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craftbeerpi4-pione/venv/lib/python3.8/site-packages/pandas/tests/io/test_sql.py
Manuel Fritsch aeabde8c4a plugin controller change
2021-01-30 22:29:33 +01:00

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"""SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: test the public API with a sqlite DBAPI
connection
- Tests for the different SQL flavors (flavor specific type conversions)
- Tests for the sqlalchemy mode: `_TestSQLAlchemy` is the base class with
common methods, `_TestSQLAlchemyConn` tests the API with a SQLAlchemy
Connection object. The different tested flavors (sqlite3, MySQL,
PostgreSQL) derive from the base class
- Tests for the fallback mode (`TestSQLiteFallback`)
"""
import csv
from datetime import date, datetime, time
from io import StringIO
import sqlite3
import warnings
import numpy as np
import pytest
from pandas.core.dtypes.common import is_datetime64_dtype, is_datetime64tz_dtype
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
concat,
date_range,
isna,
to_datetime,
to_timedelta,
)
import pandas._testing as tm
import pandas.io.sql as sql
from pandas.io.sql import read_sql_query, read_sql_table
try:
import sqlalchemy
from sqlalchemy.ext import declarative
from sqlalchemy.orm import session as sa_session
import sqlalchemy.schema
import sqlalchemy.sql.sqltypes as sqltypes
SQLALCHEMY_INSTALLED = True
except ImportError:
SQLALCHEMY_INSTALLED = False
SQL_STRINGS = {
"create_iris": {
"sqlite": """CREATE TABLE iris (
"SepalLength" REAL,
"SepalWidth" REAL,
"PetalLength" REAL,
"PetalWidth" REAL,
"Name" TEXT
)""",
"mysql": """CREATE TABLE iris (
`SepalLength` DOUBLE,
`SepalWidth` DOUBLE,
`PetalLength` DOUBLE,
`PetalWidth` DOUBLE,
`Name` VARCHAR(200)
)""",
"postgresql": """CREATE TABLE iris (
"SepalLength" DOUBLE PRECISION,
"SepalWidth" DOUBLE PRECISION,
"PetalLength" DOUBLE PRECISION,
"PetalWidth" DOUBLE PRECISION,
"Name" VARCHAR(200)
)""",
},
"insert_iris": {
"sqlite": """INSERT INTO iris VALUES(?, ?, ?, ?, ?)""",
"mysql": """INSERT INTO iris VALUES(%s, %s, %s, %s, "%s");""",
"postgresql": """INSERT INTO iris VALUES(%s, %s, %s, %s, %s);""",
},
"create_test_types": {
"sqlite": """CREATE TABLE types_test_data (
"TextCol" TEXT,
"DateCol" TEXT,
"IntDateCol" INTEGER,
"IntDateOnlyCol" INTEGER,
"FloatCol" REAL,
"IntCol" INTEGER,
"BoolCol" INTEGER,
"IntColWithNull" INTEGER,
"BoolColWithNull" INTEGER
)""",
"mysql": """CREATE TABLE types_test_data (
`TextCol` TEXT,
`DateCol` DATETIME,
`IntDateCol` INTEGER,
`IntDateOnlyCol` INTEGER,
`FloatCol` DOUBLE,
`IntCol` INTEGER,
`BoolCol` BOOLEAN,
`IntColWithNull` INTEGER,
`BoolColWithNull` BOOLEAN
)""",
"postgresql": """CREATE TABLE types_test_data (
"TextCol" TEXT,
"DateCol" TIMESTAMP,
"DateColWithTz" TIMESTAMP WITH TIME ZONE,
"IntDateCol" INTEGER,
"IntDateOnlyCol" INTEGER,
"FloatCol" DOUBLE PRECISION,
"IntCol" INTEGER,
"BoolCol" BOOLEAN,
"IntColWithNull" INTEGER,
"BoolColWithNull" BOOLEAN
)""",
},
"insert_test_types": {
"sqlite": {
"query": """
INSERT INTO types_test_data
VALUES(?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
"fields": (
"TextCol",
"DateCol",
"IntDateCol",
"IntDateOnlyCol",
"FloatCol",
"IntCol",
"BoolCol",
"IntColWithNull",
"BoolColWithNull",
),
},
"mysql": {
"query": """
INSERT INTO types_test_data
VALUES("%s", %s, %s, %s, %s, %s, %s, %s, %s)
""",
"fields": (
"TextCol",
"DateCol",
"IntDateCol",
"IntDateOnlyCol",
"FloatCol",
"IntCol",
"BoolCol",
"IntColWithNull",
"BoolColWithNull",
),
},
"postgresql": {
"query": """
INSERT INTO types_test_data
VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
""",
"fields": (
"TextCol",
"DateCol",
"DateColWithTz",
"IntDateCol",
"IntDateOnlyCol",
"FloatCol",
"IntCol",
"BoolCol",
"IntColWithNull",
"BoolColWithNull",
),
},
},
"read_parameters": {
"sqlite": "SELECT * FROM iris WHERE Name=? AND SepalLength=?",
"mysql": 'SELECT * FROM iris WHERE `Name`="%s" AND `SepalLength`=%s',
"postgresql": 'SELECT * FROM iris WHERE "Name"=%s AND "SepalLength"=%s',
},
"read_named_parameters": {
"sqlite": """
SELECT * FROM iris WHERE Name=:name AND SepalLength=:length
""",
"mysql": """
SELECT * FROM iris WHERE
`Name`="%(name)s" AND `SepalLength`=%(length)s
""",
"postgresql": """
SELECT * FROM iris WHERE
"Name"=%(name)s AND "SepalLength"=%(length)s
""",
},
"read_no_parameters_with_percent": {
"sqlite": "SELECT * FROM iris WHERE Name LIKE '%'",
"mysql": "SELECT * FROM iris WHERE `Name` LIKE '%'",
"postgresql": "SELECT * FROM iris WHERE \"Name\" LIKE '%'",
},
"create_view": {
"sqlite": """
CREATE VIEW iris_view AS
SELECT * FROM iris
"""
},
}
class MixInBase:
def teardown_method(self, method):
# if setup fails, there may not be a connection to close.
if hasattr(self, "conn"):
for tbl in self._get_all_tables():
self.drop_table(tbl)
self._close_conn()
class MySQLMixIn(MixInBase):
def drop_table(self, table_name):
cur = self.conn.cursor()
cur.execute(f"DROP TABLE IF EXISTS {sql._get_valid_mysql_name(table_name)}")
self.conn.commit()
def _get_all_tables(self):
cur = self.conn.cursor()
cur.execute("SHOW TABLES")
return [table[0] for table in cur.fetchall()]
def _close_conn(self):
from pymysql.err import Error
try:
self.conn.close()
except Error:
pass
class SQLiteMixIn(MixInBase):
def drop_table(self, table_name):
self.conn.execute(
f"DROP TABLE IF EXISTS {sql._get_valid_sqlite_name(table_name)}"
)
self.conn.commit()
def _get_all_tables(self):
c = self.conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
return [table[0] for table in c.fetchall()]
def _close_conn(self):
self.conn.close()
class SQLAlchemyMixIn(MixInBase):
def drop_table(self, table_name):
sql.SQLDatabase(self.conn).drop_table(table_name)
def _get_all_tables(self):
meta = sqlalchemy.schema.MetaData(bind=self.conn)
meta.reflect()
table_list = meta.tables.keys()
return table_list
def _close_conn(self):
pass
class PandasSQLTest:
"""
Base class with common private methods for SQLAlchemy and fallback cases.
"""
def _get_exec(self):
if hasattr(self.conn, "execute"):
return self.conn
else:
return self.conn.cursor()
@pytest.fixture(params=[("io", "data", "csv", "iris.csv")])
def load_iris_data(self, datapath, request):
import io
iris_csv_file = datapath(*request.param)
if not hasattr(self, "conn"):
self.setup_connect()
self.drop_table("iris")
self._get_exec().execute(SQL_STRINGS["create_iris"][self.flavor])
with io.open(iris_csv_file, mode="r", newline=None) as iris_csv:
r = csv.reader(iris_csv)
next(r) # skip header row
ins = SQL_STRINGS["insert_iris"][self.flavor]
for row in r:
self._get_exec().execute(ins, row)
def _load_iris_view(self):
self.drop_table("iris_view")
self._get_exec().execute(SQL_STRINGS["create_view"][self.flavor])
def _check_iris_loaded_frame(self, iris_frame):
pytype = iris_frame.dtypes[0].type
row = iris_frame.iloc[0]
assert issubclass(pytype, np.floating)
tm.equalContents(row.values, [5.1, 3.5, 1.4, 0.2, "Iris-setosa"])
def _load_test1_data(self):
columns = ["index", "A", "B", "C", "D"]
data = [
(
"2000-01-03 00:00:00",
0.980268513777,
3.68573087906,
-0.364216805298,
-1.15973806169,
),
(
"2000-01-04 00:00:00",
1.04791624281,
-0.0412318367011,
-0.16181208307,
0.212549316967,
),
(
"2000-01-05 00:00:00",
0.498580885705,
0.731167677815,
-0.537677223318,
1.34627041952,
),
(
"2000-01-06 00:00:00",
1.12020151869,
1.56762092543,
0.00364077397681,
0.67525259227,
),
]
self.test_frame1 = DataFrame(data, columns=columns)
def _load_test2_data(self):
df = DataFrame(
dict(
A=[4, 1, 3, 6],
B=["asd", "gsq", "ylt", "jkl"],
C=[1.1, 3.1, 6.9, 5.3],
D=[False, True, True, False],
E=["1990-11-22", "1991-10-26", "1993-11-26", "1995-12-12"],
)
)
df["E"] = to_datetime(df["E"])
self.test_frame2 = df
def _load_test3_data(self):
columns = ["index", "A", "B"]
data = [
("2000-01-03 00:00:00", 2 ** 31 - 1, -1.987670),
("2000-01-04 00:00:00", -29, -0.0412318367011),
("2000-01-05 00:00:00", 20000, 0.731167677815),
("2000-01-06 00:00:00", -290867, 1.56762092543),
]
self.test_frame3 = DataFrame(data, columns=columns)
def _load_raw_sql(self):
self.drop_table("types_test_data")
self._get_exec().execute(SQL_STRINGS["create_test_types"][self.flavor])
ins = SQL_STRINGS["insert_test_types"][self.flavor]
data = [
{
"TextCol": "first",
"DateCol": "2000-01-03 00:00:00",
"DateColWithTz": "2000-01-01 00:00:00-08:00",
"IntDateCol": 535852800,
"IntDateOnlyCol": 20101010,
"FloatCol": 10.10,
"IntCol": 1,
"BoolCol": False,
"IntColWithNull": 1,
"BoolColWithNull": False,
},
{
"TextCol": "first",
"DateCol": "2000-01-04 00:00:00",
"DateColWithTz": "2000-06-01 00:00:00-07:00",
"IntDateCol": 1356998400,
"IntDateOnlyCol": 20101212,
"FloatCol": 10.10,
"IntCol": 1,
"BoolCol": False,
"IntColWithNull": None,
"BoolColWithNull": None,
},
]
for d in data:
self._get_exec().execute(
ins["query"], [d[field] for field in ins["fields"]]
)
def _count_rows(self, table_name):
result = (
self._get_exec()
.execute(f"SELECT count(*) AS count_1 FROM {table_name}")
.fetchone()
)
return result[0]
def _read_sql_iris(self):
iris_frame = self.pandasSQL.read_query("SELECT * FROM iris")
self._check_iris_loaded_frame(iris_frame)
def _read_sql_iris_parameter(self):
query = SQL_STRINGS["read_parameters"][self.flavor]
params = ["Iris-setosa", 5.1]
iris_frame = self.pandasSQL.read_query(query, params=params)
self._check_iris_loaded_frame(iris_frame)
def _read_sql_iris_named_parameter(self):
query = SQL_STRINGS["read_named_parameters"][self.flavor]
params = {"name": "Iris-setosa", "length": 5.1}
iris_frame = self.pandasSQL.read_query(query, params=params)
self._check_iris_loaded_frame(iris_frame)
def _read_sql_iris_no_parameter_with_percent(self):
query = SQL_STRINGS["read_no_parameters_with_percent"][self.flavor]
iris_frame = self.pandasSQL.read_query(query, params=None)
self._check_iris_loaded_frame(iris_frame)
def _to_sql(self, method=None):
self.drop_table("test_frame1")
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", method=method)
assert self.pandasSQL.has_table("test_frame1")
num_entries = len(self.test_frame1)
num_rows = self._count_rows("test_frame1")
assert num_rows == num_entries
# Nuke table
self.drop_table("test_frame1")
def _to_sql_empty(self):
self.drop_table("test_frame1")
self.pandasSQL.to_sql(self.test_frame1.iloc[:0], "test_frame1")
def _to_sql_fail(self):
self.drop_table("test_frame1")
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", if_exists="fail")
assert self.pandasSQL.has_table("test_frame1")
msg = "Table 'test_frame1' already exists"
with pytest.raises(ValueError, match=msg):
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", if_exists="fail")
self.drop_table("test_frame1")
def _to_sql_replace(self):
self.drop_table("test_frame1")
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", if_exists="fail")
# Add to table again
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", if_exists="replace")
assert self.pandasSQL.has_table("test_frame1")
num_entries = len(self.test_frame1)
num_rows = self._count_rows("test_frame1")
assert num_rows == num_entries
self.drop_table("test_frame1")
def _to_sql_append(self):
# Nuke table just in case
self.drop_table("test_frame1")
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", if_exists="fail")
# Add to table again
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", if_exists="append")
assert self.pandasSQL.has_table("test_frame1")
num_entries = 2 * len(self.test_frame1)
num_rows = self._count_rows("test_frame1")
assert num_rows == num_entries
self.drop_table("test_frame1")
def _to_sql_method_callable(self):
check = [] # used to double check function below is really being used
def sample(pd_table, conn, keys, data_iter):
check.append(1)
data = [dict(zip(keys, row)) for row in data_iter]
conn.execute(pd_table.table.insert(), data)
self.drop_table("test_frame1")
self.pandasSQL.to_sql(self.test_frame1, "test_frame1", method=sample)
assert self.pandasSQL.has_table("test_frame1")
assert check == [1]
num_entries = len(self.test_frame1)
num_rows = self._count_rows("test_frame1")
assert num_rows == num_entries
# Nuke table
self.drop_table("test_frame1")
def _roundtrip(self):
self.drop_table("test_frame_roundtrip")
self.pandasSQL.to_sql(self.test_frame1, "test_frame_roundtrip")
result = self.pandasSQL.read_query("SELECT * FROM test_frame_roundtrip")
result.set_index("level_0", inplace=True)
# result.index.astype(int)
result.index.name = None
tm.assert_frame_equal(result, self.test_frame1)
def _execute_sql(self):
# drop_sql = "DROP TABLE IF EXISTS test" # should already be done
iris_results = self.pandasSQL.execute("SELECT * FROM iris")
row = iris_results.fetchone()
tm.equalContents(row, [5.1, 3.5, 1.4, 0.2, "Iris-setosa"])
def _to_sql_save_index(self):
df = DataFrame.from_records(
[(1, 2.1, "line1"), (2, 1.5, "line2")], columns=["A", "B", "C"], index=["A"]
)
self.pandasSQL.to_sql(df, "test_to_sql_saves_index")
ix_cols = self._get_index_columns("test_to_sql_saves_index")
assert ix_cols == [["A"]]
def _transaction_test(self):
with self.pandasSQL.run_transaction() as trans:
trans.execute("CREATE TABLE test_trans (A INT, B TEXT)")
class DummyException(Exception):
pass
# Make sure when transaction is rolled back, no rows get inserted
ins_sql = "INSERT INTO test_trans (A,B) VALUES (1, 'blah')"
try:
with self.pandasSQL.run_transaction() as trans:
trans.execute(ins_sql)
raise DummyException("error")
except DummyException:
# ignore raised exception
pass
res = self.pandasSQL.read_query("SELECT * FROM test_trans")
assert len(res) == 0
# Make sure when transaction is committed, rows do get inserted
with self.pandasSQL.run_transaction() as trans:
trans.execute(ins_sql)
res2 = self.pandasSQL.read_query("SELECT * FROM test_trans")
assert len(res2) == 1
# -----------------------------------------------------------------------------
# -- Testing the public API
class _TestSQLApi(PandasSQLTest):
"""
Base class to test the public API.
From this two classes are derived to run these tests for both the
sqlalchemy mode (`TestSQLApi`) and the fallback mode
(`TestSQLiteFallbackApi`). These tests are run with sqlite3. Specific
tests for the different sql flavours are included in `_TestSQLAlchemy`.
Notes:
flavor can always be passed even in SQLAlchemy mode,
should be correctly ignored.
we don't use drop_table because that isn't part of the public api
"""
flavor = "sqlite"
mode: str
def setup_connect(self):
self.conn = self.connect()
@pytest.fixture(autouse=True)
def setup_method(self, load_iris_data):
self.load_test_data_and_sql()
def load_test_data_and_sql(self):
self._load_iris_view()
self._load_test1_data()
self._load_test2_data()
self._load_test3_data()
self._load_raw_sql()
def test_read_sql_iris(self):
iris_frame = sql.read_sql_query("SELECT * FROM iris", self.conn)
self._check_iris_loaded_frame(iris_frame)
def test_read_sql_view(self):
iris_frame = sql.read_sql_query("SELECT * FROM iris_view", self.conn)
self._check_iris_loaded_frame(iris_frame)
def test_to_sql(self):
sql.to_sql(self.test_frame1, "test_frame1", self.conn)
assert sql.has_table("test_frame1", self.conn)
def test_to_sql_fail(self):
sql.to_sql(self.test_frame1, "test_frame2", self.conn, if_exists="fail")
assert sql.has_table("test_frame2", self.conn)
msg = "Table 'test_frame2' already exists"
with pytest.raises(ValueError, match=msg):
sql.to_sql(self.test_frame1, "test_frame2", self.conn, if_exists="fail")
def test_to_sql_replace(self):
sql.to_sql(self.test_frame1, "test_frame3", self.conn, if_exists="fail")
# Add to table again
sql.to_sql(self.test_frame1, "test_frame3", self.conn, if_exists="replace")
assert sql.has_table("test_frame3", self.conn)
num_entries = len(self.test_frame1)
num_rows = self._count_rows("test_frame3")
assert num_rows == num_entries
def test_to_sql_append(self):
sql.to_sql(self.test_frame1, "test_frame4", self.conn, if_exists="fail")
# Add to table again
sql.to_sql(self.test_frame1, "test_frame4", self.conn, if_exists="append")
assert sql.has_table("test_frame4", self.conn)
num_entries = 2 * len(self.test_frame1)
num_rows = self._count_rows("test_frame4")
assert num_rows == num_entries
def test_to_sql_type_mapping(self):
sql.to_sql(self.test_frame3, "test_frame5", self.conn, index=False)
result = sql.read_sql("SELECT * FROM test_frame5", self.conn)
tm.assert_frame_equal(self.test_frame3, result)
def test_to_sql_series(self):
s = Series(np.arange(5, dtype="int64"), name="series")
sql.to_sql(s, "test_series", self.conn, index=False)
s2 = sql.read_sql_query("SELECT * FROM test_series", self.conn)
tm.assert_frame_equal(s.to_frame(), s2)
def test_roundtrip(self):
sql.to_sql(self.test_frame1, "test_frame_roundtrip", con=self.conn)
result = sql.read_sql_query("SELECT * FROM test_frame_roundtrip", con=self.conn)
# HACK!
result.index = self.test_frame1.index
result.set_index("level_0", inplace=True)
result.index.astype(int)
result.index.name = None
tm.assert_frame_equal(result, self.test_frame1)
def test_roundtrip_chunksize(self):
sql.to_sql(
self.test_frame1,
"test_frame_roundtrip",
con=self.conn,
index=False,
chunksize=2,
)
result = sql.read_sql_query("SELECT * FROM test_frame_roundtrip", con=self.conn)
tm.assert_frame_equal(result, self.test_frame1)
def test_execute_sql(self):
# drop_sql = "DROP TABLE IF EXISTS test" # should already be done
iris_results = sql.execute("SELECT * FROM iris", con=self.conn)
row = iris_results.fetchone()
tm.equalContents(row, [5.1, 3.5, 1.4, 0.2, "Iris-setosa"])
def test_date_parsing(self):
# Test date parsing in read_sql
# No Parsing
df = sql.read_sql_query("SELECT * FROM types_test_data", self.conn)
assert not issubclass(df.DateCol.dtype.type, np.datetime64)
df = sql.read_sql_query(
"SELECT * FROM types_test_data", self.conn, parse_dates=["DateCol"]
)
assert issubclass(df.DateCol.dtype.type, np.datetime64)
assert df.DateCol.tolist() == [
pd.Timestamp(2000, 1, 3, 0, 0, 0),
pd.Timestamp(2000, 1, 4, 0, 0, 0),
]
df = sql.read_sql_query(
"SELECT * FROM types_test_data",
self.conn,
parse_dates={"DateCol": "%Y-%m-%d %H:%M:%S"},
)
assert issubclass(df.DateCol.dtype.type, np.datetime64)
assert df.DateCol.tolist() == [
pd.Timestamp(2000, 1, 3, 0, 0, 0),
pd.Timestamp(2000, 1, 4, 0, 0, 0),
]
df = sql.read_sql_query(
"SELECT * FROM types_test_data", self.conn, parse_dates=["IntDateCol"]
)
assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
assert df.IntDateCol.tolist() == [
pd.Timestamp(1986, 12, 25, 0, 0, 0),
pd.Timestamp(2013, 1, 1, 0, 0, 0),
]
df = sql.read_sql_query(
"SELECT * FROM types_test_data", self.conn, parse_dates={"IntDateCol": "s"}
)
assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
assert df.IntDateCol.tolist() == [
pd.Timestamp(1986, 12, 25, 0, 0, 0),
pd.Timestamp(2013, 1, 1, 0, 0, 0),
]
df = sql.read_sql_query(
"SELECT * FROM types_test_data",
self.conn,
parse_dates={"IntDateOnlyCol": "%Y%m%d"},
)
assert issubclass(df.IntDateOnlyCol.dtype.type, np.datetime64)
assert df.IntDateOnlyCol.tolist() == [
pd.Timestamp("2010-10-10"),
pd.Timestamp("2010-12-12"),
]
def test_date_and_index(self):
# Test case where same column appears in parse_date and index_col
df = sql.read_sql_query(
"SELECT * FROM types_test_data",
self.conn,
index_col="DateCol",
parse_dates=["DateCol", "IntDateCol"],
)
assert issubclass(df.index.dtype.type, np.datetime64)
assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
def test_timedelta(self):
# see #6921
df = to_timedelta(Series(["00:00:01", "00:00:03"], name="foo")).to_frame()
with tm.assert_produces_warning(UserWarning):
df.to_sql("test_timedelta", self.conn)
result = sql.read_sql_query("SELECT * FROM test_timedelta", self.conn)
tm.assert_series_equal(result["foo"], df["foo"].astype("int64"))
def test_complex_raises(self):
df = DataFrame({"a": [1 + 1j, 2j]})
msg = "Complex datatypes not supported"
with pytest.raises(ValueError, match=msg):
df.to_sql("test_complex", self.conn)
@pytest.mark.parametrize(
"index_name,index_label,expected",
[
# no index name, defaults to 'index'
(None, None, "index"),
# specifying index_label
(None, "other_label", "other_label"),
# using the index name
("index_name", None, "index_name"),
# has index name, but specifying index_label
("index_name", "other_label", "other_label"),
# index name is integer
(0, None, "0"),
# index name is None but index label is integer
(None, 0, "0"),
],
)
def test_to_sql_index_label(self, index_name, index_label, expected):
temp_frame = DataFrame({"col1": range(4)})
temp_frame.index.name = index_name
query = "SELECT * FROM test_index_label"
sql.to_sql(temp_frame, "test_index_label", self.conn, index_label=index_label)
frame = sql.read_sql_query(query, self.conn)
assert frame.columns[0] == expected
def test_to_sql_index_label_multiindex(self):
temp_frame = DataFrame(
{"col1": range(4)},
index=MultiIndex.from_product([("A0", "A1"), ("B0", "B1")]),
)
# no index name, defaults to 'level_0' and 'level_1'
sql.to_sql(temp_frame, "test_index_label", self.conn)
frame = sql.read_sql_query("SELECT * FROM test_index_label", self.conn)
assert frame.columns[0] == "level_0"
assert frame.columns[1] == "level_1"
# specifying index_label
sql.to_sql(
temp_frame,
"test_index_label",
self.conn,
if_exists="replace",
index_label=["A", "B"],
)
frame = sql.read_sql_query("SELECT * FROM test_index_label", self.conn)
assert frame.columns[:2].tolist() == ["A", "B"]
# using the index name
temp_frame.index.names = ["A", "B"]
sql.to_sql(temp_frame, "test_index_label", self.conn, if_exists="replace")
frame = sql.read_sql_query("SELECT * FROM test_index_label", self.conn)
assert frame.columns[:2].tolist() == ["A", "B"]
# has index name, but specifying index_label
sql.to_sql(
temp_frame,
"test_index_label",
self.conn,
if_exists="replace",
index_label=["C", "D"],
)
frame = sql.read_sql_query("SELECT * FROM test_index_label", self.conn)
assert frame.columns[:2].tolist() == ["C", "D"]
msg = "Length of 'index_label' should match number of levels, which is 2"
with pytest.raises(ValueError, match=msg):
sql.to_sql(
temp_frame,
"test_index_label",
self.conn,
if_exists="replace",
index_label="C",
)
def test_multiindex_roundtrip(self):
df = DataFrame.from_records(
[(1, 2.1, "line1"), (2, 1.5, "line2")],
columns=["A", "B", "C"],
index=["A", "B"],
)
df.to_sql("test_multiindex_roundtrip", self.conn)
result = sql.read_sql_query(
"SELECT * FROM test_multiindex_roundtrip", self.conn, index_col=["A", "B"]
)
tm.assert_frame_equal(df, result, check_index_type=True)
def test_integer_col_names(self):
df = DataFrame([[1, 2], [3, 4]], columns=[0, 1])
sql.to_sql(df, "test_frame_integer_col_names", self.conn, if_exists="replace")
def test_get_schema(self):
create_sql = sql.get_schema(self.test_frame1, "test", con=self.conn)
assert "CREATE" in create_sql
def test_get_schema_dtypes(self):
float_frame = DataFrame({"a": [1.1, 1.2], "b": [2.1, 2.2]})
dtype = sqlalchemy.Integer if self.mode == "sqlalchemy" else "INTEGER"
create_sql = sql.get_schema(
float_frame, "test", con=self.conn, dtype={"b": dtype}
)
assert "CREATE" in create_sql
assert "INTEGER" in create_sql
def test_get_schema_keys(self):
frame = DataFrame({"Col1": [1.1, 1.2], "Col2": [2.1, 2.2]})
create_sql = sql.get_schema(frame, "test", con=self.conn, keys="Col1")
constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("Col1")'
assert constraint_sentence in create_sql
# multiple columns as key (GH10385)
create_sql = sql.get_schema(
self.test_frame1, "test", con=self.conn, keys=["A", "B"]
)
constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("A", "B")'
assert constraint_sentence in create_sql
def test_chunksize_read(self):
df = DataFrame(np.random.randn(22, 5), columns=list("abcde"))
df.to_sql("test_chunksize", self.conn, index=False)
# reading the query in one time
res1 = sql.read_sql_query("select * from test_chunksize", self.conn)
# reading the query in chunks with read_sql_query
res2 = DataFrame()
i = 0
sizes = [5, 5, 5, 5, 2]
for chunk in sql.read_sql_query(
"select * from test_chunksize", self.conn, chunksize=5
):
res2 = concat([res2, chunk], ignore_index=True)
assert len(chunk) == sizes[i]
i += 1
tm.assert_frame_equal(res1, res2)
# reading the query in chunks with read_sql_query
if self.mode == "sqlalchemy":
res3 = DataFrame()
i = 0
sizes = [5, 5, 5, 5, 2]
for chunk in sql.read_sql_table("test_chunksize", self.conn, chunksize=5):
res3 = concat([res3, chunk], ignore_index=True)
assert len(chunk) == sizes[i]
i += 1
tm.assert_frame_equal(res1, res3)
def test_categorical(self):
# GH8624
# test that categorical gets written correctly as dense column
df = DataFrame(
{
"person_id": [1, 2, 3],
"person_name": ["John P. Doe", "Jane Dove", "John P. Doe"],
}
)
df2 = df.copy()
df2["person_name"] = df2["person_name"].astype("category")
df2.to_sql("test_categorical", self.conn, index=False)
res = sql.read_sql_query("SELECT * FROM test_categorical", self.conn)
tm.assert_frame_equal(res, df)
def test_unicode_column_name(self):
# GH 11431
df = DataFrame([[1, 2], [3, 4]], columns=["\xe9", "b"])
df.to_sql("test_unicode", self.conn, index=False)
def test_escaped_table_name(self):
# GH 13206
df = DataFrame({"A": [0, 1, 2], "B": [0.2, np.nan, 5.6]})
df.to_sql("d1187b08-4943-4c8d-a7f6", self.conn, index=False)
res = sql.read_sql_query("SELECT * FROM `d1187b08-4943-4c8d-a7f6`", self.conn)
tm.assert_frame_equal(res, df)
@pytest.mark.single
@pytest.mark.skipif(not SQLALCHEMY_INSTALLED, reason="SQLAlchemy not installed")
class TestSQLApi(SQLAlchemyMixIn, _TestSQLApi):
"""
Test the public API as it would be used directly
Tests for `read_sql_table` are included here, as this is specific for the
sqlalchemy mode.
"""
flavor = "sqlite"
mode = "sqlalchemy"
def connect(self):
return sqlalchemy.create_engine("sqlite:///:memory:")
def test_read_table_columns(self):
# test columns argument in read_table
sql.to_sql(self.test_frame1, "test_frame", self.conn)
cols = ["A", "B"]
result = sql.read_sql_table("test_frame", self.conn, columns=cols)
assert result.columns.tolist() == cols
def test_read_table_index_col(self):
# test columns argument in read_table
sql.to_sql(self.test_frame1, "test_frame", self.conn)
result = sql.read_sql_table("test_frame", self.conn, index_col="index")
assert result.index.names == ["index"]
result = sql.read_sql_table("test_frame", self.conn, index_col=["A", "B"])
assert result.index.names == ["A", "B"]
result = sql.read_sql_table(
"test_frame", self.conn, index_col=["A", "B"], columns=["C", "D"]
)
assert result.index.names == ["A", "B"]
assert result.columns.tolist() == ["C", "D"]
def test_read_sql_delegate(self):
iris_frame1 = sql.read_sql_query("SELECT * FROM iris", self.conn)
iris_frame2 = sql.read_sql("SELECT * FROM iris", self.conn)
tm.assert_frame_equal(iris_frame1, iris_frame2)
iris_frame1 = sql.read_sql_table("iris", self.conn)
iris_frame2 = sql.read_sql("iris", self.conn)
tm.assert_frame_equal(iris_frame1, iris_frame2)
def test_not_reflect_all_tables(self):
# create invalid table
qry = """CREATE TABLE invalid (x INTEGER, y UNKNOWN);"""
self.conn.execute(qry)
qry = """CREATE TABLE other_table (x INTEGER, y INTEGER);"""
self.conn.execute(qry)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger a warning.
sql.read_sql_table("other_table", self.conn)
sql.read_sql_query("SELECT * FROM other_table", self.conn)
# Verify some things
assert len(w) == 0
def test_warning_case_insensitive_table_name(self):
# see gh-7815
#
# We can't test that this warning is triggered, a the database
# configuration would have to be altered. But here we test that
# the warning is certainly NOT triggered in a normal case.
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# This should not trigger a Warning
self.test_frame1.to_sql("CaseSensitive", self.conn)
# Verify some things
assert len(w) == 0
def _get_index_columns(self, tbl_name):
from sqlalchemy.engine import reflection
insp = reflection.Inspector.from_engine(self.conn)
ixs = insp.get_indexes("test_index_saved")
ixs = [i["column_names"] for i in ixs]
return ixs
def test_sqlalchemy_type_mapping(self):
# Test Timestamp objects (no datetime64 because of timezone) (GH9085)
df = DataFrame(
{"time": to_datetime(["201412120154", "201412110254"], utc=True)}
)
db = sql.SQLDatabase(self.conn)
table = sql.SQLTable("test_type", db, frame=df)
# GH 9086: TIMESTAMP is the suggested type for datetimes with timezones
assert isinstance(table.table.c["time"].type, sqltypes.TIMESTAMP)
def test_database_uri_string(self):
# Test read_sql and .to_sql method with a database URI (GH10654)
test_frame1 = self.test_frame1
# db_uri = 'sqlite:///:memory:' # raises
# sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) near
# "iris": syntax error [SQL: 'iris']
with tm.ensure_clean() as name:
db_uri = "sqlite:///" + name
table = "iris"
test_frame1.to_sql(table, db_uri, if_exists="replace", index=False)
test_frame2 = sql.read_sql(table, db_uri)
test_frame3 = sql.read_sql_table(table, db_uri)
query = "SELECT * FROM iris"
test_frame4 = sql.read_sql_query(query, db_uri)
tm.assert_frame_equal(test_frame1, test_frame2)
tm.assert_frame_equal(test_frame1, test_frame3)
tm.assert_frame_equal(test_frame1, test_frame4)
# using driver that will not be installed on Travis to trigger error
# in sqlalchemy.create_engine -> test passing of this error to user
try:
# the rest of this test depends on pg8000's being absent
import pg8000 # noqa
pytest.skip("pg8000 is installed")
except ImportError:
pass
db_uri = "postgresql+pg8000://user:pass@host/dbname"
with pytest.raises(ImportError, match="pg8000"):
sql.read_sql("select * from table", db_uri)
def _make_iris_table_metadata(self):
sa = sqlalchemy
metadata = sa.MetaData()
iris = sa.Table(
"iris",
metadata,
sa.Column("SepalLength", sa.REAL),
sa.Column("SepalWidth", sa.REAL),
sa.Column("PetalLength", sa.REAL),
sa.Column("PetalWidth", sa.REAL),
sa.Column("Name", sa.TEXT),
)
return iris
def test_query_by_text_obj(self):
# WIP : GH10846
name_text = sqlalchemy.text("select * from iris where name=:name")
iris_df = sql.read_sql(name_text, self.conn, params={"name": "Iris-versicolor"})
all_names = set(iris_df["Name"])
assert all_names == {"Iris-versicolor"}
def test_query_by_select_obj(self):
# WIP : GH10846
iris = self._make_iris_table_metadata()
name_select = sqlalchemy.select([iris]).where(
iris.c.Name == sqlalchemy.bindparam("name")
)
iris_df = sql.read_sql(name_select, self.conn, params={"name": "Iris-setosa"})
all_names = set(iris_df["Name"])
assert all_names == {"Iris-setosa"}
class _EngineToConnMixin:
"""
A mixin that causes setup_connect to create a conn rather than an engine.
"""
@pytest.fixture(autouse=True)
def setup_method(self, load_iris_data):
super().load_test_data_and_sql()
engine = self.conn
conn = engine.connect()
self.__tx = conn.begin()
self.pandasSQL = sql.SQLDatabase(conn)
self.__engine = engine
self.conn = conn
yield
self.__tx.rollback()
self.conn.close()
self.conn = self.__engine
self.pandasSQL = sql.SQLDatabase(self.__engine)
@pytest.mark.single
class TestSQLApiConn(_EngineToConnMixin, TestSQLApi):
pass
@pytest.mark.single
class TestSQLiteFallbackApi(SQLiteMixIn, _TestSQLApi):
"""
Test the public sqlite connection fallback API
"""
flavor = "sqlite"
mode = "fallback"
def connect(self, database=":memory:"):
return sqlite3.connect(database)
def test_sql_open_close(self):
# Test if the IO in the database still work if the connection closed
# between the writing and reading (as in many real situations).
with tm.ensure_clean() as name:
conn = self.connect(name)
sql.to_sql(self.test_frame3, "test_frame3_legacy", conn, index=False)
conn.close()
conn = self.connect(name)
result = sql.read_sql_query("SELECT * FROM test_frame3_legacy;", conn)
conn.close()
tm.assert_frame_equal(self.test_frame3, result)
@pytest.mark.skipif(SQLALCHEMY_INSTALLED, reason="SQLAlchemy is installed")
def test_con_string_import_error(self):
conn = "mysql://root@localhost/pandas_nosetest"
msg = "Using URI string without sqlalchemy installed"
with pytest.raises(ImportError, match=msg):
sql.read_sql("SELECT * FROM iris", conn)
def test_read_sql_delegate(self):
iris_frame1 = sql.read_sql_query("SELECT * FROM iris", self.conn)
iris_frame2 = sql.read_sql("SELECT * FROM iris", self.conn)
tm.assert_frame_equal(iris_frame1, iris_frame2)
msg = "Execution failed on sql 'iris': near \"iris\": syntax error"
with pytest.raises(sql.DatabaseError, match=msg):
sql.read_sql("iris", self.conn)
def test_safe_names_warning(self):
# GH 6798
df = DataFrame([[1, 2], [3, 4]], columns=["a", "b "]) # has a space
# warns on create table with spaces in names
with tm.assert_produces_warning():
sql.to_sql(df, "test_frame3_legacy", self.conn, index=False)
def test_get_schema2(self):
# without providing a connection object (available for backwards comp)
create_sql = sql.get_schema(self.test_frame1, "test")
assert "CREATE" in create_sql
def _get_sqlite_column_type(self, schema, column):
for col in schema.split("\n"):
if col.split()[0].strip('""') == column:
return col.split()[1]
raise ValueError(f"Column {column} not found")
def test_sqlite_type_mapping(self):
# Test Timestamp objects (no datetime64 because of timezone) (GH9085)
df = DataFrame(
{"time": to_datetime(["201412120154", "201412110254"], utc=True)}
)
db = sql.SQLiteDatabase(self.conn)
table = sql.SQLiteTable("test_type", db, frame=df)
schema = table.sql_schema()
assert self._get_sqlite_column_type(schema, "time") == "TIMESTAMP"
# -----------------------------------------------------------------------------
# -- Database flavor specific tests
class _TestSQLAlchemy(SQLAlchemyMixIn, PandasSQLTest):
"""
Base class for testing the sqlalchemy backend.
Subclasses for specific database types are created below. Tests that
deviate for each flavor are overwritten there.
"""
flavor: str
@pytest.fixture(autouse=True, scope="class")
def setup_class(cls):
cls.setup_import()
cls.setup_driver()
conn = cls.connect()
conn.connect()
def load_test_data_and_sql(self):
self._load_raw_sql()
self._load_test1_data()
@pytest.fixture(autouse=True)
def setup_method(self, load_iris_data):
self.load_test_data_and_sql()
@classmethod
def setup_import(cls):
# Skip this test if SQLAlchemy not available
if not SQLALCHEMY_INSTALLED:
pytest.skip("SQLAlchemy not installed")
@classmethod
def setup_driver(cls):
raise NotImplementedError()
@classmethod
def connect(cls):
raise NotImplementedError()
def setup_connect(self):
try:
self.conn = self.connect()
self.pandasSQL = sql.SQLDatabase(self.conn)
# to test if connection can be made:
self.conn.connect()
except sqlalchemy.exc.OperationalError:
pytest.skip(f"Can't connect to {self.flavor} server")
def test_read_sql(self):
self._read_sql_iris()
def test_read_sql_parameter(self):
self._read_sql_iris_parameter()
def test_read_sql_named_parameter(self):
self._read_sql_iris_named_parameter()
def test_to_sql(self):
self._to_sql()
def test_to_sql_empty(self):
self._to_sql_empty()
def test_to_sql_fail(self):
self._to_sql_fail()
def test_to_sql_replace(self):
self._to_sql_replace()
def test_to_sql_append(self):
self._to_sql_append()
def test_to_sql_method_multi(self):
self._to_sql(method="multi")
def test_to_sql_method_callable(self):
self._to_sql_method_callable()
def test_create_table(self):
temp_conn = self.connect()
temp_frame = DataFrame(
{"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}
)
pandasSQL = sql.SQLDatabase(temp_conn)
pandasSQL.to_sql(temp_frame, "temp_frame")
assert temp_conn.has_table("temp_frame")
def test_drop_table(self):
temp_conn = self.connect()
temp_frame = DataFrame(
{"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}
)
pandasSQL = sql.SQLDatabase(temp_conn)
pandasSQL.to_sql(temp_frame, "temp_frame")
assert temp_conn.has_table("temp_frame")
pandasSQL.drop_table("temp_frame")
assert not temp_conn.has_table("temp_frame")
def test_roundtrip(self):
self._roundtrip()
def test_execute_sql(self):
self._execute_sql()
def test_read_table(self):
iris_frame = sql.read_sql_table("iris", con=self.conn)
self._check_iris_loaded_frame(iris_frame)
def test_read_table_columns(self):
iris_frame = sql.read_sql_table(
"iris", con=self.conn, columns=["SepalLength", "SepalLength"]
)
tm.equalContents(iris_frame.columns.values, ["SepalLength", "SepalLength"])
def test_read_table_absent_raises(self):
msg = "Table this_doesnt_exist not found"
with pytest.raises(ValueError, match=msg):
sql.read_sql_table("this_doesnt_exist", con=self.conn)
def test_default_type_conversion(self):
df = sql.read_sql_table("types_test_data", self.conn)
assert issubclass(df.FloatCol.dtype.type, np.floating)
assert issubclass(df.IntCol.dtype.type, np.integer)
assert issubclass(df.BoolCol.dtype.type, np.bool_)
# Int column with NA values stays as float
assert issubclass(df.IntColWithNull.dtype.type, np.floating)
# Bool column with NA values becomes object
assert issubclass(df.BoolColWithNull.dtype.type, object)
def test_bigint(self):
# int64 should be converted to BigInteger, GH7433
df = DataFrame(data={"i64": [2 ** 62]})
df.to_sql("test_bigint", self.conn, index=False)
result = sql.read_sql_table("test_bigint", self.conn)
tm.assert_frame_equal(df, result)
def test_default_date_load(self):
df = sql.read_sql_table("types_test_data", self.conn)
# IMPORTANT - sqlite has no native date type, so shouldn't parse, but
# MySQL SHOULD be converted.
assert issubclass(df.DateCol.dtype.type, np.datetime64)
def test_datetime_with_timezone(self):
# edge case that converts postgresql datetime with time zone types
# to datetime64[ns,psycopg2.tz.FixedOffsetTimezone..], which is ok
# but should be more natural, so coerce to datetime64[ns] for now
def check(col):
# check that a column is either datetime64[ns]
# or datetime64[ns, UTC]
if is_datetime64_dtype(col.dtype):
# "2000-01-01 00:00:00-08:00" should convert to
# "2000-01-01 08:00:00"
assert col[0] == Timestamp("2000-01-01 08:00:00")
# "2000-06-01 00:00:00-07:00" should convert to
# "2000-06-01 07:00:00"
assert col[1] == Timestamp("2000-06-01 07:00:00")
elif is_datetime64tz_dtype(col.dtype):
assert str(col.dt.tz) == "UTC"
# "2000-01-01 00:00:00-08:00" should convert to
# "2000-01-01 08:00:00"
# "2000-06-01 00:00:00-07:00" should convert to
# "2000-06-01 07:00:00"
# GH 6415
expected_data = [
Timestamp("2000-01-01 08:00:00", tz="UTC"),
Timestamp("2000-06-01 07:00:00", tz="UTC"),
]
expected = Series(expected_data, name=col.name)
tm.assert_series_equal(col, expected)
else:
raise AssertionError(
f"DateCol loaded with incorrect type -> {col.dtype}"
)
# GH11216
df = pd.read_sql_query("select * from types_test_data", self.conn)
if not hasattr(df, "DateColWithTz"):
pytest.skip("no column with datetime with time zone")
# this is parsed on Travis (linux), but not on macosx for some reason
# even with the same versions of psycopg2 & sqlalchemy, possibly a
# Postgresql server version difference
col = df.DateColWithTz
assert is_datetime64tz_dtype(col.dtype)
df = pd.read_sql_query(
"select * from types_test_data", self.conn, parse_dates=["DateColWithTz"]
)
if not hasattr(df, "DateColWithTz"):
pytest.skip("no column with datetime with time zone")
col = df.DateColWithTz
assert is_datetime64tz_dtype(col.dtype)
assert str(col.dt.tz) == "UTC"
check(df.DateColWithTz)
df = pd.concat(
list(
pd.read_sql_query(
"select * from types_test_data", self.conn, chunksize=1
)
),
ignore_index=True,
)
col = df.DateColWithTz
assert is_datetime64tz_dtype(col.dtype)
assert str(col.dt.tz) == "UTC"
expected = sql.read_sql_table("types_test_data", self.conn)
col = expected.DateColWithTz
assert is_datetime64tz_dtype(col.dtype)
tm.assert_series_equal(df.DateColWithTz, expected.DateColWithTz)
# xref #7139
# this might or might not be converted depending on the postgres driver
df = sql.read_sql_table("types_test_data", self.conn)
check(df.DateColWithTz)
def test_datetime_with_timezone_roundtrip(self):
# GH 9086
# Write datetimetz data to a db and read it back
# For dbs that support timestamps with timezones, should get back UTC
# otherwise naive data should be returned
expected = DataFrame(
{"A": date_range("2013-01-01 09:00:00", periods=3, tz="US/Pacific")}
)
expected.to_sql("test_datetime_tz", self.conn, index=False)
if self.flavor == "postgresql":
# SQLAlchemy "timezones" (i.e. offsets) are coerced to UTC
expected["A"] = expected["A"].dt.tz_convert("UTC")
else:
# Otherwise, timestamps are returned as local, naive
expected["A"] = expected["A"].dt.tz_localize(None)
result = sql.read_sql_table("test_datetime_tz", self.conn)
tm.assert_frame_equal(result, expected)
result = sql.read_sql_query("SELECT * FROM test_datetime_tz", self.conn)
if self.flavor == "sqlite":
# read_sql_query does not return datetime type like read_sql_table
assert isinstance(result.loc[0, "A"], str)
result["A"] = to_datetime(result["A"])
tm.assert_frame_equal(result, expected)
def test_out_of_bounds_datetime(self):
# GH 26761
data = pd.DataFrame({"date": datetime(9999, 1, 1)}, index=[0])
data.to_sql("test_datetime_obb", self.conn, index=False)
result = sql.read_sql_table("test_datetime_obb", self.conn)
expected = pd.DataFrame([pd.NaT], columns=["date"])
tm.assert_frame_equal(result, expected)
def test_naive_datetimeindex_roundtrip(self):
# GH 23510
# Ensure that a naive DatetimeIndex isn't converted to UTC
dates = date_range("2018-01-01", periods=5, freq="6H")._with_freq(None)
expected = DataFrame({"nums": range(5)}, index=dates)
expected.to_sql("foo_table", self.conn, index_label="info_date")
result = sql.read_sql_table("foo_table", self.conn, index_col="info_date")
# result index with gain a name from a set_index operation; expected
tm.assert_frame_equal(result, expected, check_names=False)
def test_date_parsing(self):
# No Parsing
df = sql.read_sql_table("types_test_data", self.conn)
expected_type = object if self.flavor == "sqlite" else np.datetime64
assert issubclass(df.DateCol.dtype.type, expected_type)
df = sql.read_sql_table("types_test_data", self.conn, parse_dates=["DateCol"])
assert issubclass(df.DateCol.dtype.type, np.datetime64)
df = sql.read_sql_table(
"types_test_data", self.conn, parse_dates={"DateCol": "%Y-%m-%d %H:%M:%S"}
)
assert issubclass(df.DateCol.dtype.type, np.datetime64)
df = sql.read_sql_table(
"types_test_data",
self.conn,
parse_dates={"DateCol": {"format": "%Y-%m-%d %H:%M:%S"}},
)
assert issubclass(df.DateCol.dtype.type, np.datetime64)
df = sql.read_sql_table(
"types_test_data", self.conn, parse_dates=["IntDateCol"]
)
assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
df = sql.read_sql_table(
"types_test_data", self.conn, parse_dates={"IntDateCol": "s"}
)
assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
df = sql.read_sql_table(
"types_test_data", self.conn, parse_dates={"IntDateCol": {"unit": "s"}}
)
assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
def test_datetime(self):
df = DataFrame(
{"A": date_range("2013-01-01 09:00:00", periods=3), "B": np.arange(3.0)}
)
df.to_sql("test_datetime", self.conn)
# with read_table -> type information from schema used
result = sql.read_sql_table("test_datetime", self.conn)
result = result.drop("index", axis=1)
tm.assert_frame_equal(result, df)
# with read_sql -> no type information -> sqlite has no native
result = sql.read_sql_query("SELECT * FROM test_datetime", self.conn)
result = result.drop("index", axis=1)
if self.flavor == "sqlite":
assert isinstance(result.loc[0, "A"], str)
result["A"] = to_datetime(result["A"])
tm.assert_frame_equal(result, df)
else:
tm.assert_frame_equal(result, df)
def test_datetime_NaT(self):
df = DataFrame(
{"A": date_range("2013-01-01 09:00:00", periods=3), "B": np.arange(3.0)}
)
df.loc[1, "A"] = np.nan
df.to_sql("test_datetime", self.conn, index=False)
# with read_table -> type information from schema used
result = sql.read_sql_table("test_datetime", self.conn)
tm.assert_frame_equal(result, df)
# with read_sql -> no type information -> sqlite has no native
result = sql.read_sql_query("SELECT * FROM test_datetime", self.conn)
if self.flavor == "sqlite":
assert isinstance(result.loc[0, "A"], str)
result["A"] = to_datetime(result["A"], errors="coerce")
tm.assert_frame_equal(result, df)
else:
tm.assert_frame_equal(result, df)
def test_datetime_date(self):
# test support for datetime.date
df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"])
df.to_sql("test_date", self.conn, index=False)
res = read_sql_table("test_date", self.conn)
result = res["a"]
expected = to_datetime(df["a"])
# comes back as datetime64
tm.assert_series_equal(result, expected)
def test_datetime_time(self):
# test support for datetime.time
df = DataFrame([time(9, 0, 0), time(9, 1, 30)], columns=["a"])
df.to_sql("test_time", self.conn, index=False)
res = read_sql_table("test_time", self.conn)
tm.assert_frame_equal(res, df)
# GH8341
# first, use the fallback to have the sqlite adapter put in place
sqlite_conn = TestSQLiteFallback.connect()
sql.to_sql(df, "test_time2", sqlite_conn, index=False)
res = sql.read_sql_query("SELECT * FROM test_time2", sqlite_conn)
ref = df.applymap(lambda _: _.strftime("%H:%M:%S.%f"))
tm.assert_frame_equal(ref, res) # check if adapter is in place
# then test if sqlalchemy is unaffected by the sqlite adapter
sql.to_sql(df, "test_time3", self.conn, index=False)
if self.flavor == "sqlite":
res = sql.read_sql_query("SELECT * FROM test_time3", self.conn)
ref = df.applymap(lambda _: _.strftime("%H:%M:%S.%f"))
tm.assert_frame_equal(ref, res)
res = sql.read_sql_table("test_time3", self.conn)
tm.assert_frame_equal(df, res)
def test_mixed_dtype_insert(self):
# see GH6509
s1 = Series(2 ** 25 + 1, dtype=np.int32)
s2 = Series(0.0, dtype=np.float32)
df = DataFrame({"s1": s1, "s2": s2})
# write and read again
df.to_sql("test_read_write", self.conn, index=False)
df2 = sql.read_sql_table("test_read_write", self.conn)
tm.assert_frame_equal(df, df2, check_dtype=False, check_exact=True)
def test_nan_numeric(self):
# NaNs in numeric float column
df = DataFrame({"A": [0, 1, 2], "B": [0.2, np.nan, 5.6]})
df.to_sql("test_nan", self.conn, index=False)
# with read_table
result = sql.read_sql_table("test_nan", self.conn)
tm.assert_frame_equal(result, df)
# with read_sql
result = sql.read_sql_query("SELECT * FROM test_nan", self.conn)
tm.assert_frame_equal(result, df)
def test_nan_fullcolumn(self):
# full NaN column (numeric float column)
df = DataFrame({"A": [0, 1, 2], "B": [np.nan, np.nan, np.nan]})
df.to_sql("test_nan", self.conn, index=False)
# with read_table
result = sql.read_sql_table("test_nan", self.conn)
tm.assert_frame_equal(result, df)
# with read_sql -> not type info from table -> stays None
df["B"] = df["B"].astype("object")
df["B"] = None
result = sql.read_sql_query("SELECT * FROM test_nan", self.conn)
tm.assert_frame_equal(result, df)
def test_nan_string(self):
# NaNs in string column
df = DataFrame({"A": [0, 1, 2], "B": ["a", "b", np.nan]})
df.to_sql("test_nan", self.conn, index=False)
# NaNs are coming back as None
df.loc[2, "B"] = None
# with read_table
result = sql.read_sql_table("test_nan", self.conn)
tm.assert_frame_equal(result, df)
# with read_sql
result = sql.read_sql_query("SELECT * FROM test_nan", self.conn)
tm.assert_frame_equal(result, df)
def _get_index_columns(self, tbl_name):
from sqlalchemy.engine import reflection
insp = reflection.Inspector.from_engine(self.conn)
ixs = insp.get_indexes(tbl_name)
ixs = [i["column_names"] for i in ixs]
return ixs
def test_to_sql_save_index(self):
self._to_sql_save_index()
def test_transactions(self):
self._transaction_test()
def test_get_schema_create_table(self):
# Use a dataframe without a bool column, since MySQL converts bool to
# TINYINT (which read_sql_table returns as an int and causes a dtype
# mismatch)
self._load_test3_data()
tbl = "test_get_schema_create_table"
create_sql = sql.get_schema(self.test_frame3, tbl, con=self.conn)
blank_test_df = self.test_frame3.iloc[:0]
self.drop_table(tbl)
self.conn.execute(create_sql)
returned_df = sql.read_sql_table(tbl, self.conn)
tm.assert_frame_equal(returned_df, blank_test_df, check_index_type=False)
self.drop_table(tbl)
def test_dtype(self):
cols = ["A", "B"]
data = [(0.8, True), (0.9, None)]
df = DataFrame(data, columns=cols)
df.to_sql("dtype_test", self.conn)
df.to_sql("dtype_test2", self.conn, dtype={"B": sqlalchemy.TEXT})
meta = sqlalchemy.schema.MetaData(bind=self.conn)
meta.reflect()
sqltype = meta.tables["dtype_test2"].columns["B"].type
assert isinstance(sqltype, sqlalchemy.TEXT)
msg = "The type of B is not a SQLAlchemy type"
with pytest.raises(ValueError, match=msg):
df.to_sql("error", self.conn, dtype={"B": str})
# GH9083
df.to_sql("dtype_test3", self.conn, dtype={"B": sqlalchemy.String(10)})
meta.reflect()
sqltype = meta.tables["dtype_test3"].columns["B"].type
assert isinstance(sqltype, sqlalchemy.String)
assert sqltype.length == 10
# single dtype
df.to_sql("single_dtype_test", self.conn, dtype=sqlalchemy.TEXT)
meta = sqlalchemy.schema.MetaData(bind=self.conn)
meta.reflect()
sqltypea = meta.tables["single_dtype_test"].columns["A"].type
sqltypeb = meta.tables["single_dtype_test"].columns["B"].type
assert isinstance(sqltypea, sqlalchemy.TEXT)
assert isinstance(sqltypeb, sqlalchemy.TEXT)
def test_notna_dtype(self):
cols = {
"Bool": Series([True, None]),
"Date": Series([datetime(2012, 5, 1), None]),
"Int": Series([1, None], dtype="object"),
"Float": Series([1.1, None]),
}
df = DataFrame(cols)
tbl = "notna_dtype_test"
df.to_sql(tbl, self.conn)
returned_df = sql.read_sql_table(tbl, self.conn) # noqa
meta = sqlalchemy.schema.MetaData(bind=self.conn)
meta.reflect()
if self.flavor == "mysql":
my_type = sqltypes.Integer
else:
my_type = sqltypes.Boolean
col_dict = meta.tables[tbl].columns
assert isinstance(col_dict["Bool"].type, my_type)
assert isinstance(col_dict["Date"].type, sqltypes.DateTime)
assert isinstance(col_dict["Int"].type, sqltypes.Integer)
assert isinstance(col_dict["Float"].type, sqltypes.Float)
def test_double_precision(self):
V = 1.23456789101112131415
df = DataFrame(
{
"f32": Series([V], dtype="float32"),
"f64": Series([V], dtype="float64"),
"f64_as_f32": Series([V], dtype="float64"),
"i32": Series([5], dtype="int32"),
"i64": Series([5], dtype="int64"),
}
)
df.to_sql(
"test_dtypes",
self.conn,
index=False,
if_exists="replace",
dtype={"f64_as_f32": sqlalchemy.Float(precision=23)},
)
res = sql.read_sql_table("test_dtypes", self.conn)
# check precision of float64
assert np.round(df["f64"].iloc[0], 14) == np.round(res["f64"].iloc[0], 14)
# check sql types
meta = sqlalchemy.schema.MetaData(bind=self.conn)
meta.reflect()
col_dict = meta.tables["test_dtypes"].columns
assert str(col_dict["f32"].type) == str(col_dict["f64_as_f32"].type)
assert isinstance(col_dict["f32"].type, sqltypes.Float)
assert isinstance(col_dict["f64"].type, sqltypes.Float)
assert isinstance(col_dict["i32"].type, sqltypes.Integer)
assert isinstance(col_dict["i64"].type, sqltypes.BigInteger)
def test_connectable_issue_example(self):
# This tests the example raised in issue
# https://github.com/pandas-dev/pandas/issues/10104
def foo(connection):
query = "SELECT test_foo_data FROM test_foo_data"
return sql.read_sql_query(query, con=connection)
def bar(connection, data):
data.to_sql(name="test_foo_data", con=connection, if_exists="append")
def main(connectable):
with connectable.connect() as conn:
with conn.begin():
foo_data = conn.run_callable(foo)
conn.run_callable(bar, foo_data)
DataFrame({"test_foo_data": [0, 1, 2]}).to_sql("test_foo_data", self.conn)
main(self.conn)
@pytest.mark.parametrize(
"input",
[{"foo": [np.inf]}, {"foo": [-np.inf]}, {"foo": [-np.inf], "infe0": ["bar"]}],
)
def test_to_sql_with_negative_npinf(self, input):
# GH 34431
df = pd.DataFrame(input)
if self.flavor == "mysql":
msg = "inf cannot be used with MySQL"
with pytest.raises(ValueError, match=msg):
df.to_sql("foobar", self.conn, index=False)
else:
df.to_sql("foobar", self.conn, index=False)
res = sql.read_sql_table("foobar", self.conn)
tm.assert_equal(df, res)
def test_temporary_table(self):
test_data = "Hello, World!"
expected = DataFrame({"spam": [test_data]})
Base = declarative.declarative_base()
class Temporary(Base):
__tablename__ = "temp_test"
__table_args__ = {"prefixes": ["TEMPORARY"]}
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True)
spam = sqlalchemy.Column(sqlalchemy.Unicode(30), nullable=False)
Session = sa_session.sessionmaker(bind=self.conn)
session = Session()
with session.transaction:
conn = session.connection()
Temporary.__table__.create(conn)
session.add(Temporary(spam=test_data))
session.flush()
df = sql.read_sql_query(sql=sqlalchemy.select([Temporary.spam]), con=conn)
tm.assert_frame_equal(df, expected)
class _TestSQLAlchemyConn(_EngineToConnMixin, _TestSQLAlchemy):
def test_transactions(self):
pytest.skip("Nested transactions rollbacks don't work with Pandas")
class _TestSQLiteAlchemy:
"""
Test the sqlalchemy backend against an in-memory sqlite database.
"""
flavor = "sqlite"
@classmethod
def connect(cls):
return sqlalchemy.create_engine("sqlite:///:memory:")
@classmethod
def setup_driver(cls):
# sqlite3 is built-in
cls.driver = None
def test_default_type_conversion(self):
df = sql.read_sql_table("types_test_data", self.conn)
assert issubclass(df.FloatCol.dtype.type, np.floating)
assert issubclass(df.IntCol.dtype.type, np.integer)
# sqlite has no boolean type, so integer type is returned
assert issubclass(df.BoolCol.dtype.type, np.integer)
# Int column with NA values stays as float
assert issubclass(df.IntColWithNull.dtype.type, np.floating)
# Non-native Bool column with NA values stays as float
assert issubclass(df.BoolColWithNull.dtype.type, np.floating)
def test_default_date_load(self):
df = sql.read_sql_table("types_test_data", self.conn)
# IMPORTANT - sqlite has no native date type, so shouldn't parse, but
assert not issubclass(df.DateCol.dtype.type, np.datetime64)
def test_bigint_warning(self):
# test no warning for BIGINT (to support int64) is raised (GH7433)
df = DataFrame({"a": [1, 2]}, dtype="int64")
df.to_sql("test_bigintwarning", self.conn, index=False)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
sql.read_sql_table("test_bigintwarning", self.conn)
assert len(w) == 0
class _TestMySQLAlchemy:
"""
Test the sqlalchemy backend against an MySQL database.
"""
flavor = "mysql"
@classmethod
def connect(cls):
return sqlalchemy.create_engine(
f"mysql+{cls.driver}://root@localhost/pandas_nosetest",
connect_args=cls.connect_args,
)
@classmethod
def setup_driver(cls):
pymysql = pytest.importorskip("pymysql")
cls.driver = "pymysql"
cls.connect_args = {"client_flag": pymysql.constants.CLIENT.MULTI_STATEMENTS}
def test_default_type_conversion(self):
df = sql.read_sql_table("types_test_data", self.conn)
assert issubclass(df.FloatCol.dtype.type, np.floating)
assert issubclass(df.IntCol.dtype.type, np.integer)
# MySQL has no real BOOL type (it's an alias for TINYINT)
assert issubclass(df.BoolCol.dtype.type, np.integer)
# Int column with NA values stays as float
assert issubclass(df.IntColWithNull.dtype.type, np.floating)
# Bool column with NA = int column with NA values => becomes float
assert issubclass(df.BoolColWithNull.dtype.type, np.floating)
def test_read_procedure(self):
import pymysql
# see GH7324. Although it is more an api test, it is added to the
# mysql tests as sqlite does not have stored procedures
df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
df.to_sql("test_procedure", self.conn, index=False)
proc = """DROP PROCEDURE IF EXISTS get_testdb;
CREATE PROCEDURE get_testdb ()
BEGIN
SELECT * FROM test_procedure;
END"""
connection = self.conn.connect()
trans = connection.begin()
try:
r1 = connection.execute(proc) # noqa
trans.commit()
except pymysql.Error:
trans.rollback()
raise
res1 = sql.read_sql_query("CALL get_testdb();", self.conn)
tm.assert_frame_equal(df, res1)
# test delegation to read_sql_query
res2 = sql.read_sql("CALL get_testdb();", self.conn)
tm.assert_frame_equal(df, res2)
class _TestPostgreSQLAlchemy:
"""
Test the sqlalchemy backend against an PostgreSQL database.
"""
flavor = "postgresql"
@classmethod
def connect(cls):
return sqlalchemy.create_engine(
f"postgresql+{cls.driver}://postgres@localhost/pandas_nosetest"
)
@classmethod
def setup_driver(cls):
pytest.importorskip("psycopg2")
cls.driver = "psycopg2"
def test_schema_support(self):
# only test this for postgresql (schema's not supported in
# mysql/sqlite)
df = DataFrame({"col1": [1, 2], "col2": [0.1, 0.2], "col3": ["a", "n"]})
# create a schema
self.conn.execute("DROP SCHEMA IF EXISTS other CASCADE;")
self.conn.execute("CREATE SCHEMA other;")
# write dataframe to different schema's
df.to_sql("test_schema_public", self.conn, index=False)
df.to_sql(
"test_schema_public_explicit", self.conn, index=False, schema="public"
)
df.to_sql("test_schema_other", self.conn, index=False, schema="other")
# read dataframes back in
res1 = sql.read_sql_table("test_schema_public", self.conn)
tm.assert_frame_equal(df, res1)
res2 = sql.read_sql_table("test_schema_public_explicit", self.conn)
tm.assert_frame_equal(df, res2)
res3 = sql.read_sql_table(
"test_schema_public_explicit", self.conn, schema="public"
)
tm.assert_frame_equal(df, res3)
res4 = sql.read_sql_table("test_schema_other", self.conn, schema="other")
tm.assert_frame_equal(df, res4)
msg = "Table test_schema_other not found"
with pytest.raises(ValueError, match=msg):
sql.read_sql_table("test_schema_other", self.conn, schema="public")
# different if_exists options
# create a schema
self.conn.execute("DROP SCHEMA IF EXISTS other CASCADE;")
self.conn.execute("CREATE SCHEMA other;")
# write dataframe with different if_exists options
df.to_sql("test_schema_other", self.conn, schema="other", index=False)
df.to_sql(
"test_schema_other",
self.conn,
schema="other",
index=False,
if_exists="replace",
)
df.to_sql(
"test_schema_other",
self.conn,
schema="other",
index=False,
if_exists="append",
)
res = sql.read_sql_table("test_schema_other", self.conn, schema="other")
tm.assert_frame_equal(concat([df, df], ignore_index=True), res)
# specifying schema in user-provided meta
# The schema won't be applied on another Connection
# because of transactional schemas
if isinstance(self.conn, sqlalchemy.engine.Engine):
engine2 = self.connect()
meta = sqlalchemy.MetaData(engine2, schema="other")
pdsql = sql.SQLDatabase(engine2, meta=meta)
pdsql.to_sql(df, "test_schema_other2", index=False)
pdsql.to_sql(df, "test_schema_other2", index=False, if_exists="replace")
pdsql.to_sql(df, "test_schema_other2", index=False, if_exists="append")
res1 = sql.read_sql_table("test_schema_other2", self.conn, schema="other")
res2 = pdsql.read_table("test_schema_other2")
tm.assert_frame_equal(res1, res2)
def test_copy_from_callable_insertion_method(self):
# GH 8953
# Example in io.rst found under _io.sql.method
# not available in sqlite, mysql
def psql_insert_copy(table, conn, keys, data_iter):
# gets a DBAPI connection that can provide a cursor
dbapi_conn = conn.connection
with dbapi_conn.cursor() as cur:
s_buf = StringIO()
writer = csv.writer(s_buf)
writer.writerows(data_iter)
s_buf.seek(0)
columns = ", ".join(f'"{k}"' for k in keys)
if table.schema:
table_name = f"{table.schema}.{table.name}"
else:
table_name = table.name
sql_query = f"COPY {table_name} ({columns}) FROM STDIN WITH CSV"
cur.copy_expert(sql=sql_query, file=s_buf)
expected = DataFrame({"col1": [1, 2], "col2": [0.1, 0.2], "col3": ["a", "n"]})
expected.to_sql(
"test_copy_insert", self.conn, index=False, method=psql_insert_copy
)
result = sql.read_sql_table("test_copy_insert", self.conn)
tm.assert_frame_equal(result, expected)
@pytest.mark.single
@pytest.mark.db
class TestMySQLAlchemy(_TestMySQLAlchemy, _TestSQLAlchemy):
pass
@pytest.mark.single
@pytest.mark.db
class TestMySQLAlchemyConn(_TestMySQLAlchemy, _TestSQLAlchemyConn):
pass
@pytest.mark.single
@pytest.mark.db
class TestPostgreSQLAlchemy(_TestPostgreSQLAlchemy, _TestSQLAlchemy):
pass
@pytest.mark.single
@pytest.mark.db
class TestPostgreSQLAlchemyConn(_TestPostgreSQLAlchemy, _TestSQLAlchemyConn):
pass
@pytest.mark.single
class TestSQLiteAlchemy(_TestSQLiteAlchemy, _TestSQLAlchemy):
pass
@pytest.mark.single
class TestSQLiteAlchemyConn(_TestSQLiteAlchemy, _TestSQLAlchemyConn):
pass
# -----------------------------------------------------------------------------
# -- Test Sqlite / MySQL fallback
@pytest.mark.single
class TestSQLiteFallback(SQLiteMixIn, PandasSQLTest):
"""
Test the fallback mode against an in-memory sqlite database.
"""
flavor = "sqlite"
@classmethod
def connect(cls):
return sqlite3.connect(":memory:")
def setup_connect(self):
self.conn = self.connect()
def load_test_data_and_sql(self):
self.pandasSQL = sql.SQLiteDatabase(self.conn)
self._load_test1_data()
@pytest.fixture(autouse=True)
def setup_method(self, load_iris_data):
self.load_test_data_and_sql()
def test_read_sql(self):
self._read_sql_iris()
def test_read_sql_parameter(self):
self._read_sql_iris_parameter()
def test_read_sql_named_parameter(self):
self._read_sql_iris_named_parameter()
def test_to_sql(self):
self._to_sql()
def test_to_sql_empty(self):
self._to_sql_empty()
def test_to_sql_fail(self):
self._to_sql_fail()
def test_to_sql_replace(self):
self._to_sql_replace()
def test_to_sql_append(self):
self._to_sql_append()
def test_to_sql_method_multi(self):
# GH 29921
self._to_sql(method="multi")
def test_create_and_drop_table(self):
temp_frame = DataFrame(
{"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}
)
self.pandasSQL.to_sql(temp_frame, "drop_test_frame")
assert self.pandasSQL.has_table("drop_test_frame")
self.pandasSQL.drop_table("drop_test_frame")
assert not self.pandasSQL.has_table("drop_test_frame")
def test_roundtrip(self):
self._roundtrip()
def test_execute_sql(self):
self._execute_sql()
def test_datetime_date(self):
# test support for datetime.date
df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"])
df.to_sql("test_date", self.conn, index=False)
res = read_sql_query("SELECT * FROM test_date", self.conn)
if self.flavor == "sqlite":
# comes back as strings
tm.assert_frame_equal(res, df.astype(str))
elif self.flavor == "mysql":
tm.assert_frame_equal(res, df)
def test_datetime_time(self):
# test support for datetime.time, GH #8341
df = DataFrame([time(9, 0, 0), time(9, 1, 30)], columns=["a"])
df.to_sql("test_time", self.conn, index=False)
res = read_sql_query("SELECT * FROM test_time", self.conn)
if self.flavor == "sqlite":
# comes back as strings
expected = df.applymap(lambda _: _.strftime("%H:%M:%S.%f"))
tm.assert_frame_equal(res, expected)
def _get_index_columns(self, tbl_name):
ixs = sql.read_sql_query(
"SELECT * FROM sqlite_master WHERE type = 'index' "
+ f"AND tbl_name = '{tbl_name}'",
self.conn,
)
ix_cols = []
for ix_name in ixs.name:
ix_info = sql.read_sql_query(f"PRAGMA index_info({ix_name})", self.conn)
ix_cols.append(ix_info.name.tolist())
return ix_cols
def test_to_sql_save_index(self):
self._to_sql_save_index()
def test_transactions(self):
self._transaction_test()
def _get_sqlite_column_type(self, table, column):
recs = self.conn.execute(f"PRAGMA table_info({table})")
for cid, name, ctype, not_null, default, pk in recs:
if name == column:
return ctype
raise ValueError(f"Table {table}, column {column} not found")
def test_dtype(self):
if self.flavor == "mysql":
pytest.skip("Not applicable to MySQL legacy")
cols = ["A", "B"]
data = [(0.8, True), (0.9, None)]
df = DataFrame(data, columns=cols)
df.to_sql("dtype_test", self.conn)
df.to_sql("dtype_test2", self.conn, dtype={"B": "STRING"})
# sqlite stores Boolean values as INTEGER
assert self._get_sqlite_column_type("dtype_test", "B") == "INTEGER"
assert self._get_sqlite_column_type("dtype_test2", "B") == "STRING"
msg = r"B \(<class 'bool'>\) not a string"
with pytest.raises(ValueError, match=msg):
df.to_sql("error", self.conn, dtype={"B": bool})
# single dtype
df.to_sql("single_dtype_test", self.conn, dtype="STRING")
assert self._get_sqlite_column_type("single_dtype_test", "A") == "STRING"
assert self._get_sqlite_column_type("single_dtype_test", "B") == "STRING"
def test_notna_dtype(self):
if self.flavor == "mysql":
pytest.skip("Not applicable to MySQL legacy")
cols = {
"Bool": Series([True, None]),
"Date": Series([datetime(2012, 5, 1), None]),
"Int": Series([1, None], dtype="object"),
"Float": Series([1.1, None]),
}
df = DataFrame(cols)
tbl = "notna_dtype_test"
df.to_sql(tbl, self.conn)
assert self._get_sqlite_column_type(tbl, "Bool") == "INTEGER"
assert self._get_sqlite_column_type(tbl, "Date") == "TIMESTAMP"
assert self._get_sqlite_column_type(tbl, "Int") == "INTEGER"
assert self._get_sqlite_column_type(tbl, "Float") == "REAL"
def test_illegal_names(self):
# For sqlite, these should work fine
df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
msg = "Empty table or column name specified"
with pytest.raises(ValueError, match=msg):
df.to_sql("", self.conn)
for ndx, weird_name in enumerate(
[
"test_weird_name]",
"test_weird_name[",
"test_weird_name`",
'test_weird_name"',
"test_weird_name'",
"_b.test_weird_name_01-30",
'"_b.test_weird_name_01-30"',
"99beginswithnumber",
"12345",
"\xe9",
]
):
df.to_sql(weird_name, self.conn)
sql.table_exists(weird_name, self.conn)
df2 = DataFrame([[1, 2], [3, 4]], columns=["a", weird_name])
c_tbl = f"test_weird_col_name{ndx:d}"
df2.to_sql(c_tbl, self.conn)
sql.table_exists(c_tbl, self.conn)
# -----------------------------------------------------------------------------
# -- Old tests from 0.13.1 (before refactor using sqlalchemy)
def date_format(dt):
"""Returns date in YYYYMMDD format."""
return dt.strftime("%Y%m%d")
_formatters = {
datetime: "'{}'".format,
str: "'{}'".format,
np.str_: "'{}'".format,
bytes: "'{}'".format,
float: "{:.8f}".format,
int: "{:d}".format,
type(None): lambda x: "NULL",
np.float64: "{:.10f}".format,
bool: "'{!s}'".format,
}
def format_query(sql, *args):
"""
"""
processed_args = []
for arg in args:
if isinstance(arg, float) and isna(arg):
arg = None
formatter = _formatters[type(arg)]
processed_args.append(formatter(arg))
return sql % tuple(processed_args)
def tquery(query, con=None, cur=None):
"""Replace removed sql.tquery function"""
res = sql.execute(query, con=con, cur=cur).fetchall()
if res is None:
return None
else:
return list(res)
@pytest.mark.single
class TestXSQLite(SQLiteMixIn):
@pytest.fixture(autouse=True)
def setup_method(self, request, datapath):
self.method = request.function
self.conn = sqlite3.connect(":memory:")
# In some test cases we may close db connection
# Re-open conn here so we can perform cleanup in teardown
yield
self.method = request.function
self.conn = sqlite3.connect(":memory:")
def test_basic(self):
frame = tm.makeTimeDataFrame()
self._check_roundtrip(frame)
def test_write_row_by_row(self):
frame = tm.makeTimeDataFrame()
frame.iloc[0, 0] = np.nan
create_sql = sql.get_schema(frame, "test")
cur = self.conn.cursor()
cur.execute(create_sql)
cur = self.conn.cursor()
ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
for idx, row in frame.iterrows():
fmt_sql = format_query(ins, *row)
tquery(fmt_sql, cur=cur)
self.conn.commit()
result = sql.read_sql("select * from test", con=self.conn)
result.index = frame.index
tm.assert_frame_equal(result, frame, rtol=1e-3)
def test_execute(self):
frame = tm.makeTimeDataFrame()
create_sql = sql.get_schema(frame, "test")
cur = self.conn.cursor()
cur.execute(create_sql)
ins = "INSERT INTO test VALUES (?, ?, ?, ?)"
row = frame.iloc[0]
sql.execute(ins, self.conn, params=tuple(row))
self.conn.commit()
result = sql.read_sql("select * from test", self.conn)
result.index = frame.index[:1]
tm.assert_frame_equal(result, frame[:1])
def test_schema(self):
frame = tm.makeTimeDataFrame()
create_sql = sql.get_schema(frame, "test")
lines = create_sql.splitlines()
for l in lines:
tokens = l.split(" ")
if len(tokens) == 2 and tokens[0] == "A":
assert tokens[1] == "DATETIME"
frame = tm.makeTimeDataFrame()
create_sql = sql.get_schema(frame, "test", keys=["A", "B"])
lines = create_sql.splitlines()
assert 'PRIMARY KEY ("A", "B")' in create_sql
cur = self.conn.cursor()
cur.execute(create_sql)
def test_execute_fail(self):
create_sql = """
CREATE TABLE test
(
a TEXT,
b TEXT,
c REAL,
PRIMARY KEY (a, b)
);
"""
cur = self.conn.cursor()
cur.execute(create_sql)
sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)', self.conn)
with pytest.raises(Exception):
sql.execute('INSERT INTO test VALUES("foo", "bar", 7)', self.conn)
def test_execute_closed_connection(self):
create_sql = """
CREATE TABLE test
(
a TEXT,
b TEXT,
c REAL,
PRIMARY KEY (a, b)
);
"""
cur = self.conn.cursor()
cur.execute(create_sql)
sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
self.conn.close()
with pytest.raises(Exception):
tquery("select * from test", con=self.conn)
def test_na_roundtrip(self):
pass
def _check_roundtrip(self, frame):
sql.to_sql(frame, name="test_table", con=self.conn, index=False)
result = sql.read_sql("select * from test_table", self.conn)
# HACK! Change this once indexes are handled properly.
result.index = frame.index
expected = frame
tm.assert_frame_equal(result, expected)
frame["txt"] = ["a"] * len(frame)
frame2 = frame.copy()
new_idx = Index(np.arange(len(frame2))) + 10
frame2["Idx"] = new_idx.copy()
sql.to_sql(frame2, name="test_table2", con=self.conn, index=False)
result = sql.read_sql("select * from test_table2", self.conn, index_col="Idx")
expected = frame.copy()
expected.index = new_idx
expected.index.name = "Idx"
tm.assert_frame_equal(expected, result)
def test_keyword_as_column_names(self):
df = DataFrame({"From": np.ones(5)})
sql.to_sql(df, con=self.conn, name="testkeywords", index=False)
def test_onecolumn_of_integer(self):
# GH 3628
# a column_of_integers dataframe should transfer well to sql
mono_df = DataFrame([1, 2], columns=["c0"])
sql.to_sql(mono_df, con=self.conn, name="mono_df", index=False)
# computing the sum via sql
con_x = self.conn
the_sum = sum(my_c0[0] for my_c0 in con_x.execute("select * from mono_df"))
# it should not fail, and gives 3 ( Issue #3628 )
assert the_sum == 3
result = sql.read_sql("select * from mono_df", con_x)
tm.assert_frame_equal(result, mono_df)
def test_if_exists(self):
df_if_exists_1 = DataFrame({"col1": [1, 2], "col2": ["A", "B"]})
df_if_exists_2 = DataFrame({"col1": [3, 4, 5], "col2": ["C", "D", "E"]})
table_name = "table_if_exists"
sql_select = f"SELECT * FROM {table_name}"
def clean_up(test_table_to_drop):
"""
Drops tables created from individual tests
so no dependencies arise from sequential tests
"""
self.drop_table(test_table_to_drop)
msg = "'notvalidvalue' is not valid for if_exists"
with pytest.raises(ValueError, match=msg):
sql.to_sql(
frame=df_if_exists_1,
con=self.conn,
name=table_name,
if_exists="notvalidvalue",
)
clean_up(table_name)
# test if_exists='fail'
sql.to_sql(
frame=df_if_exists_1, con=self.conn, name=table_name, if_exists="fail"
)
msg = "Table 'table_if_exists' already exists"
with pytest.raises(ValueError, match=msg):
sql.to_sql(
frame=df_if_exists_1, con=self.conn, name=table_name, if_exists="fail"
)
# test if_exists='replace'
sql.to_sql(
frame=df_if_exists_1,
con=self.conn,
name=table_name,
if_exists="replace",
index=False,
)
assert tquery(sql_select, con=self.conn) == [(1, "A"), (2, "B")]
sql.to_sql(
frame=df_if_exists_2,
con=self.conn,
name=table_name,
if_exists="replace",
index=False,
)
assert tquery(sql_select, con=self.conn) == [(3, "C"), (4, "D"), (5, "E")]
clean_up(table_name)
# test if_exists='append'
sql.to_sql(
frame=df_if_exists_1,
con=self.conn,
name=table_name,
if_exists="fail",
index=False,
)
assert tquery(sql_select, con=self.conn) == [(1, "A"), (2, "B")]
sql.to_sql(
frame=df_if_exists_2,
con=self.conn,
name=table_name,
if_exists="append",
index=False,
)
assert tquery(sql_select, con=self.conn) == [
(1, "A"),
(2, "B"),
(3, "C"),
(4, "D"),
(5, "E"),
]
clean_up(table_name)
@pytest.mark.single
@pytest.mark.db
@pytest.mark.skip(
reason="gh-13611: there is no support for MySQL if SQLAlchemy is not installed"
)
class TestXMySQL(MySQLMixIn):
@pytest.fixture(autouse=True, scope="class")
def setup_class(cls):
pymysql = pytest.importorskip("pymysql")
pymysql.connect(host="localhost", user="root", passwd="", db="pandas_nosetest")
try:
pymysql.connect(read_default_group="pandas")
except pymysql.ProgrammingError as err:
raise RuntimeError(
"Create a group of connection parameters under the heading "
"[pandas] in your system's mysql default file, "
"typically located at ~/.my.cnf or /etc/.my.cnf."
) from err
except pymysql.Error as err:
raise RuntimeError(
"Cannot connect to database. "
"Create a group of connection parameters under the heading "
"[pandas] in your system's mysql default file, "
"typically located at ~/.my.cnf or /etc/.my.cnf."
) from err
@pytest.fixture(autouse=True)
def setup_method(self, request, datapath):
pymysql = pytest.importorskip("pymysql")
pymysql.connect(host="localhost", user="root", passwd="", db="pandas_nosetest")
try:
pymysql.connect(read_default_group="pandas")
except pymysql.ProgrammingError as err:
raise RuntimeError(
"Create a group of connection parameters under the heading "
"[pandas] in your system's mysql default file, "
"typically located at ~/.my.cnf or /etc/.my.cnf."
) from err
except pymysql.Error as err:
raise RuntimeError(
"Cannot connect to database. "
"Create a group of connection parameters under the heading "
"[pandas] in your system's mysql default file, "
"typically located at ~/.my.cnf or /etc/.my.cnf."
) from err
self.method = request.function
def test_basic(self):
frame = tm.makeTimeDataFrame()
self._check_roundtrip(frame)
def test_write_row_by_row(self):
frame = tm.makeTimeDataFrame()
frame.iloc[0, 0] = np.nan
drop_sql = "DROP TABLE IF EXISTS test"
create_sql = sql.get_schema(frame, "test")
cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
for idx, row in frame.iterrows():
fmt_sql = format_query(ins, *row)
tquery(fmt_sql, cur=cur)
self.conn.commit()
result = sql.read_sql("select * from test", con=self.conn)
result.index = frame.index
tm.assert_frame_equal(result, frame, rtol=1e-3)
# GH#32571 result comes back rounded to 6 digits in some builds;
# no obvious pattern
def test_chunksize_read_type(self):
frame = tm.makeTimeDataFrame()
frame.index.name = "index"
drop_sql = "DROP TABLE IF EXISTS test"
cur = self.conn.cursor()
cur.execute(drop_sql)
sql.to_sql(frame, name="test", con=self.conn)
query = "select * from test"
chunksize = 5
chunk_gen = pd.read_sql_query(
sql=query, con=self.conn, chunksize=chunksize, index_col="index"
)
chunk_df = next(chunk_gen)
tm.assert_frame_equal(frame[:chunksize], chunk_df)
def test_execute(self):
frame = tm.makeTimeDataFrame()
drop_sql = "DROP TABLE IF EXISTS test"
create_sql = sql.get_schema(frame, "test")
cur = self.conn.cursor()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Unknown table.*")
cur.execute(drop_sql)
cur.execute(create_sql)
ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
row = frame.iloc[0].values.tolist()
sql.execute(ins, self.conn, params=tuple(row))
self.conn.commit()
result = sql.read_sql("select * from test", self.conn)
result.index = frame.index[:1]
tm.assert_frame_equal(result, frame[:1])
def test_schema(self):
frame = tm.makeTimeDataFrame()
create_sql = sql.get_schema(frame, "test")
lines = create_sql.splitlines()
for l in lines:
tokens = l.split(" ")
if len(tokens) == 2 and tokens[0] == "A":
assert tokens[1] == "DATETIME"
frame = tm.makeTimeDataFrame()
drop_sql = "DROP TABLE IF EXISTS test"
create_sql = sql.get_schema(frame, "test", keys=["A", "B"])
lines = create_sql.splitlines()
assert "PRIMARY KEY (`A`, `B`)" in create_sql
cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
def test_execute_fail(self):
drop_sql = "DROP TABLE IF EXISTS test"
create_sql = """
CREATE TABLE test
(
a TEXT,
b TEXT,
c REAL,
PRIMARY KEY (a(5), b(5))
);
"""
cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)', self.conn)
with pytest.raises(Exception):
sql.execute('INSERT INTO test VALUES("foo", "bar", 7)', self.conn)
def test_execute_closed_connection(self, request, datapath):
drop_sql = "DROP TABLE IF EXISTS test"
create_sql = """
CREATE TABLE test
(
a TEXT,
b TEXT,
c REAL,
PRIMARY KEY (a(5), b(5))
);
"""
cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
self.conn.close()
with pytest.raises(Exception):
tquery("select * from test", con=self.conn)
# Initialize connection again (needed for tearDown)
self.setup_method(request, datapath)
def test_na_roundtrip(self):
pass
def _check_roundtrip(self, frame):
drop_sql = "DROP TABLE IF EXISTS test_table"
cur = self.conn.cursor()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Unknown table.*")
cur.execute(drop_sql)
sql.to_sql(frame, name="test_table", con=self.conn, index=False)
result = sql.read_sql("select * from test_table", self.conn)
# HACK! Change this once indexes are handled properly.
result.index = frame.index
result.index.name = frame.index.name
expected = frame
tm.assert_frame_equal(result, expected)
frame["txt"] = ["a"] * len(frame)
frame2 = frame.copy()
index = Index(np.arange(len(frame2))) + 10
frame2["Idx"] = index
drop_sql = "DROP TABLE IF EXISTS test_table2"
cur = self.conn.cursor()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Unknown table.*")
cur.execute(drop_sql)
sql.to_sql(frame2, name="test_table2", con=self.conn, index=False)
result = sql.read_sql("select * from test_table2", self.conn, index_col="Idx")
expected = frame.copy()
# HACK! Change this once indexes are handled properly.
expected.index = index
expected.index.names = result.index.names
tm.assert_frame_equal(expected, result)
def test_keyword_as_column_names(self):
df = DataFrame({"From": np.ones(5)})
sql.to_sql(
df, con=self.conn, name="testkeywords", if_exists="replace", index=False
)
def test_if_exists(self):
df_if_exists_1 = DataFrame({"col1": [1, 2], "col2": ["A", "B"]})
df_if_exists_2 = DataFrame({"col1": [3, 4, 5], "col2": ["C", "D", "E"]})
table_name = "table_if_exists"
sql_select = f"SELECT * FROM {table_name}"
def clean_up(test_table_to_drop):
"""
Drops tables created from individual tests
so no dependencies arise from sequential tests
"""
self.drop_table(test_table_to_drop)
# test if invalid value for if_exists raises appropriate error
with pytest.raises(ValueError, match="<insert message here>"):
sql.to_sql(
frame=df_if_exists_1,
con=self.conn,
name=table_name,
if_exists="notvalidvalue",
)
clean_up(table_name)
# test if_exists='fail'
sql.to_sql(
frame=df_if_exists_1,
con=self.conn,
name=table_name,
if_exists="fail",
index=False,
)
with pytest.raises(ValueError, match="<insert message here>"):
sql.to_sql(
frame=df_if_exists_1, con=self.conn, name=table_name, if_exists="fail"
)
# test if_exists='replace'
sql.to_sql(
frame=df_if_exists_1,
con=self.conn,
name=table_name,
if_exists="replace",
index=False,
)
assert tquery(sql_select, con=self.conn) == [(1, "A"), (2, "B")]
sql.to_sql(
frame=df_if_exists_2,
con=self.conn,
name=table_name,
if_exists="replace",
index=False,
)
assert tquery(sql_select, con=self.conn) == [(3, "C"), (4, "D"), (5, "E")]
clean_up(table_name)
# test if_exists='append'
sql.to_sql(
frame=df_if_exists_1,
con=self.conn,
name=table_name,
if_exists="fail",
index=False,
)
assert tquery(sql_select, con=self.conn) == [(1, "A"), (2, "B")]
sql.to_sql(
frame=df_if_exists_2,
con=self.conn,
name=table_name,
if_exists="append",
index=False,
)
assert tquery(sql_select, con=self.conn) == [
(1, "A"),
(2, "B"),
(3, "C"),
(4, "D"),
(5, "E"),
]
clean_up(table_name)
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