Pandas combining rows based on dates

Fundamentally, I think this is a graph connectivity problem: a fast way of solving it will be some manner of graph connectivity algorithm. Pandas doesn’t include such tools, but scipy does. You can use the compressed sparse graph (csgraph) submodule in scipy to solve your problem like this:

from scipy.sparse.csgraph import connected_components

# convert to datetime, so min() and max() work
df.startDate = pd.to_datetime(df.startDate)
df.endDate = pd.to_datetime(df.endDate)

def reductionFunction(data):
    # create a 2D graph of connectivity between date ranges
    start = data.startDate.values
    end = data.endDate.values
    graph = (start <= end[:, None]) & (end >= start[:, None])

    # find connected components in this graph
    n_components, indices = connected_components(graph)

    # group the results by these connected components
    return data.groupby(indices).aggregate({'startDate': 'min',
                                            'endDate': 'max',
                                            'shipNo': 'first'})

df.groupby(['Cust']).apply(reductionFunction).reset_index('Cust')

If you want to do something different with shipNo from here, it should be pretty straightforward.

Note that the connected_components() function above is not brute force, but uses a fast algorithm to find the connections.