Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.
Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.
Arrays in Numpy
Array in Numpy is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In Numpy, number of dimensions of the array is called rank of the array.A tuple of integers giving the size of the array along each dimension is known as shape of the array. An array class in Numpy is called as ndarray. Elements in Numpy arrays are accessed by using square brackets and can be initialized by using nested Python Lists.
Creating a Numpy Array
Arrays in Numpy can be created by multiple ways, with various number of Ranks, defining the size of the Array. Arrays can also be created with the use of various data types such as lists, tuples, etc. The type of the resultant array is deduced from the type of the elements in the sequences.
Note: Type of array can be explicitly defined while creating the array.
<code># Python program for</code>
<code># Creation of Arrays</code>
<code>import</code> <code>numpy as np</code>
<code># Creating a rank 1 Array</code>
<code>arr =</code> <code>np.array([1, 2, 3])</code>
<code>print("Array with Rank 1: \n",arr)</code>
<code># Creating a rank 2 Array</code>
<code>arr =</code> <code>np.array([[1, 2, 3],</code>
<code>[4, 5, 6]])</code>
<code>print("Array with Rank 2: \n", arr)</code>
<code># Creating an array from tuple</code>
<code>arr =</code> <code>np.array((1, 3, 2))</code>
<code>print("\nArray created using "</code>
<code>"passed tuple:\n", arr)</code>
Output:
Array with Rank 1:
[1 2 3]
Array with Rank 2:
[[1 2 3]
[4 5 6]]
Array created using passed tuple:
[1 3 2]
Accessing the array Index
In a numpy array, indexing or accessing the array index can be done in multiple ways. To print a range of an array, slicing is done. Slicing of an array is defining a range in a new array which is used to print a range of elements from the original array. Since, sliced array holds a range of elements of the original array, modifying content with the help of sliced array modifies the original array content.
<code># Python program to demonstrate</code>
<code># indexing in numpy array</code>
<code>import</code> <code>numpy as np</code>
<code># Initial Array</code>
<code>arr =</code> <code>np.array([[-1, 2, 0, 4],</code>
<code>[4, -0.5, 6, 0],</code>
<code>[2.6, 0, 7, 8],</code>
<code>[3, -7, 4, 2.0]])</code>
<code>print("Initial Array: ")</code>
<code>print(arr)</code>
<code># Printing a range of Array</code>
<code># with the use of slicing method</code>
<code>sliced_arr =</code> <code>arr[:2, ::2]</code>
<code>print</code> <code>("Array with first 2 rows and"</code>
<code>" alternate columns(0 and 2):\n", sliced_arr)</code>
<code># Printing elements at</code>
<code># specific Indices</code>
<code>Index_arr =</code> <code>arr[[1, 1, 0, 3], </code>
<code>[3, 2, 1, 0]]</code>
<code>print</code> <code>("\nElements at indices (1, 3), "</code>
<code>"(1, 2), (0, 1), (3, 0):\n", Index_arr)</code>
Output:
Initial Array:
[[-1. 2. 0. 4. ]
[ 4. -0.5 6. 0. ]
[ 2.6 0. 7. 8. ]
[ 3. -7. 4. 2. ]]
Array with first 2 rows and alternate columns(0 and 2):
[[-1. 0.]
[ 4. 6.]]
Elements at indices (1, 3), (1, 2), (0, 1), (3, 0):
[ 0. 54. 2. 3.]
ACE IT SKILLS 