Python Numpy multi dimensional array

NumPy (Numerical Python) is a popular open-source Python library used for numerical computing and data analysis.

NumPy is often used in combination with other Python libraries, such as Pandas, Matplotlib, and Scikit-learn, to create a complete data analysis ecosystem in Python.

Some of the key features of NumPy :

Fast vectorized operations: NumPy allows you to perform operations on entire arrays rather than looping through each element, which can be much faster.
Broadcasting: NumPy allows you to perform operations between arrays with different shapes and sizes, which makes it easy to perform operations on data that is not the same shape.
Linear algebra: NumPy provides a suite of functions for performing linear algebra operations such as matrix multiplication, eigenvalues, and eigenvectors.
Random number generation: NumPy includes tools for generating random numbers, which are useful for simulations and statistical analysis.

Numpy is used scientific computing with Python.
We can create powerful multidimensional arrays using Numpy.
Numpy requires all elements are to be of same data type. ( This is the main difference with Pandas which can accommodate different data types. )

Installing Numpy

Use in your command prompt or the environment you are using . pip install numpy After installation you can add this line to import Numpy inside Python program.

import numpy as np

Getting the Numpy version installed in your system.

import numpy as np
print("Numpy Version : ",np.__version__)

Creating Numpy Array

import numpy as np
npr=np.array([4,5,9])  
print(npr) # [4,5,9]

l1=[3,2,8] # List 
npr=np.array(l1) # Using List to create np array
print(npr) # [3,2,8]

t1=(8,12,1) # Tuple 
npr=np.array(t1) # from tuple to np array 
print(npr) # [ 8 12  1]

Numpy installation and creating array at Colab platform using List Tuple and checking the version

Numpy

NumPy Array	Scientific computing with Python
append	Adding Data to array at the end
insert	Adding Data to array at the given index position
dtype	Data types of Numpy array
Array Methods	Methods to get result ( output ) using NumPy
array_split()	break the array to get sub-arrays
bincount	frequency of occurrence of elements
where	Return elements depending on condition check
shape	Return shape of the array
reshape	Change the array shape and dimension

Creating array

create	Creating array using Numpy
eye	array with ones and zeros
empty	array without initializing entries
empty_like	array of same shape and type
full	Array filled with input value
ones	array filled with ones
arange	creating array of fixed steps
linspace	creating array of fixed number
zeros	Aray filled with zeros

Exercise	Creating different types of array

random.randit	Random integers with lower and upper limits and size
random.rand	Uniform distribution [0,1] of random numbers of given shape & population
random.randn	Normal distribution of random numbers of given shape & population
random_sample	Continuous uniform distribution over an interval

sum()	Sum of elements, or along the axis
mean()	Mean value of elements, or along the axis
max()	Maximum value among elements, or along the axis
min()	Minimum value among elements, or along the axis
std()	Standard deviation along the axis

Exercise a	Basics of Creating Arrays

math functions	Numpy math functions

Get the list of all functions of Pandas. ( Use dir() )

import numpy as np
print(len(dir(np))) # 622

List of functions

for i in dir(np):
    print(i)

Python Tkinter Pandas Python- Tutorials

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Numpy n dimensional Arrays ( ndarray )

Some of the key features of NumPy :

Installing Numpy

Creating Numpy Array

Numpy

Creating array

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