Solve using Python Codes 10. Use NumPy to generate an array of 50 random numbers sampled from a standard normal distributionNumpy Indexing and SelectionIn [10]: # RUN THIS CELL - THIS IS OUR STARTING MATRIXimport numpy as npmatrix = np.arange (1,51).reshape(5,10)matrixOut [10]: array([[1, 2, 3,4, 5, 6, 7, 8, 9, 10],[11, 12, 13, 14, 15, 16, 17, 18, 19, 20],[21, 22, 23, 24, 25, 26, 27, 28, 29, 30],[31, 32, 33, 34, 35, 36, 37, 38, 39, 40],[41, 42, 43, 44, 45, 46, 47, 48, 49, 50]])11. Write code that reproduces the output shown below.In [11]: # Code HereIn [12] # DON'T WRITE HEREOut [12]:array([[32, 33, 34, 35, 36, 37, 38, 39, 401,[42, 43, 44, 45, 46, 47, 48, 49, 50]])12. Write code that reproduces the output shown below.In [13] # Code HereIn [15] # DON'T WRITE HERE

In this question we have to write a python code to generate a array using numpy. Question 11. Write…

Answered: 11. Write code that reproduces the…

11. Write code that reproduces the output shown below. In [11]: # Code Here In [12]: # DON'T WRITE HERE Out [12]: array([[32, 33, 34, 35, 36, 37, 38, 39, 40], [42, 43, 44, 45, 46, 47, 48, 49, 50]])

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

Passing Array as Argument

An array is a data structure that holds only fixed-size data and its contents are arranged in contiguous memory slots. The primary purpose of using an array is to store data items of the identical type together. An array can stockpile any one of the following types of data: int, float, char and so forth. The elements are stored from index 0, and the difference between the two indexes of the array is termed as an offset. The array has a fixed size. Once the size is defined during array declaration, it cannot be changed later.

Question

Solve using Python Codes

10. Use NumPy to generate an array of 50 random numbers sampled from a standard normal distribution
Numpy Indexing and Selection
In [10]: # RUN THIS CELL - THIS IS OUR STARTING MATRIX
import numpy as np
matrix = np.arange (1,51).reshape(5,10)
matrix
Out [10]: array([[1, 2, 3,
4, 5, 6, 7, 8, 9, 10],
[11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
[21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
[31, 32, 33, 34, 35, 36, 37, 38, 39, 40],
[41, 42, 43, 44, 45, 46, 47, 48, 49, 50]])
11. Write code that reproduces the output shown below.
In [11]: # Code Here
In [12] # DON'T WRITE HERE
Out [12]:
array([[32, 33, 34, 35, 36, 37, 38, 39, 401,
[42, 43, 44, 45, 46, 47, 48, 49, 50]])
12. Write code that reproduces the output shown below.
In [13] # Code Here
In [15] # DON'T WRITE HERE

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