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Development Programming Languages Data Analysis

Data Analysis with Pandas and Python

Analyze data quickly and easily with Python's powerful pandas library! All datasets included --- beginners welcome!
Bestseller
Rating: 4.7 out of 54.7 (12,969 ratings)
145,671 students
Created by Boris Paskhaver
Last updated 4/2021
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • Perform a multitude of data operations in Python's popular "pandas" library including grouping, pivoting, joining and more!
  • Learn hundreds of methods and attributes across numerous pandas objects
  • Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
  • Resolve common issues in broken or incomplete data sets
Curated for the Udemy for Business collection

Course content

15 sections • 177 lectures • 20h 39m total length

  • Preview12:15
  • About Me
    00:56
  • Completed Course Files
    00:31
  • MacOS - Download the Anaconda Distribution, our Python development environment
    04:21
  • MacOS - Install Anaconda Distribution
    10:38
  • MacOS - Access the Terminal Application
    08:34
  • MacOS - Create conda Environment and Install pandas and Jupyter Notebook
    13:07
  • MacOS - Unpack Course Materials + The Start and Shutdown Process
    12:32
  • Windows - Download the Anaconda Distribution
    04:36
  • Windows - Install Anaconda Distribution
    07:41
  • Windows - Create conda Environment and Install pandas and Jupyter Notebook
    18:12
  • Windows - Unpack Course Materials + The Startdown and Shutdown Process
    13:13
  • Intro to the Jupyter Notebook Interface
    09:51
  • Cell Types and Cell Modes in Jupyter Notebook
    07:37
  • Code Cell Execution in Jupyter Notebook
    03:13
  • Popular Keyboard Shortcuts in Jupyter Notebook
    03:41
  • Import Libraries into Jupyter Notebook
    08:31
  • Troubleshooting Issues with Jupyter Notebook
    00:14

  • Intro to the Python Crash Course
    03:34
  • Comments
    03:18
  • Basic Data Types
    10:47
  • Operators
    15:35
  • Variables
    07:48
  • Built-in Functions
    10:42
  • Custom Functions
    16:33
  • String Methods
    20:51
  • Lists
    13:17
  • Index Positions and Slicing
    15:56
  • Dictionaries
    15:22

  • Create Jupyter Notebook for the Series Module
    02:15
  • Create A Series Object from a Python List
    10:32
  • Create A Series Object from a Python Dictionary
    03:06
  • Create a Series Object
    1 question
  • Coding Exercise SOLUTION: Create a Series Object
    00:29
  • Intro to Attributes on a Series Object
    07:17
  • Intro to Methods on a Series Object
    04:42
  • Parameters and Arguments
    10:10
  • Create Series from Dataset with the pd.read_csv Method
    15:02
  • Import Series with the read_csv Function
    1 question
  • Coding Exercise SOLUTION: Import Series with the read_csv Function
    00:22
  • Use the head and tail Methods to Return Rows from Beginning and End of Dataset
    03:42
  • Passing pandas Objects to Python Built-In Functions
    05:20
  • Accessing More Series Attributes
    06:13
  • Use the sort_values method to sort a Series in ascending or descending order
    Preview06:04
  • Use the inplace Parameter to permanently mutate a pandas data structure
    05:07
  • Use the sort_index Method to Sort the Index of a pandas Series object
    04:38
  • The sort_values and sort_index Methods
    1 question
  • Coding Exercise SOLUTION: The sort_values and sort_index Methods
    00:35
  • Use Python's in Keyword to Check for Inclusion in Series values or index
    04:00
  • Extract Series Values by Index Positiox
    04:15
  • Extract Series Values by Index Label
    10:36
  • Extract Series Values by Index Position or Index Label
    1 question
  • Coding Exercise SOLUTION: Extract Series Values by Index Position or Index Label
    00:30
  • Use the get Method to Retrieve a Value for an index label in a Series
    09:39
  • Math Methods on Series Objects
    05:39
  • Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value
    03:10
  • Use the value_counts Method to See Counts of Unique Values within a Series
    03:39
  • Use the apply Method to Invoke a Function on Every Series Values
    06:46
  • The Series#map Method
    06:52
  • A Review of the Series Module
    7 questions

  • Intro to DataFrames I Module
    09:48
  • Shared Methods and Attributes between Series and DataFrames
    13:55
  • Differences between Shared Methods
    06:48
  • Select One Column from a DataFrame
    Preview07:57
  • Select One Column from a DataFrame
    1 question
  • Coding Exercise SOLUTION: Select One Column from a DataFrame
    00:23
  • Select Two or More Columns from a DataFrame
    05:12
  • Select Two or More Columns from a DataFrame
    1 question
  • Coding Exercise SOLUTION: Select Two or More Columns from a DataFrame
    00:33
  • Add New Column to DataFrame
    08:03
  • Broadcasting Operations on DataFrames
    09:07
  • A Review of the value_counts Method
    03:54
  • Drop DataFrame Rows with Null Values with the dropna Method
    06:41
  • Delete DataFrame Rows with Missing Values
    1 question
  • Coding Exercise SOLUTION: Delete DataFrame Rows with Missing Values
    00:17
  • Fill in Null DataFrame Values with the fillna Method
    04:25
  • Convert DataFrame Column Types with the astype Method
    10:38
  • Sort a DataFrame with the sort_values Method, Part I
    Preview05:46
  • Sort a DataFrame with the sort_values Method, Part II
    04:13
  • The sort_values Method on a DataFrame
    1 question
  • Coding Exercise SOLUTION: The sort_values Method on a DataFrame
    00:41
  • Sort DataFrame Indexwith the sort_index Method
    02:59
  • Rank Series Values with the rank Method
    05:53

  • This Module's Dataset + Memory Optimization
    15:51
  • Filter a DataFrame Based on A Condition
    Preview12:57
  • Filter DataFrame with More than One Condition (AND - &)
    04:41
  • Filter DataFrame with More than One Condition (OR - |)
    08:35
  • Check for Inclusion with the isin Method
    06:17
  • Check for Null and Present DataFrame Values with the isnull and notnull Methods
    05:07
  • Check For Inclusion Within a Range of Values with the between Method
    06:51
  • Check for Duplicate DataFrame Rows with the duplicated Method
    09:05
  • Delete Duplicate DataFrame Rows with the drop_duplicates Method
    08:16
  • Identify and Count Unique Values with the unique and nunique Methods
    04:22

  • Intro to the DataFrames III Module + Import Dataset
    04:55
  • Use the set_index and reset_index methods to define a new DataFrame index
    07:26
  • Retrieve Rows by Index Label with loc Accessor
    12:42
  • Retrieve Rows by Index Position with iloc Accessor
    07:23
  • Passing second arguments to the loc and iloc Accessors
    09:10
  • Set New Value for a Specific Cell or Cells In a Row
    04:34
  • Set Multiple Values in a DataFrame
    06:08
  • Rename Index Labels or Columns in a DataFrame
    Preview09:33
  • Delete Rows or Columns from a DataFrame
    07:29
  • Create Random Sample with the sample Method
    04:43
  • Use the nsmallest / nlargest methods to get rows with smallest / largest values.
    05:36
  • Filter A DataFrame with the where method
    05:03
  • Filter A DataFrame with the query method
    09:07
  • A Review of the apply Method on a pandas Series Object
    05:53
  • Apply a Function to every DataFrame Row with the apply Method
    06:49
  • Create a Copy of a DataFrame with the copy Method
    07:05

  • Intro to the Working with Text Data Section
    06:09
  • Common String Methods - lower, upper, title, and len
    Preview07:14
  • Use the str.replace method to replace all occurrences of character with another
    08:07
  • Filter a DataFrame's Rows with String Methods
    06:43
  • More DataFrame String Methods - strip, lstrip, and rstrip
    04:31
  • Invoke String Methods on DataFrame Index and Columns
    05:30
  • Split Strings by Characters with the str.split Method
    08:41
  • More Practice with the str.split method on a Series
    06:01
  • Exploring the expand and n Parameters of the str.split Method
    07:00

  • Intro to the MultiIndex Module
    04:50
  • Create a MultiIndex on a DataFrame with the set_index Method
    10:36
  • Extract Index Level Values with the get_level_values Method
    04:20
  • Change Index Level Name with the set_names Method
    04:15
  • The sort_index Method on a MultiIndex DataFrame
    08:24
  • Extract Rows from a MultiIndex DataFrame
    10:59
  • The transpose Method on a MultiIndex DataFrame
    08:16
  • The .swaplevel() Method
    03:29
  • The .stack() Method
    Preview06:01
  • The .unstack() Method, Part 1
    03:38
  • The .unstack() Method, Part 2
    06:09
  • The .unstack() Method, Part 3
    05:09
  • The pivot Method
    06:34
  • Use the pivot_table method to create an aggregate summary of a DataFrame
    10:16
  • Use the pd.melt method to create a narrow dataset from a wide one
    Preview05:59

  • Intro to the Groupby Module
    07:42
  • First Operations with groupby Object
    09:33
  • Retrieve a group from a GroupBy object with the get_group Method
    03:47
  • Methods on the Groupby Object and DataFrame Columns
    08:41
  • Grouping by Multiple Columns
    04:35
  • The .agg() Method
    Preview06:11
  • Iterating through Groups
    09:04

  • Intro to the Merging, Joining, and Concatenating Section
    04:51
  • The pd.concat Method, Part 1
    05:20
  • The pd.concat Method, Part 2
    07:06
  • Inner Joins, Part 1
    09:18
  • Inner Joins, Part 2
    09:01
  • Outer Joins
    12:23
  • Left Joins
    Preview09:19
  • The left_on and right_on Parameters
    08:54
  • Merging by Indexes with the left_index and right_index Parameters
    11:02
  • The .join() Method
    03:15
  • The pd.merge() Method
    03:06

Requirements

  • Basic / intermediate experience with Microsoft Excel or another spreadsheet software (common functions, vlookups, Pivot Tables etc)
  • Basic experience with the Python programming language
  • Strong knowledge of data types (strings, integers, floating points, booleans) etc

Description

Student Testimonials:

  • The instructor knows the material, and has detailed explanation on every topic he discusses. Has clarity too, and warns students of potential pitfalls. He has a very logical explanation, and it is easy to follow him. I highly recommend this class, and would look into taking a new class from him. - Diana

  • This is excellent, and I cannot complement the instructor enough. Extremely clear, relevant, and high quality - with helpful practical tips and advice. Would recommend this to anyone wanting to learn pandas. Lessons are well constructed. I'm actually surprised at how well done this is. I don't give many 5 stars, but this has earned it so far. - Michael

  • This course is very thorough, clear, and well thought out. This is the best Udemy course I have taken thus far. (This is my third course.) The instruction is excellent! - James


Welcome to the most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world!

Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include:

  • installing

  • sorting

  • filtering

  • grouping

  • aggregating

  • de-duplicating

  • pivoting

  • munging

  • deleting

  • merging

  • visualizing

and more!

Why learn pandas?

If you've spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you! 


Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language. 

Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets -- analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! 

I call it "Excel on steroids"!

Over the course of more than 19 hours, I'll take you step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We'll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!

Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!

Who this course is for:

  • Data analysts and business analysts
  • Excel users looking to learn a more powerful software for data analysis

Featured review

Abhishek Sengupta
Abhishek Sengupta
67 courses
6 reviews
Rating: 4.5 out of 57 months ago
One of the best tutorial for Pandas in Udemy, the lessons are explained in a very clear way, most importantly the MultiIndex, Pivot and other Transformations. Pros - Great Content Cons - Some advanced functions like apply,map, asof() etc should be included. Other resources availiable are not beginer firendly, so It will be great if the instructor includes them in future. Finally as others are also requesting : A live case study project. please add this to solidify the understandings.

Instructor

Boris Paskhaver
Software Engineer | Consultant | Author
Boris Paskhaver
  • 4.7 Instructor Rating
  • 25,014 Reviews
  • 295,937 Students
  • 6 Courses

Hi there, it's nice to meet you! I'm a New York City-based software engineer, author, and consultant who's been teaching on Udemy since 2016.

Like many of my peers, I did not follow a conventional approach to my current role as a web developer. After graduating from New York University in 2013 with a degree in Business Economics and Marketing, I worked as a business analyst, systems administrator, and data analyst for a variety of companies including a digital marketing agency, a financial services firm, and an international tech powerhouse. At one of those roles, I was fortunate enough to be challenged to build several projects with Python and JavaScript. 

There was no formal computer science education for me; I discovered coding entirely by accident. A small work interest quickly blossomed into a passionate weekend hobby. Eventually, I left my former role to complete App Academy, a rigorous full-stack web development bootcamp in NYC. The rest is history.

I've always been fascinated by the intersection of technology and education, especially since I've struggled with many of the traditional resources people use to learn how to program. My goal as an instructor is to create comprehensive step-by-step courses that break down the complex details into small, digestible pieces. I like to build the kind of material that I myself would have loved to have when I was starting out.

I'm passionate about teaching and would love to help you discover what code can do for you. See you in a course soon!

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