Answer:
import pandas as pd
# URL for university excel sheet( CSV format)
data_url = '' "
# Load the data from University URL
university = pd.read_csv(data_url)
# filter the data to contain universities from where more than 50 students out of the top 10% of their high school classes came
university_1 = university[universty.number>50 ]
n= len(university_1)
n1= (10 *n)/100
university_1.nlargest(n1, 'number')
print(university_1)
Explanation:
We are using here Pandas. And it is meant for reading from various data sources like Excel, Acess, SQL Server, etc. And first, we filter University with student number more than or equal to 50. Finally, with the nlargest, we find the top 10% of the list. And for running the above program we only need to add the URL of the university.csv. Nothing else is required. You can have the local file address as well if the CSV is on your computer.
Answer:
a) Speedup gain is 1.428 times.
b) Speedup gain is 1.81 times.
Explanation:
in order to calculate the speedup again of an application that has a 60 percent parallel component using Anklahls Law is speedup which state that:
Where S is the portion of the application that must be performed serially, and N is the number of processing cores.
(a) For N = 2 processing cores, and a 60%, then S = 40% or 0.4
Thus, the speedup is:
Speedup gain is 1.428 times.
(b) For N = 4 processing cores and a 60%, then S = 40% or 0.4
Thus, the speedup is:
Speedup gain is 1.81 times.
ROWS run horizontally and COLUMNS run vertically in a table.
Hope this helps.=^)
Answer:
batteries cant last connected to nothing forever probably
