import pandas as pd hardware = pd.Series(['Hammer', 'Saw', 'Wrench']) res=hardware.str.contains (pat='e')#series.str.contains with parameter pat searches element with pattern given print (res)# printing the result
Output with screenshot
output is at end
At 0 index and 2 index element of series 'e' is found so result is true
At element index 1 e is not found so false
mport pandas as pd hardware = pd.Series(['Hammer', 'Saw', 'Wrench']) Suppose, we wish to check if each...
Consider the vector field F(x, ) (4x3y -6ry3,2rdy - 9x2y +5y*) along the curve C given by r(t)(tsin(rt), 2t +cos(xl)), -2ss 0 To show that F is conservative we need to check a) b) We wish to find a potential for F. Let r,y be that potential, then Use the first component of F to find an expression for ф(x, y)-Po(x,y) + g(y), where ф(x,y) in the form: Differentiate ф(x,y) with respect to y and determine g(y) e Using the...
Suppose we have two integer arrays with the same type, write an AL program to check whether or not there are two integers, one from each array, with sum equal to zero. If there are such integers exist, print out all such combinations to the console window, otherise, print out "No integers in these two arrays, one from each array, with sum equal to zero." to the console window. December 3. 2018 For example, suppose we have the following two...
PYTHON
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
Our goal is to create a linear regression model to estimate
values of ln_price using ln_carat as the only feature. We will now
prepare the feature and label arrays.
"carat" "cut" "color"
"clarity" "depth" "table"
"price" "x" "y" "z"
"1" 0.23 "Ideal" "E" "SI2" 61.5 55 326
3.95 3.98 2.43
"2" 0.21 "Premium" "E" "SI1"...
18 marks] Suppose X~N(0,0). We wish to use a single value X hypothesis to test the null against the alternative hypothesis Denote by C aa) the critical region of a test at the significance level of -0.05 (a) 2 marks] What is the sample space S, the parameter space 9 space Θο of the test? and the null parameter (b) 12 marks) Computea (c) 12 marks Compute the power of the test (i.e., at 2) (d) [2 marks] Compute the...
Question 2: Suppose that we wish to fit a regression model for which the true regression line passes through the origin (0,0). The appropriate model is Y = Bx + €. Assume that we have n pairs of data (x1.yı) ... (Xn,yn). a) From first principle, derive the least square estimate of B. (write the loss function then take first derivative W.r.t coefficient etc) b) Assume that e is normally distributed what is the distribution of Y? Explain your answer...
Answer the following questions (1/2 point each, 4 points total): a. When should we use a t-test? b. When should we use a single-sample t-test? c. Why do we calculate standard deviation using N-1 rather than N like we used to? d. What is the standard error of the mean? e. Does it get larger or smaller as our sample size increases? Why? f. Does the critical value of t (i.e, the score you need for your results to be...
N(0,02). We wish to use a 1. [18 marks] Suppose X hypothesis single value X = x to test the null Ho : 0 = 1 against the alternative hypothesis H1 0 2 Denote by C aat the critical region of a test at the significance level of : α-0.05. (f [2 marks] Show that the test is also the uniformly most powerful (UMP) test when the alternative hypothesis is replaced with H1 0 > 1 (g) [2 marks Show...
Suppose you have a set of classes to schedule among a large number of lecture halls, where any class can class place in any lecture hall. Each class cj has a start time sj and finish time fj. We wish to schedule all classes using as few lecture halls as possible. Verbally describe an efficient greedy algorithm to determine which class should use which lecture hall at any given time. What is the running time of your algorithm?
Suppose that we go sequentially through a group of people asking each in turn to pick one of 5 options (e.g., "rock," "paper," "scissors," "fire," or "water") at random. Let Xi (for 0 ? i ? 4) be the random variable giving the number of additional people we have to ask before getting an option that has not been picked yet. (So X0 is the number of people we have to ask to get the first option, which will be...
Law of Large Numbers We saw in the Theoretical and Experimental Probability Lab that as we do more and more repetitions or trials of an experiment, the closer the experimental probability gets to the theoretical probability. This is called the Law of Large Numbers. Why is the Law of Large Numbers important? Why do we do experiments and find experimental probability when we could just use theoretical probability? Inferential statistics makes inferences about populations using data drawn from the population....