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test_filter.py
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test_filter.py
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# author: Elina (Ling) Lin
# date: 2021-03-05
from pyhousehunter import filter
import pandas as pd
from pytest import raises
toy_data = pd.read_csv("tests/cleaned_toy_data.csv", index_col=0)
empty_data = toy_data[toy_data["price"] == -1]
# Tests on input
def test_filter_price_range():
"""
Test to ensure min_price and max_price
non-negative and min_price <= max_price
"""
with raises(ValueError):
filter.data_filter(toy_data, -10, 1000, 500, 2, "Vancouver")
filter.data_filter(toy_data, 1000, -10, 500, 2, "Vancouver")
def test_filter_price_type():
"""
Test to ensure the max_price or min_price are both either int or float.
"""
with raises(TypeError):
filter.data_filter(toy_data, "hello", 2000, 500, 2, "Vancouver")
def test_filter_sqrt_ft_type():
"""
Test to confirm that sqrt_ft is of type integer.
"""
with raises(TypeError):
filter.data_filter(toy_data, 1000, 2000, "hellp", 2, "Vancouver")
def test_filter_num_bedroom_type():
with raises(TypeError):
filter.data_filter(toy_data, 1000, 2000, 500, 2.3, "Vancouver")
def test_filter_num_bedroom_range():
with raises(ValueError):
filter.data_filter(toy_data, 1000, 2000, 500, -3, "Vancouver")
def test_filter_city_name_type():
with raises(TypeError):
filter.data_filter(toy_data, 1000, 2000, 500, 2, 10)
# Test for output
def test_filter_output_city_case_insensitive():
assert filter.data_filter(toy_data, 1000, 2000, 600, 1, "burnaby").equals(
toy_data.iloc[[0], :]
)
def test_filter_output_empty_result():
print(empty_data)
print(
filter.data_filter(toy_data, 1000, 2000, 500, 1, "richmond").equals(empty_data)
)
assert filter.data_filter(toy_data, 1000, 2000, 500, 1, "richmond").equals(
empty_data
)
def test_filter_output_column_with_nan():
assert filter.data_filter(toy_data, 1000, 1500, 400, 2, "Surrey").equals(
toy_data.iloc[[1], :]
)