Please answer the ques in python with showing the answer
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- Python Data = { "logs": [ { "logType": “xxxxx” }, { "logType": “43435” }, { "logType": “count” }, { "logType": “4452” }, { "logType": “count” }, { "logType": “count” }, { "logType": “43435” }, { "logType": "count" }, { "logType": "count" } ] } So, please count the total of "logType": “count” My desired output: { "count”: 5 }explain every line in phytone code in colab below pls: # f(x) = tanx, f'(x) = 1 / cosx^2, f'(1), f'(2), f'(3)=? # h interval: 1/100 - 1/1000 from math import * def f(x): return tan(x) def f1(x): return 1/cos(x)**2 h = 0.001 print ("F1 Forward ", "F1 Back ", "F1 Simetric ", "F1 Exact ") for i in range (3): x = float(i+1) f1_forward = (f(x+h)-f(x))/h f1_back = (f(x)-f(x-h))/h f1_simetric = (f(x+h)-f(x-h))/(2*h) f1_exact = f1(x) print ("%.10f" %f1_forward, "%.10f" %f1_back, "%.10f" %f1_simetric, "%.10f" %f1_exact)economics R code to extract time series data from U.S. Dollars to Euro Spot Exchange Rate (DEXUSEU)(Without errors) code i wrote: # Load fredr library library(fredr) # Set your FRED API key my_key <- "bdce4c95ff2211444fe957773c117072" # Retrieve U.S. Dollars to Euro Spot Exchange Rate (DEXUSEU) data from FRED USD_EUR <- fredr_series("DEXUSEU", observation_start = "2013-04-14", observation_end = "2023-04-14", api_key = my_key) # View the first few rows of the data head(USD_EUR) error i'm getting : Error in `check_dots_empty()`: ! `...` is not empty. These dots only exist to allow for future extensions, and should be empty. Did you misspecify an argument? Run `rlang::last_trace()` to see where the error occurred.
- ASAP!! Please Write a python script that will:1. Load, normalize and concatenate the first 4 days as training data , sliding window k=360.2. Load and normalize the last day as test data, sliding window k=360.3. Perform a knn search on the train data for the test data:a. Once using SKlearn-nearestneighbors module, any base algorithm.b. Once using FAISS-library (use a flat index).c. Once using FAISS-library (use any non flat index).4. Compare the three approaches on speed as a function of n=number_of_neighboors,from n =1 to n=256.5. Compare the approaches on results similarity for n=10Read on the functions and operations of numpy and pandas. Load the given data set into Google Colab python 1. In USD, find the average salary of Data Scientists as well as the standard deviation. 2. List each unique Job Title 3. For each unique Job Title, find their average salary (USD) and standard deviations. 4. Download the file as .ipynb to pass it on this activity file: https://uc-bcf.instructure.com/courses/28339/files/5680123/download?download_frd=1&verifier=fXym8nGyMe7CmvKYy7QmRDfcunF4DZwhuqakH1rLPlease do not use a copy and pasted answer. MIPS Assembly Complete gcd_cur function, which recursively calculates the GCD (Greatest Common Divisor) of two given positive integers input using the following Euclidean algorithm (Links to an external site.). def gcd_recur(a, b): if b = 0: return a; else: return gcd_recur( b, (a mod b) ); >> a0: the 1st input argument, a>> a1: the 2nd input argument, b ############################################################################################################################################################################################## PART 2 (gcd_recur)#a0: input number#a1: input number###############################################################gcd_recur:############################### Part 2: your code begins here ## ############################### Part 2: your code ends here ##jr $ra
- need help with python. can modify/ refer from the code below import numpy as np; from pylab import *; import scipy.optimize hdata=[3.24, 2.68, 2.35, 1.87, 2.57,2.77] tdata=[0.81, 0.74, 0.69, 0.61, 0.72, 0.75]; error_in_t=np.ones(6)*0.1 def t(h,g): return (2*h/g)**0.5 results=scipy.optimize.curve_fit(t,hdata,tdata,sigma=error_in_t,absolute_sigma=True) gbest=results[0][0]; var=results[1][0][0]; se=var**0.5 print(gbest) print(var,se)(python) 10. Identify at least 10 issues with the code below. 1. def scaleData(data);2. for x in range(len(data)):3. data[x] = data[x] * 1.54. return data:5.6. listInt = ( 1, 2, 3, 4, 5 ]7. newList = scaleData[listInt]8. totalDiff == 09. for x in range(length(newList)):10. print(str(listInt[x]) + " * 1.5 = + str(newList[x]))11. totalDiff += newList[x] - listInt[x];12. print “Total increase of + str(totalDiff))MIPS Assembly Complete gcd_cur function, which recursively calculates the GCD (Greatest Common Divisor) of two given positive integers input using the following Euclidean algorithm (Links to an external site.). def gcd_recur(a, b): if b = 0: return a; else: return gcd_recur( b, (a mod b) ); >> a0: the 1st input argument, a>> a1: the 2nd input argument, b My Code: ############################################################################################################################################################################################## PART 2 (gcd_recur)#a0: input number#a1: input number###############################################################gcd_recur:############################### Part 2: your code begins here #### return value is v0move $t0, $a0 move $t1, $a1 loop: beq $t1, $0, done div $t0, $t1 move $t0, $t1 mfhi $t1 j loop done: move $v0, $t0…
- MIPS Assembly Complete gcd_cur function, which recursively calculates the GCD (Greatest Common Divisor) of two given positive integers input using the following Euclidean algorithm (Links to an external site.). def gcd_recur(a, b): if b = 0: return a; else: return gcd_recur( b, (a mod b) ); >> a0: the 1st input argument, a>> a1: the 2nd input argument, b ############################################################################################################################################################################################## PART 2 (gcd_recur)#a0: input number#a1: input number###############################################################gcd_recur:############################### Part 2: your code begins here ## ############################### Part 2: your code ends here ##jr $raWhat does the following code segment and subroutine do? .equ SIZE =128.equ TABLE_L =$60.equ TABLE_H =$00 rjmp RESET .def A =r13.def B =r14.def cnt2 =r15.def cnt1 =r16.def endL =r17.def endH =r18 mystery: mov ZL,endLmov ZH,endHmov cnt2,cnt1i_loop: ld A,Z ld B,-Z cp A,B brlo L1 st Z,A std z+1,BL1: dec cnt2 brne i_loop dec cnt1 brne mystery ret Converts temperature values from one scale to another Adds a constant to a table of bytes Sorts numbers in ascending order Converts ASCII characters from lower case to upper caseConsider the following code chunk:x <- 0while(x < 4) {x <- sample(1:3, 1)print(x)}It is not a good idea to run these lines because...a) x is an invalid argument to print().b) the condition x < 4 is never violated.c) the function sample() does not exist.d) x is initialised with the wrong type.