(Udemy) Data Analysis with Pandas and Python (Boris Paskhaver)

About Course

Learn **Data Analysis with Pandas and Python** for free! This comprehensive course, offered by Udemy, will teach you how to use one of the most popular Python libraries for data analysis. You’ll learn how to:

Install Pandas
Sort data
Filter data
Group data
Aggregate data
De-duplicate data
Pivot data
Munge data
Delete data
Merge data
Visualize data

This course is perfect for anyone who wants to take their data analysis skills to the next level. It’s also great for beginners who are new to Python and Pandas.

Why learn Pandas?

Pandas is a powerful tool that allows you to analyze, organize, sort, filter, pivot, aggregate, munge, clean, calculate, and more with colossal datasets. It’s like “Excel on steroids”!

What’s included in the course?

This course includes 19+ hours of in-depth video tutorials, dozens of datasets, and step-by-step instructions on everything from installation to visualization. You’ll learn hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library.

Get started today!

This course is completely free. Enroll now and start learning data analysis with Pandas and Python. This course is brought to you by Theetay, a platform that offers free access to top-rated courses from Udemy, Udacity, Coursera, MasterClass, NearPeer, and more. Sign up today and start learning!

Course Content

Installation and Setup

A Message from the Professor
– Introduction to Data Analysis with Pandas and Python

12:14
– About Me

00:56
– macOS

04:21
– macOS

10:38
– macOS

08:34
– macOS

13:07
– Windows

09:32
– Windows

18:12
– Windows

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

BONUS Python Crash Course

– Intro to the Python Crash Course

03:34
– Comments

03:18
– Basic Data Types

10:47
– Operators

15:35
– Variables

07:48
– Built

10:42
– Custom Functions

16:34
– String Methods

20:51
– Lists

13:17
– Index Positions and Slicing

15:56
– Dictionaries

15:22

Series

– Create Jupyter Notebook for the Series Module

02:15
– Create A Series Object from a Python List

12:41
– Create A Series Object from a Python Dictionary

06:24
– Intro to Methods

06:54
– Intro to Attributes

09:48
– Parameters and Arguments

19:06
– Import Series with the pdreadcsv Function

18:00
– Use the head and tail Methods to Return Rows from Beginning and End of Dataset

07:07
– Passing Series to Python Built

07:29
– The sortvalues Method

04:46
– The sortindex Method

04:28
– Check for Inclusion with Pythons in Keyword

05:12
– Extract Series Values by Index Position

08:27
– Extract Series Values by Index Label

04:44
– The get Method

08:21
– Overwrite a Series Value

05:25
– The copy Method

11:56
– The inplace Parameter

13:29
– Math Methods on Series Objects

04:24
– Broadcasting

04:00
– Use the valuecounts Method to See Counts of Unique Values within a Series

06:20
– Use the apply Method to Invoke a Function on Every Series Values

07:37
– The map Method

07:54

DataFrames I Introduction

– Intro to DataFrames I Module

09:48
– Methods and Attributes between Series and DataFrames

14:08
– Differences between Shared Methods

06:01
– Select One Column from a DataFrame

06:28
– Select Two or More Columns from a DataFrame

03:50
– Add New Column to DataFrame

07:16
– Create New Column from Existing Column

10:18
– A Review of the valuecounts Method

03:25
– Drop DataFrame Rows with Null Values with the dropna Method

08:48
– Fill in Missing DataFrame Values with the fillna Method

08:51
– The astype Method I

07:48
– The astype Method II

09:37
– Sort a DataFrame with the sortvalues Method Part I

08:54
– Sort a DataFrame with the sortvalues Method Part II

10:07
– Sort DataFrame Index with the sortindex Method

03:37
– Rank Series Values with the rank Method

06:07

DataFrames II Filtering Data

– This Modules Dataset Memory Optimization

15:51
– Filter a DataFrame Based on A Condition

12:56
– Filter DataFrame with More than One Condition AND

04:40
– Filter DataFrame with More than One Condition OR

08:34
– Check for Inclusion with the isin Method

06:16
– Check for Null and Present DataFrame Values with the isnull and notnull Methods

05:06
– Check For Inclusion Within a Range of Values with the between Method

06:50
– Check for Duplicate DataFrame Rows with the duplicated Method

09:04
– Delete Duplicate DataFrame Rows with the dropduplicates Method

08:15
– Identify and Count Unique Values with the unique and nunique Methods

04:21

DataFrames III Data Extraction

– Intro to the DataFrames III Module Import Dataset

04:55
– Use the setindex and resetindex methods to define a new DataFrame index

07:26
– Retrieve Rows by Index Label with loc Accessor

12:43
– 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

09:33
– Delete Rows or Columns from a DataFrame

07:28
– Create Random Sample with the sample Method

04:42
– Use the nsmallest nlargest methods to get rows with smallest largest values

05:35
– Filter A DataFrame with the where method

05:02
– Filter A DataFrame with the query method

09:06
– A Review of the apply Method on a pandas Series Object

05:52
– Apply a Function to every DataFrame Row with the apply Method

06:49
– Create a Copy of a DataFrame with the copy Method

07:04

Working with Text Data

– Intro to the Working with Text Data Section

06:09
– Common String Methods lower upper title and len

07:13
– Use the strreplace method to replace all occurrences of character with another

08:06
– Filter a DataFrames Rows with String Methods

06:42
– More DataFrame String Methods

04:30
– Invoke String Methods on DataFrame Index and Columns

05:29
– Split Strings by Characters with the strsplit Method

08:40
– More Practice with the strsplit method on a Series

06:00
– Exploring the expand and n Parameters of the strsplit Method

06:59

MultiIndex

– Intro to the MultiIndex Module

04:50
– Create a MultiIndex on a DataFrame with the setindex Method

10:36
– Extract Index Level Values with the getlevelvalues Method

04:20
– Change Index Level Name with the setnames Method

04:15
– The sortindex 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

06:00
– The unstack Method Part 1

03:37
– The unstack Method Part 2

06:08
– The unstack Method Part 3

05:08
– The pivot Method

06:33
– Use the pivottable method to create an aggregate summary of a DataFrame

10:15
– Use the pdmelt method to create a narrow dataset from a wide one

05:58

The GroupBy Object

Merging Joining and Concatenating DataFrames

– Intro to the Merging Joining and Concatenating Section

04:51
– The pdconcat Method Part 1

05:20
– The pdconcat Method Part 2

07:06
– Inner Joins Part 1

09:17
– Inner Joins Part 2

09:00
– Outer Joins

12:22
– Left Joins

09:18
– The lefton and righton Parameters

08:53
– Merging by Indexes with the leftindex and rightindex Parameters

11:01
– The join Method

03:15
– The pdmerge Method

03:06

Working with Dates and Times in Datasets

Intro to the Working with Date and Time Module

04:17
– Review of Pythons datetime Module

09:30
– The pandas Timestamp Object

07:15
– The pandas DateTimeIndex Object

05:22
– The pdtodatetime Method

11:11
– Create Range of Dates with the pddaterange Method Part 1

10:21
– Create Range of Dates with the pddaterange Method Part 2

09:03
– Create Range of Dates with the pddaterange Method Part 3

07:49
– The dt Accessor

07:28
– Install pandas

03:33
– Import Financial Data Set with pandasdatareader Library

07:56
– Selecting Rows from a DataFrame with a DateTimeIndex

12:25
– Timestamp Object Attributes and Methods

09:41
– The pdDateOffset Object

06:50
– Timeseries Offsets

12:13
– The Timedelta Object

08:22
– Timedeltas in a Dataset

09:29

Input and Output in pandas

Visualization

Options and Settings in pandas

Conclusion

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About Course

What Will You Learn?

Course Content

Installation and Setup

A Message from the Professor

– Introduction to Data Analysis with Pandas and Python

– About Me

– macOS

– macOS

– macOS

– macOS

– Windows

– Windows

– Windows

– Intro to the Jupyter Notebook Interface

– Cell Types and Cell Modes in Jupyter Notebook

– Code Cell Execution in Jupyter Notebook

– Popular Keyboard Shortcuts in Jupyter Notebook

– Import Libraries into Jupyter Notebook

BONUS Python Crash Course

– Intro to the Python Crash Course

– Comments

– Basic Data Types

– Operators

– Variables

– Built

– Custom Functions

– String Methods

– Lists

– Index Positions and Slicing

– Dictionaries

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 Methods

– Intro to Attributes

– Parameters and Arguments

– Import Series with the pdreadcsv Function

– Use the head and tail Methods to Return Rows from Beginning and End of Dataset

– Passing Series to Python Built

– The sortvalues Method

– The sortindex Method

– Check for Inclusion with Pythons in Keyword

– Extract Series Values by Index Position

– Extract Series Values by Index Label

– The get Method

– Overwrite a Series Value

– The copy Method

– The inplace Parameter

– Math Methods on Series Objects

– Broadcasting

– Use the valuecounts Method to See Counts of Unique Values within a Series

– Use the apply Method to Invoke a Function on Every Series Values

– The map Method

DataFrames I Introduction

– Intro to DataFrames I Module

– 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

– Create New Column from Existing Column

– A Review of the valuecounts Method

– Drop DataFrame Rows with Null Values with the dropna Method

– Fill in Missing DataFrame Values with the fillna Method

– The astype Method I

– The astype Method II

– Sort a DataFrame with the sortvalues Method Part I

– Sort a DataFrame with the sortvalues Method Part II

– Sort DataFrame Index with the sortindex Method

– Rank Series Values with the rank Method

DataFrames II Filtering Data

– This Modules Dataset Memory Optimization

– Filter a DataFrame Based on A Condition

– Filter DataFrame with More than One Condition AND

– Filter DataFrame with More than One Condition OR

– Check for Inclusion with the isin Method

– Check for Null and Present DataFrame Values with the isnull and notnull Methods

– Check For Inclusion Within a Range of Values with the between Method