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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

# Array Indexing

Accessing elements in the array is similar to accessing elements in a Python list:

````print(r1[0])         # 1`
`print(r1[1])         # 2` ```

The following code snippet creates another array named `r2`, which is two‐dimensional:

````list2 = [6,7,8,9,0]`
`r2 = np.array([list1,list2])     # rank 2 array`
`print(r2)`
`'''`
`[[1 2 3 4 5]`
` [6 7 8 9 0]]`
`'''`
`print(r2.shape)             # (2,5) - 2 rows and 5 columns`
`print(r2[0,0])              # 1`
`print(r2[0,1])              # 2`
`print(r2[1,0])              # 6` ```

Here, `r2` is a rank 2 array, with two rows and five columns.

Besides using an index to access elements in an array, you can also use a list as the index as follows:

````list1 = [1,2,3,4,5]`
`r1 = np.array(list1)`
`print(r1[[2,4]])    # [3 5]` ```

## Boolean Indexing

In addition to using indexing to access elements in an array, there is another very cool way to access elements in a NumPy array. Consider the following:

``print(r1>2)     # [False False  True  True  True]`...`
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