Example¶
Engarde really shines when you have a dataset that regularly receives updates. We’ll work with a data set of customer preferences on trains. This is a static dataset and isn’t being updated, but you could imagine that each month the Dutch authorities upload a new month’s worth of data.
We can start by making some very basic assertions, that the dataset is the correct shape, and that a few columns are the correct dtypes. Assertions are made as decorators to functions that return a DataFrame.
In [1]: import pandas as pd
In [2]: import engarde.decorators as ed
In [3]: pd.set_option('display.max_rows', 10)
In [4]: dtypes = dict(
...: price1=int,
...: price2=int,
...: time1=int,
...: time2=int,
...: change1=int,
...: change2=int,
...: comfort1=int,
...: comfort2=int
...: )
In [5]: @ed.is_shape((None, 11))
...: @ed.has_dtypes(items=dtypes)
...: def unload():
...: trains = pd.read_csv("data/trains.csv", index_col=0)
...: return trains
In [6]: unload()
Out[6]:
id choiceid choice price1 time1 change1 comfort1 price2 time2 \
1 1 1 choice1 2400 150 0 1 4000 150
2 1 2 choice1 2400 150 0 1 3200 130
3 1 3 choice1 2400 115 0 1 4000 115
4 1 4 choice2 4000 130 0 1 3200 150
5 1 5 choice2 2400 150 0 1 3200 150
.. .. ... ... ... ... ... ... ... ...
347 30 7 choice1 2100 135 1 1 2800 135
348 30 8 choice1 2100 125 1 1 3500 125
349 30 9 choice1 2100 150 0 0 2800 125
350 30 10 choice1 2800 125 0 1 2800 135
351 30 11 choice2 3500 125 1 0 2800 135
change2 comfort2
1 0 1
2 0 1
3 0 0
4 0 0
5 0 0
.. ... ...
347 1 0
348 1 0
349 0 1
350 1 0
351 1 0
[351 rows x 11 columns]
One very important part of the design of Engarde is that your code, the code actually doing the work, shouldn’t have to change. I don’t want a bunch of asserts cluttering up the logic of what’s happening. This is a perfect case for decorators.
The order of execution here is unload returns the DataFrame, trains.
Next, ed.has_dtypes asserts that trains has the correct dtypes, as specified with dtypes. Once that assert passes, has_dtypes passes trains along to the next check, and so on, until the original caller gets back trains.
Each row of this dataset contains a passengers preference over two routes. Each route has an associated cost, travel time, comfort level, and number of changes. Like any good economist, we’ll assume people are rational: their first choice is surely going to be better in at least one way than their second choice (faster, more comfortable, …). This is fundamental to our analysis later on, so we’ll explicitly state it in our code, and check it in our data.
In [7]: def rational(df):
...: """
...: Check that at least one criteria is better.
...: """
...: r = ((df.price1 < df.price2) | (df.time1 < df.time2) |
...: (df.change1 < df.change2) | (df.comfort1 > df.comfort2))
...: return r
...:
In [8]: @ed.is_shape((None, 11))
...: @ed.has_dtypes(items=dtypes)
...: @ed.verify_all(rational)
...: def unload():
...: trains = pd.read_csv("data/trains.csv", index_col=0)
...: return trains
...:
In [9]: df = unload()
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-9-b108f050ce4e> in <module>()
----> 1 df = unload()
/Users/tom.augspurger/sandbox/engarde/engarde/decorators.py in wrapper(*args, **kwargs)
22 @wraps(func)
23 def wrapper(*args, **kwargs):
---> 24 result = func(*args, **kwargs)
25 ck.is_shape(result, shape)
26 return result
/Users/tom.augspurger/sandbox/engarde/engarde/decorators.py in wrapper(*args, **kwargs)
115 @wraps(func)
116 def wrapper(*args, **kwargs):
--> 117 result = func(*args, **kwargs)
118 ck.has_dtypes(result, items)
119 return result
/Users/tom.augspurger/sandbox/engarde/engarde/decorators.py in wrapper(*operation_args, **operation_kwargs)
147 def wrapper(*operation_args, **operation_kwargs):
148 result = operation_func(*operation_args, **operation_kwargs)
--> 149 vfunc(result, func, *args, **kwargs)
150 return result
151 return wrapper
/Users/tom.augspurger/sandbox/engarde/engarde/generic.py in verify_all(df, check, *args, **kwargs)
40 result = check(df, *args, **kwargs)
41 try:
---> 42 assert np.all(result)
43 except AssertionError as e:
44 msg = "{} not true for all".format(check.__name__)
AssertionError: ('rational not true for all', id choiceid choice price1 time1 change1 comfort1 price2 time2 \
13 2 3 choice2 2450 121 0 0 2450 93
18 2 8 choice2 2975 108 0 0 2450 108
27 3 6 choice2 1920 106 0 0 1440 96
28 3 7 choice1 1920 106 0 0 1920 96
33 4 1 choice2 545 105 1 1 545 85
.. .. ... ... ... ... ... ... ... ...
306 27 9 choice1 3920 140 1 1 3920 125
319 28 8 choice2 2450 133 1 1 2450 108
325 28 14 choice2 2450 123 0 1 2450 108
328 28 17 choice2 2815 108 0 1 2450 108
330 29 2 choice2 2800 140 2 0 2800 120
change2 comfort2
13 0 1
18 0 1
27 0 1
28 0 1
33 1 1
.. ... ...
306 0 2
319 0 2
325 0 2
328 0 2
330 0 1
[42 rows x 11 columns])
So our check failed, apparently people aren’t rational…
Engarde has printed the name of the failed assertion and the rows that are False.
We’re simply reusing pandas printing machinery, so set pd.options.display.max_rows to display
more or fewer rows.
We’ll fix this problem by ignoring those people (why change your mind when you can change the data?).
In [16]: @ed.verify_all(rational)
....: def drop_silly_people(df):
....: r = df.query("price1 < price2 | time1 < time2 |"
....: "change1 < change2 | comfort1 > comfort2")
....: return r
....:
In [17]: @ed.is_shape((None, 11))
....: @ed.has_dtypes(items=dtypes)
....: def unload():
....: trains = pd.read_csv("data/trains.csv", index_col=0)
....: return trains
In [18]: df = unload().pipe(drop_silly_people)
In [19]: df.head()
Out[19]:
id choiceid choice price1 time1 change1 comfort1 price2 time2 \
1 1 1 choice1 2400 150 0 1 4000 150
2 1 2 choice1 2400 150 0 1 3200 130
3 1 3 choice1 2400 115 0 1 4000 115
4 1 4 choice2 4000 130 0 1 3200 150
5 1 5 choice2 2400 150 0 1 3200 150
change2 comfort2
1 0 1
2 0 1
3 0 0
4 0 0
5 0 0
All of our assertions have “passed” now, so we’re happy and our analysis can proceed.