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

# ![Pandas Tutorial | Hedaro >](https://www.dropbox.com/s/220ncn0o5danuey/pandas-ipython-tutorials-hedaro.jpg?dl=1)
# # Lesson 7

# ### Outliers

# In[1]:


import pandas as pd
import sys


# In[2]:


print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)


# In[3]:


# Create a dataframe with dates as your index
States = ['NY', 'NY', 'NY', 'NY', 'FL', 'FL', 'GA', 'GA', 'FL', 'FL'] 
data = [1.0, 2, 3, 4, 5, 6, 7, 8, 9, 10]
idx = pd.date_range('1/1/2012', periods=10, freq='MS')
df1 = pd.DataFrame(data, index=idx, columns=['Revenue'])
df1['State'] = States

# Create a second dataframe
data2 = [10.0, 10.0, 9, 9, 8, 8, 7, 7, 6, 6]
idx2 = pd.date_range('1/1/2013', periods=10, freq='MS')
df2 = pd.DataFrame(data2, index=idx2, columns=['Revenue'])
df2['State'] = States


# In[4]:


# Combine dataframes
df = pd.concat([df1,df2])
df


# # Ways to Calculate Outliers    
# 
# Note: Average and Standard Deviation are only valid for gaussian distributions.

# In[5]:


# Method 1

# make a copy of original df
newdf = df.copy()

newdf['x-Mean'] = abs(newdf['Revenue'] - newdf['Revenue'].mean())
newdf['1.96*std'] = 1.96*newdf['Revenue'].std()  
newdf['Outlier'] = abs(newdf['Revenue'] - newdf['Revenue'].mean()) > 1.96*newdf['Revenue'].std()
newdf


# In[6]:


# Method 2
# Group by item

# make a copy of original df
newdf = df.copy()

State = newdf.groupby('State')

newdf['Outlier'] = State.transform( lambda x: abs(x-x.mean()) > 1.96*x.std() )
newdf['x-Mean'] = State.transform( lambda x: abs(x-x.mean()) )
newdf['1.96*std'] = State.transform( lambda x: 1.96*x.std() )
newdf


# In[7]:


# Method 2
# Group by multiple items

# make a copy of original df
newdf = df.copy()

StateMonth = newdf.groupby(['State', lambda x: x.month])

newdf['Outlier'] = StateMonth.transform( lambda x: abs(x-x.mean()) > 1.96*x.std() )
newdf['x-Mean'] = StateMonth.transform( lambda x: abs(x-x.mean()) )
newdf['1.96*std'] = StateMonth.transform( lambda x: 1.96*x.std() )
newdf


# In[8]:


# Method 3
# Group by item

# make a copy of original df
newdf = df.copy()

State = newdf.groupby('State')

def s(group):
    group['x-Mean'] = abs(group['Revenue'] - group['Revenue'].mean())
    group['1.96*std'] = 1.96*group['Revenue'].std()  
    group['Outlier'] = abs(group['Revenue'] - group['Revenue'].mean()) > 1.96*group['Revenue'].std()
    return group

Newdf2 = State.apply(s)
Newdf2


# In[9]:


# Method 3
# Group by multiple items

# make a copy of original df
newdf = df.copy()

StateMonth = newdf.groupby(['State', lambda x: x.month])

def s(group):
    group['x-Mean'] = abs(group['Revenue'] - group['Revenue'].mean())
    group['1.96*std'] = 1.96*group['Revenue'].std()  
    group['Outlier'] = abs(group['Revenue'] - group['Revenue'].mean()) > 1.96*group['Revenue'].std()
    return group

Newdf2 = StateMonth.apply(s)
Newdf2


# Assumign a non gaussian distribution (if you plot it, it will not look like a normal distribution)

# In[10]:


# make a copy of original df
newdf = df.copy()

State = newdf.groupby('State')

newdf['Lower'] = State['Revenue'].transform( lambda x: x.quantile(q=.25) - (1.5*(x.quantile(q=.75)-x.quantile(q=.25))) )
newdf['Upper'] = State['Revenue'].transform( lambda x: x.quantile(q=.75) + (1.5*(x.quantile(q=.75)-x.quantile(q=.25))) )
newdf['Outlier'] = (newdf['Revenue'] < newdf['Lower']) | (newdf['Revenue'] > newdf['Upper']) 
newdf


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