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

# ## II. Data Cleanup 
# 
# We find the data are "messy" i.e aren't cleanly prepared for import - for instance numeric columns might have some strings in them.  This is very common in raw data especially that obtained from web sites.
# 
# Let's take a look. we're going to look at the first five rows of some specific columns that show the data dirtiness issues.

# In[1]:


get_ipython().run_line_magic('pylab', 'inline')
# first we ingest the data from the source on the web
# this contains a reduced version of the data set from Lending Club
import pandas as pd
loansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')


# In[9]:


loansData['Interest.Rate'][0:5] # first five rows of Interest.Rate


# In[10]:


loansData['Loan.Length'][0:5] # first five rows of Loan.Length


# We see here that:
# 
# * the interest rate information has "%" symbols in it.
# * loan length has " months" in it
# 
# Other than that we can also see (exploration exercise):
# 
# * there are a couple of values that are so large they must be typos
# * some values are missing "NA" values i.e. not available.
# * the FICO Range is really a numeric entity but is represented as a categorical variable in the data.

# In[11]:


loansData['FICO.Range'][0:5] # first five rows of FICO.Range


# ##Exercise
# Actually perform each of the above steps on the dataset i.e.
# 
# * import the data
# * remove the '%' suffix from each row
# * remove the ' months' suffix from each row
# * remove the outlier rows
# * remove rows with NA
# 
# Save your code in a reusable manner - these are steps you'll be doing repeatedly.
# 

# In[3]:


import matplotlib.pyplot as plt
import pandas as pd
plt.figure()
loansmin = pd.read_csv('../datasets/loanf.csv')
fico = loansmin['FICO.Score']
p = fico.hist()


# ###Box Plot
# 
# Next we take a box plot which allows us to quickly look at the distribution of interest rates based on each FICO score range.   

# In[4]:


import matplotlib.pyplot as plt
import pandas as pd
plt.figure()
loansmin = pd.read_csv('../datasets/loanf.csv')

p = loansmin.boxplot('Interest.Rate','FICO.Score')
q = p.set_xticklabels(['640','','','','660','','','','680','','','','700',
  '720','','','','740','','','','760','','','','780','','','','800','','','','820','','','','840'])

q0 = p.set_xlabel('FICO Score')
q1 = p.set_ylabel('Interest Rate %')
q2 = p.set_title('                          ')


# We're going to look at a scatterplot matrix of the five variables in our data.

# <img src="files/images/important.png" />

# In[3]:


## TRY THIS!
import pandas as pd
loansmin = pd.read_csv('../datasets/loanf.csv')
a = pd.scatter_matrix(loansmin,alpha=0.05,figsize=(10,10), diagonal='hist')
## Click on the line above
## Change 'hist' to 'kde' then hit shift-enter, with the cursor still in this box
## The plot will redraw - it takes a while. While it is recomputing you will see a 
## message-box that says 'Kernel Busy' near the top right corner
## You can change the code and hit shift-enter to re-execute the code
## Try changing the (10,10) to (8,8) and (12,12)
## Try changing the alpha value from 0.05 to 0.5 
## How does this change in alpha change your ability to interpret the data?
## Feel free to try other variations. 
## If at any time you scramble the code and forget the syntax 
## a copy of the original code is below. Copy and paste it in place. 
## Remember to remove the hashmarks.
## a = pd.scatter_matrix(loansmin, alpha=0.05,figsize=(10,10), diagonal='hist)


# In[58]:


from IPython.core.display import HTML
def css_styling():
    styles = open("../styles/custom.css", "r").read()
    return HTML(styles)
css_styling()