In [1]:

    

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')


    

In [2]:

    

from jupyterworkflow.data import get_fremont_data


    

In [3]:

    

data = get_fremont_data()

#!head Freemont.csv same as...
data.head()


    

    
Out[3]:






  

    

      

      
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 [4]:

    

data.resample('W').sum().plot()


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x10e882470>






    

In [5]:

    

# check for year trends by using rolling windows - sum in the previous 365 days
ax = data.resample('D').rolling(365).sum().plot()
ax.set_ylim(0, None)


    

    




/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:2: 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
  






    
Out[5]:





(0, 44110.330220685137)






    

In [6]:

    

# are there any time patterns?
data.groupby(data.index.time).mean().plot()


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x10e8accf8>






    

In [7]:

    

# see the same, but year-wise, using a pivot table
pivoted = data.pivot_table('Total', index=data.index.time, columns=data.index.date)
pivoted.iloc[:5, :5] #each column is a day, each row is a time


    

    
Out[7]:






  

    

      

      
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 [8]:

    

pivoted.plot(legend=False) #we now have a line for each day


    

    
Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x10df67d68>






    

In [9]:

    

pivoted.plot(legend=False, alpha=0.01) #better looking
#some days have a bimodal pattern, some have not -> weekdays vs weekends/holidays?


    

    
Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x110495ba8>






    

In [11]:

    

#this prints the docs  
get_fremont_data?


    

In [ ]:

    

#shift+tab will get params
get_fremont_data()