4.79 (355 reviews)
☑ 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.
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In The Ultimate Pandas Bootcamp (2021) you won’t be working with outdated versions of pandas, writing repetitive commands on the same boring dataset.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!
Pandas Is Not Single
Cloud vs Local
Series At A Glance
What Is A Series?
Parameters vs Arguments
What’s In The Data?
The .dtype Attribute
BONUS: What Is dtype('o'), Really?
Index And RangeIndex
Series And Index Names
The head() And tail() Methods
Extracting By Index Position
Accessing Elements By Label
BONUS: The add_prefix() And add_suffix() Methods
Using Dot Notation
Boolean Masks And The .loc Indexer
Extracting By Position With .iloc
BONUS: Using Callables With .loc And .iloc
Selecting With .get()
Series Methods And Handling
Reading In Data With read_csv()
Series Sizing With .size, .shape, And len()
Unique Values And Series Monotonicity
The count() Method
Accessing And Counting NAs
BONUS: Another Approach
The Other Side: notnull() And notna()
BONUS: Booleans Are Literally Numbers In Python
Dropping And Filling NAs
The describe() Method
mode() And value_counts()
idxmax() And idxmin()
Sorting With sort_values()
nlargest() And nsmallest()
Sorting With sort_index()
Series Arithmetics And fill_value()
BONUS: Calculating Variance And Standard Deviation
Pairwise Differences With diff()
Filtering: filter(), where(), And mask()
Transforming With update(), apply() And map()
Solution I - Reading Data
Solution II - Mean, Median, And Standard Deviation
Solution III - Z-scores
Working With DataFrames
What Is A DataFrame
Creating A DataFrame
BONUS - Four More Ways To Build DataFrames
The info() Method
Reading In Nutrition Data
Some Cleanup: Removing The Duplicated Index
The sample() Method
BONUS - Sampling With Replacement Or Weights
BONUS - How Are Random Numbers Generated?
Changing The Index
Extracting From DataFrames By Label
DataFrame Extraction by Position
Single Value Access With .at And .iat
BONUS - The get_loc() Method
More Cleanup: Going Numeric
The astype() Method
DataFrame replace() + A Glimpse At Regex
Part I: Collecting The Units
The rename() Method
BONUS - dropna() With Subset
Part II: Merging Units With Column Names
Part III: Removing Units From Values
Filtering in 2D
Using Series between() With DataFrames
BONUS - Min, Max and Idx[MinMax], And Good Foods
DataFrame nlargest() And nsmallest()
Another Skill Challenge
DataFrames In Depth
Introducing A New Dataset
Quick Review: Indexing With Boolean Masks
More Approaches To Boolean Masking
Binary Operators With Booleans
BONUS - XOR and Complement Binary Ops
Conditions As Variables
Fancy Indexing With lookup()
Sorting By Index Or Column
Sorting vs. Reordering
BONUS - Another Way
15. BONUS - Please Avoid Sorting Like This
Removing DataFrame Rows
BONUS - Removing Columns
BONUS - Another Way: pop()
BONUS - A Sophisticated Alternative
Null Values In DataFrames
Dropping And Filling DataFrame NAs
BONUS - Methods And Axes With fillna()
Calculating Aggregates With agg()
More Flexibility With apply()
Element-wise Operations With applymap()
Setting DataFrame Values
The SettingWithCopy Warning
View vs Copy
Adding DataFrame Columns
Adding Rows To DataFrames
BONUS - How Are DataFrames Stored In Memory
Working With Multiple DataFrames
Introducing (Five?) New Datasets
The Duplicated Index Issue
Enforcing Unique Indices
BONUS - Creating Multiple Indices With concat()
Column Axis Concatenation
The append() Method: A Special Case Of concat()
Concat On Different Columns
The merge() Method
The left_on And right_on Params
Inner vs Outer Joins
Left vs Right Joins
One-to-One and One-to-Many Joins
Merging By Index
The join() Method
Introducing New Data
Index And RangeIndex
Creating A MultiIndex
MultiIndex From read_csv()
Indexing Hierarchical DataFrames
Indexing Ranges And Slices
BONUS - Use : With pd.IndexSlice!
Cross Sections With xs()
The Anatomy Of A MultiIndex Object
Adding Another Level
Removing MultiIndex Levels
More MultiIndex Methods
Reshaping With stack()
The Flipside: unstack()
BONUS: Creating MultiLevel Columns Manually
An Easier Way: transpose()
BONUS - What About Panels?
GroupBy And Aggregates
New Data: Game Sales
Simple Aggregations Review
The Split-Apply-Combine Pattern
The groupby() Method
The DataFrameGroupBy Object
Customizing Index To Group Mappings
BONUS - Series groupby()
Iterating Through Groups
The filter() Method
BONUS - There's Also apply()
Reshaping With Pivots
New Data: New York City SAT Scores
What About Aggregates?
BONUS: The Problem With Average Percentage
Replicating Pivot Tables With GroupBy
MultiIndex Pivot Tables
Applying Multiple Functions
Handling Date And Time
The Python datetime Module
Parsing Dates From Text
Even Better: dateutil
From Datetime To String
Performant Datetimes With Numpy
The Pandas Timestamp
Our Dataset: Brent Prices
Date Parsing And DatetimeIndex
A Cool Shorcut: read_csv() With parse_dates
DateTimeIndex Attribute Accessors
Creating Date Ranges
Shifting Dates With pd.DateOffset
BONUS: Timedeltas And Absolute Time
Upsampling And Interpolation
What About asfreq()?
BONUS: Rolling Windows
Regex And Text Manipulation
Our Data: Boston Marathon Runners
String Methods In Python
Vectorized String Operations In Pandas
Finding Characters And Words
Strips And Whitespace
String Splitting And Concatenation
More Split Parameters
Masking With String Methods
BONUS: Parsing Indicators With get_dummies()
Introduction To Regular Expressions
More Regex Concepts
How To Approach Regex?
Is This A Valid Email?
BONUS: What's The Point Of re.compile()?
Pandas str contains(), split() And replace() With Regex
The Art Of Data Visualization
The Preliminaries Of matplotlib
Other Visualization Options
BONUS: Data Ink And Chartjunk
Data Formats And I/O
Creating Output: The to_* Family Of Methods
BONUS: Introduction To Pickling
Pickles In Pandas
The Many Other Formats
Appendix A - Rapid-Fire Python Fundamentals
Arithmetic And Augmented Assignment Operators
Ints And Floats
Booleans And Comparison Operators
Containers I: Lists
Lists vs. Strings
List Methods And Functions
Containers II: Tuples
Containers III: Sets
Containers IV: Dictionaries
Dictionary Keys And Values
Controlling Flow: if, else, And elif
Truth Value Of Non-booleans
The range() Immutable Sequence
Break And Continue
Function Arguments: Positional vs Keyword
Appendix B - Going Local: Installation And Setup
Installing Anaconda And Python - Windows
Installing Anaconda And Python - Mac
Installing Anaconda And Python - Linux
Tutor talks very slowly, I have to put it at 1,5x speed and even then I'm skipping a lot because I had already time to test it. I also find that a lot of exotic stuff is explained, only to be followed by "will hardly be used" or "you won't need this". I was hoping to get a more structured approach with also guidelines on which steps to take to approach a typical data analysis. Leave the details for later or refer to documentation, focus on the most commonly used tools and explain via projects, from basic to more advanced.
I found this course very detailed and well arranged from start to end. All concepts are explained very well from basics to advance. After taking this course i feel confident in analyzing data with Pandas.
This is an excellent course in pandas , regular expressions and string manipulation. The best part about this course is that it covers all the use cases that one can think about. The clarity and depth that Andy exhibits is truly remarkable. I am glad that I enrolled for this course. Would highly recommend this course :)
Alhamdhulilah!!! Excellent, from my own experience this course is amazing to learn python for Data analyis
Superb. I am an advanced pandas user and I find this course an excellent reference material. Highly recommended. Now I have both (the other one also 30+ hours long) pandas courses available on Udemy and they are both worth keeping. I hope both authors continue to add to the course as there is just so, so much more pandas can do. This course, as is, is a steal too. Thank you for creating it.
Best course in udemy! Such a nice, deep and brilliant course is this!.. I have learnt to much things from Andy and he is amazing person!
This is really good for me. it improve my knowledge about Python (Linear Regression). I know about Regression but i can explore my with python it help me lot. Thank You.
Course Contents are meeting my expectations. Course is going to cover each and every detail areas required to perform operations with Pandas module. Instructor is having good knowledge of Pandas, clear with his language and pronunciation.
This is the only Pandas Bootcamp that covers everything. I haven't found anything else. Andy is also using Google Colab which I like more than Jupyter Notebooks which kinda seems old. He goes on a somewhat fast paced which I really like and he teaches well. Would definately recommend this course
Impressive depth. Massive amount of very useful content. Well-broken up into bite-size pieces. Well-designed "skill challenges". I also like the sprinkling of humor and brief detours to follow his curiosity when interesting results show up in the example data sets ("Hmm...I wonder what brains are made of?" :D ). I'm only about a third of the way through, and this is my first Udemy course...so I don't know what other Udemy courses are like...but I'm learning a lot from this course of Andy's. Thank you Andy and Udemy.
could be better if there any task for us an asking some our project problem that were used pandas, but the contents all are super detail well explained
good until now, and could be better . sometimes seems a little bit confusing , but good job in general.
Konular gerçekten sade ve anlaşılır bir şekilde anlatılmış :) Verilen örnekler sayesinde konular daha iyi bir şekilde anlaşılıyor.
The author expresses himself clearly, in an ordered way and demonstrates familiarity with the topics introduced. The content is very complete, easy to understand and to follow, with exercises that cover solutions for everyday work. Great job and excellent course!
This is an amazing course !! Andy knows a lot, and his didactic is awesone, easy to understand ! It's a professional course, the instructor doesn't make jokes while teaching. Thank you Andy !