دانلود کتاب Python for Data Analysis, 2nd Edition

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PYTHON DATA ANALYTICSThe Beginner\'s Real-World Crash Course\nPYTHON DATA ANALYTICSThe Beginner\'s Real-World Crash Course\nIntroduction\nChapter 1: Introduction to Data Analytics\nChapter 1: Introduction to Data Analytics\n Difference Between Data Analytics and Data Analysis\n Necessary Skills for Becoming a Data Scientist\n Python Libraries for Data Analysis\nChapter 2: About NumPy Arrays and Vectorized Computation\nChapter 2: About NumPy Arrays and Vectorized Computation\n Creating ndarrays\n Table for Array Creation Functions\nChapter 3: Improve the System\nChapter 3: Improve the System\n Quality in Software Development\n Getting It Right the First Time\n Cycles in Learning\n 5 Tips You Can Apply to Amplify Learning\n Continuous Improvement\nChapter 4: Create Quality\nChapter 4: Create Quality\n Pair Programming\n Test Driven Development\n Regular Feedback to Inspect and Adapt\n Reduce Time Between Stages\n Automation\n Constant Integration\n Controlling Trade-Offs\n Build Quality in the Software Delivery\nChapter 5: Decide as Late as Possible\nChapter 5: Decide as Late as Possible\n Concurrent Software Development\n Rise in Cost\n Problem Solving Strategies\n Simple Rules in Software Development\nChapter 6: Fast Delivery\nChapter 6: Fast Delivery\n Why Should You Deliver Fast?\n Software Development Schedules\n Information Producers\n Cycle Time\n Slack\n To Deliver Fast, You Must Think Small\nChapter 7: Trust and Team Empowerment\nChapter 7: Trust and Team Empowerment\n Team Empowerment\n Motivation and Purpose\n The Foundations of Motivation\nChapter 8: Integrity\nChapter 8: Integrity\n The Goal to the Integrity\n Perceived Integrity\n Create a Model Approach Kind of Design\n How to Maintain the Perceived Integrity?\n Conceptual Integrity\n How to Maintain Conceptual Integrity\nChapter 9: Optimize the Whole\nChapter 9: Optimize the Whole\n System Thinking\n System Measurements\nChapter 10: Go Lean in Your Organization\nChapter 10: Go Lean in Your Organization\n Learn JIT Coaching\nChapter 11: The Relationship Between Lean and Agile Development\nChapter 11: The Relationship Between Lean and Agile Development\n The Connection Between Lean and Agile Principles\n Iterative Approach\n Connection with Lean: Deliver Fast and Delay Commitment\n Disciplined Project Management Process\n Connection with Lean: Develop Quality\n Short Feedback Loops\n Connection with Lean: Remove waste\n Lean and Agile Development\nChapter 12: Pros and Cons of Lean Software Development\nChapter 12: Pros and Cons of Lean Software Development\nConclusion\nPYTHON DATA ANALYTICSA Hands-on Guide Beyond The Basics\nPYTHON DATA ANALYTICSA Hands-on Guide Beyond The Basics\nIntroduction\nChapter One: An Introduction to Data Science And Data Analytics\nChapter One: An Introduction to Data Science And Data Analytics\n What Exactly is Data Science?\n What Information is Necessary for a Data Scientist to Know?\n Who is a Data Analyst?\n Do Data Analytics and Data Science Intersect?\n Understanding Machine Learning\n Do Machine Learning and Data Science Intersect?\n Evolution of Data Science\n Data Science: Art and Science\n Five Important Considerations in Data Science\n Ten Platforms to be Used in the Field of Data Science\nChapter Two: Types of Data Analytics\nChapter Two: Types of Data Analytics\n Descriptive Analytics\n Prescriptive Analytics\n Predictive Analytics\n Data Science Techniques that an Aspiring Data Scientist Should Know\n Classification Analysis\nChapter Three: Data Types and Variables\nChapter Three: Data Types and Variables\n Choosing the Right Identifier\n Python Keywords\n Understanding the Naming Convention\n Creating and Assigning Values to Variables\n Recognizing Different Types of Variables\n Working with Dynamic Typing\n The None Variable\n Using Quotes\n How to use Whitespace Characters\n How to Create a Text Application\n Working with Numbers\n Converting Data Types\nChapter Four: Conditional Statements\nChapter Four: Conditional Statements\n How to Compare Variables\n Manipulating Boolean Variables\n Combine Conditional Expressions\n How to Control the Process\n Nesting Loops\n For\nChapter Five: Data Structures\nChapter Five: Data Structures\n Items in Sequences\n Tuples\n List\n Stacks and Queues\n Dictionaries\nChapter Six: Working with Strings\nChapter Six: Working with Strings\n Splitting Strings\n Concatenation and Joining Strings\n Editing Strings\n Creating a Regular Expression Object\nChapter Seven: How to Use Files\nChapter Seven: How to Use Files\n How to Open Files\n Modes and Buffers\n Reading and Writing\n Closing Files\nChapter Eight: Working with Functions\nChapter Eight: Working with Functions\n Defining a Function\n Defining Parameters\n Documenting your Function\n Working with Scope\n Understanding Scope\n Manipulating Dictionaries and Lists\n Abstraction\nChapter Nine: Data Visualization\nChapter Nine: Data Visualization\n Know your Audience\n Set your Goals\n Choose the Right Type of Charts\n Number Charts\n Maps\n Pie Charts\n Gauge Charts\n The Color Theory Advantage\n Handling Big Data\n Prioritize using Ordering, Layout and Hierarchy\n Utilization of Network Diagrams and Word Clouds\n Comparisons\n Telling a Story\nChapter Ten: Visualization Tools for the Digital Age\nChapter Ten: Visualization Tools for the Digital Age\n 7 Best Data Visualization Tools\n 10 Useful Python Data Visualization Libraries\nChapter Eleven: An Introduction To Outlier Detection In Python\nChapter Eleven: An Introduction To Outlier Detection In Python\n What is an Outlier?\n Why Do We Need To Detect Outliers?\n Why Should We Use PyOD For Outlier Detection?\n Outlier Detection Algorithms Used In PyOD\n Implementation of PyOD\nChapter Twelve: An Introduction To Regression Analysis\nChapter Twelve: An Introduction To Regression Analysis\n Linear Regression Analysis\n Multiple Regression Analysis\nChapter Thirteen: Classification Algorithm\nChapter Thirteen: Classification Algorithm\n Advantages Of Decision Trees\n Disadvantages Of Decision Trees\nChapter Fourteen: Clustering Algorithms\nChapter Fourteen: Clustering Algorithms\n K-Means Clustering Algorithm\n Code for Hierarchical Clustering Algorithm\nConclusion\nReferences\nPYTHON DATA ANALYTICSThe Expert’s Guide to Real-World Solutions\nPYTHON DATA ANALYTICSThe Expert’s Guide to Real-World Solutions\nIntroduction\nChapter 1: Conceptual Approach to Data Analysis\nChapter 1: Conceptual Approach to Data Analysis\n Techniques Used in Data Analysis\n Data Analysis Procedure\n Methods Used in Data Analysis\n Types of Data Analysis\n Tools Used in Data Analysis\n Benefits of Data Analysis in Python over Excel\n Possible Shortcomings of Analyzing Data in Python\nChapter 2: Data Analysis in Python\nChapter 2: Data Analysis in Python\n Python Libraries for Data Analysis\n Installation Guide for Windows\n Installation Guide for Linux\n Installing IPython\n Building Python Libraries from Source\nChapter 3: Statistics in Python - NumPy\nChapter 3: Statistics in Python - NumPy\n Generating Arrays\n Slicing and Indexing\n Importance of NumPy Mastery\nChapter 4: Data Manipulation in Pandas\nChapter 4: Data Manipulation in Pandas\n Installing Pandas\n Fundamentals of Pandas\n Building DataFrames\n Loading Data into DataFrames\n Obtaining Data from SQL Databases\n Extracting Information from Data\n Dealing with Duplicates\n Cleaning Data in a Column\n Computation with Missing Values\n Data Imputation\n Describing Variables\n Data Manipulation\nChapter 5: Data Cleaning\nChapter 5: Data Cleaning\n Possible Causes of Unclean Data\n How to Identify Inaccurate Data\n How to Clean Data\n How to Avoid Data Contamination\nChapter 6: Data Visualization with Matplotlib in Python\nChapter 6: Data Visualization with Matplotlib in Python\n Fundamentals of Matplotlib\n Basic Matplotlib Functions\n Plotting Function Inputs\n Basic Matplotlib Plots\n Logarithmic Plots (Log Plots)\n Scatter Plots\n Display Tools in Matplotlib\n How to Create a Chart\n Using Multiple Axes and Figures\n Introducing New Elements to Your Plot\nChapter 7: Testing Hypotheses with SciPy\nChapter 7: Testing Hypotheses with SciPy\n Fundamentals of Hypothesis Testing\n Hypothesis Testing Procedure\n One-Sample T-Test\n Two-Sample T-Test\n Paired T-Test\n Using SciPy\n Installing SciPy\n SciPy Modules\n Integration\nChapter 8: Data Mining in Python\nChapter 8: Data Mining in Python\n Methods of Data Mining\n Building a Regression Model\n Building Clustering Models\nConclusion