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

# In[178]:


import glob
import pandas as pd
import os


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allFiles = glob.glob(os.getcwd() + "/*.csv")


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df_list = []
for files in allFiles:
    filename = os.path.split(files)[-1].split('.csv')[0]
    df = pd.read_csv(files, names=["date","station", "impressions"], encoding="utf-8-sig")
    df['new_index'] = df[['date', 'station']].apply(lambda x: '_'.join(map(str, x)), axis=1)
    df = df[['new_index','impressions']]
    df.set_index('new_index', inplace=True)
    df.rename(columns={'impressions' : filename}, inplace=True)
    df_list.append(df)


# In[182]:


df = pd.concat([frame for frame in df_list], axis=1)
df = df.reset_index()
df['date'] = df['new_index'].apply(lambda x: x.split('_')[0])
df['date'] = pd.to_datetime(df['date'], format="%Y-%m-%d")
df['station'] = df['new_index'].apply(lambda x: x.split('_')[1])
del df['new_index']
df.sort_values(by=['station', 'date'], inplace=True)


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heat_df = pd.DataFrame(index=df.station.unique(), columns=list(df.columns[:12]))


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from datetime import datetime
for station in df.station.unique():
    for i in xrange(12):
        pre = df[(df.station == station) & 
                 (df.date >= datetime(year=2014, day=2, month=12)) &
                 (df.date <= datetime(year=2015, day=22, month=3))]
        post = df[(df.station == station) & 
                 (df.date >= datetime(year=2015, day=1, month=12)) &
                 (df.date <= datetime(year=2016, day=20, month=3))]
        pre['delta'] = pre['date'].apply(lambda x: x - datetime(year=2014, day=2, month=12))
        post['delta'] = post['date'].apply(lambda x: x - datetime(year=2015, day=1, month=12))
        post_join = post.set_index('delta')
        post_join = post_join[[i]]
        pre_join = pre.set_index('delta')
        pre_join = pre_join[[i]]
        calc = pd.concat([pre_join, post_join], axis=1)
        calc['t'] =((calc[[1]] - calc[[0]])/((calc[[0]])))*100
        heat_df.set_value(station, df.columns[i], calc.t.mean())


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import numpy as np
import seaborn as sns
cm = sns.light_palette("green", as_cmap=True)
heat_df['aud'] = 0
df['total_adults'] = df.iloc[:,0]  + df.iloc[:,1]
for j in heat_df.index:
    heat_df.set_value(j, 'aud', df[df.station == j]['total_adults'].mean())
with pd.option_context('display.precision', 4):
    html = heat_df.dropna()[[12,2,4,6,8,10,0,3,5,7,9,11,1]].sort_values(by='aud', ascending=False).style.background_gradient(cmap=cm)


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html