answer:The output of (31+5j.real) as 31.0 and (31+5j.imag) as 5.0 (not 36.0) has to do with how Python handles complex numbers. In Python, a complex number is represented as a+bi, where a is the real part, b is the imaginary part and i is the square root of -1.The ".real" attribute returns the real part of the complex number. So, (31+5j.real) returns 31.0 because 31 is the real part of that complex number.Similarly, the ".imag" attribute returns the imaginary part (without the j). So, (31+5j.imag) should actually return 5.0, not 36.0.When you apply '.real' and '.imag' to an integer, such as 42, Python treats the integer as a complex number with a real part of 42 and an imaginary part of 0. Therefore, 42.real gives 42 and 42.imag gives 0 (not 42 as mentioned in your statement).When you do complex(a, b), a is supposed to be the real part and b is supposed to be the imaginary part of the complex number. So, complex(20+10j,5+5j) is not valid because 20+10j and 5+5j are already complex numbers. If you see (complex(10+10j,5+5j)) as (5+15j), you might be mistaken because the entries within complex() should be real numbers, not complex numbers. If you want to add two complex numbers, just do it directly: (10+10j)+(5+5j) that will result in (15+15j).

answer:It's important to note that specific solutions might differ based on the specific SQL engine and the structure of the database. Here's a simple interpretation of how one might handle this in pseudo-SQL, assuming a relevant structure.First, write a query that calculates the average scores for each player:```SQLSELECT playerId, AVG(score) as mean_scoreFROM game_scoreGROUP BY playerId```Next, incorporate this query into a larger one that orders the players by mean score and restricts the results to the lowest five:```SQLSELECT playerId, mean_scoreFROM ( SELECT playerId, AVG(score) as mean_score FROM game_score GROUP BY playerId) as player_scoresORDER BY mean_score ASCLIMIT 5```In terms of dynamically updating the table you could look into SQL triggers. A trigger is a stored procedure in database which automatically invokes whenever a particular event happens. Here's an example on INSERT trigger:```SQLCREATE TRIGGER update_mean_scoreAFTER INSERT ON game_scoreFOR EACH ROWBEGIN -- Update logic here -- -- You might need to identify which user's score was inserted, -- re-calculate their average score, and then update the appropriate tableEND;```It's also worth adding exception handling. Unfortunately exception handling in SQL varies a lot between different SQL engines. Here's a basic example of what it might look like:```SQLBEGIN TRY -- SQL query / queries hereEND TRYBEGIN CATCH -- Handle error here -- This often involves logging the error, or perhaps raising it again with more informationEND CATCH```You should replace the try and catch block commands with actual commands based on the database engine you are using.

answer:You can solve this problem by using the SELECT statement in SQL to select the needed columns, the WHERE clause to filter out the employees with a salary greater than 10000 and the ORDER BY clause to sort the resulting data. IS NOT NULL function will help in handling missing or null 'department_id'.Here is the SQL query for the problem:```sqlSELECT name, salary, department_id FROM employees WHERE salary > 10000 AND department_id IS NOT NULL ORDER BY salary DESC, name ASC;```In this SQL query:- SELECT is used to select data from a database. - WHERE is used to filter records. - "AND" is used to combine multiple conditions. - IS NOT NULL is used to test for non-empty values. - ORDER BY is used to sort the result set in ascending or descending order. - DESC is used to sort the result set in descending order and ASC for ascending order. The query selects name, salary and department_id columns from the table "employees", where the employee's salary is greater than 10,000 and the department_id is not null. The results are then sorted in descending order based on salary. If multiple people have the same salary, their names will be sorted in alphabetical order.

answer:Python does not support applying an operator as a string directly on two integer operands. You need to map the string operation to the actual Python operation. This can be done using Python's built-in operator module which allows the operations to be performed on the operand_set depending on the operator_set.To solve the problem, we define the function 'computeArithmeticSeq' which takes two lists, 'operand_set' and 'operator_set' as input. It initializes 'result' with the first number from 'operand_set'. Then it iterates over 'operator_set' and for each operator from 'operator_set', applies it on 'result' and the next number from 'operand_set'. Finally, it returns 'result', which will be the result of performing all specified operations in order.Here is the Python script:```pythonimport operatordef compute_arithmetic_seq(operator_set, operand_set): ops = { "+": operator.add, "-": operator.sub, "*": operator.mul, "//": operator.floordiv, "": operator.pow } result = operand_set[0] for index in range(len(operator_set)): result = ops[operator_set[index]](result, operand_set[index + 1]) return resultoperator_set = ['+', '*', '-'] operand_set = [2, 3, 4, 5] print(compute_arithmetic_seq(operator_set, operand_set)) # Returns: 9 ```In this script, 'ops' is a dictionary which maps operators to their associated functions in Python's operator module. For each operator 'operator_set[index]' in 'operator_set', 'ops[operator_set[index]]' returns the associated function, and '(result, operand_set[index+1])' are the operands.

answer:Creating a model to classify sentiment in TensorFlow involves several steps:1. Data preprocessing and exploration2. Building and training the model with that data3. Evaluating the model Here's a simple example of how you can create a relevant model using the TensorFlow and Keras API. This example would not directly fit into your requirement as it takes a significant amount of time and data to train such model and can't be done over the scope of this platform.```pythonimport tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersfrom tensorflow.keras.datasets import imdbfrom tensorflow.keras.preprocessing.sequence import pad_sequences# Data Preprocessingnum_words = 10000max_len = 500(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=num_words)X_train = pad_sequences(X_train, maxlen=max_len)X_test = pad_sequences(X_test, maxlen=max_len)# Building the Modelmodel = keras.models.Sequential()model.add(layers.Embedding(input_dim=num_words, output_dim=64, input_length=max_len))model.add(layers.LSTM(64))model.add(layers.Dense(16, activation='relu'))model.add(layers.Dense(1, activation='sigmoid'))# Compiling the Modelmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])# Training the Modelmodel.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test))# Evaluationloss, accuracy = model.evaluate(X_test, y_test)print('Test accuracy:', accuracy)```Again, remember this is a trivial example. Real-world sentiment analysis tasks would involve much more complex architectures, preprocessing, and techniques.Your text "This is a terrible movie, do not watch it!" is a clear example of a negative sentiment. If you had a trained model you would pre-process this text the same way as your training data and feed into your model to predict.

answer:First, we will need to import the data and necessary modules:```pythonimport pandas as pdfrom sklearn import svmfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import LabelEncoder# Data import data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional-full.csv", sep=';')# Let's see first 5 rows of the datasetdata.head()```Before applying SVM, we need to clean the data and remove any irrelevant columns or deal with null values. But in this case, we need to convert categorical into numerical data since SVM only works with numerical data:```pythonstr_cols = data.select_dtypes(include=['object']).columnslabel_encoder = LabelEncoder()for column in str_cols: data[column] = label_encoder.fit_transform(data[column])data.head()```Now that our data is ready, we will divide it into train and test sets:```pythonx = data.drop('y', axis=1)y = data['y']x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)```Now, we need to create a Support Vector Machines classifier:```pythonsvm_classifier = svm.SVC(kernel='linear') svm_classifier.fit(x_train, y_train)```We can now predict outputs for our test set and compare them to actual outputs:```pythony_pred = svm_classifier.predict(x_test)```We can also evaluate our model:```pythonfrom sklearn import metricsprint("Accuracy:", metrics.accuracy_score(y_test, y_pred))```Please note, this code assumes that the "y" column in your data is the indicator of whether a client will subscribe to a term deposit.Due to the huge dimensionality of this dataset, the linear SVM can be quite slow, consider using SVM with an 'rbf' kernel if you face performance issues. However, large datasets often require a lot of time for training in SVM, regardless of the kernel used.