#!/usr/bin/env python
# coding: utf-8

# # Data Spread
# 
# 1. Mean 
# 2. Stardard Deviation
# 3. Variance
# 
# ##### ---------------------------------------------

# In[1]:


from __future__ import division

import numpy as np
import math
import pandas as pd

print np.__version__
print pd.__version__


# ## Python Way

# In[2]:


def mean(x):
    return sum(x) / len(x)


# In[3]:


def variance(x):
    return (standard_deviation(x))**2


# In[4]:


def standard_deviation(lst):
    m = mean(lst)
    return math.sqrt(float(reduce(lambda x, y: x + y, map(lambda x: (x - m) ** 2, lst))) / len(lst))


# In[5]:


X = [1, 2, 3, 4, 5]
print 'Mean: {}'.format(mean(X))
print 'Variance: {}'.format(variance(X))
print 'Standard Deviation: {}'.format(standard_deviation(X))


# ## Numpy Way

# In[6]:


def mean(x):
    return np.mean(x)


# In[7]:


def variance(x):
    return np.var(x)


# In[8]:


def standard_deviation(x):
    return np.std(x)


# In[9]:


X = [1, 2, 3, 4, 5]
print 'Mean: {}'.format(mean(X))
print 'Variance: {}'.format(variance(X))
print 'Standard Deviation: {}'.format(standard_deviation(X))