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

# <div class="well" style="background-color:#008c9a; color:white;">	
#     <center><h1>Are Hollister CA Homes Increasing in Value or Decreasing in Value?</h1></center>
# </div>

# <!-- ---------------------------------------------------- -->
#   <div class="col-sm-4 col-md-4 col-lg-4">
# 	<!-- logo -->  
#     <div class="img-responsive">
#       <img src="https://www.dropbox.com/s/rw7t8rlyy812mba/mikaela-rojas-realtor.jpg?dl=1" title="Mikaela Rojas | Rojas Realty" alt="Mikaela Rojas | Rojas Realty">    
#     </div>
# 	<!-- logo -->	
#   </div>
# <!-- ---------------------------------------------------- --> 
#   <div class="col-sm-8 col-md-8 col-lg-8">
# 	<!-- Morgan Hill CA -->  
# 	<center>			
#         <h2>Hello!</h2>
# 		<h2>I’m Mikaela Rojas!</h2><br>
#     </center>
#     <center>
#     Thank you for choosing me as your South Bay Realtor! Let's take a <br>
#     a look at <strong>Hollister CA</strong> home values.
# 	</center>	
#     <!-- Morgan Hill CA -->	
#   </div>
# <!-- ---------------------------------------------------- -->

# <div style="color:black; border: 3px solid #008c9a;"></div>

# 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[2]:


# grab Zillow data
# Hollister,CA,San Jose,San Benito|00800
# FR = Percentage of Sales that were Foreclosures
df_iv = pd.read_csv('http://www.quandl.com/api/v3/datasets/ZILL/C00800_IV.csv')
df_dv = pd.read_csv('http://www.quandl.com/api/v3/datasets/ZILL/C00800_DV.csv')
df_dv.head();


# In[3]:


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


# In[4]:


# rename columns
df_iv.columns = ['Date','num_increasing']
df_dv.columns = ['Date','num_decreasing']


# In[5]:


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


# ## Good News for Buyers! Homes in general have been decreasing in value since 2016. 

# 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_iv.index.year >= 2016
df_iv[mask1].sort_index().plot.line(ax=axes)

# Chart 2
mask1 = df_dv.index.year >= 2016
df_dv[mask1].sort_index().plot.line(ax=axes, alpha=0.4, color='b')

# add a little sugar
axes.set_title('Number of Home Increasing or Decreasing in Value')
axes.set_ylabel('# of homes')
axes.legend(["# increasing","# decreasing"], loc='best');


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

# <div style="color:black; border: 3px solid #008c9a;"></div>

# In[7]:


from IPython.display import HTML

HTML('''<script>
code_show=true; 
function code_toggle() {
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 } else {
 $('div.input').show();
 }
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} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" class="btn btn-info btn-xs" value="Click here to toggle on/off the raw code"></form>''')