CS484_IML_Assignment_5

16-7. Automated Title: In eq_world_map_3.py, we specified the title manually when defining my_layout, which means we have to remember to update the title every time the source file changes. Instead, you can use the title for the data set in the metadata part of the JSON file. Pull this value, assign it to a variable, and use this for the title of the map when you’re defining my_layout.

V5. min jee wants to combine the sales data from each of the art fairs. switch to the combined sales worksheet and then update the worksheet as follows. in cell A5 enter a formula without using a function that references cell a5 in the Madison worksheet. copy the formula from cell a5 to the range a6:a8 without copying the formatting. in cell b5, enter a formula using the sum function, 3d references, grouped worksheets that totals the values from cell b:5 in the chicago: madison worksheets.

nycflights13::flights Q4<- flights %>%filter(carrier == "JFK") %>%summarise(average_dist = mean(distance)%>%summarise(max_dist = max(average_dist))%>%group_by(Q4, month, day)%>%head(Q4[order(dat$month),(dat$day),(dat$max_dist)],n=5) *not pictured, I did upload, tidy verse, dplyr, and the nycflights13 libraries. Error: Incomplete expression: Q4<- flights %>% filter(carrier == "JFK") %>% summarise(average_dist = mean(distance)%>% summarise(max_dist = max(average_dist))%>% group_by(Q4, month, day)%>% head(Q4[order(dat$month),(dat$day),(dat$max_dist)],n=5) RStudio Question: I can't figure out what is wrong with my code. Could someone take a look at it? See below for what I am trying to do, my code and my error output. I am trying to find what 5 days of the year had the highest mean distance from JFK airport, using the nycflights13 library. I want to format it in month, day, andvmean distance.

dataframe = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/titanic_train_raw.csv') df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/titanic_test_raw.csv') Write a function that takes in as input a dataframe and a column name, and returns the mean for numerical columns and the mode for non-numerical columns. Function Specifications: The function should take two inputs: (df, column_name), where df is a pandas DataFrame, column_name is a str. If the column_name does not exist in df, raise a ValueError. Should return as output the mean if the specified column is numerical and return a list of the mode(s) otherwise. The mean should be rounded to 2 decimal places. If there is more than one mode for a given non-numerical column, the fuction should return a list of all modes.    def calc_mean_mode(df, column_name): # your code here return calc_mean_mode(df,'Age') Expected Outputs:…

3. Given the XML document NKU_Programs.xml file, a function that prints the contents ofthe Program elements in a table as shown below.Program Name College Name Student Count Year Started Type*************************************************************************************Data Science Informatics 100 2015 UComputer Science Informatics 400 1990 UBiology Science 300 2000 G HERE IS THE NKU_Programs.xml FILE <NKUPRograms><Program studCount="100" type="Undergraduate"><Name>Data Science</Name><YearStarted>2015</YearStarted><CollegeName>Informatics</CollegeName></Program><Program studCount="400" type="Undergraduate"><Name>Computer Science</Name><YearStarted>1990</YearStarted><CollegeName>Informatics</CollegeName></Program><Program studCount="300"…

1)Consider the following DTD. All unspecified elements are #PCDATA.<!DOCTYPE bibliography [<!ELEMENT book (title,author+,year,publisher,place?)><!ELEMENT article (title,author+,journal,year,number,volume,pages?)><!ELEMENT author (last_name, first_name)>...]>Write the following queries in XQuery. Assume Last names of authors are unique.(a) List all books authored by Ullman.(b) List articles published in Vol. 32 No. 2 of ACM Transactions on Database Systems.(c) List journals that published an article on XML in 2001 (that is, “XML” appears in the title of thearticle).(d) List authors that have published a book in 2001 and another book in 2003.(e) List publishers, and for each publisher list the books they have published as subelements. Chooseappropriate tags for your output.(f) List every author, and for each author list the number of articles he/she published in 2001. Chooseappropriate tags for your output.

1)Consider the following DTD. All unspecified elements are #PCDATA.<!DOCTYPE bibliography [<!ELEMENT book (title,author+,year,publisher,place?)><!ELEMENT article (title,author+,journal,year,number,volume,pages?)><!ELEMENT author (last_name, first_name)>...]>Write the following queries in XQuery. Assume Last names of authors are unique.(a) List all books authored by Ullman.(b) List articles published in Vol. 32 No. 2 of ACM Transactions on Database Systems.(c) List journals that published an article on XML in 2001 (that is, “XML” appears in the title of thearticle).(d) List authors that have published a book in 2001 and another book in 2003.(e) List publishers, and for each publisher list the books they have published as subelements. Chooseappropriate tags for your output.(f) List every author, and for each author list the number of articles he/she published in 2001. Chooseappropriate tags for your output.   answer d,e,f questions

Exercise 6 (Operations on DataFrame) Step a: Create two dataframes df1 and df2 as follows:import numpy as npimport pandas as pdrng = np.random.RandomState(100)df1 = pd.DataFrame(rng.randint(0, 100, (4, 3)), columns=['A', 'B', 'C'])df2 = pd.DataFrame(rng.randint(0, 100, (3, 4)), columns=['A', 'B', 'C', 'D']) Step b: Create a new dataframe df which is the summation of df1 and df2; Step c: Subtract all columns of df by the half of column 'C' in df1; (Remark: the values in df should be updated) Step d: Replace the NaN in df by 10; (Remark: the values in df should be updated) Step e: Use df.apply() to calculate the summation of the numbers in each row of df, and show the result. (Remark: the result should be a vector of four values)

get_total_cases() takes the a 2D-list (similar to database) and an integer x from this set {0, 1, 2} as input parameters. Here, 0 represents Case_Reported_Date, 1 represents Age_Group and 2 represents Client_Gender (these are the fields on the header row, the integer value represents the index of each of these fields on that row). This function computes the total number of reported cases for each instance of x in the text file, and it stores this information in a dictionary in this form {an_instance_of_x : total_case}. Finally, it returns the dictionary and the total number of all reported cases saved in this dictionary.

Consider the below given iris data set. This dataset contains 3 classes of 15 instances each and each class refers to a type of iris plant. The dataset has two features: sepal length, sepal width. The third column is for species, which holds the value for these types of plants. A new plant is identified. You have to classify the Species class of new identified plant with the help of KNN algorithm. Note: Before applying KNN modify the given data by adding last two digits of your registration number. Such as if the last two digits of your registration number is 23 then first row in the given table will be 28.3and 26.3.

#Question 4 use sku.csv and WarehouseLocations.csv##############################################################def warehouse_stats(sku):"""Question 4- Read sku.csv with CSV and create a dictionary of the New SKU Statistics.- The New Sku should be the key, with the corresponding value being an innerdictionary containing the following statistics:- 350 Loc: True if not 0- Warehouse Qty- Forcasted Qty- Items/Day: can be calculated using CuFt/Day divided by Item Cube.This result should be an float rounded to5 decimals places.- In your warehouse dictionary, add an inner dictionary with key Totals whichcontains:- Total Qty in Warehouse as key "Qty": Do Not add to Totals if '350 Loc' is not a valid location.- Number of Valid 350 Loc as key "Valid"Data Cleaning Steps:- In some variates of New Sku #, the Item Cube & CuFt/Day are faulty.Fix the manufacturers mistake. If either is **less than or equal to 0**,Item Cube can be assumed to be 5.0 and CuFt/Day is 10% of theForcasted Qty of the New…

In R please provide the code and explanation for the following One Way ANOVA with the coagulation data seta. Load the coagulation data set (it is from the faraway library)b. Data Summaries & Assumption Check   i. Use the names() function to identify the column names   ii. How many rows of data are there?   iii. Create a single graph with 4 boxplots on the same scale, one for the coagulation for each of the diets. Each boxplot should be a different color. Use the plot function for this task.colNameData is the name of the column in the data set that you want to create a boxplot forcolNameCategory is the name of the column that you want to use to split the data into groups (so you want one boxplot for each category/value in this column)dataset is the name of the full dataset col=2:4 will give you 3 different colors (color indices are 1-8, then they repeat)   iv. Create 4 different data frames, one for the data corresponding to each of the 4factors. How many observations are there for…

Q1-  Suppose a group of 9 sales price records has been stored as follows: 9, 25, 28, 30, 48, 72, 78, 195, 213 Partition them into three bins then smoothing them by the following methods: 1.Partition into equal-width then smoothing by bin means.   2. Partition into equal-depth then smoothing by bin boundaries.

Python code using pandas to find the users with the highest aggregate scores (over all their posts) for the whole dataset. You should restrict your results to only those whose aggregated score is above 10,000 points, in descending order. Code should generate a dictionary of the form {author:aggregated_scores ... }.

Use python machine learning. A group of data scientists want to analyze some data. They already cleaned up the data, with the result being a Dataframe called X_train. The Dataframe X_train has 1058 rows and 13 columns. It has the below columns (picture attached on how it looks like): 'LotFrontage','LotArea','BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF','1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea','TotRmsAbvGrd','GarageArea','OpenPorchSF'   Answer the following questions: 1. Transform X_train using PCA. Assign the output to a variable X_train_pca. 2. What is wrong with above approach? Scale the data, then repeat the above.

Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with linear kernel breast cancer problem : I have already answered 1 to 3. Please provide solution from 4,5,6,7.  Programming language python

Using Pandas library in python - Calculate student grades project Pandas is a data analysis library built in Python. Pandas can be used in a Python script, a Jupyter Notebook, or even as part of a web application. In this pandas project, you’re going to create a Python script that loads grade data of 5 to 10 students (a .csv file) and calculates their letter grades in the course. The CSV file contains 5 column students' names, score in-class participation (5% of final grade), score in assignments (20% of final grade), score in discussions (20% of final grade), score in the mid term (20% of final grade), score in final (25% of final grade). Create the .csv file as part of the project submission Program Output This will happen when the program runs Enter the CSV file Student 1 named Brian Tomas has a letter grade of B+ Student 2 named Tom Blank has a letter grade of C Student 3 named Margo True has a letter grade of A Student 4 named David Atkin has a letter grade of B+ Student 5 named…

You will use the wine for this task (dataset: sklearn.datasets.load_wine()), perform NN and NearestCentroid on the dataset to perform classification. Select different parameter values (i.e., parameter tuning) and discuss the influence. Finally, report result of comparisons. The dataset can be found here https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html

google colab [jupyter notebook] Amazon Musical Instrument Reviews General Readme on Projects Web commerce sites get a substantial amount of feedback from reviews users post on various websites. It is not practical to go through all this information by hand to determine whether a user liked a particular product or not. For our project we are going to use a dataset of Amazon Musical Instrument Reviews. The main reason I selected this dataset is that it is significantly smaller than the Amazon review datasets for movies, music, and books. This dataset has a bit over 221,000 reviews. The columns in the dataset are name description verified whether the reviewer bought the product from Amazon or not reviewTime time of the review reviewerID ID of the reviewer, e.g. A2SUAM1J3GNN3B asin ID of the product reviewerName name of the reviewer reviewText the text of the body of the review summary the test of the heading of the review unixReviewTime time of the review (Unix time)…

Question 22Explain the procedure you will take to automatically choose a good value for the regularization parameter using different splits on a given dataset.

Sort results by reverse domain: Create a data category. Domain that depicts domain names, including a suitable compareTo() function where the natural order is in reverse domain name order. For example, cs.princeton.edu's mirror name is edu.princeton.cs. This is helpful for analysing site logs. Use s.split(".") to divide the string s into pieces separated by underscores. Create a client that takes normal input and displays the reverse domains in sorted order.

Representation (Metadata) Assignment  - Foundations of Data and Information §  This is a 2-part assignment. Part 1: Dublin Core Metadata Record Working the DCMI Metadata Terms http://www.dublincore.org/documents/dcmi-terms/.§ Create one “full” metadata record for an article, a webpage, a photograph, a book, OR data set representing a topic about our natural environment. For example, the object you select may represent ecology, botany, a natural disaster, weather data, etc. § Please work with the following encoding scheme: Guidelines for implementing Dublin Core inTM XML https://www.dublincore.org/specifications/dublin-core/dc-xml-guidelines/ For your metadata record (a representation): §  Use as many of the 15 DC (Dublin Core) elements (properties) as you can from the DCMES, version 1.1 when creating you’re your representation §  You may also include additional elements (full level properties) from the DCTERMS namespace. §  Show that you understand element repeatability. §…

Reverse address sort: Develop a data structure. A domain that depicts domain names and has a suitable compareTo() function with the reverse domain name's natural order as the comparison criteria. For instance, cs.princeton.edu's mirror name is edu.princeton.cs. Analysis of online logs can benefit from this. Use s.split(".") to divide the string s into pieces that are separated by spaces. Create a client that displays the reverse domains in ordered order after reading domain names from standard input.

Computer Science NODEJS How do I read specific data from csv file (for example extract data only from Canada and United States) and put that into a variable and then make a txt file and use that variable to put that data into txt file? Here is what I have so far below, I'm struggling with the ////grab data for canada section. Thanks for helping.   const csv = require('csv-parser'); const fs = require('fs'); const inputs = []; //use csv parser fs.createReadStream('input_countries.csv') .pipe(csv()) .on('data', (row) => { inputs.push(row); }) .on('end', () => { console.log('CSV file successfully processed'); });   console.log("Deleting canada.txt file if it exists"); fs.unlink('canada.txt', function (err) { if (err) { return console.error(err); } console.log("canada.txt deleted sucessfully") }); console.log("Deleting usa.txt file if it exists"); fs.unlink('usa.txt', function (err) { if (err) { return console.error(err); } console.log("usa.txt deleted sucessfully") }); const header…

Q1 Consider X to be a 100-by-100 matrix. Which of the following commands will extract elements common to every 2nd row (starting from the 2nd row) and every 3rd column (starting from the 1stcolumn)? Select one: a. X(2:2:end, 1:3:end) b. X[1:2:end, 1:3:end] c. X([2:, 3:]) d. X([2:2:end, 1:3:end]) e. X(1:2:99, 1:3:96)   Q2 Which of the following syntax performs the forward elimination process in Gaussian elimination? Here r represents the row index, c is the column index and n represents the number of rows in the augmented matrix. Select one: a. Aug(r,:) = Aug(r,:) - factor*Aug(c,:) b. Aug(r,c) = Aug(r,c) - factor*Aug(c,:) c. Aug(r,[c n+1]) = Aug(r,[c n+1]) - factor*Aug(r,[c n+1]) d. Aug(r,n+1) = Aug(r,n+1) - factor*Aug(c,:) e. Aug(r,c) = Aug(r,:) - factor*Aug(c,c) Clear my choice   Q3 Which syntax will solve for the differential equation using the in-built function ode45() with a time interval of 0 to 10 and an initial condition of 0?…

Exactly what does it mean when someone refers to the Dataset object?

Design a direct file organization using a hash function, to store an item file with item number as its primary key. The primary keys of a sample set of records of the item file are listed below. Assume that the buckets can hold two records each and the blocks in the primary storage area can accommodate a maximum of four records each. Make use of the hash function h(k) = k mod 8, where k represents the numerical value of the primary key (item number).369 760 692 871 659 975 981 115 620 208 821 111 554 781 181 965 don't copy bartleby old answer its wrong

Design a direct file organization using a hash function, to store an item file with item number as its primary key. The primary keys of a sample set of records of the item file are listed below. Assume that the buckets can hold two records each and the blocks in the primary storage area can accommodate a maximum of four records each. Make use of the hash function h(k) = k mod 8, where k represents the numerical value of the primary key (item number).369 760 692 871 659 975 981 115 620 208 821 111 554 781 181 965

The data in flat files has been provided:   INVOICE TABLE INVOICE_NUM CUSTOMER_ID INVOICE_DATE EMPLOYEE_ID COIN_ID DELIVERY_ID 8111 11011 15 May 2021 emp103 7111 511 8112 11013 15 May 2021 emp101 7116 512 8113 11012 17 May 2021 emp101 7112 513 8114 11015 17 May 2021 emp102 7111 514 8115 11011 17 May 2021 emp102 7115 515 8116 11015 18 May 2021 emp103 7115 516 8117 11012 19 May 2021 emp105 7112 517 8118 11013 19 May 2021 emp105 7112 517     COIN_RETURNS TABLE RETURN_ID RETURN_DATE REASON CUSTOMER_ID COIN_ID EMPLOYEE_ID ret001 25 May 2021 Customer not satisfied with product 11011 7116 emp101 ret002 25 May 2021 Product missing part 11013 7114 emp103     COIN TABLE COIN_ID PRODUCT PRICE QTY 7111 1oz Gold Kruger Rand R 5 999 10 7112 1oz Silver Kruger Rand R 12 999 8 7113 Gold Big 5 Uncirculated R 15 999 8 7114 Silver Big 5 Pack R 7 999 5 7115 1oz Gold Palaeontology R 11 999 15 7116 1oz Silver Palaeontology R 7 999 12   COIN_DELIVERY TABLE…

Cource : Data structure (please read the following bolded words !!!!!!) Write the code in C++ and provide a link for CPP File Please don't send a picture of the code, send the code as a text !! Write the code in C++ not C !! Problem: Create an employee Record Management system using linked listthat can perform the following operations:• Insert employee record• Delete employee record• Update employee record• Show employee• Search employee• Update salary The employee record should contain the following items• Name of Employee• ID of Employee• First day of work• Phone number of the employee• Address of the employee• Work hours• Salary Approach:With the basic knowledge of operations on Linked Lists like insertion, deletion of elementsin the Linked list, the employee record management system can be created. Below are thefunctionalities explained that are to be implemented:Check Record: It is a utility function of creating a record it checks before insertionthat the Record Already Exist or…

convert UNF for 1NF to 2NF to 3NF, progressively. UNF:Order(OrderID, OrderDate, CustID, CustName, CustPhone, CCNum, CCExpDate, CCBank, BnkContName, BnkContPhone, CustEmail, OrderIP, SiteRefFrom, ShipStreet, ShipCity, ShipSt, ShipZip, OrderLineNum, ItemID, ItemName, ItemDesc, ItemQtyOrdered,ItemListPrice, ItemSalePrice, ItemQtyShip, ShipCharge, Tax, TotalDue)

Display a function that can rank the dataset based on subjectivity on Twitter scraping

TODO 11 Using the clean_df split our columns into features and labels. Index/slice our label 'area' and store the output into the variable y. Index/slice all other features EXCEPT 'area' into the variable X. To do so you can use the Pandas DataFrame drop() method or slicing with iloc, loc or [ ]. # TODO 11.1y = display(y) todo_check([    (y.shape == (517,), 'y does not have the correct shape of (517,)'),    (np.all(np.isclose(y.values[-5:], np.array([2.00687085, 4.01259206, 2.49815188, 0.        , 0.        ]),rtol=.01)),'y has the incorrect values'),])   # TODO 11.2X = display(X) todo_check([    (X.shape == (517, 29), 'X does not have the correct shape of (517, 29)! Make sure the `area` column is not included!'),    (np.all(np.isclose(X.values[-5:, -4], np.array([27.8, 21.9, 21.2, 25.6, 11.8]),rtol=.01)),'X has the incorrect values'),])