Python is one of the most used and preferred programming languages. Edureify, the best AI Learning App with its best online coding courses gives a lot of importance to teaching students the various programming languages and tools to become efficient developers or coders.

Edureify has previously provided information on different aspects of Python like Pyscripts, Python for Loop, and more. In this article, Edureify will provide information on NumPy in Python, an array processing framework library of Python.

**What is NumPy in Python?**

NumPy stands for Numerical Python. NumPy is a simple to use but powerful data structure library of Python that is used to work with arrays. It is an n-dimensional array that is a foundation to develop most of Python’s data science toolkits.

NumPy consists of multidimensional array objects along with a collection of routines that help process the arrays. It enables performing mathematical and logical operations on arrays. NumPy works as a standard base for Python’s scientific computing.

**Features of NumPy**

The following are some features of NumPy-

- It is open-source
- Its array object is a powerful N-dimensional
- Integrates C/C++ and Fortran code
- Contains useful linear algebra, random number capabilities, and

**Why use NumPy?**

Python For Loops is a very useful framework to work with. But learning and using NumPy can also benefit developers in many ways. The following are the benefits of using NumPy-

**Speed-**NumPy’s algorithm is written in C. It, therefore, works very promptly within nanoseconds.**Less Loops-**It reduces loops. NumPy does not let things get tangled up during iteration indices.**Clear Codes-**Since NumPy reduces loops, the codes become clearer and look like the equations one is trying to calculate.**Improved Quality-**NumPy is fast, user-friendly, and bug-free.

Because of such beneficial qualities, NumPy is used for standard multidimensional arrays in Python data science. It is also for these reasons that so many popular libraries are built that are based on NumPy.

**NumPy Arrays**

The arrays in NumPy are mainly homogeneous multidimensional arrays. They are-

- A table of elements of numbers that are mostly of similar type which are shaped by a tuple of positive integers
- The dimensions in NumPy are called axes and rank is the number of axes
- The array class of NumPy is called ndarray

**Example of NumPy Arrays**

# Python program to demonstrate # basic array characteristics import numpy as np # Creating array object arr = np.array( [[ 1, 2, 3], [ 4, 2, 5]] ) # Printing type of arr object print("Array is of type: ", type(arr)) # Printing array dimensions (axes) print("No. of dimensions: ", arr.ndim) # Printing shape of array print("Shape of array: ", arr.shape) # Printing size (total number of elements) of array print("Size of array: ", arr.size) # Printing type of elements in array print("Array stores elements of type: ", arr.dtype)

Ouput:

Array is of type: No. of dimensions: 2 Shape of array: (2, 3) Size of array: 6 Array stores elements of type: int64

**Creation of Arrays in NumPy**

The following are the ways in which Arrays can be created with NumPy-

- Arrays are created from a regular Python list or tuple with the use of array functions. One can deduce this type of array from the elements’ type in the sequences.
- Elements of an array are mostly unknown but the size is known. Several functions of NumPy can create arrays with minimum placeholder content. Here, the arrays need not be grown which is an expensive operation. For example np.zeros, np.ones, np.empty, etc.
- For the creation of numbers, NumPy has a function that is similar to a range that does not return lists but arrays.
- arange- within a given interval, it returns the evenly spaced values and the step size is specified
- linspace- it also works like arange and the number of elements is returned
- Reshaping Array- one can use this method to reshape arrays
- Flatten Array- one can use this to obtain a copy of an array collapsed in one dimension

Following is an example of NumPy Array Creation-

# Python program to demonstrate # array creation techniques import numpy as np # Creating array from list with type float a = np.array([[1, 2, 4], [5, 8, 7]], dtype = 'float') print ("Array created using passed list:\n", a) # Creating array from tuple b = np.array((1 , 3, 2)) print ("\nArray created using passed tuple:\n", b) # Creating a 3X4 array with all zeros c = np.zeros((3, 4)) print ("\nAn array initialized with all zeros:\n", c) # Create a constant value array of complex type d = np.full((3, 3), 6, dtype = 'complex') print ("\nAn array initialized with all 6s." "Array type is complex:\n", d) # Create an array with random values e = np.random.random((2, 2)) print ("\nA random array:\n", e) # Create a sequence of integers # from 0 to 30 with steps of 5 f = np.arange(0, 30, 5) print ("\nA sequential array with steps of 5:\n", f) # Create a sequence of 10 values in range 0 to 5 g = np.linspace(0, 5, 10) print ("\nA sequential array with 10 values between" "0 and 5:\n", g) # Reshaping 3X4 array to 2X2X3 array arr = np.array([[1, 2, 3, 4], [5, 2, 4, 2], [1, 2, 0, 1]]) newarr = arr.reshape(2, 2, 3) print ("\nOriginal array:\n", arr) print ("Reshaped array:\n", newarr) # Flatten array arr = np.array([[1, 2, 3], [4, 5, 6]]) flarr = arr.flatten() print ("\nOriginal array:\n", arr) print ("Fattened array:\n", flarr)

Ouput:

Array created using passed list: [[ 1. 2. 4.] [ 5. 8. 7.]] Array created using passed tuple: [1 3 2] An array initialized with all zeros: [[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.]] An array initialized with all 6s. Array type is complex: [[ 6.+0.j 6.+0.j 6.+0.j] [ 6.+0.j 6.+0.j 6.+0.j] [ 6.+0.j 6.+0.j 6.+0.j]] A random array: [[ 0.46829566 0.67079389] [ 0.09079849 0.95410464]] A sequential array with steps of 5: [ 0 5 10 15 20 25] A sequential array with 10 values between 0 and 5: [ 0. 0.55555556 1.11111111 1.66666667 2.22222222 2.77777778 3.33333333 3.88888889 4.44444444 5. ] Original array: [[1 2 3 4] [5 2 4 2] [1 2 0 1]] Reshaped array: [[[1 2 3] [4 5 2]] [[4 2 1] [2 0 1]]] Original array: [[1 2 3] [4 5 6]] Fattened array: [1 2 3 4 5 6]

**Indexing an Array**

For better manipulation and analysis of array objects, it is important to know the basics of array indexing. The following are the ways of array indexing using NumPy-

**Slicing-**similar to lists in Python, NumPy arrays can be sliced. One needs to specify the slice for each dimension as the arrays can be multidimensional.**Integer array indexing-**here, for each dimension, the lists are passed for indexing. For the construction of a new arbitrary array, one-to-one mapping of corresponding elements is performed.**Boolean array indexing-**one uses this method to choose elements from an array that satisfies certain conditions.

Following is an example of indexing an array-

# Python program to demonstrate # indexing in numpy import numpy as np # An exemplar array arr = np.array([[-1, 2, 0, 4], [4, -0.5, 6, 0], [2.6, 0, 7, 8], [3, -7, 4, 2.0]]) # Slicing array temp = arr[:2, ::2] print ("Array with first 2 rows and alternate" "columns(0 and 2):\n", temp) # Integer array indexing example temp = arr[[0, 1, 2, 3], [3, 2, 1, 0]] print ("\nElements at indices (0, 3), (1, 2), (2, 1)," "(3, 0):\n", temp) # boolean array indexing example cond = arr > 0 # cond is a boolean array temp = arr[cond] print ("\nElements greater than 0:\n", temp)

Output:

Array with first 2 rows and alternatecolumns(0 and 2): [[-1. 0.] [ 4. 6.]] Elements at indices (0, 3), (1, 2), (2, 1),(3, 0): [ 4. 6. 0. 3.] Elements greater than 0: [ 2. 4. 4. 6. 2.6 7. 8. 3. 4. 2. ]

**Operations of NumPy**

NumPy can perform a diverse range of arithmetical operations with its built-in arithmetic functions.

**Single Array Operation-**Overloaded arithmetic operators can be used for element-wise operation on an array for the creation of a new array.**Unary Operators-**Under the method of ndarray class, many unary operations are provided.**Universal Functions-**Mathematical operations like sin, cos, exp, and more are provided by NumPy. To produce an array output, these functions operate elementwise on an array.

Following is an example of Operations in array-

# Python program to demonstrate # basic operations on single array import numpy as np a = np.array([1, 2, 5, 3]) # add 1 to every element print ("Adding 1 to every element:", a+1) # subtract 3 from each element print ("Subtracting 3 from each element:", a-3) # multiply each element by 10 print ("Multiplying each element by 10:", a*10) # square each element print ("Squaring each element:", a**2) # modify existing array a *= 2 print ("Doubled each element of original array:", a) # transpose of array a = np.array([[1, 2, 3], [3, 4, 5], [9, 6, 0]]) print ("\nOriginal array:\n", a) print ("Transpose of array:\n", a.T)

Ouput:

Adding 1 to every element: [2 3 6 4] Subtracting 3 from each element: [-2 -1 2 0] Multiplying each element by 10: [10 20 50 30] Squaring each element: [ 1 4 25 9] Doubled each element of original array: [ 2 4 10 6] Original array: [[1 2 3] [3 4 5] [9 6 0]] Transpose of array: [[1 3 9] [2 4 6] [3 5 0]]

Here were the various information on NumPy and its multidimensional arrays.

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**Some FAQs on NumPy-**

**1. What does NumPy stand for?**

NumPy stands for Numerical Python.

**2. What is NumPy?**

NumPy is a simple to use but powerful data structure library of Python that is used to work with arrays. It is an n-dimensional array that is a foundation to develop most of Python’s data science toolkits.

**3. Is NumPy open-source?**

Yes, NumPy is open-source.

**4. Mention the features of arrays of NumPy.**

The following are the features of arrays of NumPy-

- A table of elements of numbers that are mostly of similar type which are shaped by a tuple of positive integers
- The dimensions in NumPy are called axes and rank is the number of axes
- The array class of NumPy is called ndarray

**5. From where can I learn more about NumPy?**

Edureify has the best-certified coding courses that will teach everything about NumPy.