Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Photoshop Graphic Design Adobe Illustrator Drawing Digital Painting InDesign Character Design Figure Drawing Canva
Life Coach Training Neuro-Linguistic Programming Personal Development Mindfulness Meditation Personal Transformation Life Purpose Emotional Intelligence Neuroscience
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Google Analytics
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Modeling Data Analysis Big Data
Business Fundamentals Entrepreneurship Fundamentals Business Strategy Online Business Business Plan Startup Blogging Freelancing Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
2021-01-25 01:53:47
30-Day Money-Back Guarantee
IT & Software Other IT & Software Pandas

The Ultimate Pandas Bootcamp: Advanced Python Data Analysis

Master the powerful pandas library to analyze, manipulate and visualize data. More than 10 datasets & bonuses included!
Bestseller
Rating: 4.6 out of 54.6 (275 ratings)
18,946 students
Created by Andy Bek
Last updated 3/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn everything there is to know about pandas - from absolute scratch!
  • Gain a deep and hands-on understanding of pandas data structures.
  • Transform, clean, filter, groupby, pivot, and otherwise manipulate a any dataset.
  • Understand related computer science topics like random-number generators, binary operators, memory pointers, and more!
  • Practice reading data from the web, pickles, Excel files right within pandas.
  • Discover and learn hundreds of methods, attributes, and techniques to manipulate data in pandas and python.

Requirements

  • A computer (Windows/Mac/Linux). That's all!
  • No prior knowledge of python is required.
  • No prior knowledge of pandas is required.

Description

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 (2021) 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!

Who this course is for:

  • Anyone looking to deeply understand and master pandas
  • Anyone interested in mastering data analysis with python

Course content

15 sections • 332 lectures • 32h 4m total length

  • Preview01:25
  • 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

  • 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

  • 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

  • Preview01:40
  • 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

  • Preview02:23
  • 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

  • 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

  • Preview01:51
  • 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

  • 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

  • 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

  • Preview01:13
  • 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

Instructor

Andy Bek
Software Consultant
Andy Bek
  • 4.6 Instructor Rating
  • 275 Reviews
  • 18,946 Students
  • 1 Course

Hi! I'm Andy. I'm freelance software developer and capital markets consultant. I've spent close to a decade working with data, using code to automate business workflows, and consulting financial institutions on data-intensive applications.

Aside from teaching, I run a AI and data consulting firm that targets fintechs in the post-trade services niche.

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Impressum Kontakt
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.