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%pylab inline
import matplotlib.pyplot as plt
from workflow.data import *
from workflow.features import *
import pandas as pd
import seaborn as sns

from sqlalchemy import create_engine
import psycopg2

plt.tight_layout
plt.rcParams.update({'font.size': 22})
rc('xtick', labelsize=15) 
rc('ytick', labelsize=15) 
figure(figsize(10,7))


    

    




Populating the interactive namespace from numpy and matplotlib






    
Out[1]:





<matplotlib.figure.Figure at 0x7f5f4390ae90>






    






<matplotlib.figure.Figure at 0x7f5f4390ae90>

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# connect to SQL database

username = 'psam071'
host = 'localhost'
dbname = 'citibike'

db = create_engine('postgres://%s%s/%s' % (username,host,dbname))
con = None

con = psycopg2.connect(database = dbname, user = username, host = host)


    

In [4]:

    

# plot all counts
query_bulk = """
    SELECT * from features;
"""

bulk_df = pd.read_sql_query(query_bulk, con)
grps = bulk_df.groupby('date').sum()[['bikes_out', 'rbikes_out']]

grps['frac'] = grps.rbikes_out / grps.bikes_out
grps['frac'].plot()


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f5ea3f5f390>






    

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