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5.20 LAB: Adjust values in a list by normalizing When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows: print('{:.2f}'.format(your_value)) Ex: If the input is: 30.0 50.0 10.0 100.0 65.0 the output is: 0.30 0.50 0.10 1.00 0.65 The 5 indicates that there are five floating-point values in the list, namely 30.0, 50.0, 10.0, 100.0, and 65.0. 100.0 is the largest value in the list, so each value is divided by 100.0.

5.20 LAB: Adjust values in a list by normalizing When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows: print('{:.2f}'.format(your_value)) Ex: If the input is: 30.0 50.0 10.0 100.0 65.0 the output is: 0.30 0.50 0.10 1.00 0.65 The 5 indicates that there are five floating-point values in the list, namely 30.0, 50.0, 10.0, 100.0, and 65.0. 100.0 is the largest value in the list, so each value is divided by 100.0.

Database System Concepts
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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5.20 LAB: Adjust values in a list by normalizing When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows: print('{:.2f}'.format(your_value)) Ex: If the input is: 30.0 50.0 10.0 100.0 65.0 the output is: 0.30 0.50 0.10 1.00 0.65 The 5 indicates that there are five floating-point values in the list, namely 30.0, 50.0, 10.0, 100.0, and 65.0. 100.0 is the largest value in the list, so each value is divided by 100.0.
Transcribed Image Text:5.20 LAB: Adjust values in a list by normalizing When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows: print('{:.2f}'.format(your_value)) Ex: If the input is: 30.0 50.0 10.0 100.0 65.0 the output is: 0.30 0.50 0.10 1.00 0.65 The 5 indicates that there are five floating-point values in the list, namely 30.0, 50.0, 10.0, 100.0, and 65.0. 100.0 is the largest value in the list, so each value is divided by 100.0.
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