Array creation: There are various ways to create arrays in NumPy. O For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. O Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. For example: np.zeros, np.ones, np.full, np.empty, etc. O To create sequences of numbers, NumPy provides a function analogous to, range that returns arrays instead of lists. O arange: returns evenly spaced values within a given interval. step size is specified. O linspace: returns evenly spaced values within a given interval. num no. of elements are returned. O Reshaping array: We can use reshape method to reshape an array. Consider an array with shape (a1, a2, a3, ., aN). We can reshape and convert it into another array with shape (b1, b2, b3, ., bM). The only required condition is: al x a2 x a3 . x aN = b1 x b2 x b3 ... x bM. (i.e original size of array romainc unchannad i

Array creation: There are various ways to create arrays in NumPy. O For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. O Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. For example: np.zeros, np.ones, np.full, np.empty, etc. O To create sequences of numbers, NumPy provides a function analogous to, range that returns arrays instead of lists. O arange: returns evenly spaced values within a given interval. step size is specified. O linspace: returns evenly spaced values within a given interval. num no. of elements are returned. O Reshaping array: We can use reshape method to reshape an array. Consider an array with shape (a1, a2, a3, ., aN). We can reshape and convert it into another array with shape (b1, b2, b3, ., bM). The only required condition is: al x a2 x a3 . x aN = b1 x b2 x b3 ... x bM. (i.e original size of array romainc unchannad i

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

Array

The simplest way to start thinking about an array is as a collection, or a set of elements. In programming, these elements may be constants, variables, functions and pointers.

Question

dont give the same solution again .10 min left fast
Array creation: There are various ways to create arrays in NumPy.
O For example, you can create an array from a regular
Python list or tuple using the array function. The type of the resulting
array is deduced from the type of the elements in the sequences.
O Often, the elements of an array are originally unknown, but its size is
known. Hence, NumPy offers several functions to create arrays
with initial placeholder content. These minimize the necessity of
growing arrays, an expensive operation.
For example: np.zeros, np.ones, np.full, np.empty, etc.
O To create sequences of numbers, NumPy provides a function analogous to.
range that returns arrays instead of lists.
O arange: returns evenly spaced values within a given interval. step size is
specified.
O linspace: returns evenly spaced values within a given interval. num no. of
elements are returned.
O Reshaping array: We can use reshape method to reshape an array.
Consider an array with shape (a1, a2, a3, ., aN). We can reshape and
convert it into another array with shape (b1, b2, b3, ., bM). The only
required condition is:
al x a2 x a3 . x aN = b1 x b2 x b3 .. x bM . (i.e original size of array
ramaine unchanged

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### Import section# 3 necessary statements for numerical plotting in Jupyterimport numpy as npimport matplotlib.pyplot as plt %matplotlib inline# Factorial comes from 'math' packagefrom math import factorial ### Initialization section#array of derivatives at 0 compute by handderiv = np.array([1, 0, -1, 0, 1, 0, -1, 0])# degree of Taylor polynomialN=len(deriv)-1 # Max abs. value of N+1 deriv on interval (Compute using calculus)# (used in the remainder formula)# @@@ The N+1 derivative will be proportional to cosine, and |cos| is always # @@@ less than 1maxAbsNplus1deriv=1 #array with h valuesh = np.arange(-3,3.005,0.01) # Give the expansion point for the Taylor seriesexpansion=0 # Create an array to contain Taylor seriestaylor = np.zeros(len(h)) ### Calculation section#calculate Taylor series for all h at once using a loopfor n in range(N+1): taylor = taylor + (h**n)/factorial(n)*deriv[n] #actual function values actualFn = np.cos(expansion+h) #error actual and taylor…
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