import pandas as pd
import matplotlib.pyplot as plt # only needed for advanced plotting
import matplotlib.dates as dates
plt.style.use('ggplot')
%matplotlib inline
# 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();
# convert to date format
df_iv['Date'] = pd.to_datetime(df_iv['Date'])
df_dv['Date'] = pd.to_datetime(df_dv['Date'])
# rename columns
df_iv.columns = ['Date','num_increasing']
df_dv.columns = ['Date','num_decreasing']
# set Date to be the index
df_iv = df_iv.set_index('Date')
df_dv = df_dv.set_index('Date')
# 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');
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( 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>''')