def test_all_values():
possible_values = ["val1", "val2", "val3"]
data = pd.Series(["val1", "val3", "val1", "val2"])
expected = pd.DataFrame([(1, 0, 0), (0, 0, 1), (1, 0, 0), (0, 1, 0)], columns=possible_values)
actual = get_dummies(data, possible_values)
assert_frame_equal(expected, actual)
def test_empty_input():
possible_values = ["val1", "val2", "val3"]
data = pd.Series([])
expected = pd.DataFrame([], columns=possible_values)
actual = get_dummies(data, possible_values)
assert_frame_equal(expected, actual)
def test_only_nan():
possible_values = ["val1", "val2", "val3"]
data = pd.Series([np.nan, np.nan, np.nan, np.nan])
expected = pd.DataFrame([(np.nan, np.nan, np.nan), (np.nan, np.nan, np.nan), (np.nan, np.nan, np.nan),
(np.nan, np.nan, np.nan)], columns=possible_values)
actual = get_dummies(data, possible_values)
assert_frame_equal(expected, actual)
def make_house_type_features(db_connection):
"""
Get information whether a house is a single-family, two-family, three-family, multi-family home or mixed
used (residential + commercial)
Input:
db_connection: connection to postgres database. "set schema ..." must have been called on this connection
to select the correct schema from which to load inspections
Output:
A pandas dataframe, with one row per parcels and one column per feature.
"""
query = ("SELECT inspections.parcel_id, parcels.class "
"FROM parcels_inspections AS inspections "
"JOIN shape_files.parcels_cincy AS parcels "
"ON parcels.parcelid = inspections.parcel_id")
df = pd.read_sql(query, con=db_connection)
df = df.set_index("parcel_id")
# map use code to type of home
use_codes = {423: "mixed-used",
510: "single-family",
520: "two-family",
530: "three-family",
550: "multi-family",
554: "multi-family",
552: "multi-family",
599: "multi-family"}
df["type"] = df["class"].apply(lambda cl: use_codes.get(cl, np.nan))
df = util.get_dummies(df["type"], possible_values=["single-family", "two-family",
"three-family", "multi-family", "mixed-use"])
df = df.fillna(0)
return df
def test_illegal_value():
possible_values = ["val1", "val2", "val3"]
data = pd.Series(["val1", "val3", "val1", "val4"])
get_dummies(data, possible_values)
