What precautions should be taken when using the STRING_SPLIT function for large datasets?
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What precautions should be taken when using the STRING_SPLIT function for large datasets?
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- 3. lookup_friends_heights This function takes a friends database (a list of dictionaries, like the previous functions) and a list of friends names, and it returns a list of heights for the specified friends. If a given friend's name is not present in the friends database, we put a None in the corresponding slot. We assume that there will be at most one friend with a given name in the list. You may want to use nested for-loops for this function. Sample calls should look like this. >>> lookup_friends_heights([{"name":"todd","height":170},{"name": "sarah","height":165},{"name":"dweezil","height":175}], ["dweezil", "sarah"]) [175, 165] >>> lookup_friends_heights([{"name": "todd","height":170},{"name": "sarah","height":165},{"name": "dweezil", "height":175}], ["sarah", "elíza", "dweezil"]) [165, None, 175]3. lookup_friends_heights This function takes a friends database (a list of dictionaries, like the previous functions) and a list of friends names, and it returns a list of heights for the specified friends. If a given friend's name is not present in the friends database, we put a None in the corresponding slot. We assume that there will be at most one friend with a given name in the list. You may want to use nested for-loops for this function. Sample calls should look like this. >>> lookup_friends_heights([{"name":"todd","height":170},{"name": "sarah", "height":165},{"name":"dweezil", "height":175}], ["dweezil", "sarah"]) [175, 165] >>> lookup_friends_heights([{"name": "todd","height":170},{"name": "sarah","height":165},{"name": "dweezil", "height":175}], %3D ["sarah", "elíza", "dweezil"]) [165, None, 175]3. lookup_friends_heights This function takes a friends database (a list of dictionaries, like the previous functions) and a list of friends names, and it returns a list of heights for the specified friends. If a given friend's name is not present in the friends database, we put a None in the corresponding slot. We assume that there will be at most one friend with a given name in the list. You may want to use nested for-loops for this function. Sample calls should look like this. >>» lookup_friends_heights([{"name":"todd", "height":170},{"name":"sarah","height":165}, {"name":"dweezil","height":175}], ["dweezil", "sarah"]) [175, 165] >>> lookup_friends_heightsC[{"name":"todd","height":170},{"name":"sarah","height":165}, {"name":"dweezil","height":175}], ["sarah", "eliza", "dweezil"]) [165, None, 175] 4. class definition for Friend For the next version of the friends database (coming soon!), we want to use classes rather than dictionaries. Define a Friend class , where each Friend object…
- In cell C18 type a VLOOKUP function to find the corresponding letter grade (from column D) for the name in A18. The table array parameter is the same as in B18. Type FALSE for the range lookup parameter. Copy the formula in C18 to C19:C22Notice this formula works correctly for all cells. Range lookup of FALSE means do an exact match on the lookup value whether or not the table array is sorted by its first column. In cell E11, type an IF function that compares the score in B11 with the minimum score to pass in A5. If the comparison value is true, display Pass. Otherwise, display Fail. Copy the formula in E11 to E12:E15. Did you use appropriate absolute and relative references so the formula copied properly? In cell F11, type an IF function that compares the grade in D11 with the letter F (type F). If these two are not equal, display Pass. Otherwise, display Fail. Copy the formula in F11 to F12:F15PYTHON prov_records_per_date() takes a 2-D list (similar to the database) and an integer representing the province ID. This function returns another 2-D list, where each element of this list stores information in the following format. [ [day1, total number of patients (both icu and non-icu) in this provice reported in day1], [day2, total number of patients (both icu and non-icu) in this provice reported in day2], ... ] >>> results = prov_records_per_date(database, 35) >>> display_dict(result) >>> display_list(result) ['2022-01-31', 239] ['2022-02-05', 393] >>> results = prov_records_per_date(database, 10) >>> display_dict(result)['2022-02-02', 225] >>> results = prov_records_per_date(database, 81) >>> display_dict(result)No data in listTails Function Purpose: Produce a new column-based ( e.g. dict[str, list[str]] ) table with only the first N (a parameter) rows of data for each column. * Function name: Tails Parameters: 1. dict[str, list[str]] - a column-based table of data that_will not be mutated_ 2. int - The number of "rows" to include in the resulting list * Return type: dict[str, list[str]] Implementation strategy: 1. Establish an empty dictionary that will serve as the returned dictionary this function is building up. 2. Loop through each of the columns in the first row of the table given as a parameter. 1. Inside of the loop, establish an empty list to store each of the first N values in the column. 2. Loop through the first N items of the table's column, 1. Appending each item to the previously list established in step 2.1. 3. Assign the produced list of column values to the dictionary established in step 1. 3. Return the dictionary.
- It is used to filter data with certain criteria. a. custom sort b. advanced filter c. custom filter d. data filterPart B - reading CSV files You will need a Python repl to solve part B. Define a Python function named cheapest_rent_per_state that has one parameter. The parameter is a string representing the name of a CSV file. The CSV file will be portion of a dataset published by the US government showing the median (middle) rent in every county in the US. Each row in the CSV file has the same format Area Name, Efficiency, 1-Bedroom, 2-Bedroom, 3-Bedroom, 4-Bedroom, State Code The data in the Area Name and State Code columns are text. The data in all of the other columns are decimal numbers. Your function will need to use the accumulator pattern to return a dictionary. The keys of that dictionary will be the state codes read in from the file (state codes are at index 6). For each key in the dictionary, it's value should be the smallest median rent for an efficiency in that state (median rents for an efficiency are at index 1). Important Hints: * You will really, really want to use the built-in csv…#The Iris Dataset import sklearn.datasetsimport matplotlib.pyplot as plt import numpy as np import scipy iris = sklearn.datasets.load_iris() Write a function that takes in an index i and prints out a verbose desciption of the species and measurements for data point i. For example:Data point 5 is of the species setosaIts sepal length (cm) is 5.4Its sepal width (cm) is 3.9Its petal length (cm) is 1.7Its petal width (cm) is 0.4
- PHP Write a reduce function Write a function reduce($arr, $func) that takes an array and a function as a parameter. The reduce function should apply the parameter function to each element of the array in succession to produce a single result. Note: the current result should always be the first parameter to the function and the next element of the array should always be the second parameter. You may not use the PHP function array_reduce in your solution. For example, the result of the following should be 10. function myMax($current, $new) { return $current < $new ? $new : $current; } $arr = array(10, 5, 3, 5, 1, 2, 5, 7, 4); print("Max: " . reduce($arr, 'myMax') .JS Write a function named sum_between_indices whose parameter is a list/array of decimal numbers. Your function must return the sum of the entries at index 4 through index 15. Make certain to include the entries at index 4 and at index 15 in your sum. You SHOULD assume the parameter will have more than 15 entries.Use the accumulator pattern to write a function my_max(data), which returns the maximum number in the list data. You may assume the list data contains at least one number. You are not allowed to use the max function. It is not necessary to sort the data and any calls to sort or sorted will be disallowed as well. For example: Test Result numbers = [11, 99, 3, -6] 99 print (my_max(numbers)) negatives = [-3, -5, -9, -10] -3 print (my_max(negatives))