Data Analysis With Pandas And NumPy In Python

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Free Download Data Analysis With Pandas And NumPy In Python
Last updated 8/2024
Created by Dr Ziad Francis
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English + subtitle | Duration: 53 Lectures ( 4h 46m ) | Size: 1.4 GB

NumPy and Pandas for Data Analysis and Financial Applications, Examples in Trading Market Analysis
What you'll learn
Data manipulation: working with data, filter, sort, and transform large datasets
Data analysis: perform a wide range of data analysis tasks, including aggregating data, performing statistical calculations
Data visualization: create a variety of visualizations to help understand data and communicate findings
Data wrangling: cleaning and preparing data for analysis, handling missing data, merge datasets, and reshape data
Requirements
Python basics, for loops, condition statements, python containers; lists, sets, tuples and dictionnaries.
Description
This online course is designed to equip you with the skills and knowledge needed to efficiently and effectively manipulate and analyze data using two powerful Python libraries: Pandas and NumPy.In this course, you will start by learning the fundamentals of data wrangling, including the different types of data and data cleaning techniques. You will then dive into the NumPy library, exploring its powerful features for working with N-dimensional arrays and universal functions.Next, you will explore the Pandas library, which offers powerful tools for data manipulation, including data structures and data frame manipulation. You will learn how to use advanced Pandas functions, manipulate time and time series data, and read and write data with Pandas.Throughout the course, you will engage in hands-on exercises and practice problems to reinforce your learning and build your skills. By the end of the course, you will be able to effectively wrangle and analyze data using Pandas and NumPy, and create compelling data visualizations using these tools.Whether you're a data analyst, data scientist, or data enthusiast, this course will give you the skills you need to take your data wrangling and analysis to the next level.Content Table:Lesson 1: Introduction to Data WranglingLesson 2: Introduction to NumPyLesson 3: Data structure in PandasLesson 4: Pandas DataFrame ManipulationLesson 5: Advanced Pandas FunctionsLesson 6: Time and Time Series in PandasLesson 7: Reading and Writing Data with PandasLesson 8: Data Visualization with PandasPractice Exercises
Who this course is for
Beginner in Python building Data Science skills for real world applications
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Data Analysis With Pandas And Numpy In Python
Published 3/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.48 GB | Duration: 4h 46m​

NumPy and Pandas for Data Analysis and Financial Applications, Examples in Trading Market Analysis

What you'll learn

Data manipulation: working with data, filter, sort, and transform large datasets

Data analysis: perform a wide range of data analysis tasks, including aggregating data, performing statistical calculations

Data visualization: create a variety of visualizations to help understand data and communicate findings

Data wrangling: cleaning and preparing data for analysis, handling missing data, merge datasets, and reshape data

Requirements

Python basics, for loops, condition statements, python containers; lists, sets, tuples and dictionnaries.

Description

This online course is designed to equip you with the skills and knowledge needed to efficiently and effectively manipulate and analyze data using two powerful Python libraries: Pandas and NumPy.In this course, you will start by learning the fundamentals of data wrangling, including the different types of data and data cleaning techniques. You will then dive into the NumPy library, exploring its powerful features for working with N-dimensional arrays and universal functions.Next, you will explore the Pandas library, which offers powerful tools for data manipulation, including data structures and data frame manipulation. You will learn how to use advanced Pandas functions, manipulate time and time series data, and read and write data with Pandas.Throughout the course, you will engage in hands-on exercises and practice problems to reinforce your learning and build your skills. By the end of the course, you will be able to effectively wrangle and analyze data using Pandas and NumPy, and create compelling data visualizations using these tools.Whether you're a data analyst, data scientist, or data enthusiast, this course will give you the skills you need to take your data wrangling and analysis to the next level.Content Table:Lesson 1: Introduction to Data WranglingLesson 2: Introduction to NumPyLesson 3: Data structure in PandasLesson 4: Pandas DataFrame ManipulationLesson 5: Advanced Pandas FunctionsLesson 6: Time and Time Series in PandasLesson 7: Reading and Writing Data with PandasLesson 8: Data Visualization with PandasPractice Exercises

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: NumPy or Numerical Python

Lecture 2 NumPy Installation

Lecture 3 NumPy Basic Functions

Lecture 4 NumPy Slicing

Lecture 5 NumPy Multidimentional Arrays

Lecture 6 NumPy DTypes

Lecture 7 NumPy Structured Arrays

Lecture 8 NumPy Reading And Writing Data Files

Lecture 9 NumPy Arithmetic Operations

Lecture 10 NumPy Logical Operations

Lecture 11 NumPy Array Broadcasting

Lecture 12 NumPy Conditional Indexing

Section 3: NumPy Exercises

Lecture 13 Exercises And Solutions

Lecture 14 Exercise 1

Lecture 15 Exercise 2

Lecture 16 Exercise 3

Lecture 17 Exercise 4

Lecture 18 Exercise 5

Lecture 19 Exercise 6

Section 4: Data Structure in Pandas

Lecture 20 Pandas Series

Lecture 21 Series Missing Values

Lecture 22 Applying Functions to Series

Lecture 23 Pandas DataFrames

Section 5: DataFrame Manipulation

Lecture 24 Columns And Indexes In Pandas

Lecture 25 Accessing DataFrames With Loc[] and iLoc[]

Lecture 26 Accessing Scalars/Values In DataFrames at[] And iat[]

Lecture 27 Filling And Replacing Values In DataFrames

Lecture 28 Arithmetic Operations On DataFrames

Lecture 29 Concatenating DataFrames

Lecture 30 Merging And Joining DataFrames

Section 6: Advanced Pandas Function

Lecture 31 Recap And Planning This Lesson

Lecture 32 Pivot Tables

Lecture 33 GroupBy In DataFrames

Lecture 34 Binning Values And The Cut Function

Lecture 35 MultiLevel Indexing In DataFrames

Lecture 36 Filling Missing Values

Section 7: Time and Time Series in Pandas

Lecture 37 Date Time In Python

Lecture 38 Time Zones And Time Deltas In Python

Lecture 39 Rolling And Shift Functions

Section 8: Reading and Writing Data with Pandas

Lecture 40 Reading And Writing Files With Pandas

Section 9: Data Visualization with Pandas

Lecture 41 Plotting Graphs Bars And Histograms

Lecture 42 Boxplots

Lecture 43 Area Plots

Lecture 44 Scatter Points

Lecture 45 Pie Charts

Lecture 46 Conclusion

Section 10: Pandas Exercises

Lecture 47 Pandas Exercises

Lecture 48 Exercise 1 Financial Data Analysis

Lecture 49 Exercise 2 Stacked BarPlots In Pandas

Lecture 50 Exercise 3 Dinner With Friends

Lecture 51 Exercise 4 Oil spill in water: Data cleaning example

Lecture 52 Exercise 5 Financial Trading Analysis/Prediction

Lecture 53 Exercise 6 Financial Trading: analyzing the engulfing candles

Beginner in Python building Data Science skills for real world applications

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Data Analysis With Pandas And Python
Last updated 7/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.26 GB | Duration: 22h 0m​

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

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
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. - DianaThis 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. - MichaelThis 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! - JamesWelcome 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:installingsortingfilteringgroupingaggregatingde-duplicatingpivotingmungingdeletingmergingvisualizingand 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!

Overview

Section 1: Installation and Setup

Lecture 1 Introduction to Data Analysis with Pandas and Python

Lecture 2 About Me

Lecture 3 Completed Course Files

Lecture 4 macOS - Download the Anaconda Distribution, our Python development environment

Lecture 5 macOS - Install Anaconda Distribution

Lecture 6 macOS - Access the Terminal Application

Lecture 7 macOS - Create conda Environment and Install pandas and Jupyter Notebook

Lecture 8 macOS - Unpack Course Materials + The Start and Shutdown Process

Lecture 9 Windows - Find Out if Your System is 32-bit or 64-bit

Lecture 10 Windows - Download and Install the Anaconda Distribution

Lecture 11 Windows - Create conda Environment and Install pandas and Jupyter Notebook

Lecture 12 Windows - Unpack Course Materials + The Startdown and Shutdown Process

Lecture 13 Intro to the Jupyter Notebook Interface

Lecture 14 Cell Types and Cell Modes in Jupyter Notebook

Lecture 15 Code Cell Execution in Jupyter Notebook

Lecture 16 Popular Keyboard Shortcuts in Jupyter Notebook

Lecture 17 Import Libraries into Jupyter Notebook

Lecture 18 Troubleshooting Issues with Jupyter Notebook

Section 2: BONUS: Python Crash Course

Lecture 19 Intro to the Python Crash Course

Lecture 20 Comments

Lecture 21 Basic Data Types

Lecture 22 Operators

Lecture 23 Variables

Lecture 24 Coding Exercise Solution: Declare Variables

Lecture 25 Built-in Functions

Lecture 26 Coding Exercise Solution: Built-in Functions

Lecture 27 Custom Functions

Lecture 28 Coding Exercise Solution: Custom Functions

Lecture 29 String Methods

Lecture 30 Coding Exercise Solution: String Methods

Lecture 31 Lists

Lecture 32 Coding Exercise Solution: Creating Lists

Lecture 33 Index Positions and Slicing

Lecture 34 Coding Exercise Solution: Index Positions and Slicing

Lecture 35 Dictionaries

Lecture 36 Coding Exercise Solution: Creating Dictionaries

Lecture 37 Completed Jupyter Notebook for this Section

Section 3: Series

Lecture 38 Create Jupyter Notebook for the Series Module

Lecture 39 Create A Series Object from a Python List

Lecture 40 Create A Series Object from a Python Dictionary

Lecture 41 Coding Exercise Solution: Create a Series Object

Lecture 42 Intro to Methods

Lecture 43 Intro to Attributes

Lecture 44 Coding Exercise Solution: Attributes and Methods on a Series

Lecture 45 Parameters and Arguments

Lecture 46 Coding Exercise Solution: Parameters and Arguments

Lecture 47 Import Series with the pd.read_csv Function

Lecture 48 Coding Exercise Solution: Import Series with the read_csv Function

Lecture 49 Use the head and tail Methods to Return Rows from Beginning and End of Dataset

Lecture 50 Coding Exercise Solution: The head and tail Methods

Lecture 51 Passing Series to Python Built-In Functions

Lecture 52 The sort_values Method

Lecture 53 Coding Exercise Solution: The sort_values Method

Lecture 54 The sort_index Method

Lecture 55 Coding Exercise Solution: The sort_index Method

Lecture 56 Check for Inclusion with Python's in Keyword

Lecture 57 Coding Exercise Solution: Check for Inclusion with Python's in Keyword

Lecture 58 Extract Series Values by Index Position

Lecture 59 Extract Series Values by Index Label

Lecture 60 Coding Exercise Solution: Extract Series Values by Index Position or Index Label

Lecture 61 The get Method

Lecture 62 Overwrite a Series Value

Lecture 63 The copy Method

Lecture 64 The inplace Parameter

Lecture 65 Math Methods on Series Objects

Lecture 66 Broadcasting

Lecture 67 Use the value_counts Method to See Counts of Unique Values within a Series

Lecture 68 Coding Exercise Solution: The value_counts Method

Lecture 69 Use the apply Method to Invoke a Function on Every Series Values

Lecture 70 The map Method

Lecture 71 Completed Jupyter Notebook for this Section

Section 4: DataFrames I: Introduction

Lecture 72 Intro to DataFrames I Module

Lecture 73 Methods and Attributes between Series and DataFrames

Lecture 74 Differences between Shared Methods

Lecture 75 Select One Column from a DataFrame

Lecture 76 Coding Exercise Solution: Select One Column from a DataFrame

Lecture 77 Select Two or More Columns from a DataFrame

Lecture 78 Coding Exercise Solution: Select Two or More Columns from a DataFrame

Lecture 79 Add New Column to DataFrame

Lecture 80 Create New Column from Existing Column

Lecture 81 A Review of the value_counts Method

Lecture 82 Drop DataFrame Rows with Null Values with the dropna Method

Lecture 83 Coding Exercise Solution: Delete DataFrame Rows with Missing Values

Lecture 84 Fill in Missing DataFrame Values with the fillna Method

Lecture 85 The astype Method I

Lecture 86 The astype Method II

Lecture 87 Coding Exercise Solution: The astype Method

Lecture 88 Sort a DataFrame with the sort_values Method, Part I

Lecture 89 Sort a DataFrame with the sort_values Method, Part II

Lecture 90 Coding Exercise Solution: The sort_values Method on a DataFrame

Lecture 91 Sort DataFrame Index with the sort_index Method

Lecture 92 Rank Series Values with the rank Method

Lecture 93 Completed Jupyter Notebook for this Section

Section 5: DataFrames II: Filtering Data

Lecture 94 This Module's Dataset + Memory Optimization

Lecture 95 Filter a DataFrame Based on A Condition

Lecture 96 Coding Exercise Solution: Filter a DataFrame Based on A Condition

Lecture 97 Filter DataFrame with More than One Condition (AND - &)

Lecture 98 Coding Exercise Solution: Filter DataFrame with More than One Condition (AND)

Lecture 99 Filter DataFrame with More than One Condition (OR - |)

Lecture 100 Coding Exercise Solution: Filter DataFrame with More than One Condition (OR)

Lecture 101 Check for Inclusion with the isin Method

Lecture 102 Coding Exercise Solution: Check for Inclusion with the isin Method

Lecture 103 Check for Null and Present DataFrame Values with the isnull and notnull Methods

Lecture 104 Check For Inclusion Within a Range of Values with the between Method

Lecture 105 Coding Exercise Solution: The between Method

Lecture 106 Check for Duplicate DataFrame Rows with the duplicated Method

Lecture 107 Delete Duplicate DataFrame Rows with the drop_duplicates Method

Lecture 108 Identify and Count Unique Values with the unique and nunique Methods

Section 6: DataFrames III: Data Extraction

Lecture 109 Intro to the DataFrames III Module + Import Dataset

Lecture 110 Use the set_index and reset_index methods to define a new DataFrame index

Lecture 111 Retrieve Rows by Index Label with loc Accessor

Lecture 112 Retrieve Rows by Index Position with iloc Accessor

Lecture 113 Passing second arguments to the loc and iloc Accessors

Lecture 114 Set New Value for a Specific Cell or Cells In a Row

Lecture 115 Set Multiple Values in a DataFrame

Lecture 116 Rename Index Labels or Columns in a DataFrame

Lecture 117 Delete Rows or Columns from a DataFrame

Lecture 118 Create Random Sample with the sample Method

Lecture 119 Use the nsmallest / nlargest methods to get rows with smallest / largest values.

Lecture 120 Filter A DataFrame with the where method

Lecture 121 Filter A DataFrame with the query method

Lecture 122 A Review of the apply Method on a pandas Series Object

Lecture 123 Apply a Function to every DataFrame Row with the apply Method

Lecture 124 Create a Copy of a DataFrame with the copy Method

Section 7: Working with Text Data

Lecture 125 Intro to the Working with Text Data Section

Lecture 126 Common String Methods - lower, upper, title, and len

Lecture 127 Coding Exercise Solution: Common String Methods

Lecture 128 Use the str.replace method to replace all occurrences of character with another

Lecture 129 Filter a DataFrame's Rows with String Methods

Lecture 130 More DataFrame String Methods - strip, lstrip, and rstrip

Lecture 131 Invoke String Methods on DataFrame Index and Columns

Lecture 132 Split Strings by Characters with the str.split Method

Lecture 133 More Practice with the str.split method on a Series

Lecture 134 Exploring the expand and n Parameters of the str.split Method

Section 8: MultiIndex

Lecture 135 Intro to the MultiIndex Module

Lecture 136 Create a MultiIndex on a DataFrame with the set_index Method

Lecture 137 Coding Exercise Solution: Create a MultiIndex on a DataFrame

Lecture 138 Extract Index Level Values with the get_level_values Method

Lecture 139 Coding Exercise Solution: Extract Index Level Values with the get_level_values M

Lecture 140 Change Index Level Name with the set_names Method

Lecture 141 The sort_index Method on a MultiIndex DataFrame

Lecture 142 Extract Rows from a MultiIndex DataFrame

Lecture 143 Coding Exercise Solution: Extract Rows from a MultiIndex DataFrame

Lecture 144 The transpose Method on a MultiIndex DataFrame

Lecture 145 The swaplevel Method

Lecture 146 The stack Method

Lecture 147 The unstack Method, Part 1

Lecture 148 The unstack Method, Part 2

Lecture 149 The unstack Method, Part 3

Lecture 150 The pivot Method

Lecture 151 Use the pivot_table method to create an aggregate summary of a DataFrame

Lecture 152 Use the pd.melt method to create a narrow dataset from a wide one

Lecture 153 Coding Exercise Solution: The pd.melt Method

Section 9: The GroupBy Object

Lecture 154 Intro to the GroupBy Module

Lecture 155 First Operations with groupby Object

Lecture 156 Retrieve a group from a GroupBy object with the get_group Method

Lecture 157 Methods on the Groupby Object and DataFrame Columns

Lecture 158 Grouping by Multiple Columns

Lecture 159 The agg Method

Lecture 160 Iterating through Groups

Section 10: Merging, Joining, and Concatenating DataFrames

Lecture 161 Intro to the Merging, Joining, and Concatenating Section

Lecture 162 The pd.concat Method, Part 1

Lecture 163 The pd.concat Method, Part 2

Lecture 164 Inner Joins, Part 1

Lecture 165 Inner Joins, Part 2

Lecture 166 Outer Joins

Lecture 167 Left Joins

Lecture 168 The left_on and right_on Parameters

Lecture 169 Merging by Indexes with the left_index and right_index Parameters

Lecture 170 The .join() Method

Lecture 171 The pd.merge() Method

Section 11: Working with Dates and Times in Datasets

Lecture 172 Intro to the Working with Dates and Times Module

Lecture 173 Review of Python's datetime Module

Lecture 174 The pandas Timestamp Object

Lecture 175 The pandas DateTimeIndex Object

Lecture 176 The pd.to_datetime() Method

Lecture 177 Create Range of Dates with the pd.date_range() Method, Part 1

Lecture 178 Create Range of Dates with the pd.date_range() Method, Part 2

Lecture 179 Create Range of Dates with the pd.date_range() Method, Part 3

Lecture 180 The .dt Accessor

Lecture 181 Install pandas-datareader Library

Lecture 182 Import Financial Data Set with pandas_datareader Library

Lecture 183 Selecting Rows from a DataFrame with a DateTimeIndex

Lecture 184 Timestamp Object Attributes and Methods

Lecture 185 The pd.DateOffset Object

Lecture 186 Timeseries Offsets

Lecture 187 The Timedelta Object

Lecture 188 Timedeltas in a Dataset

Section 12: Input and Output in pandas

Lecture 189 Intro to the Input and Output Section

Lecture 190 Pass a URL to the pd.read_csv Method

Lecture 191 Quick Object Conversions

Lecture 192 Export CSV File with the to_csv Method

Lecture 193 Install xlrd and openpyxl Libraries to Read and Write Excel Files

Lecture 194 Import Excel File into pandas with the read_excel Method

Lecture 195 Export Excel File with the to_excel Method

Section 13: Visualization

Lecture 196 Intro to Visualization Section

Lecture 197 Use the plot Method to Render a Line Chart

Lecture 198 Modifying Plot Aesthetics with matplotlib Templates

Lecture 199 Creating Bar Graphs to Show Counts

Lecture 200 Creating Pie Charts to Represent Proportions

Section 14: Options and Settings in pandas

Lecture 201 Introduction to the Options and Settings Module

Lecture 202 Changing pandas Options with Attributes and Dot Syntax

Lecture 203 Changing pandas Options with Methods

Lecture 204 The precision Option

Section 15: Conclusion

Lecture 205 Conclusion

Lecture 206 Bonus!

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

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Data Analysis With Pandas And Python
Last updated 7/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.26 GB | Duration: 22h 0m​

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

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
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. - DianaThis 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. - MichaelThis 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! - JamesWelcome 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:installingsortingfilteringgroupingaggregatingde-duplicatingpivotingmungingdeletingmergingvisualizingand 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!

Overview

Section 1: Installation and Setup

Lecture 1 Introduction to Data Analysis with Pandas and Python

Lecture 2 About Me

Lecture 3 Completed Course Files

Lecture 4 macOS - Download the Anaconda Distribution, our Python development environment

Lecture 5 macOS - Install Anaconda Distribution

Lecture 6 macOS - Access the Terminal Application

Lecture 7 macOS - Create conda Environment and Install pandas and Jupyter Notebook

Lecture 8 macOS - Unpack Course Materials + The Start and Shutdown Process

Lecture 9 Windows - Find Out if Your System is 32-bit or 64-bit

Lecture 10 Windows - Download and Install the Anaconda Distribution

Lecture 11 Windows - Create conda Environment and Install pandas and Jupyter Notebook

Lecture 12 Windows - Unpack Course Materials + The Startdown and Shutdown Process

Lecture 13 Intro to the Jupyter Notebook Interface

Lecture 14 Cell Types and Cell Modes in Jupyter Notebook

Lecture 15 Code Cell Execution in Jupyter Notebook

Lecture 16 Popular Keyboard Shortcuts in Jupyter Notebook

Lecture 17 Import Libraries into Jupyter Notebook

Lecture 18 Troubleshooting Issues with Jupyter Notebook

Section 2: BONUS: Python Crash Course

Lecture 19 Intro to the Python Crash Course

Lecture 20 Comments

Lecture 21 Basic Data Types

Lecture 22 Operators

Lecture 23 Variables

Lecture 24 Coding Exercise Solution: Declare Variables

Lecture 25 Built-in Functions

Lecture 26 Coding Exercise Solution: Built-in Functions

Lecture 27 Custom Functions

Lecture 28 Coding Exercise Solution: Custom Functions

Lecture 29 String Methods

Lecture 30 Coding Exercise Solution: String Methods

Lecture 31 Lists

Lecture 32 Coding Exercise Solution: Creating Lists

Lecture 33 Index Positions and Slicing

Lecture 34 Coding Exercise Solution: Index Positions and Slicing

Lecture 35 Dictionaries

Lecture 36 Coding Exercise Solution: Creating Dictionaries

Lecture 37 Completed Jupyter Notebook for this Section

Section 3: Series

Lecture 38 Create Jupyter Notebook for the Series Module

Lecture 39 Create A Series Object from a Python List

Lecture 40 Create A Series Object from a Python Dictionary

Lecture 41 Coding Exercise Solution: Create a Series Object

Lecture 42 Intro to Methods

Lecture 43 Intro to Attributes

Lecture 44 Coding Exercise Solution: Attributes and Methods on a Series

Lecture 45 Parameters and Arguments

Lecture 46 Coding Exercise Solution: Parameters and Arguments

Lecture 47 Import Series with the pd.read_csv Function

Lecture 48 Coding Exercise Solution: Import Series with the read_csv Function

Lecture 49 Use the head and tail Methods to Return Rows from Beginning and End of Dataset

Lecture 50 Coding Exercise Solution: The head and tail Methods

Lecture 51 Passing Series to Python Built-In Functions

Lecture 52 The sort_values Method

Lecture 53 Coding Exercise Solution: The sort_values Method

Lecture 54 The sort_index Method

Lecture 55 Coding Exercise Solution: The sort_index Method

Lecture 56 Check for Inclusion with Python's in Keyword

Lecture 57 Coding Exercise Solution: Check for Inclusion with Python's in Keyword

Lecture 58 Extract Series Values by Index Position

Lecture 59 Extract Series Values by Index Label

Lecture 60 Coding Exercise Solution: Extract Series Values by Index Position or Index Label

Lecture 61 The get Method

Lecture 62 Overwrite a Series Value

Lecture 63 The copy Method

Lecture 64 The inplace Parameter

Lecture 65 Math Methods on Series Objects

Lecture 66 Broadcasting

Lecture 67 Use the value_counts Method to See Counts of Unique Values within a Series

Lecture 68 Coding Exercise Solution: The value_counts Method

Lecture 69 Use the apply Method to Invoke a Function on Every Series Values

Lecture 70 The map Method

Lecture 71 Completed Jupyter Notebook for this Section

Section 4: DataFrames I: Introduction

Lecture 72 Intro to DataFrames I Module

Lecture 73 Methods and Attributes between Series and DataFrames

Lecture 74 Differences between Shared Methods

Lecture 75 Select One Column from a DataFrame

Lecture 76 Coding Exercise Solution: Select One Column from a DataFrame

Lecture 77 Select Two or More Columns from a DataFrame

Lecture 78 Coding Exercise Solution: Select Two or More Columns from a DataFrame

Lecture 79 Add New Column to DataFrame

Lecture 80 Create New Column from Existing Column

Lecture 81 A Review of the value_counts Method

Lecture 82 Drop DataFrame Rows with Null Values with the dropna Method

Lecture 83 Coding Exercise Solution: Delete DataFrame Rows with Missing Values

Lecture 84 Fill in Missing DataFrame Values with the fillna Method

Lecture 85 The astype Method I

Lecture 86 The astype Method II

Lecture 87 Coding Exercise Solution: The astype Method

Lecture 88 Sort a DataFrame with the sort_values Method, Part I

Lecture 89 Sort a DataFrame with the sort_values Method, Part II

Lecture 90 Coding Exercise Solution: The sort_values Method on a DataFrame

Lecture 91 Sort DataFrame Index with the sort_index Method

Lecture 92 Rank Series Values with the rank Method

Lecture 93 Completed Jupyter Notebook for this Section

Section 5: DataFrames II: Filtering Data

Lecture 94 This Module's Dataset + Memory Optimization

Lecture 95 Filter a DataFrame Based on A Condition

Lecture 96 Coding Exercise Solution: Filter a DataFrame Based on A Condition

Lecture 97 Filter DataFrame with More than One Condition (AND - &)

Lecture 98 Coding Exercise Solution: Filter DataFrame with More than One Condition (AND)

Lecture 99 Filter DataFrame with More than One Condition (OR - |)

Lecture 100 Coding Exercise Solution: Filter DataFrame with More than One Condition (OR)

Lecture 101 Check for Inclusion with the isin Method

Lecture 102 Coding Exercise Solution: Check for Inclusion with the isin Method

Lecture 103 Check for Null and Present DataFrame Values with the isnull and notnull Methods

Lecture 104 Check For Inclusion Within a Range of Values with the between Method

Lecture 105 Coding Exercise Solution: The between Method

Lecture 106 Check for Duplicate DataFrame Rows with the duplicated Method

Lecture 107 Delete Duplicate DataFrame Rows with the drop_duplicates Method

Lecture 108 Identify and Count Unique Values with the unique and nunique Methods

Section 6: DataFrames III: Data Extraction

Lecture 109 Intro to the DataFrames III Module + Import Dataset

Lecture 110 Use the set_index and reset_index methods to define a new DataFrame index

Lecture 111 Retrieve Rows by Index Label with loc Accessor

Lecture 112 Retrieve Rows by Index Position with iloc Accessor

Lecture 113 Passing second arguments to the loc and iloc Accessors

Lecture 114 Set New Value for a Specific Cell or Cells In a Row

Lecture 115 Set Multiple Values in a DataFrame

Lecture 116 Rename Index Labels or Columns in a DataFrame

Lecture 117 Delete Rows or Columns from a DataFrame

Lecture 118 Create Random Sample with the sample Method

Lecture 119 Use the nsmallest / nlargest methods to get rows with smallest / largest values.

Lecture 120 Filter A DataFrame with the where method

Lecture 121 Filter A DataFrame with the query method

Lecture 122 A Review of the apply Method on a pandas Series Object

Lecture 123 Apply a Function to every DataFrame Row with the apply Method

Lecture 124 Create a Copy of a DataFrame with the copy Method

Section 7: Working with Text Data

Lecture 125 Intro to the Working with Text Data Section

Lecture 126 Common String Methods - lower, upper, title, and len

Lecture 127 Coding Exercise Solution: Common String Methods

Lecture 128 Use the str.replace method to replace all occurrences of character with another

Lecture 129 Filter a DataFrame's Rows with String Methods

Lecture 130 More DataFrame String Methods - strip, lstrip, and rstrip

Lecture 131 Invoke String Methods on DataFrame Index and Columns

Lecture 132 Split Strings by Characters with the str.split Method

Lecture 133 More Practice with the str.split method on a Series

Lecture 134 Exploring the expand and n Parameters of the str.split Method

Section 8: MultiIndex

Lecture 135 Intro to the MultiIndex Module

Lecture 136 Create a MultiIndex on a DataFrame with the set_index Method

Lecture 137 Coding Exercise Solution: Create a MultiIndex on a DataFrame

Lecture 138 Extract Index Level Values with the get_level_values Method

Lecture 139 Coding Exercise Solution: Extract Index Level Values with the get_level_values M

Lecture 140 Change Index Level Name with the set_names Method

Lecture 141 The sort_index Method on a MultiIndex DataFrame

Lecture 142 Extract Rows from a MultiIndex DataFrame

Lecture 143 Coding Exercise Solution: Extract Rows from a MultiIndex DataFrame

Lecture 144 The transpose Method on a MultiIndex DataFrame

Lecture 145 The swaplevel Method

Lecture 146 The stack Method

Lecture 147 The unstack Method, Part 1

Lecture 148 The unstack Method, Part 2

Lecture 149 The unstack Method, Part 3

Lecture 150 The pivot Method

Lecture 151 Use the pivot_table method to create an aggregate summary of a DataFrame

Lecture 152 Use the pd.melt method to create a narrow dataset from a wide one

Lecture 153 Coding Exercise Solution: The pd.melt Method

Section 9: The GroupBy Object

Lecture 154 Intro to the GroupBy Module

Lecture 155 First Operations with groupby Object

Lecture 156 Retrieve a group from a GroupBy object with the get_group Method

Lecture 157 Methods on the Groupby Object and DataFrame Columns

Lecture 158 Grouping by Multiple Columns

Lecture 159 The agg Method

Lecture 160 Iterating through Groups

Section 10: Merging, Joining, and Concatenating DataFrames

Lecture 161 Intro to the Merging, Joining, and Concatenating Section

Lecture 162 The pd.concat Method, Part 1

Lecture 163 The pd.concat Method, Part 2

Lecture 164 Inner Joins, Part 1

Lecture 165 Inner Joins, Part 2

Lecture 166 Outer Joins

Lecture 167 Left Joins

Lecture 168 The left_on and right_on Parameters

Lecture 169 Merging by Indexes with the left_index and right_index Parameters

Lecture 170 The .join() Method

Lecture 171 The pd.merge() Method

Section 11: Working with Dates and Times in Datasets

Lecture 172 Intro to the Working with Dates and Times Module

Lecture 173 Review of Python's datetime Module

Lecture 174 The pandas Timestamp Object

Lecture 175 The pandas DateTimeIndex Object

Lecture 176 The pd.to_datetime() Method

Lecture 177 Create Range of Dates with the pd.date_range() Method, Part 1

Lecture 178 Create Range of Dates with the pd.date_range() Method, Part 2

Lecture 179 Create Range of Dates with the pd.date_range() Method, Part 3

Lecture 180 The .dt Accessor

Lecture 181 Install pandas-datareader Library

Lecture 182 Import Financial Data Set with pandas_datareader Library

Lecture 183 Selecting Rows from a DataFrame with a DateTimeIndex

Lecture 184 Timestamp Object Attributes and Methods

Lecture 185 The pd.DateOffset Object

Lecture 186 Timeseries Offsets

Lecture 187 The Timedelta Object

Lecture 188 Timedeltas in a Dataset

Section 12: Input and Output in pandas

Lecture 189 Intro to the Input and Output Section

Lecture 190 Pass a URL to the pd.read_csv Method

Lecture 191 Quick Object Conversions

Lecture 192 Export CSV File with the to_csv Method

Lecture 193 Install xlrd and openpyxl Libraries to Read and Write Excel Files

Lecture 194 Import Excel File into pandas with the read_excel Method

Lecture 195 Export Excel File with the to_excel Method

Section 13: Visualization

Lecture 196 Intro to Visualization Section

Lecture 197 Use the plot Method to Render a Line Chart

Lecture 198 Modifying Plot Aesthetics with matplotlib Templates

Lecture 199 Creating Bar Graphs to Show Counts

Lecture 200 Creating Pie Charts to Represent Proportions

Section 14: Options and Settings in pandas

Lecture 201 Introduction to the Options and Settings Module

Lecture 202 Changing pandas Options with Attributes and Dot Syntax

Lecture 203 Changing pandas Options with Methods

Lecture 204 The precision Option

Section 15: Conclusion

Lecture 205 Conclusion

Lecture 206 Bonus!

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