Basketball Players project in Python Data Science Course

Question

The given code includes a list of heights for various basketball players.
You need to calculate and output how many players are in the range of one standard deviation from the mean.
Here is my code:
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import math
players = [180, 172, 178, 185, 190, 195, 192, 200, 210, 190]
sum = 0
for nums in players:
sum+=nums

mean = sum / len(players)
new_list = []
for i in players:
new_list.append(mean-i)

new_sum = 0
for k in new_list:
new_sum+=k**2

variance = new_sum/len(players)
deviation = math.sqrt(variance)
#print(variance)
#print(deviation)
#print(round(variance))
#print(round(deviation))

num_players = []
for j in players:
if j in range(round(deviation),round(mean)):
num_players.append(j)
else:
pass

print(len(num_players))
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So what I did here is that I found the mean, variance, deviation of the list, I have checked and verified that the mean of this is 112 and the deviation is 11 by rounding them up. I created a new list that contains any numbers from the original list that are in range of 11,112 and I print out the len of that list. I did this in my code playground and I got 4. But the answer is wrong. I am not sure if anything is wrong with my code. Please help me. Thank you

Answer 1

OMG I AM SORRY I FIGURED THIS OUT AGAIN
import math
players = [180, 172, 178, 185, 190, 195, 192, 200, 210, 190]
sum = 0
for nums in players:
sum+=nums

mean = sum / len(players)
new_list = []
for i in players:
new_list.append(mean-i)

new_sum = 0
for k in new_list:
new_sum+=k**2

variance = new_sum/len(players)
deviation = math.sqrt(variance)
#print(variance)
#print(deviation)
#print(round(variance))
#print(round(deviation))
range1 = mean - deviation
range2 = mean + deviation
num_players = []
for j in players:
if j in range(round(range1),round(range2)):
num_players.append(j)
else:
pass

print(len(num_players))

Answer 2

players = [180, 172, 178, 185, 190, 195, 192, 200, 210, 190]

# we need to find the SD
# How many are in b/w mean and SD to be calculated
sumofplyht = 0
for i in players:
sumofplyht = sumofplyht + i
median = sumofplyht / len(players) # Median of height
variance = 0
for i in players:
variance = variance + ((i - median)**2)
sd = ((variance/len(players))**(1/2)) #Standard deviation
count = 0
for i in players:
if sd <= i <= mean:
count = count+1
print("Players within the range of SD and Mean :",count)

Answer i got is 4 but program says its wrong pls give the solution for this problem

Answer 3

import numpy as np

players = [180, 172, 178, 185, 190, 195, 192, 200, 210, 190]
players = sorted(players)

data = np.array(players)
mean = np.mean(data)
std_dev = np.sqrt(np.var(data))
lower_bound = mean - std_dev
upper_bound = mean + std_dev
print(len([i for i in players if lower_bound <= i <= upper_bound]))

Answer 4

That's really cool, awesome you got it figured out. For what it's with i tried to do the same thing, but with Pandas

https://code.sololearn.com/cX325Sqr4QTl/?ref=app

Answer 5

Above answers are good .lets make it more smaller

Use this to calculate mean and standard deviation :)

import numpy
mean=numpy.mean(players )
std=numpy.std(players)#standard deviation

Answer 6

I see there is a lot I can do to shorten my code, but I am a newbie, så I am proud to get it working at all. :-)

import math
players = [180, 172, 178, 185, 190, 195, 192, 200, 210, 190]

mean = sum(players)/len(players). #calculate mean value
std_dev = 0 #define variable for standard deviation

for i in players: #calculate variance using a for-loop
variance = (sum((i-mean)**2 for i in players)/len(players))

std_dev = (math.sqrt(variance)) #calculate standard deviation
in_range = 0
for i in players: #count the number of players within the given range
if i >= mean + (std_dev/2) or i <= mean - (std_dev/2):
in_range += 1
else:
continue

print(in_range) #output number of players

Answer 7

This code is normal for pycharm, but not for sandbox in sololearn:

import numpy as np

players = np.array([180, 172, 178, 185, 190, 195, 192, 200, 210, 190])

s = []

mean = np.sum(players)/players.size
variance = np.sum((players - mean)**2)/players.size
std = np.sqrt(variance)

for i in players:
a = abs(i - mean)
if 0 < abs(a-std) < 2:
s.append(a)

print(len(s))

Answer 8

package main
import "fmt"

func main() {
team := map[string]float32{
"P1": 1.98,
"P2": 2.05,
"P3": 1.89,
"P4": 2.0,
"P5": 2.11}
var sum float32 = 0.0
for _, x := range team{
sum += x
}
fmt.Println(sum/5)

}