Python-for-Beginners/MeanAndVariance.py at master · Harry830/Python-for-Beginners · GitHub

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importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""

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importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""

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importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""

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importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""

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importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""

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importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""

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importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""

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19
20
21
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23
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25
26
27
28
29
importnumpyasnp

lst= []
n=int(input("Enter the value on N: "))

foriinrange(0, n):
ele=int(input())
lst.append(ele)
x=np.array(lst)
print("\nOriginal array: ")
print(x)
r1=np.mean(x)
r2=np.average(x)
assertnp.allclose(r1, r2)
print("\nMean: ", r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x)) **2 ))
assertnp.allclose(r1, r2)
print("\nstd: ", 1)
r1=np.var(x)
r2=np.mean((x-np.mean(x)) **2 )
assertnp.allclose(r1, r2)
print("\nvariance: ", r1)

"""
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data
The sample mean is simply the average of all the measurements in the sample.
"""