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## You're reading fromPython Machine Learning (Wiley)

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee

1. Cover
2. Introduction
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index

# Reshaping Arrays

You can reshape an array to another dimension using the `reshape()` function. Using the `b5` (which is a rank 1 array) example, you can reshape it to a rank 2 array as follows:

````b5 = b5.reshape(1,-1)`
`print(b5)`
`'''`
`[[9 8 7 6 5]]`
`'''` ```

In this example, you call the `reshape()` function with two arguments. The first `1` indicates that you want to convert it into rank 2 array with 1 row, and the `‐1` indicates that you will leave it to the `reshape()` function to create the correct number of columns. Of course, in this example, it is clear that after reshaping there will be five columns, so you can call the `reshape()` function as `reshape(1,5)`. In more complex cases, however, it is always convenient to be able to use `‐1` to let the function decide on the number of rows or columns to create.

Here is another example of how to reshape `b4` (which is a rank 2 array) to rank 1:

````b4.reshape(-1,)`
`'''`
`[9 8 7 6 5]`
`'''` ```

The `‐...`

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