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Data Analysis with Pandas and Python [Video]

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Data Analysis with Pandas and Python [Video]

Boris Paskhaver

Wednesday, October 18, 2017

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.

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[]

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

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

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

Boris Paskhaver

Software Engineer – the author is an NYC-based web developer with experience building apps in React / Redux and Ruby on Rails. Raised in New Jersey, he graduated from the Stern School of Business at New York University in 2013 with a double major in Business Economics and Marketing. Since graduation, his work has taken him in a wide variety of directions -- he spent a year in marketing, then financial services, and now the tech industry. He has worked everywhere from a 50-person digital agency to an international tech powerhouse with thousands of employees. He always had a love of learning but have struggled with the traditional resources available for education. He had used that as inspiration for his work here. His goal is to create comprehensive courses that break down the complex details into small, digestible pieces. He hopes to see you in a course soon.

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