NumPy Tutorial

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Introduction

NumPy is a powerful library for numerical computing in Python. It provides support for arrays, matrices, and many mathematical functions to operate on these data structures. This tutorial covers the basics of using NumPy, including creating arrays, indexing, slicing, reshaping, and performing various operations.

Table of Contents

1. NumPy Getting Started
2. NumPy Creating Arrays
3. NumPy Array Indexing
4. NumPy Array Slicing
5. NumPy Data Types
6. NumPy Copy vs View
7. NumPy Array Shape
8. NumPy Array Reshape
9. NumPy Array Iterating
10. NumPy Array Join
11. NumPy Array Split
12. NumPy Array Search
13. NumPy Array Sort
14. NumPy Array Filter

1. NumPy Getting Started

To get started with NumPy, you need to install the library. You can install NumPy using pip.

pip install numpy 

Example

import numpy as np # Checking the version of NumPy print(np.__version__) 

2. NumPy Creating Arrays

NumPy arrays can be created using various methods such as array(), zeros(), ones(), arange(), and linspace().

Example

# Creating an array from a list arr = np.array([1, 2, 3, 4, 5]) print(arr) # Creating an array of zeros zeros = np.zeros(5) print(zeros) # Creating an array of ones ones = np.ones(5) print(ones) # Creating an array with a range of values range_array = np.arange(1, 10, 2) print(range_array) # Creating an array with evenly spaced values linspace_array = np.linspace(0, 1, 5) print(linspace_array) 

3. NumPy Array Indexing

You can access elements of an array using indexing.

Example

arr = np.array([10, 20, 30, 40, 50]) # Accessing individual elements print(arr[0]) # Output: 10 print(arr[3]) # Output: 40 # Accessing elements in a 2D array arr_2d = np.array([[1, 2, 3], [4, 5, 6]]) print(arr_2d[0, 1]) # Output: 2 print(arr_2d[1, 2]) # Output: 6 

4. NumPy Array Slicing

Slicing allows you to extract a portion of an array.

Example

arr = np.array([10, 20, 30, 40, 50]) # Slicing elements from index 1 to 3 print(arr[1:4]) # Output: [20 30 40] # Slicing elements with a step print(arr[::2]) # Output: [10 30 50] # Slicing a 2D array arr_2d = np.array([[1, 2, 3], [4, 5, 6]]) print(arr_2d[:, 1:3]) # Output: [[2 3] [5 6]] 

5. NumPy Data Types

NumPy supports various data types, and you can specify the data type of an array during its creation.

Example

arr = np.array([1, 2, 3, 4], dtype='int32') print(arr.dtype) # Output: int32 # Changing data type of an array arr_float = arr.astype('float64') print(arr_float.dtype) # Output: float64 

6. NumPy Copy vs View

In NumPy, a copy is a new array, and changes to the copy do not affect the original array, whereas a view is a new array object that looks at the same data.

Example

arr = np.array([1, 2, 3, 4, 5]) # Creating a copy arr_copy = arr.copy() arr_copy[0] = 10 print(arr) # Output: [1 2 3 4 5] print(arr_copy) # Output: [10 2 3 4 5] # Creating a view arr_view = arr.view() arr_view[0] = 10 print(arr) # Output: [10 2 3 4 5] print(arr_view) # Output: [10 2 3 4 5] 

7. NumPy Array Shape

The shape of an array is the number of elements in each dimension.

Example

arr = np.array([[1, 2, 3], [4, 5, 6]]) # Getting the shape of the array print(arr.shape) # Output: (2, 3) 

8. NumPy Array Reshape

You can change the shape of an array using the reshape() method.

Example

arr = np.array([1, 2, 3, 4, 5, 6]) # Reshaping the array to 2x3 reshaped_arr = arr.reshape((2, 3)) print(reshaped_arr) 

9. NumPy Array Iterating

You can iterate over the elements of an array using loops.

Example

arr = np.array([1, 2, 3, 4, 5]) # Iterating over a 1D array for x in arr: print(x) # Iterating over a 2D array arr_2d = np.array([[1, 2, 3], [4, 5, 6]]) for row in arr_2d: for element in row: print(element) 

10. NumPy Array Join

You can join arrays using functions like concatenate(), stack(), and hstack().

Example

arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) # Concatenating arrays concatenated = np.concatenate((arr1, arr2)) print(concatenated) # Output: [1 2 3 4 5 6] # Stacking arrays stacked = np.stack((arr1, arr2)) print(stacked) # Output: [[1 2 3] [4 5 6]] 

11. NumPy Array Split

You can split arrays using the split() function.

Example

arr = np.array([1, 2, 3, 4, 5, 6]) # Splitting the array into 3 parts split_arr = np.split(arr, 3) print(split_arr) # Output: [array([1, 2]), array([3, 4]), array([5, 6])] 

12. NumPy Array Search

You can search for elements in an array using the where() function.

Example

arr = np.array([1, 2, 3, 4, 5, 6]) # Searching for elements equal to 4 result = np.where(arr == 4) print(result) # Output: (array([3]),) 

13. NumPy Array Sort

You can sort arrays using the sort() function.

Example

arr = np.array([3, 1, 4, 1, 5, 9]) # Sorting the array sorted_arr = np.sort(arr) print(sorted_arr) # Output: [1 1 3 4 5 9] 

14. NumPy Array Filter

You can filter elements in an array using a boolean index list.

Example

arr = np.array([1, 2, 3, 4, 5, 6]) # Creating a boolean index list filter_arr = arr % 2 == 0 # Filtering the array filtered_arr = arr[filter_arr] print(filtered_arr) # Output: [2 4 6] 

Conclusion

NumPy is a powerful library for numerical computing in Python, providing support for arrays, matrices, and a wide range of mathematical functions. By understanding how to create, manipulate, and perform operations on arrays, you can efficiently handle large datasets and perform complex computations in your Python projects. This tutorial covered the basics of NumPy, including creating arrays, indexing, slicing, reshaping, and performing various operations.