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Development Programming Languages Pandas

The Complete Pandas Bootcamp 2021: Data Science with Python

Pandas fully explained | 150+ Exercises | Must-have skills for Machine Learning & Finance | + Scikit-Learn and Seaborn
Rating: 4.7 out of 54.7 (1,802 ratings)
12,620 students
Created by Alexander Hagmann
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Bring your Data Handling & Data Analysis skills to an outstanding level.
  • Learn and practice all relevant Pandas methods and workflows with Real-World Datasets
  • Learn Pandas based on NEW Version 1.x (the days of versions 0.x are over)
  • Import, clean, and merge messy Data and prepare Data for Machine Learning
  • Master a complete Machine Learning Project A-Z with Pandas, Scikit-Learn, and Seaborn
  • Analyze, visualize, and understand your Data with Pandas, Matplotlib, and Seaborn
  • Practice and master your Pandas skills with Quizzes, 150+ Exercises, and Comprehensive Projects
  • Import Financial/Stock Data from Web Sources and analyze them with Pandas
  • Learn and master the most important Pandas workflows for Finance
  • Learn how to best transition from Versions 0.x to new Version 1.x
  • Learn the Basics of Pandas and Numpy Coding (Appendix)
  • Learn and master important Statistical Concepts with scipy
Curated for the Udemy for Business collection

Course content

31 sections • 325 lectures • 33h 51m total length

  • Preview09:19
  • Preview05:27
  • Preview04:26
  • More FAQ / Important Information
    02:37
  • Installation of Anaconda
    08:08
  • Opening a Jupyter Notebook
    09:29
  • How to use Jupyter Notebooks
    14:00
  • How to tackle Pandas Version 1.0
    03:07

  • Preview04:19
  • Download: Part 1 Course Materials
    02:22

  • Create your very first Pandas DataFrame (from csv)
    09:09
  • Pandas Display Options and the methods head() & tail()
    06:41
  • First Data Inspection
    11:25
  • Built-in Functions, Attributes and Methods with Pandas
    09:34
  • Make it easy: TAB Completion and Tooltip
    08:57
  • First Steps
    3 questions
  • Explore your own Dataset: Coding Exercise 1 (Intro)
    03:46
  • Preview04:14
  • Selecting Columns
    06:05
  • Selecting one Column with the "dot notation"
    02:16
  • Zero-based Indexing and Negative Indexing
    03:04
  • Selecting Rows with iloc (position-based indexing)
    10:07
  • Slicing Rows and Columns with iloc (position-based indexing)
    04:39
  • Preview00:02
  • Selecting Rows with loc (label-based indexing)
    03:14
  • Slicing Rows and Columns with loc (label-based indexing)
    10:21
  • Label-based Indexing Cheat Sheets
    00:02
  • Indexing and Slicing with reindex()
    05:30
  • Summary, Best Practices and Outlook
    06:30
  • Indexing and Slicing
    6 questions
  • Coding Exercise 2 (Intro)
    01:20
  • Coding Exercise 2 (Solution)
    03:58
  • Advanced Indexing and Slicing (optional)
    05:22

  • Intro
    00:17
  • First Steps with Pandas Series
    03:53
  • Analyzing Numerical Series with unique(), nunique() and value_counts()
    13:50
  • Analyzing non-numerical Series with unique(), nunique(), value_counts()
    07:17
  • Creating Pandas Series (Part 1)
    06:12
  • Creating Pandas Series (Part 2)
    05:40
  • Indexing and Slicing Pandas Series
    10:08
  • Sorting of Series and Introduction to the inplace - parameter
    08:59
  • nlargest() and nsmallest()
    03:48
  • idxmin() and idxmax()
    05:20
  • Manipulating Pandas Series
    07:47
  • Pandas Series
    5 questions
  • Coding Exercise 3 (Intro)
    00:04
  • Coding Exercise 3 (Solution)
    06:18
  • First Steps with Pandas Index Objects
    05:57
  • Creating Index Objects from Scratch
    03:16
  • Changing Row Index with set_index() and reset_index()
    10:07
  • Changing Column Labels
    03:20
  • Renaming Index & Column Labels with rename()
    03:51
  • Pandas Index objects
    3 questions
  • Coding Exercise 4 (Intro)
    00:04
  • Coding Exercise 4 (Solution)
    04:00

  • Intro
    00:09
  • Filtering DataFrames by one Condition
    10:20
  • Filtering DataFrames by many Conditions (AND)
    04:45
  • Filtering DataFrames by many Conditions (OR)
    05:04
  • Advanced Filtering with between(), isin() and ~
    08:35
  • any() and all()
    04:07
  • Removing Columns
    05:18
  • Removing Rows
    07:06
  • Adding new Columns to a DataFrame
    03:27
  • Creating Columns based on other Columns
    06:37
  • Adding Columns with insert()
    02:43
  • Preview07:43
  • Adding new Rows (hands-on approach)
    02:55
  • DataFrame Basics II
    4 questions
  • Coding Exercise 5 (Intro)
    00:04
  • Coding Exercise 5 (Solution)
    08:49

  • Intro
    00:31
  • Best Practice (How you should do it)
    09:15
  • Chained Indexing: How you should NOT do it (Part 1)
    09:35
  • Chained Indexing: How you should NOT do it (Part 2)
    08:42
  • View vs. Copy
    05:58
  • Simple Rules what to do when...
    08:09
  • Manipulating DataFrames / Slices
    3 questions
  • Coding Exercise 6 (Intro)
    00:04
  • Coding Exercise 6 (Solution)
    05:55

  • Intro
    00:14
  • Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update)
    09:09
  • Ranking DataFrames with rank()
    08:07
  • nunique() and nlargest() / nsmallest() with DataFrames
    05:30
  • Summary Statistics and Accumulations
    10:26
  • The agg() method
    03:27
  • Coding Exercise 7 (Intro)
    00:04
  • Coding Exercise 7 (Solution)
    05:03
  • User-defined Functions with apply(), map() and applymap()
    13:46
  • Hierarchical Indexing (Part 1)
    10:43
  • Hierarchical Indexing (Part 2)
    11:18
  • String Operations (Part 1)
    07:20
  • String Operations (Part 2)
    09:36
  • Coding Exercise 8 (Intro)
    00:04
  • Coding Exercise 8 (Solution)
    08:50

  • Intro
    00:12
  • The plot() method
    08:48
  • Customization of Plots
    12:56
  • Histograms (Part 1)
    04:34
  • Histograms (Part 2)
    06:27
  • Barcharts and Piecharts
    04:00
  • Scatterplots
    07:18
  • Coding Exercise 9 (Intro)
    00:04
  • Coding Exercise 9 (Solution)
    04:56

  • Welcome to PART 2: Full Data Workflow A-Z
    00:17
  • Download: Part 2 Course Materials
    00:04

  • Importing csv-files with pd.read_csv
    14:13
  • Importing messy csv-files with pd.read_csv
    09:44
  • Importing Data from Excel with pd.read_excel()
    Preview11:11
  • Importing messy Data from Excel with pd.read_excel()
    08:06
  • Importing Data from the Web with pd.read_html()
    07:09
  • Coding Exercise 10
    00:17

Requirements

  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming videos.
  • Ideally some Spreadsheet Basics/Programming Basics (not mandatory, the course guides you through the basics)

Description

######### UPDATE (November 2020) ###########

  • Added: Introduction to Machine Learning with Pandas and scikit-learn - incl. a comprehensive ML Project A-Z

  • Added: Another comprehensive Final Project (Explanatory Data Analysis) to test your skills

  • Updated to latest Pandas Version 1.1! This is the first course that covers Pandas 1.x. It gives optimal guidance on how to transition from version 0.x to version 1.x!

##########################################


Welcome to the web´s most comprehensive Pandas Bootcamp with 34 hours of video content, 150+ exercises, and two large and comprehensive Final Projects that test your skills! This course has one goal: Bringing your data handling skills to the next level to build your career in Data Science, Machine Learning, Finance & co.

This course has five parts:

  • Pandas Basics - from Zero to Hero (Part 1).

  • The complete data workflow A-Z with Pandas: Importing, Cleaning, Merging, Aggregating, and Preparing Data for Machine Learning. (Part 2)

  • Two Comprehensive Project Challenges that are frequently used in Data Science job recruiting/assessment centers: Test your skills! (Part 3).

  • Application 1: Pandas for Finance, Investing and other Time Series Data (Part 4)

  • Application 2: Machine Learning with Pandas and scikit-learn (Part 5)


Why should you learn Pandas?

The world is getting more and more data-driven. Data Scientists are gaining ground with $100k+ salaries. It´s time to switch from soapbox cars (spreadsheet software like Excel) to High Tuned Racing Cars (Pandas)!

Python is a great platform/environment for Data Science with powerful Tools for Science, Statistics, Finance, and Machine Learning. The Pandas Library is the Heart of Python Data Science. Pandas enables you to import, clean, join/merge/concatenate, manipulate, and deeply understand your Data and finally prepare/process Data for further Statistical Analysis, Machine Learning, or Data Presentation. In reality, all of these tasks require a high proficiency in Pandas! Data Scientists typically spend up to 85% of their time manipulating Data in Pandas.


Can you start right now?

A frequently asked question of Python Beginners is: "Do I need to become an expert in Python coding before I can start working with Pandas?"

The clear answer is: "No! Do you need to become a Microsoft Software Developer before you can start with Excel? Probably not!"

You require some Python Basics like data types, simple operations/operators, lists and numpy arrays. In the Appendix of this course, you can find a Python crash course. This Python Introduction is tailor-made and sufficient for Data Science purposes!

In addition, this course covers fundamental statistical concepts (coding with scipy).   

As a Summary, if you primarily want to use Python for Data Science or as a replacement for Excel, this course is a perfect match!


Why should you take this Course?

  • It is the most relevant and comprehensive course on Pandas.

  • It is the most up-to-date course and the first that covers Pandas Version 1.x. The Pandas Library has experienced massive improvements in the last couple of months. Working with and relying on outdated code can be painful.

  • Pandas isn´t an isolated tool. It is used together with other Libraries: Matplotlib and Seaborn for Data Visualization | Numpy, Scipy and Scikit-Learn for Machine Learning, scientific and statistical computing. This course covers all these Libraries.

  • In real-world projects, coding and the business side of things are equally important. This is probably the only Pandas course that teaches both: in-depth Pandas Coding and Big-Picture Thinking.

  • It serves as a Pandas Encyclopedia covering all relevant methods, attributes, and workflows for real-world projects. If you have problems with any method or workflow, you will most likely get help and find a solution in this course.

  • It shows and explains the full real-world Data Workflow A-Z: Starting with importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Explanatory Data Analysis through to preparing and processing data for Statistics, Machine Learning, Finance, and Data Presentation. 

  • It explains Pandas Coding on real Data and real-world Problems. No toy data! This is the best way to learn and understand Pandas.

  • It gives you plenty of opportunities to practice and code on your own. Learning by doing. In the exercises, you can select the level of difficulty with optional hints and guidance/instruction.

  • Pandas is a very powerful tool. But it also has pitfalls that can lead to unintended and undiscovered errors in your data. This course also focuses on commonly made mistakes and errors and teaches you, what you should not do.

  • Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.


I am looking forward to seeing you in the course!

Who this course is for:

  • Everyone who want to step into Data Science. Pandas is Key to everything.
  • Data Scientists who want to improve their Data Handling/Manipulation skills.
  • Everyone who want to switch Data Projects from Excel to more powerful tools (e.g. in Research/Science)
  • Investment/Finance Professionals who reached the limits of Excel.

Featured review

Itzel Beltrán Mata
Itzel Beltrán Mata
53 courses
2 reviews
Rating: 5.0 out of 5a year ago
Definitely this is the best course i've ever seen. All the classes are perfect and the teacher is the best. He answer all the questions. The only thing i would change is that we work with the same data (3 differents data frames in all course). Englisch is not my first language and sometimes I had problems with the captions but i'm sure I learn a lot of great things. Thank you so much.

Instructor

Alexander Hagmann
Data Scientist | Finance Professional | Entrepreneur
Alexander Hagmann
  • 4.7 Instructor Rating
  • 3,470 Reviews
  • 35,215 Students
  • 8 Courses

Alexander is a Data Scientist and Finance Professional with more than 10 years of experience in the Finance and Investment Industry.

He is also a Bestselling Udemy Instructor for

- Data Analysis/Manipulation with Pandas

- (Financial) Data Science

- Python for Business and Finance

- Algorithmic Trading

Alexander started his career in the traditional Finance sector and moved step-by-step into Data-driven and Artificial Intelligence-driven Finance roles. He is currently working on cutting-edge Fintech projects and creates solutions for Algorithmic Trading and Robo Investing. And Alexander is excited to share his knowledge with others here on Udemy. Students who completed his courses work in the largest and most popular tech and finance companies all over the world.

Alexander´s courses have one thing in common: Content and concepts are practical and real-world proven. The clear focus is on acquiring skills and understanding concepts rather than memorizing things.   

Alexander holds a Master´s degree in Finance and passed all three CFA Exams (he is currently no active member of the CFA Institute).   

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