I'm trying to use hypothesis to generate pandas dataframes where some column values are dependant on other column values. So far, I haven't been able to 'link' two columns.
This code snippet:
from hypothesis import strategies as st
from hypothesis.extra.pandas import data_frames , column, range_indexes
def create_dataframe():
id1 = st.integers().map(lambda x: x)
id2 = st.shared(id1).map(lambda x: x * 2)
df = data_frames(index = range_indexes(min_size=10, max_size=100), columns=[
column(name='id1', elements=id1, unique=True),
column(name='id2', elements=id2),
])
return df
Produces a dataframe with a static second column:
id1 program_id
0 1.170000e+02 110.0
1 3.600000e+01 110.0
2 2.876100e+04 110.0
3 -1.157600e+04 110.0
4 5.300000e+01 110.0
5 2.782100e+04 110.0
6 1.334500e+04 110.0
7 -3.100000e+01 110.0
I think that you're after the rows argument, which allows you to compute some column values from other columns. For example, if we wanted a full_price and a sale_price column where the sale price has some discount applied:
from hypothesis import strategies as st
from hypothesis.extra.pandas import data_frames, range_indexes
def create_dataframe():
full = st.floats(1, 1000) # all items cost $1 to $1,000
discounts = st.sampled_from([0, 0.1, 0.25, 0.5])
rows = st.tuples(full, discounts).map(
lambda xs: dict(price=xs[0], sale_price=xs[0] * (1-xs[1]))
)
return data_frames(
index = range_indexes(min_size=10, max_size=100),
rows = rows
)
price sale_price
0 757.264509 378.632254
1 824.384095 618.288071
2 401.187339 300.890504
3 723.193610 650.874249
4 777.171038 699.453934
5 274.321034 205.740776
So what went wrong with your example code? It looks like you imagined that the id1 and id2 strategies were defined relative to each other on a row-wise basis, but they're actually independent - and the shared() strategy shares a single value between every row in the column.