Nilotpal C.
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Online Course
Data Preprocessing with NumPy
with
Viktor Mehandzhiyski
Master Python’s key NumPy package: Apply essential techniques for efficient data preprocessing and analysis
25,655 students already enrolled
Skill level:
Intermediate
Duration:
8 hours
-
Lessons (7 hours)
-
Practice exams (1.2 hours)
- Lessons (7 hours)
- Practice exams (1.2 hours)
CPE credits:
12.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited
certificate
What you learn
- Add the NumPy library to your data analysis toolkit.
- Learn how to install and import Python packages.
- Gain proficiency in NumPy’s ndarray.
- Explore various techniques to clean and preprocess data.
- Solve real-world data preprocessing problems using NumPy.
Topics & tools
PythonProgrammingData AnalysisData ProcessingNumpyData Preprocessing
Your instructor
Data Analyst | Former Data Scientist at NielsenIQ | Course Author at 365 Data Science
Track record
Bringing real-world expertise from leading global companies
Academic background
Bachelor's degree, Mathematics & Economics
Course OVERVIEW
Description
CPE Credits: 12.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
This course is designed to show you how to work with one of Python’s fundamental packages – NumPy. You will learn what a “package” is and see how to install, upgrade and import it. By the time you finish the course, you’ll be comfortable with NumPy’ ndarray class, how to slice and reduce the dimensions of its instances, as well as how to quickly refer to the documentation. Furthermore, you’ll be ready to take advantage of NumPy’s various built-in functions and methods, which we’ll use to generate random and non-random data, import and export data to and from Python, find statistical values for a dataset, and clean and preprocess ndarrays.
Prerequisites
- Python (version 3.8 or later), NumPy library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
- Completion of an introductory Python course is required.
- Mathematics
Advanced preparation
Curriculum
68 lessons
85 exercises
5 exams
- 2. Why do we use NumPy?20 minThis 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.20 minThis 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 FreeExercise FreeNdarrays FreeCoding exercise FreeArrays vs Lists FreeExercise FreeCoding exercise Free
- 3. NumPy Fundamentals29 minHere, 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.29 minHere, 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.IndexingExerciseCoding exerciseAssigning ValuesCoding exerciseElementwise PropertiesCoding exerciseTypes of Data Supported by NumPyExerciseCharacteristics of NumPy Functions - Part 1Characteristics of NumPy Functions - Part 2Coding exerciseExercise
- 4. Working with Arrays27 minThis 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.27 minThis 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 SlicingCoding exerciseStepwise SlicingCoding exerciseConditional SlicingCoding exerciseDimensions and the Squeeze FunctionExercisePractice exam
- 5. Generating Data with NumPy32 minThis 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.32 minThis 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 1sCoding exercise"_like" functions in NumPyCoding exerciseA Non-Random Sequence of NumbersExerciseRandom Generators and SeedsCoding exerciseExerciseBasic Random Functions in NumPyCoding exerciseCoding exerciseProbability Distributions in NumPyExerciseCoding exerciseApplications of Random Data in NumPyPractice exam
- 6. Importing and Saving Data with NumPy39 minThis 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.39 minThis 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()Coding exerciseSimple Cleaning when ImportingCoding exerciseString vs Object vs NumbersCoding exerciseExercisenp.save()np.savez()np.savetxt()Exercise
- 7. Statistics with NumPy42 minIn 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.42 minIn 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 NumPyCoding exerciseMinimal and Maximal Values in NumPyCoding exerciseStatistical Order Functions in NumPyCoding exerciseExerciseAverages and Variance in NumPyCoding exerciseCovariance and Correlation in NumPyCoding exerciseExerciseHistograms in NumPy (Part 1)Histograms in NumPy (Part 2)NAN Equivalent Functions in NumPyExercisePractice exam
- 8. Data Manipulation with NumPy95 minThis 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.95 minThis 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 NdarraysCoding exerciseSubstituting Missing Values in NdarraysCoding exerciseExerciseReshaping NdarraysRemoving Values from NdarraysCoding exerciseSorting NdarraysExerciseArgument Sort in NumPyArgument Where in NumPyCoding exerciseShuffling NdarraysCasting NdarraysExerciseStriping Values from NdarraysStacking NdarraysCoding exerciseCoding exerciseConcatenating NdarraysFinding Unique Vaules in NdarraysPractice exam
- 9. A Loan Data Practical Example with NumPy88 minIn 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.88 minIn 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 ExampleSetting Up: Importing the Data SetSetting Up: Checking for Incomplete DataSetting Up: Splitting the DatasetSetting Up: Creating CheckpointsManipulating Text Data: Issue DateManipulating Text Data: Loan Status and TermManipulating Text Data: Grade and Sub GradeManipulating Text Data: Verification Status & URLManipulating Text Data: State AddressManipulating Text Data: Converting Strings and Creating a CheckpointManipulating Numeric Data: Substitute Filler ValuesManipulating Numeric Data: Currency Change – The Exchange RateManipulating Numeric Data: Currency Change - From USD to EURCompleting the Dataset:
- 10. Course exam70 min70 minCourse exam
Free lessons
1.1 Course Introduction
5 min
1.2 The NumPy Package and Its Applications
4 min
1.3 Installing and Upgrading NumPy
2 min
1.5 What is an array?
3 min
1.8 Using The NumPy Documentation
5 min
1.10 Frequently Asked Questions
1 min
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after moving to an AI and data science career
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of our students recommend
365 Data Science.
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of AI and data science graduates
successfully change
or advance their careers.
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