Preprocessing Data With Numpy

NumPy, ndarrays, Slicing, Random Generators, Importing and Saving Data, Statistics, Data Manipulation, Preprocessing

What you'll learn

Arrays.

The definition of a package/library.

Installing and Upgrading a package.

Navigating the documentation.

A history of NumPy.

The relationship between arrays and vectors.

Arrays vs Lists.

Indexing.

Assigning values to arrays.

Elementwise properties and operations.

Datatypes supported by ndarrays.

Broadcasting and type casting.

Running a function or method over a given axis.

Slicing, Stepwise Slicing, Conditional Slicing

Dimensionality reduction in arrays.

Generating arrays full of identical values.

Generating non-random sequences of data.

Generating random data with Random Generators.

Generating random samples from a random probability distribution.

Importing and exporting data with and from NumPy.

NPY and NPZ files.

Maximums and Minimums.

Percentiles and Quantiles.

Mean and Variance.

Covariance and Correlation.

Calculating histograms.

Higher dimension histograms.

Finding and filling up missing values.

Substituting "filler" values.

Reshaping arrays.

Removing parts of arrays.

Removing parts of individual elements within arrays. (Stripping)

Sorting and Shuffling.

Argument Functions.

Stacking and Concatenating.

Finding the unique values within an array.

A comprehensive practical example of data cleaning and preprocessing.

Requirements

You'll need to install Python.

No prior experience with NumPy is required.

Some general understanding of coding languages is preferred, but not required.

Description

The problemMost data analyst, data science, and coding courses miss a crucial practical step. They don’t teach you how to work with raw data, how to clean and preprocess it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.The bootcamps we have seen online, and even live classes neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favor. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.The solutionOur goal is to provide you with complete preparation using the NumPy package. This course will turn you into capable data analyst with a fantastic understanding of one of the most prominent computing packages in the world. To take you there, we will cover the following topics extensively.· The ndarray class and why we use it· The type of data arrays usually contain· Slicing and squeezing datasets· Dimensions of arrays, and how to reduce them· Generating pseudo-random data· Importing data from external text files· Saving/Exporting data to external files· Computing the statistics of the dataset (max, min, mean, variance, etc.)· Data cleaning· Data preprocessing· Final practical exampleEach of these subjects builds on the previous ones. And this is precisely what makes our curriculum so valuable. Everything is shown in the right order and we guarantee that you are not going to get lost along the way, as we have provided all necessary steps in video (not a single one skipped). In other words, we are not going to teach you how to concatenate datasets before you know how to index or slice them.So, to prepare you for the long journey towards a data science position, we created a course that will show you all the tools for the job: The Preprocessing Data with NumPy course [MG1] .We believe that this resource will significantly boost your chances of landing a job, as it will prepare you for practical tasks and concepts that are frequently included in interviews.NumPy is Python’s fundamental package for scientific computing. It has established itself as the go-to tool when you need to compute mathematical and statical operations.Why learn it?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. What’s more, the package introduces multi-dimensional array structures and provides a plethora of built-in functions and methods to use while working with them. In other words, NumPy can be described as a computationally stable state-of-the-art Python instrument that provides great flexibility and can take your analysis to the next level.Some of the topics we will cover:1. Fundamentals of NumPy2. Random Generators3. Working with text files4. Statistics with NumPy5. Data preprocessing6. Final practical example1. Fundamentals of NumPyTo fully grasp the capabilities of NumPy, we need to start from the fundamentals. In this part of the course, we’ll examine the ndarray class, discuss why it’s so popular and get familiar with terms like “indexing”, “slicing”, “dimensions” and “reducing”.Why learn it?As stated above, NumPy is the quintessential package for scientific computing, and to understand its true value, we need to start from its very core – the ndarray class. The better we comprehend the basics, the easier it’s going to be to grasp the more difficult concepts. That’s why it’s fundamental to lay a good foundation on which to build our NumPy skills.2. Random GeneratorsAfter we’ve learned the basics, we’ll move on to pseudo-random data and random generators. These generators will help construct a set of arbitrary variables from a given probability distribution, or a fixed set of viable options.Why learn it?Working in a data-driven field, we sometimes need to construct partially arbitrary tests to see if our code works as intended. And here lies the value of random generators, as they allow us to construct datasets of pseudo-random data. The added benefit of random generators is that we can set a seed if we wish to replicate a particular randomization, but we’ll go into all the details in the course itself.3. Working with text filesExchanging information with text files is practically how we exchange information today. In this part of the course, we will use the Python, pandas, and NumPy tools covered earlier to give you the essentials you need when importing or saving data.Why learn it?In many courses, you are just given a dataset to practice your analytical and programming skills. However, we don’t want to close our eyes to reality, where converting a raw dataset from an external file into a workable Python format can be a massive challenge.4. Statistics with NumPyOnce we’ve learned how to import large sets of information from external text files, we’ll finally be ready to explore one of NumPy’s strengths – statistics. Since the package is extremely computationally durable, we often rely on its functions and methods to calculate the statistics of a sample dataset. These include the likes of the mean, the standard deviation, and much more.Why learn it?To become a data scientist, you not only need to be able to preprocess a dataset, but also to extract valuable insights. One way to learn more about a dataset is by examining its statistics. So, we’ll use the package to understand more about the data and how to convert this knowledge into crucial information we can use for forecasting.5. Data preprocessingEven when your dataset is in clean and comprehensible shape, it isn’t quite ready to be processed for visualizations and analysis just yet. There is a crucial step in between, and that’s data preprocessing.Why learn it?Data preprocessing is where a data analyst can demonstrate how good or great they are at their job. This stage of the work requires the ability to choose the right statistical tool that will improve the quality of your dataset and the knowledge to implement it with advanced pandas and NumPy techniques. Only when you’ve completed this step can you say that your dataset is preprocessed and ready for the next part, which is data visualization.6. Practical exampleThe course contains plenty of exercises and practical cases. What’s more, in the end, we have included a comprehensive practical example that will show you how everything you have learned along the way comes nicely together. This is where you will be able to appreciate how far you have come in your journey on mastering NumPy in your pursuit of a data career.What you get· Active Q&A support· All the NumPy knowledge to become a data analyst· A community of aspiring data analysts· A certificate of completion· Access to frequent future updates· Real-world trainingGet ready to become a NumPy data analyst from scratchWhy wait? Every day is a missed opportunity.Click the “Buy Now” button and become a part of our data analyst program today.

Overview

Section 1: Introduction to NumPy

Lecture 1 What Does the Course Cover?

Lecture 2 Download All Resources

Lecture 3 FAQ

Lecture 4 The NumPy Package and Its Applications

Lecture 5 Installing and Upgrading NumPy

Lecture 6 What is an array?

Lecture 7 Using the NumPy Documentation

Lecture 8 Introduction to NumPy - Exercise

Section 2: Why Do We Use NumPy?

Lecture 9 A Brief History of NumPy

Lecture 10 ndarrays

Lecture 11 Arrays vs Lists

Lecture 12 Why Do We Use NumPy - Exercise

Section 3: NumPy Fundamentals

Lecture 13 Indexing

Lecture 14 Assigning Values

Lecture 15 Elementwise Properties

Lecture 16 NumPy Datatypes

Lecture 17 Characteristics of NumPy Functions - Part 1

Lecture 18 Characteristics of NumPy Functions - Part 2

Lecture 19 NumPy Fundamentals - Exercise

Section 4: Working with Arrays

Lecture 20 Basic Slicing

Lecture 21 Stepwise Slicing

Lecture 22 Conditional Slicing

Lecture 23 Dimensions and the Squeeze Function

Lecture 24 Working with Arrays - Exercise

Section 5: Generating Data with NumPy

Lecture 25 Empty Arrays, Arrays of Identical Values

Lecture 26 _like Functions

Lecture 27 A Sequence of Numbers - np.arange()

Lecture 28 Random Generators and Seeds

Lecture 29 Random Integers, Probabilities and Choices

Lecture 30 Random Probability Distributions

Lecture 31 Applications of Random Generators

Lecture 32 Generating Data with NumPy - Exercise

Section 6: Importing and Saving Data

Lecture 33 Importing Data with Numpy - np.loadtxtx() vs np.genfromtxt()

Lecture 34 Importing Data with NumPy - Simple Cleaning when Importing

Lecture 35 Importing Data with NumPy - String vs Object vs Numbers

Lecture 36 Importing Data with NumPy - Exercise

Lecture 37 Saving Data with NumPy - NPY

Lecture 38 Saving Data with NumPy - NPZ

Lecture 39 Saving Data with NumPy - CSV

Lecture 40 Importing and Saving Data - Exercise

Section 7: Statistics with NumPy

Lecture 41 Using NumPy Statistical Functions

Lecture 42 Minimal and Maximal Values

Lecture 43 Percentiles and Quantiles

Lecture 44 Averages and Variance

Lecture 45 Covariance and Correlation

Lecture 46 Histogram - Part 1: 1-D Histograms

Lecture 47 Histogram - Part 2: Higher Dimension Histograms

Lecture 48 N-A-N Equivalent Functions

Lecture 49 Statistics with NumPy - Exercise

Section 8: Manipulation Data with NumPy

Lecture 50 Checking for Missing Values

Lecture 51 Substituting Filler Values

Lecture 52 Reshaping Arrays

Lecture 53 Removing Values

Lecture 54 Sorting Arrays

Lecture 55 Argument Functions - Part 1: Argument Sort

Lecture 56 Argument Functions - Part 1: Argument Where

Lecture 57 Shuffling Data

Lecture 58 Casting Arrays

Lecture 59 Stripping Symbols from Arrays

Lecture 60 Stacking Arrays

Lecture 61 Concatenating Arrays

Lecture 62 Finding Unique Values in Arrays

Section 9: A NumPy Practical Example

Lecture 63 Setting Up: Introduction to the Practical Example

Lecture 64 Setting Up: Importing the Data Set

Lecture 65 Setting Up: Checking for Incomplete Data

Lecture 66 Setting Up: Splitting the Dataset

Lecture 67 Setting Up: Creating Checkpoints

Lecture 68 Manipulating Text Data: Issue Date

Lecture 69 Manipulating Text Data: Loan Status and Term

Lecture 70 Manipulating Text Data: Grade and Sub Grade

Lecture 71 Manipulating Text Data: Verification Status & URL

Lecture 72 Manipulating Text Data: State Address

Lecture 73 Manipulating Text Data: Converting Strings and Creating a Checkpoint

Lecture 74 Manipulating Numeric Data: Substitute Filler Values

Lecture 75 Manipulating Numeric Data: Currency Change – The Exchange Rate

Lecture 76 Manipulating Numeric Data: Currency Change - From USD to EUR

Lecture 77 Completing the Dataset

Aspiring data analysts.,Programming beginners.,People interested in analyzing data through Python.,Analysts who wish to specialize in Python.,Finance graduates and professionals who need to better apply their knowledge in Python.