Numpy Array Object

Numpy (12 Part Series)

1 Introduction to NumPy
2 Numpy Array Object
… 8 more parts…
3 NumPy Unleashed: Exploring the Power of Special Arrays
4 Understanding Indexing and Slicing in NumPy Arrays
5 Understanding NumPy Data Types.
6 Understanding NumPy: Datatypes, Memory Storage, and Structured Arrays.
7 Understanding NumPy Array Shapes in Python
8 Array Manipulation: A Deep Dive into Insertions and Deletions
9 How to Master Joining and Splitting Numpy Arrays: A Comprehensive Guide
10 Exploring Data with NumPy: A Guide to Statistical Functions in Python
11 Element-Wise Numerical Operations in NumPy: A Practical Guide with Examples
12 Efficient Array Sorting and File I/O Operations in NumPy: A Comprehensive Guide

Understanding NumPy Arrays: Homogeneous Multidimensional Data Structures.

Multidimensional Homogeneous Array:
- A NumPy array is a fundamental object that represents a multidimensional, homogeneous data structure.
- “Homogeneous” means that all elements in the array have the same data type (e.g., all integers, all floats, etc.).
Comparison with Python Lists:
- Unlike Python lists, which can contain elements of different types, a NumPy array enforces a consistent data type for all its elements.
Dimensions and Axes:
- The dimension of a NumPy array refers to the number of axes it has.
- For example:
  - A 1-D array (e.g., [1, 2, 3]) behaves like a single axis with three elements. Its length is 3.
  - A 2-D array (e.g., [[1.0, 2.0, 3.0], [4.0, 6.0, 7.0]]) has two axes:
  - The first axis (axis=0) has a length of 2 (i.e., two rows).
  - The second axis (axis=1) has a length of 3 (i.e., three columns).

Creating the NumPy array:

In NumPy, we create N-D arrays using the array() function by passing Python lists or tuples.

Here is the example for 1-D array:


<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span># Using a tuple to create a 1D NumPy array </span><span>array_from_tuple</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>((</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>))</span>
<span># Using a list to create a 1D NumPy array </span><span>array_from_list</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>6</span><span>,</span> <span>7</span><span>,</span> <span>8</span><span>,</span> <span>9</span><span>,</span> <span>10</span><span>])</span>
<span>print</span><span>(</span><span>"</span><span>Array from tuple:</span><span>"</span><span>,</span> <span>array_from_tuple</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Array from list:</span><span>"</span><span>,</span> <span>array_from_list</span><span>)</span>
<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>

<span># Using a tuple to create a 1D NumPy array </span><span>array_from_tuple</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>((</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>))</span>

<span># Using a list to create a 1D NumPy array </span><span>array_from_list</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>6</span><span>,</span> <span>7</span><span>,</span> <span>8</span><span>,</span> <span>9</span><span>,</span> <span>10</span><span>])</span>

<span>print</span><span>(</span><span>"</span><span>Array from tuple:</span><span>"</span><span>,</span> <span>array_from_tuple</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Array from list:</span><span>"</span><span>,</span> <span>array_from_list</span><span>)</span>
import numpy as np

# Using a tuple to create a 1D NumPy array array_from_tuple = np.array((1, 2, 3, 4, 5))

# Using a list to create a 1D NumPy array array_from_list = np.array([6, 7, 8, 9, 10])

print("Array from tuple:", array_from_tuple)
print("Array from list:", array_from_list)

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<span>Output</span><span>:</span>
<span>Array</span> <span>from</span> <span>tuple</span><span>:</span> <span>[</span><span>1</span> <span>2</span> <span>3</span> <span>4</span> <span>5</span><span>]</span>
<span>Array</span> <span>from</span> <span>list</span><span>:</span> <span>[</span> <span>6</span>  <span>7</span>  <span>8</span>  <span>9</span> <span>10</span><span>]</span>
<span>Output</span><span>:</span>
<span>Array</span> <span>from</span> <span>tuple</span><span>:</span> <span>[</span><span>1</span> <span>2</span> <span>3</span> <span>4</span> <span>5</span><span>]</span>
<span>Array</span> <span>from</span> <span>list</span><span>:</span> <span>[</span> <span>6</span>  <span>7</span>  <span>8</span>  <span>9</span> <span>10</span><span>]</span>
Output:
Array from tuple: [1 2 3 4 5]
Array from list: [ 6  7  8  9 10]

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In NumPy, we can explicitly specify the data types of an array using the dtype option in the array() function.
Here is the example:


<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span># Using a tuple to create a 1D NumPy array </span><span>array_from_tuple</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>((</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>),</span> <span>dtype</span><span>=</span><span>float</span><span>)</span>
<span># Using a list to create a 1D NumPy array </span><span>array_from_list</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>6</span><span>,</span> <span>7</span><span>,</span> <span>8</span><span>,</span> <span>9</span><span>,</span> <span>10</span><span>],</span><span>dtype</span><span>=</span><span>complex</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Array from tuple:</span><span>"</span><span>,</span> <span>array_from_tuple</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Array from list:</span><span>"</span><span>,</span> <span>array_from_list</span><span>)</span>
<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>

<span># Using a tuple to create a 1D NumPy array </span><span>array_from_tuple</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>((</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>),</span> <span>dtype</span><span>=</span><span>float</span><span>)</span>

<span># Using a list to create a 1D NumPy array </span><span>array_from_list</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>6</span><span>,</span> <span>7</span><span>,</span> <span>8</span><span>,</span> <span>9</span><span>,</span> <span>10</span><span>],</span><span>dtype</span><span>=</span><span>complex</span><span>)</span>

<span>print</span><span>(</span><span>"</span><span>Array from tuple:</span><span>"</span><span>,</span> <span>array_from_tuple</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Array from list:</span><span>"</span><span>,</span> <span>array_from_list</span><span>)</span>
import numpy as np

# Using a tuple to create a 1D NumPy array array_from_tuple = np.array((1, 2, 3, 4, 5), dtype=float)

# Using a list to create a 1D NumPy array array_from_list = np.array([6, 7, 8, 9, 10],dtype=complex)

print("Array from tuple:", array_from_tuple)
print("Array from list:", array_from_list)

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<span>Output</span><span>:</span>
<span>Array</span> <span>from</span> <span>tuple</span><span>:</span> <span>[</span><span>1.</span> <span>2.</span> <span>3.</span> <span>4.</span> <span>5.</span><span>]</span>
<span>Array</span> <span>from</span> <span>list</span><span>:</span> <span>[</span> <span>6.</span><span>+</span><span>0.j</span>  <span>7.</span><span>+</span><span>0.j</span>  <span>8.</span><span>+</span><span>0.j</span>  <span>9.</span><span>+</span><span>0.j</span> <span>10.</span><span>+</span><span>0.j</span><span>]</span>
<span>Output</span><span>:</span>
<span>Array</span> <span>from</span> <span>tuple</span><span>:</span> <span>[</span><span>1.</span> <span>2.</span> <span>3.</span> <span>4.</span> <span>5.</span><span>]</span>
<span>Array</span> <span>from</span> <span>list</span><span>:</span> <span>[</span> <span>6.</span><span>+</span><span>0.j</span>  <span>7.</span><span>+</span><span>0.j</span>  <span>8.</span><span>+</span><span>0.j</span>  <span>9.</span><span>+</span><span>0.j</span> <span>10.</span><span>+</span><span>0.j</span><span>]</span>
Output:
Array from tuple: [1. 2. 3. 4. 5.]
Array from list: [ 6.+0.j  7.+0.j  8.+0.j  9.+0.j 10.+0.j]

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I will try to post a separate article on various types of datatypes in NumPy in the upcoming posts.

The array() function in NumPy transforms sequences (such as [1, 2, 3, 4, 5] and (2, 3, 4, 5)) into 1-D arrays. To create a 2-D array, we need to pass sequences of sequences, and for a 3-D array, we use sequences of sequences of sequences and so on.
Here is the following example of 2-D array creation using the array() function:


<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span># Create a 2-D array with specific values </span><span>my_array</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([(</span><span>10</span><span>,</span> <span>20</span><span>,</span> <span>30</span><span>),</span> <span>(</span><span>40</span><span>,</span> <span>50</span><span>,</span> <span>60</span><span>)])</span>
<span>print</span><span>(</span><span>my_array</span><span>)</span>
<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>

<span># Create a 2-D array with specific values </span><span>my_array</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([(</span><span>10</span><span>,</span> <span>20</span><span>,</span> <span>30</span><span>),</span> <span>(</span><span>40</span><span>,</span> <span>50</span><span>,</span> <span>60</span><span>)])</span>

<span>print</span><span>(</span><span>my_array</span><span>)</span>
import numpy as np

# Create a 2-D array with specific values my_array = np.array([(10, 20, 30), (40, 50, 60)])

print(my_array)

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<span>Output</span><span>:</span>
<span>[[</span><span>10</span> <span>20</span> <span>30</span><span>]</span>
 <span>[</span><span>40</span> <span>50</span> <span>60</span><span>]</span>
<span>]</span>
<span>Output</span><span>:</span>
<span>[[</span><span>10</span> <span>20</span> <span>30</span><span>]</span>
 <span>[</span><span>40</span> <span>50</span> <span>60</span><span>]</span>
<span>]</span>
Output:
[[10 20 30]
 [40 50 60]
]

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In this code snippet, passing a list of two tuples to the array() function results in a 2-D array, where the first dimension has a length of two(ie.,Rows) and the second, a length of three(ie.,Columns).

Creating the array using numeric range series:-

NumPy provides the arange function, which allows for the creation of an array by specifying a numeric range. This function requires three arguments: start, stop, and step, enabling the construction of an array with the desired sequence of numbers.
Let’s see the following example:


<span># Create an array with even numbers from 10 to 18 </span><span>even_numbers</span> <span>=</span> <span>np</span><span>.</span><span>arange</span><span>(</span><span>start</span><span>=</span><span>10</span><span>,</span> <span>stop</span><span>=</span><span>20</span><span>,</span> <span>step</span><span>=</span><span>2</span><span>)</span>
<span>print</span><span>(</span><span>even_numbers</span><span>)</span>
<span># Create an array with even numbers from 10 to 18 </span><span>even_numbers</span> <span>=</span> <span>np</span><span>.</span><span>arange</span><span>(</span><span>start</span><span>=</span><span>10</span><span>,</span> <span>stop</span><span>=</span><span>20</span><span>,</span> <span>step</span><span>=</span><span>2</span><span>)</span>

<span>print</span><span>(</span><span>even_numbers</span><span>)</span>
# Create an array with even numbers from 10 to 18 even_numbers = np.arange(start=10, stop=20, step=2)

print(even_numbers)

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<span>[</span><span>10</span> <span>12</span> <span>14</span> <span>16</span> <span>18</span><span>]</span>
<span>[</span><span>10</span> <span>12</span> <span>14</span> <span>16</span> <span>18</span><span>]</span>
[10 12 14 16 18]

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Remember, the stop value is not included in the array, and the step defines the difference between each number in the sequence. You can also use non-integer steps, such as 0.5, to create arrays with decimal numbers.

In summary, NumPy arrays provide efficient and flexible ways to work with multi-dimensional data, ensuring consistent data types across elements.

Numpy (12 Part Series)

原文链接：Numpy Array Object

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