Data Analysis with Pandas and Python [Video]

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Data Analysis with Pandas and Python [Video]

Boris Paskhaver

4 customer reviews
Analyze data quickly and easily with Python's powerful panda library! All datasets included --- beginners welcome!
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Video Details

ISBN 139781788622394
Course Length18 hours 47 minutes

Video Description

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"!

Style and Approach

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.

Table of Contents

Installation and Setup
Introduction to the Course
Mac OS - Download the Anaconda Distribution
Mac OS - Install Anaconda Distribution
Mac OS - Access the Terminal
Mac OS - Update Anaconda Libraries
Mac OS - Unpack Course Materials + The Startdown 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
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 Label with .loc[]
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
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
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
Visualization
Intro to Visualization Module
The .plot() Method
Modifying Aesthetics with Templates
Bar Graphs
Pie Charts
Histograms
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

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

Authors

Table of Contents

Installation and Setup
Introduction to the Course
Mac OS - Download the Anaconda Distribution
Mac OS - Install Anaconda Distribution
Mac OS - Access the Terminal
Mac OS - Update Anaconda Libraries
Mac OS - Unpack Course Materials + The Startdown 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
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 Label with .loc[]
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
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
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
Visualization
Intro to Visualization Module
The .plot() Method
Modifying Aesthetics with Templates
Bar Graphs
Pie Charts
Histograms
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

Video Details

ISBN 139781788622394
Course Length18 hours 47 minutes
Read More
From 4 reviews

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