Generate a Pandas Dataframe with python hypothesis library where one row is dependant on another-CodePudding

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

CodePudding user response：

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 bases, but they're actually independent - and the shared() strategy shares a single value between every row in the column.