:14
Skill Challenge
01:00
Solution
04:22
Slicing Substrings
05:50
Masking With String Methods
07:02
BONUS: Parsing Indicators With get_dummies()
10:21
Text Replacement
07:33
Introduction To Regular Expressions
14:24
More Regex Concepts
11:10
How To Approach Regex?
11:43
Is This A Valid Email?
16:43
BONUS: What's The Point Of re.compile()?
03:27
Pandas str contains(), split() And replace() With Regex
14:37
Skill Challenge
01:21
Solution
12:21
Section Recap Notebook
00:04

Visualizing Data

13 lectures
Section Intro
01:21
The Art Of Data Visualization
02:52
The Preliminaries Of matplotlib
12:08
Line Graphs
11:11
Bar Charts
10:57
Pie Plots
10:18
Histograms
10:08
Scatter Plots
11:13
Other Visualization Options
08:47
BONUS: Data Ink And Chartjunk
03:26
Skill Challenge
01:33
Solution
08:13
Section Recap Notebook
00:04

Data Formats And I/O

11 lectures
Section Intro
00:52
Reading JSON
05:01
Reading HTML
14:12
Reading Excel
07:58
Creating Output: The to_* Family Of Methods
14:56
BONUS: Introduction To Pickling
06:24
Pickles In Pandas
04:53
The Many Other Formats
04:09
Skill Challenge
02:41
Solution
07:10
Section Recap Notebook
00:04

Appendix A - Rapid-Fire Python Fundamentals

29 lectures
Section Intro
01:45
Data Types
02:35
Variables
08:27
Arithmetic And Augmented Assignment Operators
07:16
Ints And Floats
08:54
Booleans And Comparison Operators
05:12
Strings
07:52
Methods
06:29
Containers I: Lists
06:08
Lists vs. Strings
06:53
List Methods And Functions
07:54
Containers II: Tuples
04:43
Containers III: Sets
10:32
Containers IV: Dictionaries
05:15
Dictionary Keys And Values
08:14
Membership Operators
04:28
Controlling Flow: if, else, And elif
08:21
Truth Value Of Non-booleans
03:28
For Loops
05:05
The range() Immutable Sequence
05:10
While Loops
05:55
Break And Continue
04:15
Zipping Iterables
03:39
List Comprehensions
07:47
Defining Functions
10:19
Function Arguments: Positional vs Keyword
06:54
Lambdas
05:28
Importing Modules
05:38
Section Recap Notebook
00:04

Appendix B - Going Local: Installation And Setup

3 lectures
Installing Anaconda And Python - Windows
07:17
Installing Anaconda And Python - Mac
02:27
Installing Anaconda And Python - Linux
04:13

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