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What is Numpy ?
Numpy stands for Numerical python. NumPy is the most important Python package for scientific computing. It's a Python library that includes a multidimensional array object, derived objects (such as masked arrays and matrices), and a variety of routines for performing fast array operations, such as mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and more.
What is the use of Numpy ?
Numpy is mainly used for multidimensional arrays. Multidimensional arrays means 1-Dimensional, 2-Dimensional arrays, 3-Dimensional arrays and so on. We can use this for calculating the matrix. Matrix is the mathematical term that is mainly used in statistics, graphs, images analysis etc. Also numpy is mainly used a lot of mathematical functions because it has a number of predefined mathematical functions, these functions you can reuse so that is the great use of numpy.
Difference between standard python sequence and numpy arrays
Unlike Python lists, NumPy arrays have a fixed size when created. When the size of an ndarray is changed, a new array is created and the old one is deleted.
The elements of a NumPy array must all be of the same data type and so have the same memory footprint. The exception is that arrays of objects can be used, allowing for arrays of various sizes.
NumPy arrays make it easier to do advanced mathematical and other operations on massive amounts of data. Such actions are typically performed more quickly and with less code than utilising Python's built-in sequences.
Why it is so popular and Fast ?
It is quite simple and faster than regular python arrays. Vectorization refers to the lack of explicit looping, indexing, and other operations in the code; these operations, of course, occur "behind the scenes" in optimised, pre-compiled C code. There are numerous advantages to vectorized code, including:
Code that is vectorized is more concise and easier to read.
There are fewer bugs when there are fewer lines of code.
The code matches traditional mathematical notation more closely (making it easier, typically, to correctly code mathematical constructs)
More "Pythonic" code arises from vectorization.
Now we will discuss the numpy installation, import library and Important functions.
How to Install numpy Library ?
The below command is used to install the latest numpy version of it else you can also install specific version using (==) like pip install numpy == version number (1.15.4)
pip install numpy How to import Numpy Library ?
The below command is used to import the numpy library. Referred as np instead of numpy. After running this one line of code library imported.
import numpy as np How to check the version of Numpy library we are using ?
The below one line of code will show us version of library.
print(np.__version__)Output
How to create new Numpy array ?
The following code will create numpy array. array() funtion takes list of element as argument.
array = np.array([4,8,7,9,6,5]) # creating ndarray
print(array)This is also known as One-Dimensional array.
Output
How to Create Two Dimensional Array ?
In Two dimensional Array, array within array. It has two indices instead of one. It represent the data in table format such row and column. The below code will create two dimensional array.
array = np.array([[4, 8, 3,4], [4, 9, 6,7]])
print(array)Output
How to create one-dimensional array using linespace() function ?
This function create a one-dimensional array. it takes start, stop and number of elelments to create as argument. The following code will one-dimensional array.
array = np.linspace(5, 10, 5)
print(array)Output
How to Create Three-Dimensional Array ?
Following example let assume List containing x nested lists, y nested lists inside each of the x nested lists, and z values inside each of the y nested lists to create a x-by-y-by-z 3D NumPy array. The following code will create Three dimensional array.
array3 = np.array([[[5,7,8,9],[8,4,6,9]],[[3,4,6,9],[8,7,9,2]]])
print(array3)Output
Some Important Numpy functions :
arange()
array()
arrange()
linspace()
unique()
Argmax and argmin
Random.random
Random.randint
Random.shuffle
Reshape
Count_nonzero
Ravel
Hsplit and vsplit
Transpose
Abs and absolute
arange() :
It create an array within specified range and increament.
np.arange(5,30,5)Output
linspace() :
It creates an array in a specified range with equal line space distance elements.
np.linspace(0,10,5)Output
Unique() :
This function will return the unique element from the given array.
arr = np.array([1,1,1,5,5,4,7,8,8,1,5,3])
np.unique(arr)Output :
Argsmax and argmin :
argsmax returns the indices of maximum value in every row and argsmin returns the indices of minimum value in every columns.
arr = np.array([[7,8,4,5],[8,4,1,6],[9,4,1,5],[7,8,4,9]])Output
Random.random() :
It create an array with random values between 0 and 1
np.random.random(10)Output
random.randint() :
It create an array with specified range and number.
np.random.randint(4,10,(2,3))Output
random.shuffle() :
It shuffle the value of an array.
arr = np.array([1,5,4,7,8,1,6,3])
np.random.shuffle(arr)
arrOutput
Reshape :
It change the shape of an array
arr = np.array([[7,8,4,5],[8,4,1,6],[9,4,1,5],[7,8,4,9]])
arr.reshape(2,8)Output
count_nonzero() :
It will count the number of non zero values in the array.
arr = np.array([[7,8,4,0],[8,4,0,6],[9,0,0,5],[7,8,4,9]])
np.count_nonzero(arr)Output
Ravel :
It will flatten an array.
#ravel
arr = np.array([[7,8,4,0],[8,4,0,6],[9,0,0,5],[7,8,4,9]])
np.ravel(arr)Output
Hsplit and Vsplit :
It split the array vertically or horizontally.
arr = np.array([[7,8,4,0],[8,4,0,6],[9,0,0,5],[7,8,4,9]])
np.hsplit(arr,4)[0]Output
Array splited into four parts horizontally and print the 0 indices data.
Transpose :
It switches rows and columns
#Transpose
arr = np.array([[7,8,4,0],[8,4,0,6],[9,0,0,5]])
arr.transpose()Output
Absolute :
It will returns the absolute values of an array.
#Abs and absolute
arr = np.array([[-7,8,-4,0],[8,-4,0,6],[9,0,0,-5]])
arr
np.abs(arr)
output
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