pandas - lesson 07

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• Lesson 07

#!/usr/bin/env python
# coding: utf-8
 
# Lesson 7
 
# Outliers
 
import pandas as pd
import sys
 
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
 
# 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
 
# Combine dataframes
df = pd.concat([df1,df2])
df
 
# Ways to Calculate Outliers    
# Note: Average and Standard Deviation are only valid for gaussian distributions.
 
# 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
 
# 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
 
# 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
 
# 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
 
# 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)
 
# 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