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test_file_data_source.py
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test_file_data_source.py
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import pytest
from dfsql.data_sources import DataSource
from dfsql.engine import pd
import numpy as np
import os
import json
from dfsql.exceptions import QueryExecutionException
from dfsql.functions import AggregateFunction
from dfsql.table import Table
@pytest.fixture()
def data_source_googleplay(googleplay_csv, tmpdir):
ds = DataSource(metadata_dir=str(tmpdir))
ds.add_table_from_file(googleplay_csv)
return ds
class TestDataSource:
def test_created_from_dir(self, csv_file):
dir_path = csv_file.dirpath()
ds = DataSource.from_dir(metadata_dir=dir_path, files_dir_path=dir_path)
assert ds.tables and len(ds.tables) == 1
table = ds.tables['titanic']
assert table.name == csv_file.purebasename
assert pd.read_csv(csv_file).shape == table.dataframe.shape
def test_add_from_file(self, csv_file):
ds = DataSource(metadata_dir=csv_file.dirpath())
assert not ds.tables and len(ds.tables) == 0
ds.add_table_from_file(str(csv_file))
table = ds.tables['titanic']
assert table.name == csv_file.purebasename
assert pd.read_csv(csv_file).shape == table.dataframe.shape
def test_save_metadata(self, csv_file):
assert not [f for f in os.listdir(csv_file.dirpath()) if f.endswith('.json')]
ds = DataSource(metadata_dir=csv_file.dirpath())
assert 'datasource_tables.json' in [f for f in os.listdir(csv_file.dirpath()) if f.endswith('.json')]
json_data = json.load(open(os.path.join(csv_file.dirpath(), 'datasource_tables.json')))
assert json_data == {}
ds.add_table_from_file(csv_file)
assert ds.tables['titanic']
json_data = json.load(open(os.path.join(csv_file.dirpath(), 'datasource_tables.json')))
assert json_data.get('titanic') and list(json_data.keys()) == ['titanic']
assert json_data['titanic']['type'] == 'FileTable'
assert json_data['titanic']['name'] == 'titanic'
assert json_data['titanic']['fpath'] == str(csv_file)
with pytest.raises(QueryExecutionException):
# Can't implicitly overwrite table metadata
DataSource(metadata_dir=csv_file.dirpath(), tables=[Table(name='titanic')])
# Metadata is loaded if a data source is created from the same dir
ds2 = DataSource(metadata_dir=csv_file.dirpath())
assert ds2.tables['titanic']
# Metadata is cleared when requested explicitly
ds3 = DataSource.create_new(metadata_dir=csv_file.dirpath())
assert not ds3.tables
def test_simple_select(self, data_source):
sql = "SELECT 1 AS result"
assert data_source.query(sql) == 1
sql = "SELECT 1"
assert data_source.query(sql) == 1
def test_create_table(self, csv_file):
ds = DataSource(metadata_dir=csv_file.dirpath())
assert not ds.tables and len(ds.tables) == 0
sql = f"CREATE TABLE ('{str(csv_file)}')"
query_result = ds.query(sql)
assert query_result == 'OK'
assert ds.tables and len(ds.tables) == 1
table = ds.tables['titanic']
assert table.name == csv_file.purebasename
assert pd.read_csv(csv_file).shape == table.dataframe.shape
def test_create_table_error_on_recreate(self, csv_file, data_source):
assert data_source.tables['titanic']
sql = f"CREATE TABLE ('{str(csv_file)}')"
with pytest.raises(QueryExecutionException):
query_result = data_source.query(sql)
def test_drop_table(self, data_source):
assert data_source.tables['titanic']
sql = f"DROP TABLE titanic"
query_result = data_source.query(sql)
assert query_result == 'OK'
assert not data_source.tables and len(data_source.tables) == 0
def test_select_column(self, csv_file, data_source):
df = pd.read_csv(csv_file)
sql = "SELECT passenger_id FROM titanic"
query_result = data_source.query(sql)
assert query_result.name == 'passenger_id'
values_left = df['passenger_id'].values
values_right = query_result.values
assert (values_left == values_right).all()
def test_select_all(self, csv_file, data_source):
df = pd.read_csv(csv_file)
sql = "SELECT * FROM titanic"
query_result = data_source.query(sql)
assert (query_result.columns == df.columns).all()
values_left = df.values
values_right = query_result.values
assert values_left.shape == values_right.shape
def test_select_table_case_insensitive(self, csv_file, tmpdir):
from dfsql import DataSource
dir_path = csv_file.dirpath()
data_source = DataSource.from_dir(metadata_dir=str(tmpdir),
files_dir_path=dir_path,
case_sensitive=False)
df = pd.read_csv(csv_file)
sql = "SELECT * FROM TiTaNiC"
query_result = data_source.query(sql)
assert (query_result.columns == df.columns).all()
values_left = df.values
values_right = query_result.values
assert values_left.shape == values_right.shape
def test_select_column_case_insensitive(self, csv_file, tmpdir):
from dfsql import DataSource
dir_path = csv_file.dirpath()
data_source = DataSource.from_dir(metadata_dir=str(tmpdir),
files_dir_path=dir_path,
case_sensitive=False)
df = pd.read_csv(csv_file)
df = df[['passenger_id', 'embarked']]
sql = "SELECT PassEnGer_ID, EmBarKED FROM TiTaNiC"
query_result = data_source.query(sql)
values_left = df.values
values_right = query_result.values
assert values_left.shape == values_right.shape
def test_select_column_alias(self, csv_file, data_source):
df = pd.read_csv(csv_file)
sql = "SELECT passenger_id AS p1 FROM titanic"
query_result = data_source.query(sql)
assert query_result.name == 'p1'
values_left = df['passenger_id'].values
values_right = query_result.values
assert (values_left == values_right).all()
def test_select_distinct(self, csv_file, data_source):
sql = "SELECT DISTINCT survived FROM titanic"
query_result = data_source.query(sql)
assert query_result.name == 'survived'
assert list(query_result.values) == [0, 1]
def test_select_limit_offset(self, csv_file, data_source):
sql = "SELECT passenger_id FROM titanic LIMIT 2 OFFSET 2"
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)['passenger_id']
df = df.iloc[2:4]
assert query_result.shape == df.shape
assert (df.values == query_result.values).all().all()
def test_select_multiple_columns(self, csv_file, data_source):
df = pd.read_csv(csv_file)
sql = "SELECT passenger_id, survived FROM titanic"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'survived']
values_left = df[['passenger_id', 'survived']].values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_select_const(self, csv_file, data_source):
df = pd.read_csv(csv_file)
df['const'] = 1
sql = "SELECT passenger_id, 1 AS const FROM titanic"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'const']
values_left = df[['passenger_id', 'const']].values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_select_operation(self, csv_file, data_source):
df = pd.read_csv(csv_file)
df['col_sum'] = df['passenger_id'] + df['survived']
df['col_diff'] = df['passenger_id'] - df['survived']
df = df[['col_sum', 'col_diff']]
sql = "SELECT passenger_id + survived AS col_sum, passenger_id - survived AS col_diff FROM titanic"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['col_sum', 'col_diff']
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_select_where(self, csv_file, data_source):
df = pd.read_csv(csv_file)
out_df = df[df['survived'] == 1][['passenger_id', 'survived']]
sql = "SELECT passenger_id, survived FROM titanic WHERE survived = 1"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'survived']
values_left = out_df[['passenger_id', 'survived']].values
values_right = query_result.values
assert values_left.shape == values_right.shape
assert (values_left == values_right).all()
sql = "SELECT passenger_id, survived FROM titanic WHERE titanic.survived = 1"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'survived']
values_left = out_df[['passenger_id', 'survived']].values
values_right = query_result.values
assert values_left.shape == values_right.shape
assert (values_left == values_right).all()
out_df = df[df.survived == 1]
out_df = out_df[out_df.sex != "male"]
out_df = out_df[out_df.p_class > 0]
out_df = out_df[['passenger_id', 'survived']]
sql = "SELECT passenger_id, survived FROM titanic WHERE survived = 1 AND sex != \"male\" AND p_class > 0"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'survived']
values_left = out_df[['passenger_id', 'survived']].values
values_right = query_result.values
assert values_left.shape == values_right.shape
assert (values_left == values_right).all()
def test_select_where_alias(self, csv_file, data_source):
sql = "SELECT passenger_id, titanic.survived as ts FROM titanic WHERE titanic.survived = 1"
df = pd.read_csv(csv_file)
out_df = df[df['survived'] == 1][['passenger_id', 'survived']]
out_df.columns = ['passenger_id', 'ts']
query_result = data_source.query(sql)
values_left = out_df.values
values_right = query_result.values
assert values_left.shape == values_right.shape
assert (values_left == values_right).all()
def test_select_where_empty_result(self, csv_file, data_source):
sql = "SELECT passenger_id, survived FROM titanic WHERE survived = 3"
query_result = data_source.query(sql)
assert query_result.empty
assert list(query_result.columns) == ['passenger_id', 'survived']
def test_where_operator_order(self, csv_file, data_source):
df = pd.read_csv(csv_file)
# And surviving females or children
out_df = df[((df.survived == 1) & (df.sex == "female")) | (df.p_class < 1)][['passenger_id', 'survived', 'sex', 'age']]
sql = "SELECT passenger_id, survived, sex, age FROM titanic WHERE survived = 1 AND sex = \"female\" OR p_class < 1"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'survived', 'sex', 'age']
values_left = out_df.values
values_right = query_result.values
assert values_left.shape == values_right.shape
assert (values_left == values_right).all()
out_df = df[(df.survived == 1) & ((df.sex == "female") | (df.p_class < 1))][
['passenger_id', 'survived', 'sex', 'age']]
sql = "SELECT passenger_id, survived, sex, age FROM titanic WHERE survived = 1 AND (sex = \"female\" OR p_class < 1)"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'survived', 'sex', 'age']
values_left = out_df.values
values_right = query_result.values
assert values_left.shape == values_right.shape
assert (values_left == values_right).all()
def test_select_where_string(self, csv_file, data_source):
df = pd.read_csv(csv_file)
out_df = df[df['sex'] == "male"]['passenger_id']
sql = "SELECT passenger_id FROM titanic WHERE sex = \"male\""
query_result = data_source.query(sql)
assert query_result.name == 'passenger_id'
values_left = out_df.values
values_right = query_result.values
assert values_left.shape == values_right.shape
assert (values_left == values_right).all()
def test_select_groupby_wrong_column(self, csv_file, data_source):
sql = "SELECT survived, p_class, count(passenger_id) AS count_passenger_id FROM titanic GROUP BY survived"
with pytest.raises(QueryExecutionException):
query_result = data_source.query(sql)
def test_select_aggregation_function_no_groupby(self, csv_file, data_source):
df = pd.read_csv(csv_file)
tdf = pd.DataFrame({'col_sum': [df['passenger_id'].sum()], 'col_avg': [df['passenger_id'].mean()]})
sql = "SELECT sum(passenger_id) AS col_sum, avg(passenger_id) AS col_avg FROM titanic"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['col_sum', 'col_avg']
values_left = tdf.values
values_right = query_result.values
assert (values_left == values_right).all().all()
sql = "SELECT count(passenger_id) AS count1 FROM titanic"
query_result = data_source.query(sql)
assert (query_result == df['passenger_id'].count())
def test_groupby(self, csv_file, data_source):
sql = "SELECT survived, p_class, count(passenger_id) AS count_passenger_id FROM titanic GROUP BY survived, p_class HAVING survived = 1"
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df.groupby(['survived', 'p_class']).agg({'passenger_id': 'count'}).reset_index()
df.columns = ['survived', 'p_class', 'count_passenger_id']
df = df[df['survived'] == 1]
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
assert (query_result.survived == 1).all()
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
# Same, but no alias
sql = "SELECT survived, p_class, count(passenger_id) FROM titanic GROUP BY survived, p_class HAVING survived = 1"
query_result = data_source.query(sql)
df.columns = ['survived', 'p_class', 'count(passenger_id)']
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
assert (query_result.survived == 1).all()
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_group_by_alias(self, csv_file, data_source):
sql = "SELECT survived as col1, count(passenger_id) AS count_passenger_id FROM titanic GROUP BY survived"
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df.groupby(['survived']).agg({'passenger_id': 'count'}).reset_index()
df.columns = ['col1', 'count_passenger_id']
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_group_by_case_insensitive(self, csv_file, tmpdir):
from dfsql import DataSource
dir_path = csv_file.dirpath()
data_source = DataSource.from_dir(metadata_dir=str(tmpdir),
files_dir_path=dir_path,
case_sensitive=False)
sql = "SELECT SuRViveD as COL1, count(PASSENGER_ID) AS count_passenger_id FROM titanic GROUP BY SURVIVED"
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df.groupby(['survived']).agg({'passenger_id': 'count'}).reset_index()
df.columns = ['COL1', 'count_passenger_id']
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_groupby_function(self, data_source, csv_file):
df = pd.read_csv(csv_file)
df['lower(name)'] = df.name.str.lower()
df = df.groupby(['lower(name)']).agg({'passenger_id': 'count'}).reset_index()
df = df.rename(columns={'passenger_id': 'count'})
sql = "SELECT lower(name), COUNT(passenger_id) as count FROM titanic GROUP BY lower(name)"
query_result = data_source.query(sql)
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_groupby_function_with_alias(self, data_source, csv_file):
df = pd.read_csv(csv_file)
df['somealias'] = df.name.str.lower()
df = df.groupby(['somealias']).agg({'passenger_id': 'count'}).reset_index()
df = df.rename(columns={'passenger_id': 'count'})
sql = "SELECT lower(name) as somealias, COUNT(passenger_id) as count FROM titanic GROUP BY lower(name)"
query_result = data_source.query(sql)
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
# TODO
# def test_groupby_function_nested(self, data_source, csv_file):
# df = pd.read_csv(csv_file)
# df['somealias'] = df.name.str.lower()
# df = df.groupby(['somealias']).agg({'passenger_id': 'count'}).reset_index()
# df = df.rename(columns={'passenger_id': 'count'})
#
# sql = "SELECT name as somealias, COUNT(passenger_id) as count FROM titanic GROUP BY upper(lower(name))"
#
# query_result = data_source.query(sql)
# assert (query_result.columns == df.columns).all()
# assert query_result.shape == df.shape
#
# values_left = df.values
# values_right = query_result.values
# assert (values_left == values_right).all().all()
def test_groupby_custom_aggregate_func(self, csv_file, data_source):
sql = "SELECT sex, mode(survived) AS mode_survived FROM titanic GROUP BY sex"
class ModeFunc(AggregateFunction):
def get_output(self, args):
return args[0].value_counts(dropna=False).index[0]
data_source.custom_functions['mode'] = ModeFunc()
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df.groupby(['sex']).agg({'survived': lambda x: x.value_counts(dropna=False).index[0]}).reset_index()
df.columns = ['sex', 'mode_survived']
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_groupby_register_aggregate_func(self, csv_file, data_source):
sql = "SELECT sex, mode(survived) AS mode_survived FROM titanic GROUP BY sex"
func = lambda x: x.value_counts(dropna=False).index[0]
data_source.register_function('mode', func)
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df.groupby(['sex']).agg({'survived': func}).reset_index()
df.columns = ['sex', 'mode_survived']
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_groupby_register_two_aggregate_funcs(self, csv_file, data_source):
sql = "SELECT sex, mode1(survived) AS mode1_survived, mode2(survived) AS mode2_survived FROM titanic GROUP BY sex"
func = lambda x: x.value_counts(dropna=False).index[0]
data_source.register_function('mode1', func)
data_source.register_function('mode2', func)
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df.groupby(['sex']).agg({'survived': func}).reset_index()
df.columns = ['sex', 'mode1_survived']
df['mode2_survived'] = df['mode1_survived']
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_group_by_columns_select(self, csv_file, data_source):
df = pd.read_csv(csv_file)
df = df.groupby(['survived', 'p_class']).agg({'passenger_id': 'count'}).reset_index()
df.columns = ['survived', 'p_class', 'count_passenger_id']
sql = "SELECT survived, p_class, count(passenger_id) AS count_passenger_id FROM titanic GROUP BY survived, p_class"
query_result = data_source.query(sql)
assert (query_result.columns == df.columns).all()
assert query_result.shape == df.shape
values_left = df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
sql = "SELECT p_class, count(passenger_id) FROM titanic GROUP BY survived, p_class"
query_result = data_source.query(sql)
values_left = df.drop(columns=['survived']).values
values_right = query_result.values
assert (values_left == values_right).all().all()
sql = "SELECT count(passenger_id) FROM titanic GROUP BY survived, p_class"
query_result = data_source.query(sql)
values_left = df.drop(columns=['survived', 'p_class']).values.flatten()
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_inner_join(self, csv_file, data_source):
df = pd.read_csv(csv_file)
merge_df = pd.merge(df, df, how='inner', left_on=['passenger_id'], right_on=['p_class'])[['passenger_id_x', 'p_class_y']]
merge_df.columns = ['passenger_id', 'p_class']
sqls = ["SELECT passenger_id, p_class FROM titanic AS t1 INNER JOIN titanic AS t2 ON t1.passenger_id = t2.p_class",
"SELECT passenger_id, p_class FROM titanic AS t1 INNER JOIN titanic AS t2 ON t2.p_class = t1.passenger_id"]
for sql in sqls:
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'p_class']
values_left = merge_df[['passenger_id', 'p_class']].values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_inner_join_no_aliases(self, csv_file, tmpdir):
p = tmpdir.join('titanic2.csv')
content = """passenger_id,survived,p_class,name,sex,age,sib_sp,parch,ticket,fare,cabin,embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
"""
p.write_text(content, encoding='utf-8')
dir_path = csv_file.dirpath()
data_source = DataSource.from_dir(metadata_dir=dir_path, files_dir_path=dir_path)
assert len(data_source.tables) == 2
df = pd.read_csv(csv_file)
merge_df = pd.merge(df, df, how='inner', left_on=['passenger_id'], right_on=['p_class'])[['passenger_id_x', 'p_class_y']]
merge_df.columns = ['passenger_id', 'p_class']
sql = "SELECT passenger_id, p_class FROM titanic INNER JOIN titanic2 ON titanic.passenger_id = titanic2.p_class"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'p_class']
values_left = merge_df[['passenger_id', 'p_class']].values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_inner_join_col_access(self, csv_file, data_source):
df = pd.read_csv(csv_file)
merge_df = pd.merge(df, df, how='inner', left_on=['passenger_id'], right_on=['p_class'])[['passenger_id_x', 'p_class_y', 'sex_x']]
merge_df.columns = ['passenger_id', 'p_class', 't1.sex']
sql = "SELECT passenger_id, p_class, t1.sex FROM titanic AS t1 INNER JOIN titanic AS t2 ON t1.passenger_id = t2.p_class"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'p_class', 't1.sex']
values_left = merge_df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
sql = "SELECT passenger_id, p_class, t1.sex AS sex FROM titanic AS t1 INNER JOIN titanic AS t2 ON t1.passenger_id = t2.p_class"
query_result = data_source.query(sql)
assert list(query_result.columns) == ['passenger_id', 'p_class', 'sex']
values_left = merge_df.values
values_right = query_result.values
assert (values_left == values_right).all().all()
def test_left_right_outer_joins(self, csv_file, data_source):
df = pd.read_csv(csv_file)
merge_df = pd.merge(df, df, how='left', left_on=['passenger_id'], right_on=['p_class'])[['passenger_id_x', 'p_class_y']]
merge_df.columns = ['passenger_id', 'p_class']
sql = "SELECT passenger_id, p_class FROM titanic AS t1 LEFT JOIN titanic AS t2 ON t1.passenger_id = t2.p_class"
query_result = data_source.query(sql)
assert merge_df.shape == query_result.shape
assert list(query_result.columns) == ['passenger_id', 'p_class']
values_left = merge_df.dropna().values
values_right = query_result.dropna().values
assert (values_left == values_right).all().all()
merge_df = pd.merge(df, df, how='right', left_on=['passenger_id'], right_on=['p_class'])[
['passenger_id_x', 'p_class_y']]
merge_df.columns = ['passenger_id', 'p_class']
sql = "SELECT passenger_id, p_class FROM titanic AS t1 RIGHT JOIN titanic AS t2 ON t1.passenger_id = t2.p_class"
query_result = data_source.query(sql)
assert merge_df.shape == query_result.shape
assert list(query_result.columns) == ['passenger_id', 'p_class']
values_left = merge_df.dropna().values
values_right = query_result.dropna().values
assert (values_left == values_right).all().all()
merge_df = pd.merge(df, df, how='outer', left_on=['passenger_id'], right_on=['p_class'])[
['passenger_id_x', 'p_class_y']]
merge_df.columns = ['passenger_id', 'p_class']
sql = "SELECT passenger_id, p_class FROM titanic AS t1 FULL JOIN titanic AS t2 ON t1.passenger_id = t2.p_class"
query_result = data_source.query(sql)
assert merge_df.shape == query_result.shape
assert list(query_result.columns) == ['passenger_id', 'p_class']
values_left = merge_df.dropna().values
values_right = query_result.dropna().values
assert (values_left == values_right).all().all()
def test_subquery_simple(self, csv_file, data_source):
sql = "SELECT * FROM (SELECT * FROM titanic) AS t1"
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
assert query_result.shape == df.shape
values_left = df.dropna().values
values_right = query_result.dropna().values
assert (values_left == values_right).all()
def test_subquery_groupby(self, csv_file, data_source):
sql = "SELECT survived, p_class, count(passenger_id) AS count FROM (SELECT * FROM titanic WHERE survived = 1) AS t1 GROUP BY survived, p_class"
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df[df.survived == 1]
df = df.groupby(['survived', 'p_class']).agg({'passenger_id': 'count'}).reset_index()
assert query_result.shape == df.shape
values_left = df.dropna().values
values_right = query_result.dropna().values
assert (values_left == values_right).all()
def test_subquery_where(self, csv_file, data_source):
sql = "SELECT survived, p_class, passenger_id FROM titanic WHERE passenger_id IN (SELECT passenger_id FROM titanic WHERE survived = 1)"
query_result = data_source.query(sql)
df = pd.read_csv(csv_file)
df = df[df.survived == 1]
df = df[['survived', 'p_class', 'passenger_id']]
assert query_result.shape == df.shape
values_left = df.dropna().values
values_right = query_result.dropna().values
assert (values_left == values_right).all()
def test_subquery_select(self, csv_file, data_source):
sql = "SELECT survived, (SELECT passenger_id FROM titanic LIMIT 1) AS pid FROM titanic"
query_result = data_source.query(sql)
assert (query_result['pid'] == 1).all()
def test_show_tables(self, csv_file, data_source):
sql = "SHOW TABLES"
query_result = data_source.query(sql)
assert (query_result.values == np.array([['titanic', str(csv_file)]])).all()
data_source.drop_table('titanic')
query_result = data_source.query(sql)
assert query_result.empty
def test_cast(self, csv_file, data_source):
sql = "SELECT CAST (4 AS str) AS result"
query_result = data_source.query(sql)
assert query_result == "4" and isinstance(query_result, str)
sql = "SELECT CAST (\"4\" AS int) AS result"
query_result = data_source.query(sql)
assert query_result == 4 and isinstance(query_result, np.int64)
sql = "SELECT CAST (\"4\" AS float) AS result"
query_result = data_source.query(sql)
assert query_result == 4.0 and isinstance(query_result, np.float64)
def test_count_distinct(self, csv_file, data_source):
sql = "SELECT COUNT(DISTINCT survived) AS uniq_survived FROM titanic"
query_result = data_source.query(sql)
assert query_result == 2
def test_large_where_and(self, data_source_googleplay, googleplay_csv):
df = pd.read_csv(googleplay_csv)
out_df = df[(df.Category == "FAMILY") & (df.Price == '0')][['App', 'Category']]
sql = "SELECT App, Category FROM googleplaystore WHERE Category = \"FAMILY\" AND Price = \"0\""
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
def test_large_not(self, data_source_googleplay, googleplay_csv):
df = pd.read_csv(googleplay_csv)
out_df = df[~(df.Category == "FAMILY")][['App', 'Category']]
sql = "SELECT App, Category FROM googleplaystore WHERE NOT Category = \"FAMILY\""
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
def test_large_order_by(self, data_source_googleplay, googleplay_csv):
df = pd.read_csv(googleplay_csv)
out_df = df.sort_values(by='App')[['App', 'Category']]
sql = "SELECT App, Category FROM googleplaystore ORDER BY App"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
out_df = df.sort_values(by='App', ascending=False)[['App', 'Category']]
sql = "SELECT App, Category FROM googleplaystore ORDER BY App DESC"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
out_df = df.sort_values(by=['App', 'Category'])[['App', 'Category']]
sql = "SELECT App, Category FROM googleplaystore ORDER BY App, Category"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
out_df = df.sort_values(by=['App', 'Category'], ascending=[False, False])[['App', 'Category']]
sql = "SELECT App, Category FROM googleplaystore ORDER BY App DESC, Category DESC"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
out_df = df.sort_values(by=['App', 'Category'], ascending=[False, True])[['App', 'Category']]
sql = "SELECT App, Category FROM googleplaystore ORDER BY App DESC, Category ASC"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
out_df = df.groupby(['Category']).agg({'App': 'count'}).reset_index()
out_df.columns = ['Category', 'count_app']
out_df = out_df.sort_values(by=['count_app'], ascending=[False])[:10]
sql = "SELECT Category, count(App) AS count_app FROM googleplaystore GROUP BY Category ORDER BY count_app DESC LIMIT 10"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all().all()
def test_string_concat(self, data_source, csv_file):
sql = "SELECT \"a\" || \"b\""
query_result = data_source.query(sql)
assert query_result == 'ab'
sql = "SELECT \"b\" || \"a\""
query_result = data_source.query(sql)
assert query_result == 'ba'
df = pd.read_csv(csv_file)
out_series = df['name'] + df['embarked']
sql = "SELECT name || embarked FROM titanic"
query_result = data_source.query(sql)
assert (query_result.values == out_series.values).all()
df = pd.read_csv(csv_file)
out_series = df['embarked'] + df['name']
sql = "SELECT embarked || name FROM titanic"
query_result = data_source.query(sql)
assert (query_result.values == out_series.values).all()
out_series = df['name'] + 'a'
sql = "SELECT name || \"a\" FROM titanic"
query_result = data_source.query(sql)
assert (query_result.values == out_series.values).all()
out_series = "a" + df['name']
sql = "SELECT \"a\" || name FROM titanic"
query_result = data_source.query(sql)
assert (query_result.values == out_series.values).all()
def test_string_upper_lower(self, data_source, csv_file):
sql = "SELECT upper(\"a\")"
query_result = data_source.query(sql)
assert query_result == 'A'
sql = "SELECT lower(\"A\")"
query_result = data_source.query(sql)
assert query_result == 'a'
df = pd.read_csv(csv_file)
out_series = df['name'].apply(lambda x: x.upper())
sql = "SELECT upper(name) FROM titanic"
query_result = data_source.query(sql)
assert (query_result.values == out_series.values).all()
out_series = df['name'].apply(lambda x: x.lower())
sql = "SELECT lower(name) FROM titanic"
query_result = data_source.query(sql)
assert (query_result.values == out_series.values).all()
def test_string_like(self, data_source, csv_file):
sql = "SELECT \"a\" LIKE \".*\" "
query_result = data_source.query(sql)
assert query_result == True
df = pd.read_csv(csv_file)
sql = "SELECT name FROM titanic WHERE name LIKE \".*\""
query_result = data_source.query(sql)
assert (query_result.values == df['name'].values).all()
sql = "SELECT name FROM titanic WHERE name LIKE \".*Owen.*\""
query_result = data_source.query(sql)
assert query_result == 'Braund, Mr. Owen Harris'
def test_in(self, data_source, csv_file):
sql = "SELECT name FROM titanic WHERE name IN (\"Braund, Mr. Owen Harris\", \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\")"
query_result = data_source.query(sql)
assert (query_result.values == np.array(['Braund, Mr. Owen Harris', 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)'])).all()
def test_custom_function_select(self, data_source, csv_file):
def custom(x):
return x + '_custom_addition'
data_source.register_function('custom', custom)
sql = "SELECT custom(\"a\")"
query_result = data_source.query(sql)
assert query_result == 'a_custom_addition'
df = pd.read_csv(csv_file)
sql = "SELECT custom(name) FROM titanic"
query_result = data_source.query(sql)
assert (query_result.values == df.name.values + '_custom_addition').all()
def test_custom_function_where(self, data_source, csv_file):
df = pd.read_csv(csv_file)
def did_survive(survived):
return survived == 1
data_source.register_function('did_survive', did_survive)
sql = "SELECT passenger_id FROM titanic WHERE did_survive(survived)"
query_result = data_source.query(sql)
assert (query_result.values == df[df.survived == 1]['passenger_id'].values).all()
def test_is_null(self, data_source_googleplay, googleplay_csv):
df = pd.read_csv(googleplay_csv)
out_df = df[df.Rating.isnull()]['App']
sql = "SELECT App FROM googleplaystore WHERE Rating IS NULL"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all()
out_df = df[~df.Rating.isnull()]['App']
sql = "SELECT App FROM googleplaystore WHERE Rating IS NOT NULL"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all()
def test_is_true(self, data_source_googleplay, googleplay_csv):
df = pd.read_csv(googleplay_csv)
out_df = df[df.Price == '0']['App']
sql = "SELECT App FROM googleplaystore WHERE (Price = '0') IS TRUE"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all()
out_df = df[df.Price != '0']['App']
sql = "SELECT App FROM googleplaystore WHERE (Price = '0') IS NOT TRUE"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all()
def test_is_false(self, data_source_googleplay, googleplay_csv):
df = pd.read_csv(googleplay_csv)
out_df = df[df.Price != '0']['App']
sql = "SELECT App FROM googleplaystore WHERE (Price = '0') IS FALSE"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all()
out_df = df[df.Price == '0']['App']
sql = "SELECT App FROM googleplaystore WHERE (Price = '0') IS NOT FALSE"
query_result = data_source_googleplay.query(sql)
assert (out_df.dropna().values == query_result.dropna().values).all()
def test_subquery_alias(self, googleplay_csv, data_source_googleplay):
df = pd.read_csv(googleplay_csv)
out_df = df.App
sql = "SELECT tab_alias.app FROM (SELECT App as app FROM googleplaystore) AS tab_alias"
query_result = data_source_googleplay.query(sql)
assert query_result.name == 'tab_alias.app'
assert (out_df.dropna().values == query_result.dropna().values).all()
def test_subquery_alias_case_insensitive(self, root_directory, googleplay_csv, tmpdir):
from dfsql import DataSource
dir_path = os.path.join(root_directory, 'tests')
data_source_googleplay = DataSource.from_dir(metadata_dir=str(tmpdir),
files_dir_path=dir_path,
case_sensitive=False)
df = pd.read_csv(googleplay_csv)
out_df = df.App
sql = "SELECT tab_alias.app FROM (SELECT App as APP FROM googleplaystore) AS tab_alias"
query_result = data_source_googleplay.query(sql)
assert query_result.name == 'tab_alias.app'
assert (out_df.dropna().values == query_result.dropna().values).all()
def test_multi_word_identifier(self, googleplay_csv, data_source_googleplay):
df = pd.read_csv(googleplay_csv)
out_df = df[['App', 'Content Rating']]
sql = "SELECT App, `Content Rating` FROM googleplaystore"
query_result = data_source_googleplay.query(sql)
assert (query_result.columns == out_df.columns).all()
assert (out_df.dropna().values == query_result.dropna().values).all()