Data Preprocessing with NumPy

Curriculum

• Introduction to NumPy Free

  6 Lessons 19 Min

  This introductory section presents the NumPy package and its applications. You’ll learn how to install and upgrade NumPy, before quickly learning about its most important assets – “arrays”. We’ll also go over how to use the documentation - an extremely useful component for our work later on in the course.

  Course Introduction Free The NumPy Package and Its Applications Free Installing and Upgrading NumPy Free What is an array? Free Using The NumPy Documentation Free Frequently Asked Questions Free
• Why do we use NumPy? Free

  3 Lessons 20 Min

  This section follows NumPy’s role in the development of Python and takes a closer look at ndarrays. We discuss what makes them so useful and compare them to another similarly-looking data structure – NumPy lists.

  History of NumPy Free Ndarrays Free Arrays vs Lists Free
• NumPy Fundamentals

  6 Lessons 29 Min

  Here, we focus on the basic NumPy syntax. You’ll learn about “indexing” and the different ways of assigning values to an array. This section also explains the elementwise properties of arrays, as we go over the different types of data we can store in them. In addition, we’ll take a look at some of the most important characteristics and properties of NumPy functions.

  Indexing Assigning Values Elementwise Properties Types of Data Supported by NumPy Characteristics of NumPy Functions - Part 1 Characteristics of NumPy Functions - Part 2
• Working with Arrays

  4 Lessons 27 Min

  This section explores the concept of slicing and how its many variations can be applied to ndarrays. You’ll grasp what “dimensions” are when it comes to arrays and learn how the “reduce” function and method work.

  Basic Slicing Stepwise Slicing Conditional Slicing Dimensions and the Squeeze Function
• Generating Data with NumPy

  7 Lessons 32 Min

  This part of the course explains how to generate arrays of random and non-random data. We begin by creating “empty” arrays, as well as basic arrays of 1s and 0s, before moving on to random generators. Then, we introduce NumPy’s capabilities of generating pseudo-random data pulled from a probability distribution. The section concludes with the applications of generating pseudo-random data.

  Arrays of 0s and 1s "_like" functions in NumPy A Non-Random Sequence of Numbers Random Generators and Seeds Basic Random Functions in NumPy Probability Distributions in NumPy Applications of Random Data in NumPy
• Importing and Saving Data with NumPy

  6 Lessons 39 Min

  This part of the course explains how to generate arrays of random and non-random data. We begin by creating “empty” arrays, as well as basic arrays of 1s and 0s, before moving on to random generators. Then, we introduce NumPy’s capabilities of generating pseudo-random data pulled from a probability distribution. The section concludes with the applications of generating pseudo-random data.

  np.loadtxt() vs np.genfromtxt() Simple Cleaning when Importing String vs Object vs Numbers np.save() np.savez() np.savetxt()
• Statistics with NumPy

  8 Lessons 42 Min

  In this section of the course, we focus on importing and exporting, also known as saving data using the NumPy package. We discuss the differences between “np.loadtxt()” and “np.genfromtxt()” and their applications. We’ll examine NumPy’s capabilities to partially clean datasets as we import them. Later in the section, you’ll learn why you need to import a file into a specific datatype and how choosing the incorrect one can affect your results. We continue with the topic of saving ndarrays to external files where you’ll discover what N-P-Y and N-P-Z files are and when (and how) to export arrays in those formats. Finally, we provide you with a more conventional approach and showcase how to save arrays as text files.

  Using Statistical Functions in NumPy Minimal and Maximal Values in NumPy Statistical Order Functions in NumPy Averages and Variance in NumPy Covariance and Correlation in NumPy Histograms in NumPy (Part 1) Histograms in NumPy (Part 2) NAN Equivalent Functions in NumPy
• Data Manipulation with NumPy

  13 Lessons 95 Min

  This section revolves around NumPy’s capabilities to compute important characteristics or statistics from an array. These include minimal and maximal values, various forms of averages, covariances, correlations as well as histograms. In addition, you’ll also learn about nan equivalent functions and how to use them.

  Checking for Missing Values in Ndarrays Substituting Missing Values in Ndarrays Reshaping Ndarrays Removing Values from Ndarrays Sorting Ndarrays Argument Sort in NumPy Argument Where in NumPy Shuffling Ndarrays Casting Ndarrays Striping Values from Ndarrays Stacking Ndarrays Concatenating Ndarrays Finding Unique Vaules in Ndarrays
• A Loan Data Practical Example with NumPy

  15 Lessons 88 Min

  In this part of the NumPy course, we explore ways to clean and preprocess data in NumPy. You’ll understand how to find and fill missing values, reshape an array, delete excess data as well as sort, shuffle and cast ndarrays. The section also explains what argument functions are and why they are so useful, and introduces ways to combining arrays by stacking and concatenating them. Finally, you’ll discover how to extract the unique values of an array and why this can be important for your analysis.

  Setting Up: Introduction to the Practical Example Setting Up: Importing the Data Set Setting Up: Checking for Incomplete Data Setting Up: Splitting the Dataset Setting Up: Creating Checkpoints Manipulating Text Data: Issue Date Manipulating Text Data: Loan Status and Term Manipulating Text Data: Grade and Sub Grade Manipulating Text Data: Verification Status & URL Manipulating Text Data: State Address Manipulating Text Data: Converting Strings and Creating a Checkpoint Manipulating Numeric Data: Substitute Filler Values Manipulating Numeric Data: Currency Change – The Exchange Rate Manipulating Numeric Data: Currency Change - From USD to EUR Completing the Dataset: