def test_join_exact_matching(self):
fbi_data1 = pandas.DataFrame(
{
'index': ['california_sunnyvale', 'alabama_montgomery'],
'foo': [1, 2],
'State': ['CA', 'AL'],
'City': ['Sunnyvale', 'Montgomery'],
'Population': [100, 200],
},
index=[0, 1])
fbi_data2 = pandas.DataFrame(
{
'index': ['alabama_montgomery', 'california_sunnyvale'],
'bar': [3, 4],
'State': ['AL', 'CA'],
'City': ['Montgomery', 'Sunnyvale'],
'Population': [200, 100],
},
index=[0, 1])
fbi_table1 = fbi_data_table(data=fbi_data1, suffix='_table1')
fbi_table2 = fbi_data_table(data=fbi_data2, suffix='_table2')
# Note that the indices,
# index=['california_sunnyvale', 'alabama_montgomery']
# will be ignored.
expected_data = pandas.DataFrame({
'foo': [1, 2],
'bar': [4, 3],
'State_table1': ['CA', 'AL'],
'City_table1': ['Sunnyvale', 'Montgomery'],
'Population_table1': [100, 200],
'State_table2': ['CA', 'AL'],
'City_table2': ['Sunnyvale', 'Montgomery'],
'Population_table2': [100, 200],
'index': ['california_sunnyvale', 'alabama_montgomery']
}).sort_index(axis=1)
actual_data = fbi_table1.join_exact_matching(fbi_table2).data.sort_index(
axis=1)
# We sort the pandas DataFrame columns in order to compare
self.assertTrue(expected_data.equals(actual_data))
def test_init_from_data(self):
# Test initializing an `Fbi` DataTable from pandas dataframe.
df = pandas.DataFrame(
{
'foo': 1,
'State': 'CA',
'City': 'Sunnyvale',
'Population': 100,
'state': 'ignored',
'city': 'ignored'
},
index=[0])
fbi_table = fbi_data_table(data=df)
self.assertTrue(fbi_table.data.equals(df))
def test_get_fuzzy_matching_key(self):
df = pandas.DataFrame(
{
'foo': 1,
'state': 'CA',
'city': 'Sunnyvale',
'population': 100,
'State': 'ignored',
'City': 'ignored'
},
index=[0])
fbi_table = fbi_data_table(data=df)
self.assertEqual(
fbi_table.get_fuzzy_matching_key(df.iloc[0]),
FuzzyMatchingKey(state='CA', city='Sunnyvale', population=100))
def test_join_fuzzy_matching(self):
fbi_data1 = pandas.DataFrame(
{
'foo': [1, 2, 3],
'state': ['CA', 'AL', 'Hidden'],
'city': ['Sunnyvale', 'Montgomery', 'Lost City'],
'population': [100, 200, 300],
'index':
['california_sunnyvale', 'alabama_montgomery', 'atlantas_lost_city'],
},
index=['california_sunnyvale', 'alabama_montgomery', 'atlantis'])
census_data2 = pandas.DataFrame(
{
'bar': [5, 3, 4],
'state': ['Isle of Man', 'AL', 'CA'],
'city': ['Avalon', 'Montgomery', 'Sunnyvale'],
'Target Geo Id2': ['???', '1620000US0151000', '1620000US0677000'],
get_header('Population Estimate (as of July 1) - 2017', 'census_2017'):
[1, 200, 100],
},
index=[0, 1, 2])
fbi_table1 = fbi_data_table(data=fbi_data1, suffix='_fbi')
census_table2 = census_data_table(data=census_data2, suffix='_census')
joined_table = fbi_table1.join_fuzzy_matching(census_table2)
# The output table from matching should be the same class as the left table.
self.assertTrue(isinstance(joined_table, fbi_data_table))
actual_data = joined_table.data.sort_index(axis=1)
expected_data = pandas.DataFrame({
'city_fbi': ['Montgomery', 'Sunnyvale'],
'population': [200, 100],
get_header('Population Estimate (as of July 1) - 2017', 'census_2017'):
[200, 100],
'state_census': ['AL', 'CA'],
'Target Geo Id2': ['1620000US0151000', '1620000US0677000'],
'bar': [3, 4],
'foo': [2, 1],
'city_census': ['Montgomery', 'Sunnyvale'],
'index': ['alabama_montgomery', 'california_sunnyvale'],
'state_fbi': ['AL', 'CA'],
}).sort_index(axis=1)
self.assertTrue(expected_data.equals(actual_data))
def test_init_from_file(self):
fbi_table = fbi_data_table(
file_path=
'data/fbi/Table_8_Offenses_Known_to_Law_Enforcement_by_State_by_City_2017.xls'
)
self.assertEqual(len(fbi_table.data), 9589)