answer:Here is a Python script that will display the indices of every instance of a specified element within a provided array using list comprehension.```pythondef find_indices(array, element): return [i for i, x in enumerate(array) if x == element]# test the functionarray = [1, 2, 3, 2, 4, 2, 5, 6, 7, 2, 8]element = 2print(find_indices(array, element))```In the script, `enumerate()` is a built-in Python function that allows us to loop over a list and have an automatic counter. `i` will be the counter and `x` will be the item of the list. The list comprehension checks every `x` in the list if it equals the `element` we want to find and returns the counter `i` when the condition is true.

answer:Here is the upgraded Python code:```pythonfrom typing import Listdef count_zeros(matrix: List[List[int]], strict_zero: bool = False) -> int: """The data input is a matrix of integers. The task at hand is to devise a function which can quantify the zero occurrences in the matrix. An optional parameter 'strict_zero' has been added to the function to increase the level of difficulty. If this parameter is turned on (strict_zero=True), the function counts only true zeros, discarding any units that could potentially qualify as false in a boolean context like '' or False. If off (strict_zero=False), these units should also be categorized as zeros. Examples: >>> count_zeros([[1, 0, 3], [4, 0, 0]]) 3 >>> count_zeros([[1, '', 3], [4, '0', '-0']]) 0 >>> count_zeros([[1, '', 3], [4, '0', '-0']], False) 3 """ zero_count = 0 for row in matrix: for num in row: if (strict_zero and num == 0) or (not strict_zero and not num): zero_count += 1 return zero_count```The problem with the initial code was the incorrect syntax for comparison in the if condition, the equality operator `==` should be used instead of the assignment operator `=`. Apart from this, the function has been extended in order to interpret false values as zeros or not based on the `strict_zero` parameter.

answer:I will provide an example of how you might use Boto3 to extract information from a DynamoDB table. I'll return data from a hypothetical table named "TimeSeriesData" which hypothetically contains "timestamp" as its primary key, and a corresponding "value". Then, I'll use pandas for time series analysis.Please note that this is a hypothetical time-series analysis scenario and the actual analysis could be different based on the requirements and the data in your DynamoDB table. Also, make sure that AWS credentials are configured properly in your environment before you use Boto3.```pythonimport boto3import pandas as pddef dynamodb_time_series_analysis(table_name, primarykey): dynamodb = boto3.resource('dynamodb') table = dynamodb.Table(table_name) response = table.scan() data = response['Items'] while 'LastEvaluatedKey' in response: response = table.scan(ExclusiveStartKey=response['LastEvaluatedKey']) data.extend(response['Items']) df = pd.DataFrame(data) df[primarykey] = pd.to_datetime(df[primarykey]) # convert to datetime format df.sort_values(by=[primarykey], inplace=True, ascending=True) # sort by datetime # here, we can start performing time-series analysis # Let's say we want to calculate the rolling average of our "values" df['value'] = df['value'].astype(float) # make sure "value" column is float type df['Rolling_Mean'] = df['value'].rolling(window=7).mean() return dfresult_df = dynamodb_time_series_analysis('TimeSeriesData', 'timestamp')print(result_df)```In this script, we first define a DynamoDB resource object and then use the `.scan()` method to get all items from the specified DynamoDB table. If the data size is large and cannot be returned in a single `Scan` operation, we use the `LastEvaluatedKey` value to continue to receive remaining data in subsequent `Scan` operations. We convert the 'timestamp' column to datetime format and sort the dataframe based on time. Then, we convert the 'value' column into float (this is based on the assumption that the 'value' column contains numeric data). A time series analysis is shown here by calculating the rolling mean of 'value'.Please adjust the function according to your data and analysis needs. Make sure to properly handle AWS credentials and exceptions in your production code. DISCLAIMER: The sample code described herein is provided on an "as is" basis, without warranty of any kind.

answer:This is a data normalization problem, which can be solved in Python using numpy and the sklearn library. We use sklearn's MinMaxScaler to scale each feature (dimension) to a given range:```pythonfrom sklearn.preprocessing import MinMaxScalerimport numpy as npdef normalize_data(data): # Converting to numpy array for processing data_array = np.array(data) # Using MinMaxScaler to scale to range [0,1] scaler = MinMaxScaler() normalized_data = scaler.fit_transform(data_array) return normalized_data.tolist()data = [(500, 200, -100, 0), (50, -25, 300, 500), (-25, 100, 500, 800), (100, -100, 0, -50)]print(normalize_data(data))```

answer:Here is a Python solution for the problem. It is a generic routine that can handle large datasets efficiently.```pythonfrom collections import Counter, defaultdictdef compare_pairs(pairs): results = [] counter_results = defaultdict(int) for pair in pairs: if all(isinstance(i, (int, float)) for i in pair): if pair[0] > pair[1]: results.append('superior') elif pair[0] < pair[1]: results.append('subordinate') else: results.append('identical') else: results.append('Invalid pair') counter_results = Counter(results) return results, dict(counter_results)```Tests to validate the function:```pythondef tests(): pairs = [[1,2], [3, 3], [5,4], ['a', 1], [7,7]] result, statistics = compare_pairs(pairs) assert result == ['subordinate', 'identical', 'superior', 'Invalid pair', 'identical'], "Test Case 1 Failed" assert statistics == {'subordinate': 1, 'identical': 2, 'superior': 1, 'Invalid pair': 1}, "Test Case 2 Failed" print("All Test Cases Passed")tests()```This solution has an optimal time complexity of O(n) as it only loops through the data once. It also has an optimal space complexity of O(n) as it stores the results and statistical data of the processing. The Counter class from collections module efficiently generates a histogram from a list allowing us to quickly count the number of 'superior', 'subordinate', and 'identical' labels in the list. This solution also handles non-numeric elements without crashing by using isinstance() to check if the elements in the pair are either integer or float before comparison.

answer:def gcd(x: int, y: int) -> int: while y: x, y = y, x % y return xdef lcm(x: int, y: int) -> int: return x * y // gcd(x, y)if __name__ == '__main__': print(lcm(4, 6)) print(lcm(21, 14)) print(lcm(10, 25)) print(lcm(12, 15)) print(lcm(9, 3))