def test_data_schema():
crime_df = pd.read_csv(os.path.join(RESOURCES, 'sacramento_crime.csv'))
realestate_df = pd.read_csv(
os.path.join(RESOURCES, 'sacramento_realestate.csv'))
tables = jupytab.Tables()
tables['sacramento_crime'] = \
jupytab.DataFrameTable('Sacramento Crime', dataframe=crime_df)
tables['sacramento_realestate'] = \
jupytab.DataFrameTable('Sacramento RealEstate', dataframe=realestate_df)
schema = tables.schema()
assert schema[0]['id'] == 'sacramento_crime'
assert schema[0]['alias'] == 'Sacramento Crime'
columns = schema[0]['columns']
assert len(columns) == 9
raw_output = '[{"id": "sacramento_crime", "alias": "Sacramento Crime", "columns": [{"id": "cdat\
etime", "dataType": "string"}, {"id": "address", "dataType": "string"}, {"id": "district", "dataTyp\
e": "int"}, {"id": "beat", "dataType": "string"}, {"id": "grid", "dataType": "int"}, {"id": "crimed\
escr", "dataType": "string"}, {"id": "ucr_ncic_code", "dataType": "int"}, {"id": "latitude", "dataT\
ype": "float"}, {"id": "longitude", "dataType": "float"}]}, {"id": "sacramento_realestate", "alias"\
: "Sacramento RealEstate", "columns": [{"id": "street", "dataType": "string"}, {"id": "city", "data\
Type": "string"}, {"id": "zip", "dataType": "int"}, {"id": "state", "dataType": "string"}, {"id": "\
beds", "dataType": "int"}, {"id": "baths", "dataType": "int"}, {"id": "sq__ft", "dataType": "int"},\
{"id": "type", "dataType": "string"}, {"id": "sale_date", "dataType": "string"}, {"id": "price", "\
dataType": "int"}, {"id": "latitude", "dataType": "float"}, {"id": "longitude", "dataType": "float"\
}]}]'
assert raw_output == tables.render_schema(do_print=False)
def test_data_schema():
arrays = [['A', 'A', 'a', 'a', 0, 0, 'a$_!#àz', 'a$_!#àz'],
[
'A', 'A', 0, 1, 'z$_"_àéça"', 'z_èà[|]a',
'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789',
'abcdefghijklmnopqrstuvwxyz0123456789'
]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
complex_df = pd.DataFrame(np.random.randn(len(index), len(index)),
index=index,
columns=index)
tables = jupytab.Tables()
tables['complex_df_no_index_{}[]#!'] = \
jupytab.DataFrameTable('A multi-index Dataframe ({}[]#!)',
dataframe=complex_df)
tables['complex_df_with_index_{}[]#!'] = \
jupytab.DataFrameTable('A multi-index Dataframe ({}[]#!)',
dataframe=complex_df,
include_index=True)
schema = tables.schema()
assert schema[0]['id'] == 'complex_df_no_index_{}[]#!'
assert schema[0]['alias'] == 'A multi-index Dataframe ({}[]#!)'
assert schema[1]['id'] == 'complex_df_with_index_{}[]#!'
assert schema[1]['alias'] == 'A multi-index Dataframe ({}[]#!)'
raw_output = '[{"id": "complex_df_no_index_{}[]#!", "alias": "A multi-index Dataframe ({}[]#!' \
')", "columns": [{"id": "A_A_1", "dataType": "float"}, {"id": "A_A_2", "dataType' \
'": "float"}, {"id": "a_0", "dataType": "float"}, {"id": "a_1", "dataType": "flo' \
'at"}, {"id": "0_z____aeca_", "dataType": "float"}, {"id": "0_z_ea_a", "dataType' \
'": "float"}, {"id": "a___az_ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789", "dataType": ' \
'"float"}, {"id": "a___az_abcdefghijklmnopqrstuvwxyz0123456789", "dataType": "fl' \
'oat"}]}, {"id": "complex_df_with_index_{}[]#!", "alias": "A multi-index Datafra' \
'me ({}[]#!)", "columns": [{"id": "first_", "dataType": "string"}, {"id": "secon' \
'd_", "dataType": "string"}, {"id": "A_A_1", "dataType": "float"}, {"id": "A_A_2' \
'", "dataType": "float"}, {"id": "a_0", "dataType": "float"}, {"id": "a_1", "dat' \
'aType": "float"}, {"id": "0_z____aeca_", "dataType": "float"}, {"id": "0_z_ea_a' \
'", "dataType": "float"}, {"id": "a___az_ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789", ' \
'"dataType": "float"}, {"id": "a___az_abcdefghijklmnopqrstuvwxyz0123456789", "da' \
'taType": "float"}]}]'
raw_schema = tables.render_schema(do_print=False)
assert raw_output == raw_schema
def test_large_data_content():
row_count = 1000000
col_count = 10
np.random.seed(0)
large_df = pd.DataFrame(np.random.randn(row_count, col_count))
tables = jupytab.Tables()
tables['large_df'] = \
jupytab.DataFrameTable('A very large Dataframe',
dataframe=large_df)
request = json.dumps({
'args': {
'table_name': ['large_df'],
'format': ['json'],
'from': [5100],
'to': [5102]
}
})
start = timer()
raw_data = tables.render_data(request, do_print=False)
end = timer()
print(
f"Elapsed time in second to retrieve one row in a large dataframe : {(end - start)} s"
)
assert (end - start) < 0.1
print(raw_data)
assert raw_data == '[{"0":0.2307805099,"1":0.7823326556,"2":0.9507107694,"3":1.4595805778,' \
'"4":0.6798091111,"5":-0.8676077457,"6":0.3908489554,"7":1.0838125793,' \
'"8":0.6227587338,"9":0.0919146565},{"0":0.6267312321,"1":0.7369835911,' \
'"2":-0.4665488934,"3":1.5379716957,"4":-1.0313145219,"5":1.0398963231,' \
'"6":0.8687854819,"7":0.2055855947,"8":-1.7716643336,"9":0.2428264886}]'