pandas DataFrame:
Constructor:
iterables = [[date(2018,5,31),date(2018,6,26),date(2018,6,29),date(2018,7,1)],
['test1','test2']]
indx = pd.MultiIndex.from_product(iterables, names=['date','tests'])
col = ['tests_passing', 'tests_total']
data = np.array([[834,3476],[229,256],[1524,1738],[78,144],[1595,1738],[78,144],[1595,1738],[142,144]])
df = pd.DataFrame(data, index=indx, columns=col)
df = df.assign(tests_remaining= df['tests_total'] - df['tests_passing'])
Dataframe:
tests_passing tests_total tests_remaining
date tests
2018-05-31 test1 834 3476 2642
test2 229 256 27
2018-06-26 test1 1524 1738 214
test2 78 144 66
2018-06-29 test1 1595 1738 143
test2 78 144 66
2018-07-01 test1 1595 1738 143
test2 142 144 2
This data consists of a number of test measurements (test1,test2,...,etc) each collected on some date.
I want to create a new column in this dataframe named 'progress' which would in general select all rows where test = unique test (test1 for example) across all dates and subtract the 'tests_remaining' column value for that row at date0 with the next value for row at date1,date2,...,etc so basically:
df.loc[(date0,test0),'progress'] = df.loc[(date0,test0),'tests_remaining']-df.loc[(date1,test0),'tests_remaining]
(with the one exception that the first date would have a progress value of 0 since it was the first collected date).
The desired output will look like this:
tests_passing tests_total tests_remaining progress
date tests
5/31/2018 test1 834 3476 2642 0
test2 229 256 27 0
6/26/2018 test1 1524 1738 214 2428
test2 78 144 66 -39
6/29/2018 test1 1595 1738 143 71
test2 78 144 66 0
7/1/2018 test1 1595 1738 143 0
test2 142 144 2 64
So far I have been able to use loc[] with slices to select a single test at a time and perform this calculation as a resultant pandas Series, but I am unable to do this in general across all tests without specifying the test name explicitly in the split. This is not a reasonable solution for me as in the real data there are hundreds of tests.
All = slice(None)
df_slice = df.loc[(All,'test1'),'tests_remaining']
sub = df_slice.diff(periods=-1).shift(1).fillna(0);sub
date tests
2018-05-31 test1 0.0
2018-06-26 test1 2428.0
2018-06-29 test1 71.0
2018-07-01 test1 0.0
Name: tests_remaining, dtype: float64
Is there a more pandas idiomatic way to create the desired column as described?
Thanks in advance for your help!
You can groupby
level test and do diff
df.groupby(level='tests').tests_remaining.diff().mul(-1)
Out[662]:
date tests
2018-05-31 test1 NaN
test2 NaN
2018-06-26 test1 2428.0
test2 -39.0
2018-06-29 test1 71.0
test2 -0.0
2018-07-01 test1 -0.0
test2 64.0
Name: tests_remaining, dtype: float64