What is another way to set the core_data data frame besides using the functions read.csv() and file.choose()?

Write a program that reads movie data from a CSV (comma separated values) file and output the data in a formatted table. The program first reads the name of the CSV file from the user. The program then reads the CSV file and outputs the contents according to the following requirements: Each row contains the title, rating, and all showtimes of a unique movie. A space is placed before and after each vertical separator ('|') in each row. Column 1 displays the movie titles and is left justified with a minimum of 44 characters. If the movie title has more than 44 characters, output the first 44 characters only. Column 2 displays the movie ratings and is right justified with a minimum of 5 characters. Column 3 displays all the showtimes of the same movie, separated by a space. Each row of the CSV file contains the showtime, title, and rating of a movie. Assume data of the same movie are grouped in consecutive rows. Hints: Use the find() function to find the index of a comma in each row of…

Create a structure called applianceTvpe with the following members. Use the proper variable TYPES for the members: name; SUpplierlD: modelNo: cost; Create an array variable of type applianceType that can hold 50 components. Write the code segment to read from a file into the structure array. Declare an input file stream identifier for this purpose. Open the file and read until end of file. Keep a count of how many rows actually read. The data file contains the following data: RefrigeratorABC 80 4657 521.62 ToasterOven 100 3245 245.96 MicroWave 95 7878 345.67 Using a looping construct, output to the console (cout) the data read into the structure array. Make sure you do not go outside the bounds of the array or beyond the number of rows actually read. You do not need to write any #include statements, using statements and you do not have to write code to open the file. Assume the file is open. You do need to declare variables needed for the program segment.

In python

please provide code  and explanation with output (list student)

Write a function named csv_to_kvs that takes a string as a parameter representing the name of a CSV file with 4 columns in the format "<string>,<float>,<float>,<float>" and returns a new dictionary mapping strings to decimal numbers. The returned dictionary will have a key-value pair for each row in the file. The key for each pair is the data in the initial column of the CSV file and values are the data from the column at index 2. (in python )

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:

Please help me write the following function (see the photos for context of the problem): follow_trail(filename, treasure_map, start_row, start_col): Takes as inputs a string corresponding to a filename, a list of lists corresponding to a treasure map, and two non-negative integers representing a row and column index. Follows the trail in the given treasure map, starting at the given row and column index. Following the trail means to look at each character of the trail and perform the appropriate operation for that character: '>', '<', 'v', ' ∧': Continues following the trail by moving to the character to the right, left, below or above the current trail character, respectively. '.': Creates a new map file, with 'new_' prepended to the current map filename, and stores the same treasure map but with an X at the current position. Then, returns a tuple of three elements: the first being -1, and the latter two being the current row and column index. '*': Returns a tuple of three…

TODO 12 Let's now split our input data X and labels y into a train and test set using the train_test_split() function (docs). Here we'll use the 80-20 split rule where we use 80% of the data for training and 20% for testing. Lastly, we'll seed our split using the random_state keyword argument which will make sure we create the same split every time we run the function. Use the train_test_split() function to get a train and test split. Store the output into X_train, X_test, y_train, and y_test. Pass the required arguments X and y. Further specify we want to use 20% of the training data by setting the test_size keyword argument. Lastly, pass the keyword argument random_state=42 to set the random seed so we get the same split every time we run this code. Print the shape for X_train. Print the shape for y_train. Print the shape for X_test. Print the shape for y_test. # TODO 12.1X_train, X_test, y_train, y_test =  todo_check([    (X_train.shape == (413, 29), 'X_train does not have the…

Create a datafile that contains the first name, last name, gender, age, height, smoking preference, eye color and phone number. Add a variety of records to the file. A sample file looks like:  Write a program that opens the file and reads the records one by one. The program will skip any records where the gender preference is not a match. Of those records that match the gender preference, check to see if the age and height are between the maximum and minum preferences. Then check to see if the smoking preference and eye color are also a match. If at least 3 of the remaining fields match, consider the record a partial match, and print it in the report. If all 4 of the remaining fields match, the record is a perfect match and print it in the report with an asterisk next to it. At the end of the program, close the file and report how many total records there were of the specified gender, how many were a partial match, and how many were a perfect match.