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numpy Array (1D and 2’D) creation and operation

Python Numpy

numpy Array (1D and 2’D) creation and operation
numpy array itreating
numpy array join
numpy Array slicing and indexing
numpy array sort and Filter
numpy array split and search
numpy shape and reshape

numpy Array (1D and 2’D) creation and operation

Create a NumPy ndarray Object

NumPy is used to work with arrays. The array object in NumPy is called ndarray. We can create a NumPy ndarray object by using the array() function 

import numpy as np
 arr = np.array([1, 2, 3, 4, 5])
 print(arr)
 print(type(arr))

Create a NumPy ndarray Object

To create an ndarray, we can pass a list, tuple or any array-like object into the array() method, and it will be converted into an ndarray 

import numpy as np
 arr = np.array((1, 2, 3, 4, 5))
 print(arr)

0-D Arrays

0-D arrays, or Scalars, are the elements in an array. Each value in an array is a 0-D array. 

import numpy as np
 arr = np.array(42)
 print(arr)

1-D Arrays

An array that has 0-D arrays as its elements is called uni-dimensional or 1-D array. These are the most common and basic arrays. 

 import numpy as np
 arr = np.array([1, 2, 3, 4, 5])
 print(arr)

2-D Arrays

An array that has 1-D arrays as its elements is called a 2-D array. These are often used to represent matrix or 2nd order tensors. 

import numpy as np
 arr = np.array([[1, 2, 3], [4, 5, 6]])
 print(arr)

3-D arrays

An array that has 2-D arrays (matrices) as its elements is called 3-D array. These are often used to represent a 3rd order tensor Create a 3-D array with two 2-D arrays, both containing two arrays with the values 1,2,3 and 4,5,6: 

import numpy as np
 arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
 print(arr)

Check Number of Dimensions?

NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have. 

 import numpy as np
 a = np.array(42)
 b = np.array([1, 2, 3, 4, 5])
 c = np.array([[1, 2, 3], [4, 5, 6]])
 d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
 print(a.ndim)
 print(b.ndim)
 print(c.ndim)
 print(d.ndim)

Higher Dimensional Arrays

An array can have any number of dimensions. When the array is created, you can define the number of dimensions by using the ndmin argument 

 import numpy as np
 arr = np.array([1, 2, 3, 4], ndmin=5)
 print(arr)
 print('number of dimensions :', arr.ndim)

numpy Array (1D and 2’D) creation and operation

Create a NumPy ndarray Object NumPy is used to work with arrays. The array object in NumPy is called ndarray. We can create a NumPy ndarray object by using the array() function. 

import numpy as np
 arr = np.array([1, 2, 3, 4, 5])
 print(arr)
 print(type(arr))

numpy Array (1D and 2’D) creation and operation

To create an ndarray, we can pass a list, tuple or any array-like object into the array() method, and it will be converted into an ndarray: 

import numpy as np
 arr = np.array((1, 2, 3, 4, 5))
 print(arr)

0-D Arrays

0-D arrays, or Scalars, are the elements in an array. Each value in an array is a 0-D array 

import numpy as np
 arr = np.array(42)
print(arr)

1-D Arrays

An array that has 0-D arrays as its elements is called uni-dimensional or 1-D array. These are the most common and basic arrays. 

import numpy as np
 arr = np.array([1, 2, 3, 4, 5])
 print(arr)

2-D Arrays

An array that has 1-D arrays as its elements is called a 2-D array. These are often used to represent matrix or 2nd order tensors. 

 import numpy as np
 arr = np.array([[1, 2, 3], [4, 5, 6]])
 print(arr)

3-D arrays

An array that has 2-D arrays (matrices) as its elements is called 3-D array. These are often used to represent a 3rd order tensor. 

import numpy as np
 arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
 print(arr)

Check Number of Dimensions?

NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have 

import numpy as np
 a = np.array(42)
 b = np.array([1, 2, 3, 4, 5])
 c = np.array([[1, 2, 3], [4, 5, 6]])
 d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
 print(a.ndim)
 print(b.ndim)
 print(c.ndim)
 print(d.ndim)

Higher Dimensional ArrayS

An array can have any number of dimensions. When the array is created, you can define the number of dimensions by using the ndmin argument 

 import numpy as np
 arr = np.array([1, 2, 3, 4], ndmin=5)
 print(arr)
 print('number of dimensions :', arr.ndim)