In [8]:

    

import pg8000
import pandas as pd
import matplotlib
import numpy
%matplotlib inline
conn = pg8000.connect(host="training.c1erymiua9dx.us-east-1.rds.amazonaws.com", port=5432, database="training", user="dot_student", password="qgis")


    

In [9]:

    

cursor = conn.cursor()


    

In [10]:

    

conn.rollback()


    

In [60]:

    

df = pd.read_sql("select unique_key,created_date,closed_date from noise_311", conn)


    

In [61]:

    

df.head()
df.columns=['unique_key','created_date','closed_date']


    

In [62]:

    

df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 37615 entries, 0 to 37614
Data columns (total 3 columns):
unique_key      37615 non-null int64
created_date    37615 non-null datetime64[ns]
closed_date     37615 non-null datetime64[ns]
dtypes: datetime64[ns](2), int64(1)
memory usage: 881.7 KB

In [63]:

    

df.head()


    

    
Out[63]:






  

    

      

      
unique_key
      
created_date
      
closed_date
    
  
  

    

      
0
      
28792167
      
2014-08-31 23:59:00
      
2014-09-01 03:52:00
    
    

      
1
      
28789088
      
2014-08-31 23:56:00
      
2014-09-01 06:17:00
    
    

      
2
      
28791854
      
2014-08-31 23:54:00
      
2014-09-01 01:29:00
    
    

      
3
      
28789936
      
2014-08-31 23:52:00
      
2014-09-01 02:53:00
    
    

      
4
      
28789931
      
2014-08-31 23:47:00
      
2014-09-01 01:06:00
    
  

In [64]:

    

df['opentime'] = df['closed_date']-df['created_date']


    

In [65]:

    

df.index=df['opentime']


    

In [58]:

    

df[]


    

    
Out[58]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x116e2a6a0>]], dtype=object)






    

In [ ]: