Data

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%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

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# Import libraries
from __future__ import absolute_import, division, print_function

# Ignore warnings
import warnings
warnings.filterwarnings('ignore')


import numpy as np
import pandas as pd
import math

# Graphing Libraries
import matplotlib.pyplot as pyplt
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 6

import seaborn as sns
sns.set_style("white") 


from IPython.display import display


    

In [3]:

    

def load_citibike():
    data_mine = pd.read_csv('data/nyc_citibike.csv')
    data_mine['one'] = 1
    data_mine['starttime'] = pd.to_datetime(data_mine.starttime)
    data_starttime = data_mine.set_index("starttime")
    data_resampled = data_starttime.resample("3h").sum().fillna(0)
    return data_resampled.one


    

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citibike = load_citibike()


    

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citibike.head()


    

    
Out[5]:





starttime
2013-07-01 00:00:00     409
2013-07-01 03:00:00     206
2013-07-01 06:00:00    3055
2013-07-01 09:00:00    1395
2013-07-01 12:00:00    1408
Freq: 3H, Name: one, dtype: int64

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len(citibike.index)


    

    
Out[6]:





248

In [7]:

    

plt.figure(figsize=(10, 3))
x_values = pd.date_range(start=citibike.index.min(), end=citibike.index.max(), freq='D')
plt.plot(citibike, linewidth=1)
plt.xticks(x_values, x_values.strftime("%a %m-%d"), rotation='vertical');
plt.xlabel("Date")
plt.ylabel("Rentals");


    

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y = citibike.values


    

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X = citibike.index.astype('int64')// 10 ** 9


    

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X


    

    
Out[15]:





Int64Index([1372636800, 1372647600, 1372658400, 1372669200, 1372680000,
            1372690800, 1372701600, 1372712400, 1372723200, 1372734000,
            ...
            1375207200, 1375218000, 1375228800, 1375239600, 1375250400,
            1375261200, 1375272000, 1375282800, 1375293600, 1375304400],
           dtype='int64', name=u'starttime', length=248)

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