2a) Shuffle DataAn important part of the data science process is to make sure that your dataset is not skewed or biased. Let us look at the various category values innews_df for example.Run the two cells below to look at the category breakdown.In [26]: A breakdwon in firsst 15888 valuesnews_df['category I[:15880). value_counts()Out [26]: computerrecreational43893957science2754politicsreligionnisc20791439382Name: category, dtype: int64In [27]: breakdown from 1500e to the end of dfnews_df['category ][15800:]. value_counts()Out[27): science1180religionniscpoliticsconputerNane: category, dtype: int64973578546454As you can see in the above results, there is no data point for recreational category (numerical category 2) after 15000 datapoints in the current data. If wewere to split the data into training and test sets naively, we might end up with O datapoints for recreational category in the test set and thus our analysis wouldremain biased.Randomly shuffle the dataframe using sample() method in pandas such that the resulting dataset has the same number of rows as news_df butrandomly shuffled. Store this back into news_df.Also, provide random_state-randon_state to use the value defined in randon state at the start of the assignment as an argument to the method.And, be sure to reset the indices in the resulting news_df : be sure to do this such that there is no index column in the resuiting dataframe (see the dropparameter in the reset index() method.In [28]: rs= 369new_df=df.sample(frac=1, random_state=rs).reset_index(drop=True)news_df.head()NameErrorTraceback (nost recent call last) in 1 rs = 369--> 2 new_df-df.sample(frac-1, randon_state=rs).reset_index(drop-True)3 news_df.head ()NameError: nane 'df' is not definedIn [ ]: assert news_df.shape == (18738, 6)* if you fail the following assertW go back and be sure you followed instructionsassert news_df.loc[e, "subject'] == 'Re: Is 988-1MB/sec. HD transfer slow for 486Dx-58 EISA with Ultrastor24F 2b) Numerical LabelsWe need to convert the category labels to numerical labels. Use the following mapping to convert the values in category into numerical labels. Store thenumeric values in a new column called 'category_num• politics -> 1• recreational -> 2• computer > 3religion ->4• sclence -> 5• nisc -> 6Hint you can use the replace() method from pandas.In [29]: category = ['politics', "recreational', "computer','religion', "science', "nisc']news_df = pd. DataFrame({'news' :news, 'category':category})news_dfNameErrorTraceback (nost recent call last) in 1 category = l'politics', 'recreational, 'conputer', 'religion", 'sclence", 'misc']----> 2 news_dt = pd. DataFrane({'news':news, category':category})3 news_dfNameError: nane 'news is not definedIn [ ]: assert set(np.unique(news_df['category_num' ])) == {1,2,3,4,5,6}assert sum(neNs_df['category_num"] == 2) == 39562c) Convert Text data into vrector

Code: import pandas as pd category = ['politics', 'recreational', 'computer', 'religion',…

We need to convert the category labels to numerical labels. Use the following mapping to convert the values in category into numerical labeis. Store the numeric values in a new column called category_num • politics 1 • recreational ->2 • conputer - 3 • religion 4 • science 5 • nisc 6 Hint you can use the replace) method from pandas. category L'politics','recreational', "computer', 'relligion", science', nisc] news df = pd. Datafrane({'news :news, 'category":category}) news df

We need to convert the category labels to numerical labels. Use the following mapping to convert the values in category into numerical labeis. Store the numeric values in a new column called category_num • politics 1 • recreational ->2 • conputer - 3 • religion 4 • science 5 • nisc 6 Hint you can use the replace) method from pandas. category L'politics','recreational', "computer', 'relligion", science', nisc] news df = pd. Datafrane({'news :news, 'category":category}) news df

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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CREATE DATABASE COUNTRIES; USE COUNTRIES; DROP TABLE IF EXISTS `City`; CREATE TABLE `City` ( `ID` int(11) NOT NULL AUTO_INCREMENT, `Name` char(35) NOT NULL DEFAULT '', `CountryCode` char(3) NOT NULL DEFAULT '', `District` char(20) NOT NULL DEFAULT '', `Population` int(11) NOT NULL DEFAULT '0', PRIMARY KEY (`ID`) ) ENGINE=MyISAM AUTO_INCREMENT=4080 DEFAULT CHARSET=latin1; -- -- Dumping data for table `City` -- -- ORDER BY: `ID` INSERT INTO `City` VALUES (1,'Kabul','AFG','Kabol',1780000); INSERT INTO `City` VALUES (2,'Qandahar','AFG','Qandahar',237500); INSERT INTO `City` VALUES (3,'Herat','AFG','Herat',186800); INSERT INTO `City` VALUES (4,'Mazar-e-Sharif','AFG','Balkh',127800); INSERT INTO `City` VALUES (5,'Amsterdam','NLD','Noord-Holland',731200); INSERT INTO `City` VALUES (6,'Rotterdam','NLD','Zuid-Holland',593321); INSERT INTO `City` VALUES (7,'Haag','NLD','Zuid-Holland',440900); INSERT INTO `City` VALUES (3068,'Berlin','DEU','Berliini',3386667); INSERT INTO `City` VALUES…
Given the following sequence of operations on an empty Deque. Provide a visualrepresentation of the Deque at each operation.add_last("customer 1")add_first("customer 2")add_first("customer 3")first()delete_last()
89. Using OQL, you may do which of the following? a. Return an entire collection of elements including duplicates. b. Return a collection of elements without duplicates. c. Return a specific subset of elements using a given criteria. d. All of the above.
Why are narrowing or widening conversions never safe, or why are they always safe, and can you provide instances to back your claim?
Although this dataset does not havee any NaN S, you could utilizee the dropna function to drop rows with missing values. We will not be providing links to the relevant page in the Pandas API anymore. Instead, you should develop the habit of Googling and finding out how to use the API on your own, which is an important skillfor working with Python libraries. Once you are done with that, print out summary information on the table again to ensure that the changes are saved. Your code here spotify_na- ? spotify_na.?
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…
A table called "game1" contains three columns and twenty rows, but "game2" has the same column as game1 (ie 3) and fifteen rows. Both tables have 5 rows in common. What is the degree and cardinality of the resulting table if we take union?
Given the following scheme, what kind of key is (VIN) (check all that apply)? automobile_scheme = {VIN, license_plate_state, owner_last_name, owner_first_name, license_plate_number, color, model, make, year} • color is a simple name, like 'blue' to describe the predominant color of the vehicle. • VIN is the vehicle identification number. No two vehicles in the world have the same VIN. • License plate numbers are only unique within a state. There could be more than one automobile with the same license plate number, as long as they are registered in different states. • Make is the manufacturer - like Chevrolet. • Model is the name of the vehicle - like Toyota Prius. • Year is the year that the automobile was made. ✓candidate key external key minor key ✓superkey
USE supermarkett; CREATE TABLE product(product_id INT NOT NULL,product_name VARCHAR(45) NOT NULL,price FLOAT NOT NULL,prod_discount INT NOT NULL,product_type VARCHAR(45) NOT NULL,PRIMARY KEY (product_id)); CREATE TABLE stock (stock_id INT NOT NULL,product_id INT NOT NULL,quantity INT NOT NULL,product_status BOOLEAN NOT NULL,PRIMARY KEY (stock_id),FOREIGN KEY (product_id) REFERENCES product(product_id));CREATE TABLE employee (emp_id INT NOT NULL,emp_name VARCHAR(45) NOT NULL,emp_number INT NOT NULL,address VARCHAR(45) NOT NULL,PRIMARY KEY (emp_id));CREATE TABLE supplier (sup_id INT NOT NULL,sup_name VARCHAR(45) NOT NULL,sup_number INT NOT NULL,address VARCHAR(45) NOT NULL,PRIMARY KEY (sup_id));CREATE TABLE login (login_id INT NOT NULL,password VARCHAR(45) NOT NULL,PRIMARY KEY (login_id)); ALTER TABLE stockADD FOREIGN KEY (product_id) REFERENCES product(product_id); ------------------------------------------------------------------------------- make 2 different -stored procedure- query…
Subject: Technology of Large Databases. You have a table AUDITORIUMS with a nested table that represents the classes that take place in that auditorium: a. Auditorium number b. Class CLASS_TYPE_TABLE c. Max people allowed to be in an auditorium Check, if the class with parameters ("Databases", DSP101) is registered in auditorium 405!
MariaDB [dreamhome]> select * from Staff1; | staffNo | FName | 1Name | position | sex | Mary | Howe | | David | Ford | | Ann | Beech | Susan | Brand | Assistant | F Supervisor | M Assistant | F Manager | F | | John | White | Manager | M | Julie | Lee | Assistant | F | SA9 | SG14 | SG37 Find all staff fname and direct supervisor's fname. | SG5 | SL21 | SL41 | staff | supervisor | | Mary | Susan | David | Susan | Ann | Susan | Susan | John | Julie | John Output salary | branchNo | superno | | SG5 | SG5 | SG5 | DOB | | 1970-02-19 | 9000.00 | B007 | 1958-03-24 | 18000.00 | B003 | 1960-11-10 | 12000.00 | B003 1940-06-03 | 24000.00 | B003 | 1945-10-01 | 30000.00 | B005 | 1965-06-13 | 9000.00 | B005 SL21 | NULL | SL21