Estimating standard deviation from DataSet

[jomardee]

SO I am having a hard time trying to calculate the standard deviation given the graph. I was wondering what the steps were? Here is a code of what I have so far, but is not getting the right output. the DataSet contains thousands of random numbers such as:
9.8254457980e-1 1.0293906530e+0 8.6314178340e-1 8.7754757930e-1 8.2216021950e-1 9.8155318390e-1 1.0215753050e+0 1.0064994180e+0 1.0300426240e+0 8.7195144970e-1 9.4140464140e-1 1.0811751280e+0 8.5982980390e-1



#Worksheet 1.3A - make plot of DataSet vs row Nums
from numpy import *
from matplotlib.pyplot import *
import matplotlib.pyplot as plt

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# Import data as a list of numbers
with open("DataSet1.dat", "r") as textFile:
data = textFile.read().split() # split based on spaces
data = [float(point) for point in data] # convert strings to floats
rms = 0
#This is my code for calculating the square of the deviation
sqrt_rms = square(rms)
variance = average([square(i) for i in textFile]) - average1**2
print("The square of the standard deviation is:", variance)
print("The standard deviation is:", sqrt(variance))
#^Would square root of variance calculate the standard deviation?

plt.xlabel('x axis')
plt.ylabel('y axis')
plt.title('Plot of DataSet vs Row Numbers')
plt.plot(data)
plt.show()