Based on the ERD below, display the assignment number, employee number and assigned hours for either assign job 501 or 502 with assigned hours more than 3.
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Based on the ERD below, display the assignment number, employee number and assigned hours for either assign job 501 or 502 with assigned hours more than 3.
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Based on the ERD below, for each project calculate the difference between the project value and the project balance. Display the result in a new column name “Remaining Balance”. Include the project number and project name in the result output.
- Please modify the code below for "Current Date" ********************************************************************** from pandas_datareader import data as pdrimport yfinance as yfin # Work around until# pandas_datareader is fixed.import datetimeimport pandas as pdimport matplotlib.pyplot as pltimport numpy as np# Show all columns.pd.set_option('display.max_columns', None)pd.set_option('display.width', 1000)def getStock(stk, numDays):print(stk)# Only gets up until day before during# trading hoursdt = datetime.date.today()# For some reason, must add 1 day to get current stock prices# during trade hours. (Prices are about 15 min behind actual prices.)dtNow = dt + datetime.timedelta(days=1)dtNowStr = dtNow.strftime("%Y-%m-%d")dtPast = dt + datetime.timedelta(days=-numDays)dtPastStr = dtPast.strftime("%Y-%m-%d")yfin.pdr_override()df = pdr.get_data_yahoo(stk, start=dtPastStr, end=dtNowStr)return df################################################################### CONFIGURATION…# Create Custom Transformer Create a custom transformer, just as we did in the lecture video entitled "Custom Transformers", that performs two computations: 1. Adds an attribute to the end of the numerical data (i.e. new last column) that is equal to $\frac{x_1^3}{x_5}$ for each observation. In other words, for each instance, you will cube the $x_1$ column and then divide by the $x_5$ column. 2. Drops the entire $x_4$ feature column if the passed function argument `drop_x4` is `True` and doesn't drop the column if `drop_x4` is `False`. (See further instructions below.) You must name your custom transformer class `Assignment4Transformer`. Your class should include an input parameter called `drop_x4` with a default value of `True` that deletes the $x_4$ feature column when its value is `True`, but preserves the $x_4$ feature column when you pass a value of `False`. This transformer will be used in a pipeline. In that pipeline, an imputer will be run *before* this transformer. Keep…As a software engineer, you are hired to arrange cricket players based on their country and type. You have given all the player’s information in a tuple called my_tuple. The players are from 3 countries which are Bangladesh, Australia and England. There are 2 types of players which are “Batters” and “Bowlers” You are given the following nested tuple where each element in the my_tuple is a tuple consisting of a name and a 4 digit ID. my_tuple = (("Mominul", 1101),("Mustafiz", 1202),("Bell", 2101),("Cook", 2103),("Smith", 3101),("Finch", 3102),("Starc", 3203), ("Imrul", 1103), ("Taijul", 1204)) Write a python program that will convert this tuple into the following dictionary using the process described below: Your code should work for all types of similar tuple if the sample input is changed. Output dictionary (You just have to print the resultant dictionary. No need to follow the pattern below) { 'Bangladesh': { 'Batters': ('Mominul', 'Imrul'), 'Bowlers':…
- As a software engineer, you are hired to arrange cricket players based on their country and type. You have given all the player’s information in a tuple called my_tuple. The players are from 3 countries which are Bangladesh, Australia and England. There are 2 types of players which are “Batters” and “Bowlers”You are given the following nested tuple where each element in the my_tuple is a tuple consisting of a name and a 4 digit ID.my_tuple = (("Mominul", 1101),("Mustafiz", 1202),("Bell", 2101),("Cook", 2103),("Smith", 3101),("Finch", 3102),("Starc", 3203), ("Imrul", 1103), ("Taijul", 1204))Write a python program that will convert this tuple into the following dictionary using the process described below: Your code should work for all types of similar tuple if the sample input is changed.Output dictionary (You just have to print the resultant dictionary. No need to follow the pattern below){'Bangladesh': { 'Batters': ('Mominul', 'Imrul'), 'Bowlers': ('Mustafiz', 'Taijul')…JAVASCRIPT 9. populateSelectMenu a. Depends on the createSelectOptions function we created b. Receives the users JSON data as a parameter c. Selects the #selectMenu element by id d. Passes the users JSON data to createSelectOptions() e. Receives an array of option elements from createSelectOptions f. Loops through the options elements and appends each option element to the select menu g. Return the selectMenu element NOTE: The next functions use Async / Await to request data from an API. We cover this in Week 13. I do not recommend proceeding beyond this point until you have completed the learning module for Week 13. */ function populateSelectMenu(){ }Here is my entire code(average is not working yet). The response I got from the professor is that I have to create a DRY solution replacing the five display_functions with a single get_array function you call five times like common_array = get_array(plant_xml, "COMMON") etc. from urllib.request import urlopenfrom xml.etree.ElementTree import parse def read_data(data_file): plant_url = urlopen(data_file) plant_xml = parse(plant_url) return plant_xml def display_common_items(plant_xml): plant_root = plant_xml.getroot() common_array = [] for item in plant_root.findall("PLANT"): common_array.append(item.find("COMMON").text) return common_array def display_botanical_items(plant_xml): plant_root = plant_xml.getroot() botanical_array = [] for item in plant_root.findall("PLANT"): botanical_array.append(item.find("BOTANICAL").text) return botanical_array def display_zone_items(plant_xml): plant_root = plant_xml.getroot() zone_array = []…
- struct DateTag{ int nMonth; int nDay; int nYear; }; struct NameTag{ char strFirst[ 31 ]; char strLast[ 21 ]; }; struct EmployeeTag{ int nId; struct NameTag sName; struct DateTag sBirth; }; struct DepartmentTag{ char strName[ 21 ]; char strDescription[ 51 ]; struct EmployeeTag sManager; struct EmployeeTag arrEmployees[ 15 ]; }; struct CompanyTag{ char strName[ 31 ]; struct DepartmentTag arrDepartments[ 10 ]; }; struct CompanyTag c1, c2; struct DepartmentTag d1, d2; struct EmployeeTag e1, e2; struct EmployeeTag *pEmp; 1. Which statement(s) is/are syntactically correct? a) e1 = d1.sManager; b) e1 = d1.arrEmployees; 2. Which statement(s) is/are syntactically correct? a) d1.arrEmployees[ 0 ] = pEmp; b) pEmp = d1.arrEmployees; 3. Which statement(s) is/are syntactically correct? a) c1.d1 = d2; b) c2.arrDepartments[1] = d2;./llcpInt.cpp:28:14: error: no viable overloaded '=' temp=headPtr; ~~~~^~~~~~~~./llcpInt.h:6:8: note: candidate function (the implicit copy assignment operator) not viable: no known conversion from 'Node *' to 'const Node' for 1st argument; dereference the argument with *struct Node ^./llcpInt.h:6:8: note: candidate function (the implicit move assignment operator) not viable: no known conversion from 'Node *' to 'Node' for 1st argument; dereference the argument with *struct Node ^./llcpInt.cpp:30:10: error: cannot delete expression of type 'Node' delete temp; ^ ~~~~./llcpInt.cpp:38:15: error: no viable overloaded '=' temp=currentNode->link; ~~~~^~~~~~~~~~~~~~~~~~./llcpInt.h:6:8: note: candidate function (the implicit copy assignment operator) not viable: no known conversion from 'Node *' to 'const Node' for 1st argument; dereference the argument with *struct Node ^./llcpInt.h:6:8: note: candidate function (the…#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
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