In [1]:

    

import numpy as np
import pandas as pd

pd.set_option('display.max_columns', 60)


    

In [2]:

    

df_status = pd.read_csv('DATA/babs_master/status_master.csv')


    

In [3]:

    

df_status.drop('docks_available', axis=1, inplace=True)


    

In [4]:

    

df_status.head()


    

    
Out[4]:






  

    

      

      
station_id
      
bikes_available
      
time
    
  
  

    

      
0
      
2
      
2
      
2013/08/29 12:06:01
    
    

      
1
      
2
      
2
      
2013/08/29 12:07:01
    
    

      
2
      
2
      
2
      
2013/08/29 12:08:01
    
    

      
3
      
2
      
2
      
2013/08/29 12:09:01
    
    

      
4
      
2
      
2
      
2013/08/29 12:10:01
    
  

In [5]:

    

df_status.shape


    

    
Out[5]:





(71984434, 3)

In [6]:

    

df_status['time'] = pd.to_datetime(df_status.time)


    

In [7]:

    

df_status.set_index('time', drop=True, inplace=True)


    

In [8]:

    

df_status_hourly = df_status.groupby('station_id').resample('60T', how='mean')


    

In [9]:

    

df_status_hourly['bikes_available'] = np.round(df_status_hourly['bikes_available'], 0)


    

In [10]:

    

df_status_hourly.reset_index(inplace=True)


    

In [11]:

    

df_status_hourly.head()


    

    
Out[11]:






  

    

      

      
station_id
      
time
      
bikes_available
    
  
  

    

      
0
      
2
      
2013-08-29 12:00:00
      
2
    
    

      
1
      
2
      
2013-08-29 13:00:00
      
3
    
    

      
2
      
2
      
2013-08-29 14:00:00
      
2
    
    

      
3
      
2
      
2013-08-29 15:00:00
      
2
    
    

      
4
      
2
      
2013-08-29 16:00:00
      
2
    
  

In [12]:

    

df_status_hourly.shape


    

    
Out[12]:





(1208635, 3)

In [13]:

    

df_status_hourly.dropna(inplace=True)


    

In [14]:

    

df_status_hourly.shape


    

    
Out[14]:





(1204800, 3)

In [15]:

    

df_status_hourly.to_csv('DATA/babs_master/status_master_60m.csv', index=False)


    

In [ ]: