pandas python

The Complete Pandas Bootcamp 2022: Data Science with Python Course is the most comprehensive Pandas Bootcamp on the web. It features 34 hours of video content, 150+ exercises, and two real-world projects to put theoretical knowledge into practice.

The objective of the course is to advance the student’s understanding of data handling and manipulation so that they can advance their Data Science career. The course is divided into 5 sections that focus on the basics of Pandas, Pandas for Finance and Investing, Pandas and Scikit-Learn for Machine Learning, etc. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get The Complete Pandas Bootcamp 2022: Data Science with Python Course for INR 499.

Who all can opt for this course?

  • Anyone who wishes to enter the field of data science. The key to everything is the Panda.
  • Data Scientists looking to develop their data handling and manipulation abilities.
  • Anyone who wants to move their data projects from Excel to more powerful tools (for example, in science/research).
  • Finance/Investment professionals who reached the limits of Excel.

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration33 Hours
Student Enrollment21,561 students
InstructorAlexander Hagmann
Topics CoveredPandas basics, Machine Learning with Pandas and sci-kit-learn, DataFrame basics
Course LevelBeginner
Total Student Reviews2,965

Learning Outcomes

  • Brush up your Data Handling & Data Analysis skills
  • Study and practice Pandas’ workflows and methodologies by using real-world datasets
  • Understand and learn Pandas based on the new version 1.x
  • Import, clean, and integrate messy data for machine learning
  • Learn how to use Pandas, Scikit-Learn, and Seaborn to master a whole machine-learning project from A to Z
  • Utilize Seaborn, Pandas, and Matplotlib to analyze, visualize, and comprehend your data
  • Utilize quizzes, 150+ exercises, and comprehensive projects to hone your Pandas skills
  • Import financial and stock data from the web and use Pandas to analyze it
  • Learn the basics of coding with Pandas and Numpy
  • Learn and master the important statistical concepts with scipy

Course Content

S.No.Module (Duration)Topics
1.Getting Started (56 minutes)Overview / Student FAQ
Tips: How to get the most out of this course
Did you know that…?
More FAQ / Important Information
Installation of Anaconda
Opening a Jupyter Notebook
How to use Jupyter Notebooks
How to tackle Pandas Version 1.0
2.—- PART 1: PANDAS FROM ZERO TO HERO (BUILDING BLOCKS) —- (06 minutes)Intro to Tabular Data / Pandas
Download: Part 1 Course Materials
3.Pandas Basics (DataFrame Basics I) (01 hour 56 minutes)Create your very first Pandas DataFrame (from csv)
Pandas Display Options and the methods head() & tail()
First Data Inspection
Built-in Functions, Attributes and Methods with Pandas
Make it easy: TAB Completion and Tooltip
First Steps
Explore your own Dataset: Coding Exercise 1 (Intro)
Explore your own Dataset: Coding Exercise 1 (Solution)
Selecting Columns
Selecting one Column with the “dot notation”
Zero-based Indexing and Negative Indexing
Selecting Rows with iloc (position-based indexing)
Slicing Rows and Columns with iloc (position-based indexing)
Position-based Indexing Cheat Sheets
Selecting Rows with loc (label-based indexing)
Slicing Rows and Columns with loc (label-based indexing)
Label-based Indexing Cheat Sheets
Indexing and Slicing with reindex()
Summary, Best Practices and Outlook
Indexing and Slicing
Coding Exercise 2 (Intro)
Coding Exercise 2 (Solution)
Advanced Indexing and Slicing (optional)
4.Pandas Series and Index Objects (01 hour 50 minutes)Intro
First Steps with Pandas Series
Analyzing Numerical Series with unique(), nunique() and value_counts()
Analyzing non-numerical Series with unique(), nunique(), value_counts()
Creating Pandas Series (Part 1)
Creating Pandas Series (Part 2)
Indexing and Slicing Pandas Series
Sorting of Series and Introduction to the inplace – parameter
nlargest() and nsmallest()
idxmin() and idxmax()
Manipulating Pandas Series
Pandas Series
Coding Exercise 3 (Intro)
Coding Exercise 3 (Solution)
First Steps with Pandas Index Objects
Creating Index Objects from Scratch
Changing Row Index with set_index() and reset_index()
Changing Column Labels
Renaming Index & Column Labels with rename()
Pandas Index objects
Coding Exercise 4 (Intro)
Coding Exercise 4 (Solution)
5.DataFrame Basics II (01 hour 17 minutes)Intro
Filtering DataFrames by one Condition
Filtering DataFrames by many Conditions (AND)
Filtering DataFrames by many Conditions (OR)
Advanced Filtering with between(), isin() and ~
any() and all()
Removing Columns
Removing Rows
Adding new Columns to a DataFrame
Creating Columns based on other Columns
Adding Columns with insert()
Creating DataFrames from Scratch with pd.DataFrame()
Adding new Rows (hands-on approach)
DataFrame Basics II
Coding Exercise 5 (Intro)
Coding Exercise 5 (Solution)
6.Manipulating Elements in a DataFrame / Slice +++Important, know the Pitfalls!+++ (48 minutes)Intro
Best Practice (How you should do it)
Chained Indexing: How you should NOT do it (Part 1)
Chained Indexing: How you should NOT do it (Part 2)
View vs. Copy
Simple Rules what to do when…
Manipulating DataFrames / Slices
Coding Exercise 6 (Intro)
Coding Exercise 6 (Solution)
7.DataFrame Basics III (01 hour 43 minutes)Intro
Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update)
Ranking DataFrames with rank()
nunique() and nlargest() / nsmallest() with DataFrames
Summary Statistics and Accumulations
The agg() method
Coding Exercise 7 (Intro)
Coding Exercise 7 (Solution)
User-defined Functions with apply(), map() and applymap()
Hierarchical Indexing (Part 1)
Hierarchical Indexing (Part 2)
String Operations (Part 1)
String Operations (Part 2)
Coding Exercise 8 (Intro)
Coding Exercise 8 (Solution)
8.Visualization with Matplotlib (49 minutes)Intro
The plot() method
Customization of Plots
Histograms (Part 1)
Histograms (Part 2)
Barcharts and Piecharts
Coding Exercise 9 (Intro)
Coding Exercise 9 (Solution)
9.—- PART 2: FULL DATA WORKFLOW A-Z —- (22 seconds)Welcome to PART 2: Full Data Workflow A-Z
Download: Part 2 Course Materials
10.Importing Data (50 minutes)Importing csv-files with pd.read_csv
Importing messy csv-files with pd.read_csv
Importing Data from Excel with pd.read_excel()
Importing messy Data from Excel with pd.read_excel()
Importing Data from the Web with pd.read_html()
Coding Exercise 10
11.Cleaning Data (02 hours 24 minutes)First Inspection & Handling of inconsistent Data
String Operations
Changing Datatype of Columns with astype()
Intro NA values / missing values
Detection of missing Values
Removing missing values
Replacing missing values
Intro Duplicates
Detection of Duplicates
Handling / Removing Duplicates
The ignore_index parameter (NEW in Pandas 1.0)
Detection of Outliers
Handling / Removing Outliers
Categorical Data
Pandas Version 1.0: New dtypes and pd.NA
Coding Exercise 11 (Intro)
Coding Exercise 11 (Solution)
12.Merging, Joining, and Concatenating Data (01 hour 33 minutes)Intro
Adding Rows with append() and pd.concat() (Part 1)
Adding Rows with pd.concat() (Part 2)
Arithmetic with Pandas Objects / Data Alignment
EXCURSUS: Comparing two DataFrames / Identify Differences
Outer Joins with merge()
Inner Joins with merge()
Outer Joins (without Intersection) with merge()
Left Joins (without Intersection) with merge()
Right Joins (without Intersection) with merge()
Left Joins with merge()
Right Joins with merge()
Joining on different Column Names / Indexes
Joining on more than one Column
pd.merge() and join()
Coding Exercise 12
13.GroupBy Operations (01 hour 41 minutes)Intro
Understanding the GroupBy Object
Splitting with many Keys
split-apply-combine explained
split-apply-combine applied
GroupBy 1
Advanced aggregation with agg()
GroupBy Aggregation with Relabeling (NEW – Pandas Version 0.25)
Transformation with transform()
Replacing NA Values by group-specific Values
Generalizing split-apply-combine with apply()
Hierarchical Indexing with Groupby
stack() and unstack()
GroupBy 2
Coding Exercise 13 (Intro)
Coding Exercise 13 (Solution)
14.Reshaping and Pivoting DataFrames (01 hour 06 minutes)Intro
Transposing Rows and Columns
Pivoting DataFrames with pivot()
Limits of pivot()
melting DataFrames with melt()
Coding Exercise 14
15.Data Preparation and Feature Creation (01 hour 34 minutes)Intro
Arithmetic Operations (Part 1)
Arithmetic Operations (Part 2)
Transformation/Mapping with map()
Conditional Transformation
Discretization and Binning with pd.cut() (Part 1)
Discretization and Binning with pd.cut() (Part 2)
Discretization and Binning with pd.qcut()
Floors and Caps
Scaling / Standardization
Creating Dummy Variables
String Operations
Coding Exercise 15
16.Advanced Visualization with Seaborn (40 minutes)Intro
First Steps in Seaborn
Categorical Plots
Joint Plots / Regression Plots
Matrixplots / Heatmaps
Coding Exercise 16
17.—- PART 3: COMPREHENSIVE PROJECT CHALLENGES —- (18 seconds)Intro and Downloads
18.Data Manipulation and Aggregation Challenge (Olympic Medal Tables) (36 minutes)Olympic Medal Tables (Instruction & Hints)
Olympic Medal Tables (Solution Part 1)
Olympic Medal Tables (Solution Part 2)
Olympic Medal Tables (Solution Part 3)
19.Explanatory Data Analysis Challenge (01 hour 09 minutes)Challenge Introduction and Overview
Merging and Concatenating (Solution Part 1)
Data Cleaning 1 (Solution Part 2)
Data Cleaning 2 (Solution Part 3)
The most successful Countries (Solution Part 4)
Impact of GDP, Population and Politics (Solution Part 5)
Statistical Analysis and Hypothesis Testing (Solution Part 6)
Aggregating and Ranking (Solution Part 7)
Summer Games vs. Winter Games – does Location matter? (Solution Part 8)
Men vs. Women – do Culture & Religion matter? (Solution Part 9)
National Sports and Traditions (Solution Part 10)
20.—- PART 4: PANDAS FOR FINANCE, INVESTING & TIME SERIES —- (25 seconds)Welcome to PART 4: Finance and Investments with Pandas
Download: Part 4 Course Materials
21.Time Series Basics (01 hour 26 minutes)Importing Time Series Data from csv-files
Converting strings to datetime objects with pd.to_datetime()
Initial Analysis / Visualization of Time Series
Indexing and Slicing Time Series
Creating a customized DatetimeIndex with pd.date_range()
More on pd.date_range()
Downsampling Time Series with resample() (Part 1)
Downsampling Time Series with resample (Part 2)
The PeriodIndex object
Advanced Indexing with reindex()
22.Pandas for Finance and Investing (01 hour 16 minutes)Intro
Getting Ready (Installing required package)
Importing Stock Price Data from Yahoo Finance (it still works!)
Initial Inspection and Visualization
Normalizing Time Series to a Base Value (100)
The shift() method
The methods diff() and pct_change()
Measuring Stock Performance with MEAN Returns and STD of Returns
Financial Time Series – Return and Risk
Financial Time Series – Covariance and Correlation
Helpful DatetimeIndex Attributes and Methods
Filling NA Values with bfill, ffill and interpolation
Coding Exercise 17
23.—- PART 5: MACHINE LEARNING WITH PANDAS AND SCIKIT-LEARN —- (13 seconds)Overview & Downloads
24.Introduction to Regression and Classification (38 minutes)Machine Learning – an Overview
Linear Regression with scikit-learn – a simple Introduction
Making Predictions with Linear Regression
Logistic Regression with scikit-learn – a simple Introduction (Part 1)
Logistic Regression with scikit-learn – a simple Introduction (Part 2)
25.BONUS: Machine Learning Project A-Z (Regression) (01 hour 00 minutes)Project Intro
Importing the Dataset and first Inspection
Cleaning the Data and Creating more Features
Explanatory Data Analysis (Part 1)
Explanatory Data Analysis (Part 2)
Feature Engineering (Part 1)
Feature Engineering (Part 2)
Splitting the Data into Training Set and Test Set
Training the Machine Learning Model
Testing/Evaluating the Model with the Test Set
Feature Importance
26.+++ WHAT´S NEW IN PANDAS VERSION 1.0? – A HANDS-ON GUIDE +++ (47 minutes)Intro and Overview
How to update Pandas to Version 1.0
Downloads for this Section
Important Recap: Pandas Display Options (Changed in Version 0.25)
Info() method – new and extended output
NEW Extension dtypes (“nullable” dtypes): Why do we need them?
Creating the NEW extension dtypes with convert_dtypes()
NEW pd.NA value for missing values
The NEW “nullable” Int64Dtype
The NEW StringDtype
The NEW “nullable” BooleanDtype
Addition of the ignore_index parameter
Removal of prior Version Deprecations
27.—- APPENDIX: PYTHON BASICS, NUMPY & STATISTICS —- (07 seconds)Welcome to the Appendix
28.Python Basics (02 hours 49 minutes)Downloads
First Steps
Data Types: Integers and Floats
Data Types: Strings
Data Types: Lists (Part 1)
Data Types: Lists (Part 2)
Data Types: Tuples
Data Types: Sets
Operators & Booleans
Conditional Statements (if, elif, else, while)
For Loops
Key words break, pass, continue
Generating Random Numbers
User Defined Functions (Part 1)
User Defined Functions (Part 2)
User Defined Functions (Part 3)
Visualization with Matplotlib
Python Basics
Python Basics Quiz: Solution
29.The Numpy Package (01 hour 39 minutes)Downloads
Introduction to Numpy Arrays
Numpy Arrays: Vectorization
Numpy Arrays: Indexing and Slicing
Numpy Arrays: Shape and Dimensions
Numpy Arrays: Indexing and Slicing of multi-dimensional Arrays
Numpy Arrays: Boolean Indexing
Generating Random Numbers
Performance Issues
Case Study: Numpy vs. Python Standard Library
Summary Statistics
Visualization and (Linear) Regression
Numpy Quiz: Solution
30.Statistical Concepts (03 hours 02 minutes)Statistics – Overview, Terms and Vocabulary
Downloads for this Section
Population vs. Sample
Visualizing Frequency Distributions with plt.hist()
Relative and Cumulative Frequencies with plt.hist()
Measures of Central Tendency (Theory)
Coding Measures of Central Tendency – Mean and Median
Coding Measures of Central Tendency – Geometric Mean
Variability around the Central Tendency / Dispersion (Theory)
Minimum, Maximum and Range with Python/Numpy
Percentiles with Python/Numpy
Variance and Standard Deviation with Python/Numpy
Skew and Kurtosis (Theory)
How to calculate Skew and Kurtosis with scipy.stats
How to generate Random Numbers with Numpy
Reproducibility with np.random.seed()
Probability Distributions – Overview
Discrete Uniform Distributions
Continuous Uniform Distributions
The Normal Distribution (Theory)
Creating a normally distributed Random Variable
Normal Distribution – Probability Density Function (pdf) with scipy.stats
Normal Distribution – Cumulative Distribution Function (cdf) with scipy.stats
The Standard Normal Distribution and Z-Values
Properties of the Standard Normal Distribution (Theory)
Probabilities and Z-Values with scipy.stats
Confidence Intervals with scipy.stats
Covariance and Correlation Coefficient (Theory)
Cleaning and preparing the Data – Movies Database (Part 1)
Cleaning and preparing the Data – Movies Database (Part 2)
How to calculate Covariance and Correlation in Python
Correlation and Scatterplots – visual Interpretation
What is Linear Regression? (Theory)
A simple Linear Regression Model with numpy & Scipy
How to interpret Intercept and Slope Coefficient
Case Study (Part 1): The Market Model (Single Factor Model)
Case Study (Part 2): The Market Model (Single Factor Model)
31.What´s next? (outlook and additional resources) (03 minutes)Bonus Lecture

Resources Required

  • A computer that has either Windows, Mac, or Linux, and is capable of storing and running Anaconda. The course will walk students through installing the necessary free software.
  • A stable and strong internet connection capable of streaming videos.
  • Ideally, some fundamental knowledge of spreadsheets and programming (not mandatory, the course guides you through the basics).

Featured Review

Bhanurdra Narayan Mohapatra (5/5): Thanks Alex for such a great course. This is the best course for anyone like to learn Pandas. I can sense that you have brought the do’s and don’t’s from your real life work experience. The course is curated such a way anyone can learn. Anyone wants to learn Machine Learning, this is the starting point. Only thing I would like to some Videos regarding handling of Json, databases, except that it is perfect. Thanks Alex for such great course !!


  • Husain Merchant (5/5): Alexander is an excellent teacher, and his explanations can always be understood.
  • Itzel Beltrán Mata (5/5): All the classes are perfect and the teacher is the best.
  • Michelle Price (4/5): Excellent approach to explaining in detail each element of the lines of code.
  • Ks (5/5): This is one of the best structured and presented courses I ever took in my life.


  • Kevin M. (2.5/5): Your voice is one of the problems but currently on Part 1 you are not explaining why you use some these pandas function but are just showcasing what they do.
  • Felice S. (2/5): Video and audio quality should be drastically improved.
  • Juan Miguel R. (1.5/5): Nothing personal, but it was too slow in my opinion. A lot of time used to learning something new in every lesson.
  • Giorgos M. (1/5): The structure of the content is really bad. Not recommended.

About the Author

Alexander Hagmann is the instructor of the course. He is a Data Scientist, Finance Professional, and Entrepreneur. He offers a total of 15 courses and holds a 4.6 instructor rating and 9,878 reviews on Udemy. He has taught 85,415 students so far.

  • Alexander has more than 10 years of experience in the finance and investment industry as a data scientist and finance professional.
  • Additionally, he is a top-rated Udemy instructor for data analysis and manipulation using pandas.
  • Alexander began his career in the conventional finance industry before advancing gradually into data-driven and AI-driven finance roles.
  • He presently develops solutions for Robo Investing and Algorithmic Trading while working on cutting-edge Fintech initiatives.
  • His graduates hold positions in some of the biggest and most well-known IT and banking firms worldwide.
  • One thing all of Alexander’s courses have in common is their practical, real-world-proven content and principles.
  • The emphasis is clearly on understanding concepts and developing abilities rather than memorization.
  • Alexander has a Master’s in Finance and has successfully completed all three CFA Exams (he is currently no active member of the CFA Institute).

Comparison Table

ParametersThe Complete Pandas Bootcamp 2022: Data Science with PythonPython for Financial Analysis and Algorithmic TradingManage Finance Data with Python & Pandas: Unique Masterclass
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration34 hours16.5 hours27 hours
Rating4.6 /54.5 /54.8 /5
Student Enrollments21,561113,9928,470
InstructorsAlexander HagmannJose PortillaAlexander Hagmann
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