Data Analysis with Pandas and Python

Analyze data quickly and easily with Python's powerful pandas library! All datasets included --- beginners welcome!

4.71 (17402 reviews)
Udemy
platform
English
language
Data Science
category
instructor
169,203
students
22 hours
content
Jul 2022
last update
$19.99
regular price

What you will 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

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!

Screenshots

Data Analysis with Pandas and Python - Screenshot_01Data Analysis with Pandas and Python - Screenshot_02Data Analysis with Pandas and Python - Screenshot_03Data Analysis with Pandas and Python - Screenshot_04

Content

Installation and Setup

Introduction to the Course
About Me
Completed Course Files
MacOS - Download the Anaconda Distribution
MacOS - Install Anaconda Distribution
MacOS - Access the Terminal
MacOS - Create Conda Environment and Install Pandas
MacOS - Unpack Course Materials + The Start and Shutdown Process
Windows - Download the Anaconda Distribution
Windows - Install Anaconda Distribution
Windows - Access the Command Prompt and Update Anaconda Libraries
Windows - Unpack Course Materials + The Startdown and Shutdown Process
Intro to the Jupyter Notebook Interface
Cell Types and Cell Modes
Code Cell Execution
Popular Keyboard Shortcuts
Import Libraries into Jupyter Notebook
Python Crash Course, Part 1 - Data Types and Variables
Python Crash Course, Part 2 - Lists
Python Crash Course, Part 3 - Dictionaries
Python Crash Course, Part 4 - Operators
Python Crash Course, Part 5 - Functions

Series

Create Jupyter Notebook for the Series Module
Create A Series Object from a Python List
Create A Series Object from a Python Dictionary
Intro to Attributes
Intro to Methods
Parameters and Arguments
Import Series with the .read_csv() Method
The .head() and .tail() Methods
Python Built-In Functions
More Series Attributes
The .sort_values() Method
The inplace Parameter
The .sort_index() Method
Python's in Keyword
Extract Series Values by Index Position
Extract Series Values by Index Label
The .get() Method on a Series
Math Methods on Series Objects
The .idxmax() and .idxmin() Methods
The .value_counts() Method
The .apply() Method
The .map() Method
A Review of the Series Module

DataFrames I

Intro to DataFrames I Module
Shared Methods and Attributes between Series and DataFrames
Differences between Shared Methods
Select One Column from a DataFrame
Select Two or More Columns from a DataFrame
Add New Column to DataFrame
Broadcasting Operations
A Review of the .value_counts() Method
Drop Rows with Null Values
Fill in Null Values with the .fillna() Method
The .astype() Method
Sort a DataFrame with the .sort_values() Method, Part I
Sort a DataFrame with the .sort_values() Method, Part II
Sort DataFrame with the .sort_index() Method
Rank Values with the .rank() Method

DataFrames II

This Module's Dataset + Memory Optimization
Filter a DataFrame Based on A Condition
Filter with More than One Condition (AND - &)
Filter with More than One Condition (OR - |)
The .isin() Method
The .isnull() and .notnull() Methods
The .between() Method
The .duplicated() Method
The .drop_duplicates() Method
The .unique() and .nunique() Methods

DataFrames III

Intro to the DataFrames III Module + Import Dataset
The .set_index() and .reset_index() Methods
Retrieve Rows by Index Label with .loc[]
Retrieve Rows by Index Position with .iloc[]
The Catch-All .ix[] Method
Second Arguments to .loc[], .iloc[], and .ix[] Methods
Set New Values for a Specific Cell or Row
Set Multiple Values in DataFrame
Rename Index Labels or Columns in a DataFrame
Delete Rows or Columns from a DataFrame
Create Random Sample with the .sample() Method
The .nsmallest() and .nlargest() Methods
Filtering with the .where() Method
The .query() Method
A Review of the .apply() Method on Single Columns
The .apply() Method with Row Values
The .copy() Method

Working with Text Data

Intro to the Working with Text Data Module
Common String Methods - lower, upper, title, and len
The .str.replace() Method
Filtering with String Methods
More String Methods - strip, lstrip, and rstrip
String Methods on Index and Columns
Split Strings by Characters with .str.split() Method
More Practice with Splits
The expand and n Parameters of the .str.split() Method

MultiIndex

Intro to the MultiIndex Module
Create a MultiIndex with the set_index() Method
The .get_level_values() Method
The .set_names() Method
The sort_index() Method
Extract Rows from a MultiIndex DataFrame
The .transpose() Method and MultiIndex on Column Level
The .swaplevel() Method
The .stack() Method
The .unstack() Method, Part 1
The .unstack() Method, Part 2
The .unstack() Method, Part 3
The .pivot() Method
The .pivot_table() Method
The pd.melt() Method

GroupBy

Intro to the Groupby Module
First Operations with groupby Object
Retrieve A Group with the .get_group() Method
Methods on the Groupby Object and DataFrame Columns
Grouping by Multiple Columns
The .agg() Method
Iterating through Groups

Merging, Joining, and Concatenating

Intro to the Merging, Joining, and Concatenating Module
The pd.concat() Method, Part 1
The pd.concat() Method, Part 2
The .append() Method on a DataFrame
Inner Joins, Part 1
Inner Joins, Part 2
Outer Joins
Left Joins
The left_on and right_on Parameters
Merging by Indexes with the left_index and right_index Parameters
The .join() Method
The pd.merge() Method

Working with Dates and Times

Intro to the Working with Dates and Times Module
Review of Python's datetime Module
The pandas Timestamp Object
The pandas DateTimeIndex Object
The pd.to_datetime() Method
Create Range of Dates with the pd.date_range() Method, Part 1
Create Range of Dates with the pd.date_range() Method, Part 2
Create Range of Dates with the pd.date_range() Method, Part 3
The .dt Accessor
Install pandas-datareader Library
Import Financial Data Set with pandas_datareader Library
Selecting Rows from a DataFrame with a DateTimeIndex
Timestamp Object Attributes
The .truncate() Method
pd.DateOffset Objects
More Fun with pd.DateOffset Objects
The pandas Timedelta Object
Timedeltas in a Dataset

Panels

DEPRECATION WARNING
Intro to the Module + Fetch Panel Dataset from Google Finance
The Axes of a Panel Object
Panel Attributes
Use Bracket Notation to Extract a DataFrame from a Panel
Extracting with the .loc, .iloc, and .ix Methods
Convert Panel to a MultiIndex DataFrame (and Vice Versa)
The .major_xs() Method
The .minor_xs() Method
Transpose a Panel with the .transpose() Method
The .swapaxes() Method
A Review of the Panels Module

Input and Output

Intro to the Input and Output Module
Feed pd.read_csv() Method a URL Argument
Quick Object Conversions
Export DataFrame to CSV File with the .to_csv() Method
Install xlrd and openpyxl Libraries to Read and Write Excel Files
Import Excel File into pandas
Export Excel File
Input and Output

Visualization

Intro to Visualization Module
The .plot() Method
Modifying Aesthetics with Templates
Bar Graphs
Pie Charts
Histograms
Visualization

Options and Settings

Introduction to the Options and Settings Module
Changing pandas Options with Attributes and Dot Syntax
Changing pandas Options with Methods
The precision Option

Conclusion

Conclusion

Reviews

Richard
July 12, 2022
This is the best course I've ever done on Udemy. Well explained and good for beginners. It explains even the small details so people don't get lost.
Noppharat
July 4, 2022
This course lead me step by step and finally gave me a strong foundation of understanding the Pandas.
Moi
June 28, 2022
Overall, the content of the course is very well prepared. The explanations and examples or sample data used demonstrate the concepts very well. However, I would suggest that at the end of each tutorial, an exercise should be provided for the student to reinforce their understanding of the concepts. In some of the sections, I lost focus due to a lack of exercise and I had to rewatch some of the videos. It would be great if the suggestion could be taken into consideration.
Lai
June 27, 2022
yes, it was perfect for me.. plenty of examples. What would make it even more cool is to have coding exercises at the end of every topic
Andrew
June 19, 2022
Great course for Data Analysis with Pandas and Python. Would have been good to cover dash/plotly for visualizations though. As a beginner with Pandas I felt the learning curve was fair.
Russell
June 16, 2022
Great blast through many the wonders of pandas. It's excellent an intro to using pandas for data processing.
Robert
June 14, 2022
It's a little wordy. One thing I WISH Udemy courses like this would offer is "let me test out of parts of this" - in this case, I have Python experience, so going through all of the Python "get you up to date" lessons is time consuming and doesn't cover something I already know. I can't tell if simply checking off boxes and skipping to the new (to me) material is allowed (i.e., you can't get the certificate without viewing all of the video - Udemy doesn't make that clear, and I suppose then people would just check everything without actually performing the tasks). (And this has nothing whatsoever to do with the course itself or the instructor.) OK - in terms of the actual course - so far I'm enjoying the actual Pandas content. One peeve: in the quizzes the datasets are not within the ZIP file of CSV files. This is annoying because you have to code things blindly for the quiz. It would be nice if these were also included so that a) you can examine the data if you want to; and b) you can test out solutions, or alternate paths to get to the desired result(s).
Alexandra
June 9, 2022
The instructor knows his subject and is really helpful with the small details. I never feel lost or left behind when I'm following along. The pacing of this course is just right.
Guilherme
June 7, 2022
Conteúdo excepcional, mesmo estando com 6 anos. Único ponto a melhorar é a legenda que muitas vezes não reflete o que está sendo falado pelo professor.
Ronald
June 6, 2022
This is really an amazing course! Fantastically done! Really enjoying the practical application of code and the instructor is thought provoking.
Paul
May 28, 2022
Solid course! Easy instructions. Exams to affirm your knowledge along the way. Definitely recommend if you want to learn Pandas. I used it to re-learn python as well.
Jonathan
May 28, 2022
This course has been incredible so far. Unlike the Jose - "Python Bootcamp" course, our instructor lays definitions and examples out so clearly that I can retain the information and relate to it. I am so pleased with the method of teaching in this course.
Olivier
May 17, 2022
Way too many examples - we don't need 3 examples every single time - a module like head/tail could be covered in 1 minute instead of 7 minutes
Akshay
April 29, 2022
The course was very illustrative on Python Pandas Module and the content was very meaningful. Adding some more modules for advanced concept of Python Programming could add more value to the course. Thankyou!!
Jmp
April 15, 2022
I don't remember reviewing the course, I've not even finished it yet ! I must have miss clicked something. All I can say is so far so good. I'll default to 4.5 stars until the end of the course, then I'm sure I'll bump it to 5 stars :o)

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udemy ID
8/16/2016
course created date
6/13/2019
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