Consider we have loaded our data frame, named NBA, using the data in the file below. Write a single statement that can find a numberof unique playing positions?NameТeamNumberPositionAgeHeightAvery Bradley Boston CelticsPG256-2Jae Crowder Boston Celtics99SF256-6John Holland Boston Celtics30SG276-5RJ. HunterBoston Celtics28SG226-5Jonas Jerebko Boston Celtics8PF296-10Amir Johnson Boston Celtics90PF296-9Jordan Mickey Boston Celtics55PF216-8Kelly Olynyk Boston CelticsTerry Rozier Boston Celtics41257-012PG226-2Marcus Smart Boston Celtics36PG226-4Jared Sullinger Boston Celtics7246-9Isaiah Thomas Boston Celtics4PG275-9Evan TumerBoston Celtics11SG276-7O a. NBA [Position].unique ()O b. NBA.position.unique ()O . NBA[ 'Position'].unique ()O d. NBA. Position.notRepeat()

Pandas Index.unique() function return unique values in the index. Uniques are returned in order of…

Consider we have loaded our data frame, named NBA, using the data in the file below. Write a single statement that can find a number of unique playing positions? Name Team Number Position Age Height 6-2 Avery Bradley Boston Celtics PG 25 Jae Crowder Boston Celtics 99 SF 25 6-6 John Holland Boston Celtics 30 SG 27 6-5 RJ. Hunter Boston Celtics 28 SG 22 6-5 Jonas Jerebko Boston Celtics PF 29 6-10 Amir Johnson Boston Celtics 90 PF 29 6-9 Jordan Mickey Boston Celtics Kelly Olynyk Boston Celtics Terry Rozier Boston Celtics 55 PF 21 6-8 41 25 7-0 12 PG 22 6-2 Marcus Smart Boston Celtics 36 PG 6-4 Jared Sullinger Boston Celtics 7 24 6-9 Isaiah Thomas Boston Celtics 4 PG 27 5-9 Evan Turner Boston Celtics SG 27 6-7 11 O a. NBA (Position].unique () O b. NBA.position.unique () O C. NBA[ 'Position'].unique () O d. NBA.Position.notRepeat()

Consider we have loaded our data frame, named NBA, using the data in the file below. Write a single statement that can find a number of unique playing positions? Name Team Number Position Age Height 6-2 Avery Bradley Boston Celtics PG 25 Jae Crowder Boston Celtics 99 SF 25 6-6 John Holland Boston Celtics 30 SG 27 6-5 RJ. Hunter Boston Celtics 28 SG 22 6-5 Jonas Jerebko Boston Celtics PF 29 6-10 Amir Johnson Boston Celtics 90 PF 29 6-9 Jordan Mickey Boston Celtics Kelly Olynyk Boston Celtics Terry Rozier Boston Celtics 55 PF 21 6-8 41 25 7-0 12 PG 22 6-2 Marcus Smart Boston Celtics 36 PG 6-4 Jared Sullinger Boston Celtics 7 24 6-9 Isaiah Thomas Boston Celtics 4 PG 27 5-9 Evan Turner Boston Celtics SG 27 6-7 11 O a. NBA (Position].unique () O b. NBA.position.unique () O C. NBA[ 'Position'].unique () O d. NBA.Position.notRepeat()

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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import numpy as np import pandas as pd np.random.seed(1) import datetime today datetime.date.today0 someday = datetime.date(2016, 1, 1) diff = today - someday rows, cols = diff.days,6 data = np.random.rand(rows,cols) tidx = pd.date_range(2016-01-01', periods=rows, freq="D') data frame = pd.DataFrame(data, columns=['a','b','c','d','e',"f], index-tidx) print(data_frame) Question- Show the first 30 rows of your data (Using a pandas data frame function0
Fragment the Client table so that clients of Consultant 19 form a fragment named ClientConslt19, clients of Consultant 22 form a fragment named ClientConslt22, clients of Consultant 35 form a fragment named ClientConslt35, and clients of Consultant 51 form a fragment named ClientConslt51. (Include all fields from the Client table in each fragment.) In addition, you need to fragment the WorkOrders table so that orders are distributed and stored with the clients that placed the orders. For example, fragment OrdersConslt19 consists of those orders placed by clients of Consultant 19. Write the SQL-type statements to create these fragments.
How would you use personal firewalls and network firewalls to safeguard your home network?
Suppose that we have an item table and a shipment table. The Item table contains all of the items and the Shipment table contains items received by the company. Shipments Table itemNo description Items Table itemNo 342 345 346 347 description MacBook Pro Lenovo PC 4GB Memory 500GB HD qtyOnHand 10 55 100 6 342 345 347 352 What is the result of executing the following query on this data? MacBook Pro Lenovo PC 500GB HD ITB External Drive qty Received SELECT I.itemNo "Item No", qtyOnHand "Quantity On Hand", S.itemNo "Item Number", qtyReceived "Quantity Received" FROM Items I RIGHT JOIN Shipments S ON (1.itemNo= S.itemNo); O c. 'NULL' appears in the column Item Number. O d. '352' appears in the column Item No. 1 5 6 4 Identify in the list below, a value and the name of a column in which it appears. O a. '346' appears in the column Item No. O b. 'NULL' appears in the column Item No.
hello please i want SQL Statements (DDL & DML) full code based on this ERD diagram Patient patientiD PK Pharmacy BloodBank PAame Medicine pharmacylD PK bankID bloodType patientiD PK PAge treatmentiD 0.. PK treatmentID FK pHeathissue medName FK peloodType medPrice FK medPrice 0. Appointment appointmentiD Doctor DoctorClinic 1 appointmentiD FK PK doctoriD PK 0.appointmentDate 0. dName treamentiD FK medPrice doctoriD FK appointment_price FK patientiD FK FK appointmentStatus appoinment_price
calculate_trip_time( iata_src: str, iata_dst: str, flight_walk: List[str], flights: Flight Dir float: def ) -> """ Return a float corresponding to the amount of time required to travel from the source airport to the destination airport to the destination airport, as outlined by the flight_walk. The start time of the trip should be considered zero. In other words, assuming we start the trip at 12:00am, this function should return the time it takes for the trip to finish, including all the waiting times before, and between the flights. If there is no path available, return -1.0 >>> calculate_trip_time("AA1", "AA2", ["AA1", "AA2"], TEST_FLIGHTS_DIR_FOUR_ 2.0 "AA7", ["AA7", "AA1"], TEST_FLIGHTS_DIR_FOUR_ "AA7", ["AA1", "AA7"], TEST_FLIGHTS_DIR_FOUR_ "AA1", ["AA1"], TEST_FLIGHTS_DIR_FOUR_CITIES) "AA2", ["AA4", "AA1", "AA2"], TEST_FLIGHTS_DI "AA3", ["AA1", "AA2", "AA3"], TEST_FLIGHTS_DI >>> calculate_trip_time("AA1", "AA4", ["AA1", "AA4"], TEST_FLIGHTS_DIR_FOUR_ 2.0 || || || >>>…
from matplotlib import cmimport matplotlib.pyplot as pltimport platformimport numpy as npfrom mlxtend.data import loadlocal_mnist from sklearn.datasets import load_digitsfrom sklearn.model_selection import train_test_splitimport pickle plt.rcParams.update({'font.size': 12})plt.rcParams['figure.figsize'] = [8, 4] X, y = loadlocal_mnist(images_path='train-images-idx3-ubyte', labels_path='train-labels-idx1-ubyte') # Keeping only digits 0 and 1index = y <2X = X[index]y = y[index] # Normalizing the dataX = X/X.max() # Splitting to training and testing groups.X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False) def sigmoid(t): reasonable = (t>-100).astype(float) t = t*reasonable s = 1/(1+np.exp(-t)) s = s*reasonable return s Nsamples,Nfeatures = X_train.shapeNclasses = 2a = np.random.randn(Nfeatures+1,Nclasses-1)Xtilde = np.concatenate((X_train,np.ones((Nsamples,1))),axis=1)gamma = 1e-1for iter in range(1500): # YOUR CODE HERE…
Q3 Using the Spark DataFrame below, add a new column to each letter with value 1. In [0]: df = spark.createDataFrame([(i,) for i in data], schema='letters string') display(df) In [0]: Q4 Using the Spark DataFrame from Q3, find the count of each individual letters.
1. Break the table down to 1NF, 2NF anf 3NF.
Create a MySQL stored procedure to validate an attempt to create a new submission. The stored procedure should have parameters for the user, the user’s password, the id of the problem, the programming language (as a varchar), and the source code. An output parameter returns the result of the submission, which is one of the strings that can return or “Bad Parameters”, which handles cases such as incorrect password for the user, or the attempt to submit using a language that the program does not support (e.g., Malbolge or Visual Basic). The stored procedure can simulate the program judging process: if there is no Bad Parameters error, then randomly choose between Accepted, Wrong Answer, Time Limit Exceeded, and Run Time Error with equal probability. The submission should gets recorded in the database.
Data Frame: import numpy as npimport pandas as pdnp.random.seed(1)import datetimetoday = datetime.date.today()someday = datetime.date(2016, 1, 1)diff = today - somedayrows,cols = diff.days,6data = np.random.rand(rows,cols) tidx = pd.date_range('2016-01-01', periods=rows, freq='D')data_frame = pd.DataFrame(data, columns=['a','b','c','d','e','f'], index=tidx)print(data_frame) Question - Calculate the cumulative sum of the Data frame (using the <DataFrame>.cumsum() function) andstore it in a new data frame called: df_accumulate and Plot the six sets of row values of df_accumulate in a graph
Suppose I want to find the teachers' name and the courses they have taken in different semester, year including details of classroom information. Write DML. Sample output is given below: 画Results Messages course_id sec_id semester year building room_no capacity 2009 Taylor 2009 Taylor name Spring Spring Spring 1 Brandt CS-190 1 3128 92 Brandt CS-190 3128 92 3 Brandt CS-319 2 2010 Taylor 3128 92 Crick BIO-101 1 Summer 2009 Painter 514 100 Crick BIO-301 1 Summer 2010 Painter 514 100 6 Einstein PHY-101 1 Fall 2009 Watson 100 96 El Said Spring Spring Spring 7 HIS-351 2010 Painter 514 100 8. Katz CS-101 1 2010 Packard 101 98 9. Katz CS-319 2010 Watson 100 96 2009 Taylor Spring Spring 10 Kim EE-181 1 3128 92 11 Mozart MU-199 1 2010 Packard 101 98 12 Sriniva. CS-101 1 Fall 2009 Packard 101 98 13 Sriniva. CS-347 Fall 2009 Taylor 3128 92 14 Sriniva. CS-315 1 Spring 2010 Watson 120 94 15 Wu FIN-201 1 Spring 2010 Packard 101 98