rform the following operation: Increase the grade of the house by 1
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please code in python
use pandas for this
Write a function to perform the following operation:
Increase the grade of the house by 1 if the sqft of the house is greater than 0 and less than
equal to 400. If the total sqft of the house is greater than 400, increase the grade of the house
by 2.
After creating the above function, which syntax will be using when using apply function to run
the above created function in the dataset?
A. data.apply(your_function_name(arg1))
B. data.apply_fun(function_name())
C. data.app(data)
D. None of the above
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- Write a script that creates and calls a function named fnStudentUnits that calculates the total course units of a student in the StudentCourses table. To do that, this function should accept one parameter for the student ID, and it should return the sum of the course units for the student. The SELECT statement that calls the function should return the StudentID from the StudentCourses table, the CourseNumber and CourseUnits from the Courses table, and the value return by the fnStudentUnits function for that student.Create a function in your own database that takes two parameters: A year parameter A month parameter The function then calculates and returns the total sales of the requested period for each territory. Include the territory id, territory name, and total sales dollar amount in the returned data. Format the total sales as an integer. Hints: a) Use the TotalDue column of the Sales.SalesOrderHeader table in an AdventureWorks database for calculating the total sale. b) The year and month parameters should have the SMALLINT data type.6. Create a table-valued function named fnDateRange, similar to the stored procedure of exercise 3. The function requires two parameters of data type date. Don’t validate the parameters. Return a result set that includes the InvoiceNumber, InvoiceDate, InvoiceTotal, and Balance for each invoice for which the InvoiceDate is within the date range. Invoke the function from within a SELECT statement to return those invoices with InvoiceDate between October 10 and October 20, 2019.
- Assume that there is a huge number of employees working in an organization and the organization wants to digitalize their data. write a program that stores the emp_id, emp_name and the salary of the employee. Choose the most suitable data structure. Your task is to display the details of an employee with highest and lowest salary. Create a function showData which display all the records in sorted order with respect to salary. It is upto you, whether you create an additional data structure for this task or sort the data in the existing data structure. If the data is stored in a data structure as follows: Ali Arif Asif Atif 7986 9854 8342 6587 80000 70000 85000 75000Assume that there is a huge number of employees working in an organization and the organization wants to digitalize their data. write a program that stores the emp_id, emp_name and the salary of the employee. Choose the most suitable data structure. Your task is to display the details of an employee with highest and lowest salary. Create a function showData which display all the records in sorted order with respect to salary. It is upto you, whether you create an additional data structure for this task or sort the data in the existing data structure.I need to merge three datasets so that I can create a custom function that would do the following: Create a function called theme_by_year that takes as input a year (as an integer) and shows the theme ids and theme names (listed in order by theme id) that were in sets that year. The column names must be id and name_themes (to differentiate between the name of a theme and the name of a set) in that order. The index should be reset and go from 0 to n-1. Each theme should only be listed once even if it appeared in more than one set from that year -- duplicate themes should be based on theme id and not name since there are some themes with the same name but with a different id. Hint: It will help if you were to think about merging appropriate DataFrames to help you get this answer. The desired output should look like this: theme_by_year(1960) Output: id name_themes0 371 Supplemental1 497 Books2 513 Classic Here's what I tried, but I did not get the desired output:
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- Write the function plot_all_time_series (filename) that takes a filename and plots lines showing the TL13 over time for all lakes in the file. Notes: • Your function must set the names for each data plot (using label=...) and display a legend, in the default location (that is, you don't need to specify the legend location - leave it to matplotlib). o Be aware, the label for each line won't be listed if you don't turn on the legend. • The graph title should be generated using the following f-string, f'TLI3 over time for all lakes in (filename}'. • Other than that your graph be just like your single series graph except that it has multiple plots drawn on the same axes. • You should include and call, at least, your float_array and read_raw_data_dict functions. Don't reinvent the wheel! In fact you can include all your code so far :) • No function definition can contain more than 20 lines of code (excluding docstring and comments). Example plot plot_all_time_series('time_series_01.csv')…HW7_3 Ball bearings are hardened through a process known as quenching-submersion of the heated ball bearing in oil or water in order to cool it rapidly. The data below represent the temperature of the ball at various points in the cooling process time (sec) 0 30 Temp (C) 800 457 269 79 46 60 150 240 Plot the data (as discrete points). In the same figure window, also plot a) interpolating polynomial, (use polyfit and polyval) b) piecewise cubic interpolation (not-a-knot) and c) piecewise cubic interpolation (shape-preserving) Include a legend for the three methods used: polynomial interpolation, not-a-knot, shape-preserving. Then looking at the plot, choose the method you believe to be the best and smoothest fit of all these to predict the temperature after 3 minutes of cooling. Print the result using fprintf.Write a function called divide_dataset_into_training_and_testing as shown below that aims to split the dataset into training and testing. It must contain arugment called training_size that meet following: If training_size is float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train examples.
