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Data Preprocessing with NumPy
Intro to NumPy
The NumPy Package (4:03)
Installing/Upgrading Numpy (1:51)
What is an array? (3:06)
The NumPy Documentation (4:47)
Intro to NumPy - Exercise
Why NumPy?
History of Num Py (3:17)
ndarrays (9:41)
Arrays vs Lists (6:47)
Why NumPy? - Exercise
NumPy Fundamentals
Indexing (5:51)
Assigning values (4:16)
Elementwise Properties (4:29)
Types of data supported by NumPy (5:56)
Characteristics of NumPy Functions Part 1 (4:43)
Characteristics of NumPy Functions Part 2 (3:30)
NumPy Fundamentals - Exercise
Working with Arrays
Slicing (10:03)
Stepwise Slicing (4:58)
Conditional Slicing (4:51)
Dimensions and the Squeeze Function (6:52)
Working with Arrays - Exercise
Generating Data
np.empty, np.zeros, np.ones, np.full (5:32)
"_like" functions (3:13)
Generating a Sequence of Numbers (np.arange) (5:02)
Random Generators and Seeds (5:21)
np.integers(), np.random(), np.choice() (3:56)
Probability Distributions (5:19)
Applications (4:02)
Generating Data - Exercise
Importing and Saving Data
np.loadtxt() vs np.genfromtxt() (10:32)
Simple Cleaning when Importing (7:18)
String vs Objet vs Numbers (6:54)
Importing Data - Exercise
np.save() (5:23)
np.savez() (5:12)
np.savetxt() (4:02)
Saving Data - Exercise
Statistics
Using NumPy functions - np.mean() (7:44)
Min & Max values (min, amin, minimum and equivalent max, amax, maximum) (6:02)
Statistical Order Functions (np.ptp, np.percentile, np.quantile) (6:25)
Averages and Variances (mean, median, average, var, std etc.) (4:17)
Correlation and Covariance (2:59)
Histograms in NumPy Part 1 (7:35)
Histograms in NumPy Part 2 (4:15)
N-A-N Equivalent Functions (3:08)
Statistics with NumPy - Exercise
Preprocessing
Checking for Missing Values (9:23)
Substituting Missing Values (8:29)
Reshaping (6:31)
Removing Values (4:20)
Sorting Data (9:45)
Argument Functions - Argument Sort (5:48)
Argument Functions - Argument Where (11:12)
Shuffling Data (6:51)
Assigning DataTypes (6:14)
Striping Data (4:43)
Stacking Data (10:31)
Concatenating Data (6:27)
Unique Values in Arrays (5:04)
A Loan Dataset Practical Example with NumPy
Setting Up: Introduction to the Practical Example (4:50)
Setting Up: Importing the Data Set (4:09)
Setting Up: Checking for Incomplete Data (4:35)
Setting Up: Splitting the Dataset (5:27)
Setting Up: Creating Checkpoints (2:50)
Manipulating Text Data: Issue Date (5:26)
Manipulating Text Data: Loan Status and Term (7:08)
Manipulating Text Data: Grade and Sub Grade (8:54)
Manipulating Text Data: Verification Status & URL (5:20)
Manipulating Text Data: State Address (6:01)
Manipulating Text Data: Converting Strings and Creating a Checkpoint (3:28)
Manipulating Numeric Data: Substitute Filler Values (7:51)
Manipulating Numeric Data: Currency Change – The Exchange Rate (6:32)
Manipulating Numeric Data: Currency Change - From USD to EUR (8:22)
Completing the Dataset (7:46)
np.integers(), np.random(), np.choice()
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