Using the supplied file (lab10.json), write a program that prints each personâs name, along with their friendsâ LAST names. For example, the first line should look like this:Daisy Ingram is friends with Chase, Horton, Rollins. Hint: This particular JSON file starts with a square bracket [. That means that when you load it using the JSON library, it will be a list, rather than a dict.

Geneva’s manager would like to see some additional information to make sales decisions.enter a formula in cell j4 using the if function to it whether or not the item beaded earrings should go on sale. it will go on sale if its current sales rank (in cell g4) is 8th or lower. (hint: if the cell value is greater than or equal to 8, it will go on sale.)if this condition is true, the item will go on sale. (hint: the if_true value should be “yes”.)if this condition is false, the item doesn’t need to go on sale. (hint: the if_false value should be “-”.)copy the formula created in cell j4 to the range j5: j14

Your task this week is to write a very simple spam classifier in python. it will classify messages as either spam (unwanted) or ham (wanted). the program will have a set of spam_words, words that are known to appear in spam messages. that set is included in the template file spam.pypreview the document. you will also define a spam threshold which reflects the allowed percentage of spam words in the message. you'll compute a 'spam indicator', which is the ratio of spam words to the total number of unique words in the message. if the spam indicator exceeds the spam threshold, the message is classified as spam. otherwise it is classified as ham. we'll assume that the spam threshold is a constant and has a value of 0.10. your program will prompt the user for a message and then will print the corresponding spam indicator with two decimal digits and the corresponding classification (spam or ham). the program will be case insensitive. the spam words are detected whether they are in lower case or upper case or mixed case. each word, spam or not, is counted once (even if it appears multiple times in the message.) the program will remove punctuation from the message before identifying the words and computing the spam indicator. for example '! ' must be identified as the spam word 'now'.

An endless cycle of creation and response on the internet is called