slip.8

(Save by .html

<label for="smallString">Small String:</label>

<label for="bigString">Big String:</label>

</form>

(Save by . setb1ph.php)

<?php

if ($_SERVER["REQUEST_METHOD"] == "POST") {

$smallString = $_POST["smallString"];

$bigString = $_POST["bigString"];

// a. Find whether the small string appears at the start of the large string

$startsWith = (strpos($bigString, $smallString) === 0);

// b. Find the position of the small string in the big string

$position = strpos($bigString, $smallString);

// c. Compare both strings for the first n characters (case insensitive)

$n = 5; // Change this value to the desired number of characters to compare

$comparison = strncasecmp($smallString, $bigString, $n);

// Output the results

echo "Starts with small string: " . ($startsWith ? "Yes" : "No") . "<br>";

echo "Position of small string: " . ($position !== false ? $position : "Not found") .

"<br>"; echo "Comparison of first $n characters: " . ($comparison == 0 ? "Equal" :

"Not equal") . "<br>";

}

2...

import pandas as pd

import numpy as np

import scipy.stats as s

from sklearn import preprocessing

data = pd.read_csv('winequality-red.csv')

print("\n Initial Mean : ", s.tmean(data).round(2))

print("\n Initial Standard Deviation :\n ", round(data.std(), 2))

standard = preprocessing.scale(data)

standard.mean(axis=0)

standard.std(axis=0)

print("\n*** Standardized Data ***\n", standard.round(2))

print("\n Scaled Mean : ", s.tmean(standard).round(2))

print("\n Scaled Standard Deviation : ", round(standard.std(), 2))

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