Jupyter Data Science Workflow

From exploratory analysis to reproducible science

Jake VanderPlas

University of Washington eScience Institute



In [1]:

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



In [2]:

    
# pd.DataFrame? question mark can return documentation in Jupyter
# pd.DataFrame?? for source code

from jupyterworkflow.data import get_data



In [3]:

    
data = get_data()
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();
# ax.set_ylim(0, None);



In [5]:

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



In [6]:

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



In [7]:

    
pivoted.plot(legend=False, alpha=0.01); #Transparency

Unit Testing

Using PyTest to validate your functions are working.

Making a note to describe the purpose of Pytest. When packaging a function, you want to make sure it does what it set out to accomplish. Unit tests provide a validation, and are especially useful when testing out new code.

The interesting part is that it also provides a speed test of sorts to determine how efficient the function was for its purposes.

python -m pytest jupyterworkflow/



In [ ]:

	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

	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