In [15]:

    

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, filename)
    data = pd.read_csv('Fremont.csv', index_col='Date', parse_dates=True)   
    data.columns = ['West','East']
    data['Total'] = data['West'] + data['East']
    return data


    

In [16]:

    

data = get_fremont_data()


    

In [17]:

    

data.head()


    

    
Out[17]:






  

    

      

      
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 [18]:

    

%matplotlib inline
data.plot()
data.resample('W').sum().plot()


    

    
Out[18]:





<matplotlib.axes._subplots.AxesSubplot at 0x1548e9ffdd8>






    













    

In [19]:

    

import seaborn as sns
sns.set()
data.resample('W').sum().plot()


    

    
Out[19]:





<matplotlib.axes._subplots.AxesSubplot at 0x1548f84db00>






    

In [20]:

    

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


    

    




C:\Users\HRIDAY\Anaconda3\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: 
.resample() is now a deferred operation
You called rolling(...) on this deferred object which materialized it into a dataframe
by implicitly taking the mean.  Use .resample(...).mean() instead
  if __name__ == '__main__':






    

In [21]:

    

data.groupby(data.index.time).mean().plot()


    

    
Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x1548f4ce320>






    

In [22]:

    

pivoted = data.pivot_table('Total',index=data.index.time,columns=data.index.date)
pivoted.iloc[:5, :5]


    

    
Out[22]:






  

    

      

      
2012-10-03
      
2012-10-04
      
2012-10-05
      
2012-10-06
      
2012-10-07
    
  
  

    

      
00:00:00
      
13.0
      
18.0
      
11.0
      
15.0
      
11.0
    
    

      
01:00:00
      
10.0
      
3.0
      
8.0
      
15.0
      
17.0
    
    

      
02:00:00
      
2.0
      
9.0
      
7.0
      
9.0
      
3.0
    
    

      
03:00:00
      
5.0
      
3.0
      
4.0
      
3.0
      
6.0
    
    

      
04:00:00
      
7.0
      
8.0
      
9.0
      
5.0
      
3.0
    
  

In [23]:

    

pivoted.plot(legend=False,alpha=0.01)


    

    
Out[23]:





<matplotlib.axes._subplots.AxesSubplot at 0x1548e9612b0>