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

# <div class="well" style="background-color:#008c9a; color:white;">	
#     <center><h1>What Percentage of Sales were Foreclosures in Morgan Hill CA?</h1></center>
# </div>

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# 	<center>			
#         <h2>Hello!</h2>
# 		<h2>I’m Mikaela Rojas!</h2><br>
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#     <center>
#     Thank you for choosing me as your South Bay Realtor! Let's take a <br>
#     a look at <strong>Morgan Hill CA</strong> Foreclosure rates.
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# In[1]:


import pandas as pd
import matplotlib.pyplot as plt # only needed for advanced plotting
import matplotlib.dates as dates
plt.style.use('ggplot')
get_ipython().run_line_magic('matplotlib', 'inline')


# In[8]:


# grab Zillow data
# Morgan Hill,CA,San Jose,Santa Clara|01424
# FR = Percentage of Sales that were Foreclosures
df = pd.read_csv('http://www.quandl.com/api/v3/datasets/ZILL/C01424_FR.csv')
df.head();


# In[3]:


# convert to date format
df['Date'] = pd.to_datetime(df['Date'])


# In[4]:


# rename columns
df.columns = ['Date','Foreclosure_pct']


# In[5]:


# set Date to be the index
df = df.set_index('Date')


# ## Foreclosures for 2015 and 2016 seem to be total opposites. What kind of trend will we have for 2017?

# In[6]:


# Advanced Plotting
####################
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(13, 5))
fig.subplots_adjust(hspace=1.0) ## Create space between plots

# Chart 1
mask1 = df.index.year == 2015
df[mask1].sort_index().plot.bar(ax=axes)

# Chart 2
mask1 = df.index.year == 2016
df[mask1].sort_index().plot.bar(ax=axes, alpha=0.4, color='w')

# add a little sugar
axes.set_title('Forclosure Percentages: 2015 vs 2016')
axes.set_ylabel('Forclosure Percentages')
axes.legend(["2015","2016"], loc='best');


# <center><h1><strong>Morgan Hill CA Realtor:</strong> <a href="http://www.rojasrealty.us" target="_blank">ROJAS REALTY</a></h1>    </center>
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# <br>
# <center><p class="label label-default">the information came from quandl and Zillow</p></center>

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# In[7]:


from IPython.display import HTML

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