![1-D Array
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)](https://image.slidesharecdn.com/rajnumpy-210917045014/85/NumPy-9-320.jpg)
![2-D Array
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)](https://image.slidesharecdn.com/rajnumpy-210917045014/85/NumPy-10-320.jpg)
![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)](https://image.slidesharecdn.com/rajnumpy-210917045014/85/NumPy-11-320.jpg)
![np.array([1,2,3]) 1d array
np.array([(1,2,3),(4,5,6)]) 2darray
np.arrange(start,stop,step) rangearray
np.linespace(0,2,9) Addevenlyspacedvaluesbtwintervaltoarrayof length
np.zeros((1,2)) Createandarrayfilledwithzeros
np.ones((1,2)) Createsanarrayfilledwithones
np.random.random((5,5)) Createsrandomarray
np.empty((2,2)) Createsanempty array
INITIALIZTION](https://image.slidesharecdn.com/rajnumpy-210917045014/85/NumPy-12-320.jpg)
![Operations
Operator Description
np.add(x,y) x + y Addition
np.substract(x,y) x –y Subtraction
np.divide(x,y)
x / y
Division
np.multiply(x,y) x *y Multiplication
np.sqrt(x) SquareRoot
np.sin(x) Element-wisesine
np.cos(x) Element-wisecosine
np.log(x) Element-wisenaturallog
np.dot(x,y) Dotproduct
np.roots([1,0,-4]) Rootsofagivenpolynomialcoefficients](https://image.slidesharecdn.com/rajnumpy-210917045014/85/NumPy-15-320.jpg)
NumPy
- 1. Name : Abhijeet Anand Dept : A.I&D.S Roll No. : 20AD001 Reg no : 201201001 Date : 07-09-2021 Topic : NumPy
- 2. Numpy PRESENTED BY - Abhijeet Anand (A.I & D.S)
- 4. What is NumPy ? NumPy Stands For Numerical Python. NumPy is a Python library used for working with array NumPy is the fundamental package for scientific computing in Python. It also has functions for working in domain of linear algebra, Fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.
- 5. Why Use NumPy ? In Python we have lists that serve the purpose of arrays, but they are slow to process. NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The Array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.
- 6. Why is NumPy Faster Than Lists? NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called locality of reference in computer science. This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.
- 7. Arrays in NumPy An Array is a collection of values, organized in a specific order. NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy dimensions are called axis. The number of axis is rank. NumPy’s array class is called ndarray. It is also known by the alias array.
- 9. 1-D Array 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)
- 10. 2-D Array 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)
- 11. 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)
- 12. np.array([1,2,3]) 1d array np.array([(1,2,3),(4,5,6)]) 2darray np.arrange(start,stop,step) rangearray np.linespace(0,2,9) Addevenlyspacedvaluesbtwintervaltoarrayof length np.zeros((1,2)) Createandarrayfilledwithzeros np.ones((1,2)) Createsanarrayfilledwithones np.random.random((5,5)) Createsrandomarray np.empty((2,2)) Createsanempty array INITIALIZTION
- 13. Array Properties Syntax Description array.shape Dimensions(Rows,Columns) len(array) Lengthof Array array.ndim NumberofArrayDimensions array.size NumberofArrayElements array.dtype DataType type(array) TypeofArray
- 14. Copying & Shorting np.copy(array) Creates copy of array array.sort() Sortsanarray array.sort(axis=0) Sortsaxisofarray
- 15. Operations Operator Description np.add(x,y) x + y Addition np.substract(x,y) x –y Subtraction np.divide(x,y) x / y Division np.multiply(x,y) x *y Multiplication np.sqrt(x) SquareRoot np.sin(x) Element-wisesine np.cos(x) Element-wisecosine np.log(x) Element-wisenaturallog np.dot(x,y) Dotproduct np.roots([1,0,-4]) Rootsofagivenpolynomialcoefficients
- 16. Thank You... Abhijeet Anand (A.I & D.S)