Data Preprocessing with NumPy

This course will guide you through one of Python’s most notable packages – NumPy. We’ll explain why it’s so popular and discuss the numerous applications of its crown jewel – the ndarray class.

with Viktor Mehandzhiyski

Course Overview

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.

68 High Quality Lessons

6 Practical Tasks

7 Hours of Video

Certificate of Achievement

Skills you will gain

data analysisdata processingprogrammingpython

What You'll Learn

Do you already know how to use Python? Improve your skills by adding another tool to your arsenal – one of the most notable packages, NumPy!

Create arrays with NumPy

Use basic NumPy syntax

Generate data with NumPy

Implement statistical functions

Substitute missing values in Ndarrays

Explore ways to clean and preprocess data in NumPy

Curriculum

Introduction to NumPy Free

6 Lesson 19 Min

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 Lesson 20 Min

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

6 Lesson 29 Min

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 Lesson 27 Min

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

7 Lesson 32 Min

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 Lesson 39 Min

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

8 Lesson 42 Min

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 Lesson 95 Min

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 Lesson 88 Min

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:

“A large portion of a data analyst’s work is dedicated to preprocessing datasets. Unquestionably, this involves tons of mathematical and statistical techniques that NumPy is renowned for. NumPy can be described as a computationally stable state-of-the-art Python instrument that provides flexibility and can take your analysis to the next level.”

Viktor Mehandzhiyski

Content Creator at 365 Data Science

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