In [1]:

    

import numpy as np
import os
from urllib.request import urlretrieve
import pandas as pd
URL = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'
def get_fremont_data(filename = 'Fremont.csv', url = URL, force_download=False):
    if force_download or not os.path.exists(filename):
        urlretrieve(URL,'Fremont.csv')
    data = pd.read_csv('Fremont.csv',index_col='Date', parse_dates=True)
    data.columns = ('West', 'East')
    data['Total'] = data['East'] + data['West']
    return data


    

In [2]:

    

data = get_fremont_data()
data.head()


    

    
Out[2]:






  

    

      

      
West
      
East
      
Total
    
    

      
Date
      

      

      

    
  
  

    

      
2012-10-03 00:00:00
      
4.0
      
9.0
      
13.0
    
    

      
2012-10-03 01:00:00
      
4.0
      
6.0
      
10.0
    
    

      
2012-10-03 02:00:00
      
1.0
      
1.0
      
2.0
    
    

      
2012-10-03 03:00:00
      
2.0
      
3.0
      
5.0
    
    

      
2012-10-03 04:00:00
      
6.0
      
1.0
      
7.0
    
  

In [3]:

    

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('dark_background')
data.resample('W').sum().plot()


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x25a0a61db00>






    

In [4]:

    

ax = data.resample('D').sum().rolling(365).sum().plot()
ax.set_ylim(0,None)


    

    
Out[4]:





(0, 1100000.0)






    

In [5]:

    

data.groupby(data.index.time).mean().plot() # Groupby the time for the last 4years


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x25a0a70ddd8>






    

In [6]:

    

pivoted = data.pivot_table('Total', index=data.index.time, columns=data.index.date)
pivoted.iloc[:5,:5]
pivoted.plot(legend = False,alpha=0.01);