Numpy python

Numpy is a library in python

What is numpy?
import numpy as np
How to create arry function in python?
[6, 7.5, 8, 0, 1]
How to create equal lengh list function in python?
array([[1, 2, 3, 4],
[5, 6, 7, 8]])
How to create zero matrix in python?
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]])
[0.000000e+000, 0.000000e+000],
[0.000000e+000, 0.000000e+000]],
[[0.000000e+000, 0.000000e+000],
[0.000000e+000, 0.000000e+000],
[0.000000e+000, 0.000000e+000]]])
How to see data type in python?
[16., 25., 36.]])
[0., 0., 0.]])
[0.25 , 0.2 , 0.16666667]])
[2. , 2.23606798, 2.44948974]])
How to see individual element in python?
9])
Higher dimensional arrays, you have
 many more options.
 In a two-dimensional
array, the elements at each index are no longer scalars
 but rather one-dimensional arrays

 Numpy is most required library of python is numpy.Numpy is a core library for computing with python that provides a foundation for all computational libraries and its usage patterns is a fundamental skill for python.

import pandas as pd

import matplotlib.pyplot as plt

Describe Numpy arry:

# ARRYS AND VECTRISED COMPUTATION

NumPy, short for Numerical Python, is the fundamental package. required for high performance scientific computing and data analysis. ndarray, a fast and space-efficient multidimensional array providing vectorized arithmetic operations and sophisticated broadcasting capabilities • Standard mathematical functions for fast operations on entire arrays of data without having to write loops • Tools for reading / writing array data to disk and working with memory-mapped files • Linear algebra, random number generation, and Fourier transform capabilities • Tools for integrating code written in C, C++, and Fortran

# Creating ndarrays

#The easiest way to create an array is 

to use the array function

data1 = [6, 7.5, 8, 0, 1]

data1

data1*10

data1+data1

[6, 7.5, 8, 0, 1, 6, 7.5, 8, 0, 1]

arr1 = np.array(data1)

arr1

array([6. , 7.5, 8. , 0. , 1. ])

#Nested sequences, like a list of equal-length

 lists, will be converted 

into a multidimensional array:

data2 = [[1, 2, 3, 4], [5, 6, 7, 8]]

arr2 = np.array(data2)

arr2

arr2.ndim

2

arr2.shape

(2, 4)

arr1.dtype

dtype('float64')

arr2.dtype

dtype('int64')

#In addition to np.array, there are a

 number of other functions for 

creating new arrays.

# As examples, zeros and ones create

 arrays of 0’s or 1’s, respectively,

 with a given length or shape. empty

np.zeros(10)

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

np.zeros((3, 6)) 

array([[0., 0., 0., 0., 0., 0.],

np.empty((2, 3, 2))

array([[[4.669867e-310, 0.000000e+000],

#arange is an array-valued version of the built-in

 Python range function:

np.arange(15)

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

array Convert input data (list, tuple, array, or other sequence type)

 to an ndarray either by inferring a dtype or explicitly specifying a dtype.

 Copies the input data by default. asarray Convert input to ndarray,

 but do not copy if the input is already an ndarray arange 

Like the built-in range but returns an ndarray instead of a list.

 ones, ones_like Produce an array of all 1’s with the given

 shape and dtype. ones_like takes another array and produces

 a ones array of the same shape and dtype. zeros, zeros_like 

Like ones and ones_like but producing arrays of 0’s instead

arr1 = np.array([1, 2, 3], dtype=np.float64)

arr1

array([1., 2., 3.])

arr2 = np.array([1, 2, 3], dtype=np.int32)

arr2

array([1, 2, 3], dtype=int32)

arr1.dtype

dtype('float64')

arr2.dtype

dtype('int32')

arr = np.array([1, 2, 3, 4, 5])

arr.dtype

dtype('int64')

float_arr = arr.astype(np.float64)

float_arr

array([1., 2., 3., 4., 5.])

float_arr.dtype
dtype('float64')
#Operations between Arrays and Scalars
Arrays are important because they enable you to express
 batch operations on data without writing any for loops. 
This is usually called vectorization. Any arithmetic operations 
between equal-size arrays applies the operation elementwise:
arr = np.array([[1., 2., 3.], [4., 5., 6.]])
arr*arr
array([[ 1.,  4.,  9.],
arr - arr
array([[0., 0., 0.],
1 / arr
array([[1.        , 0.5       , 0.33333333],
arr ** 0.5
array([[1.        , 1.41421356, 1.73205081],
Basic Indexing and Slicing *NumPy array 
indexing is a rich topic, as there are many ways
 you may want to select a subset of your data or 
individual elements. One-dimensional arrays are 
simple; on the surface they act similarly to Python lists: *
arr = np.arange(10)
arr
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
arr[5]
5arr[5:8]
array([5, 6, 7])
arr[5:8] = 12
arr
array([ 0,  1,  2,  3,  4, 12, 12, 12,  8,  9])
arr_slice = arr[5:8]

arr_slice[1] = 12345
arr
array([    0,     1,     2,     3,     4,    12, 12345,    12,     8,
arr_slice[:] = 64
arr
array([ 0,  1,  2,  3,  4, 64, 64, 64,  8,  9])
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