import keyring
import os
from pathlib import Path
import pandas as pd
from configparser import ConfigParser
import sqlalchemy
import logging
import base64
_logger = logging.getLogger(__name__)
_CONFIG_PTH = Path.home() / ".dwopt"
_KEYRING_SERV_ID = Path(__file__).parent.resolve().as_posix()
_TEST_PG_URL = "postgresql://dwopt_tester:1234@localhost/dwopt_test"
_TEST_LT_URL = "sqlite://"
_TEST_OC_URL = (
"oracle://dwopt_test:1234@localhost:1521/?service_name=XEPDB1"
"&encoding=UTF-8&nencoding=UTF-8"
)
[docs]def save_url(db_nme, url, method="keyring"):
"""Save encoded database engine url to keyring or config.
The package can also read a manually set environment variable storing the raw url.
See examples for quick-start.
On package import, default url are taken firstly from keyring if available,
then the config file if available, then the environment variable
if available, lastly a set of hard-coded testing urls.
A `sqlalchemy engine url <https://docs.sqlalchemy.org/en/14/core/
engines.html#database-urls>`_
combines the user name, password, database names, etc
into a single string.
*Details on credential locations:*
* The system keyring service is accessed via the
`keyring <https://pypi.org/project/keyring/>`_
package. The service on Windows is the Windows Credential Manager.
The service id is the full path to the dwopt package files.
For example: ``E:\\python\\python3.9\\Lib\\site-packages\\dwopt``.
The user name will be either ``pg``, ``lt``, or ``oc``.
The url will be encoded before saving.
* The config file is created with name ``.dwopt``
on the system ``HOME`` directory.
There will be a url section on the config file, with option names being
the database names. The url will be encoded before saving.
* The environment variables should be manually made with the names
``dwopt_pg``, ``dwopt_lt`` or ``dwopt_oc``, and the value being
the raw url string.
Base 64 encoding is done to prevent raw password being stored on files.
User could rewrite the ``_encode`` and the ``_decode`` functions to implement
custom encryption algorithm.
Parameters
----------
db_nme : str
Relevant database code. Either ``pg`` for postgre, ``lt`` for sqlite,
or ``oc`` for oracle.
url : str
Sqlalchemy engine url.
method: str
Method used to save, either 'keyring', or 'config'.
Default 'keyring'.
Returns
-------
str
Message anouncing completion.
Examples
--------
Save connection urls in various methods for various databases.
Also manually create following environment variable
``variable = value`` pair: ``dwopt_lt = sqlite:///:memory:``.
>>> import dwopt
>>> dwopt.save_url('pg', 'postgresql://dwopt_tester:1234@localhost/dwopt_test')
'Saved pg url to keyring'
>>> url = ("oracle://dwopt_test:1234@localhost:1521/?service_name=XEPDB1"
... "&encoding=UTF-8&nencoding=UTF-8")
>>>
>>> dwopt.save_url('oc', url, 'config')
'Saved oc url to config'
Exit and re-enter python for changes to take effect.
>>> from dwopt import pg, lt, oc
>>> pg.eng
Engine(postgresql://dwopt_tester:***@localhost/dwopt_test)
>>> lt.eng
Engine(sqlite:///:memory:)
>>> str(oc.eng)[:50]
'Engine(oracle://dwopt_test:***@localhost:1521/?enc'
"""
url = _encode(url)
if method == "keyring":
keyring.set_password(_KEYRING_SERV_ID, db_nme, url)
elif method == "config":
cfg = ConfigParser()
cfg.read(_CONFIG_PTH)
if not cfg.has_section("url"):
cfg.add_section("url")
cfg.set("url", db_nme, url)
with open(_CONFIG_PTH, "w") as f:
cfg.write(f)
else:
raise ValueError("Invalid method, either 'keyring', or 'config'")
return f"Saved {db_nme} url to {method}"
def _get_url(db_nme):
"""Get url if available, else dummy url."""
url = None
try:
url = _decode(keyring.get_password(_KEYRING_SERV_ID, db_nme))
except Exception as e:
_logger.warning(e)
if url is not None:
_logger.debug(f"{db_nme} url obtained from keyring")
return url
cfg = ConfigParser()
cfg.read(_CONFIG_PTH)
if cfg.has_option("url", db_nme):
url = _decode(cfg.get("url", db_nme))
if url is not None:
_logger.debug(f"{db_nme} url obtained from config")
return url
url = os.environ.get(f"dwopt_{db_nme}")
if url is not None:
_logger.debug(f"{db_nme} url obtained from environ")
return url
if db_nme == "pg":
url = _TEST_PG_URL
elif db_nme == "lt":
url = _TEST_LT_URL
elif db_nme == "oc":
url = _TEST_OC_URL
else:
raise ValueError("Invalid db_nme, either 'pg', 'lt' or 'oc'")
_logger.debug(f"{db_nme} url obtained from hardcoded dummy")
return url
def _encode(x):
if x is not None:
return base64.b64encode(x.encode("UTF-8")).decode("UTF-8")
def _decode(x):
if x is not None:
return base64.b64decode(x.encode("UTF-8")).decode("UTF-8")
[docs]def make_eng(url):
"""Make database connection engine.
Use the database connection engine to instantiate the database opeartor class.
This function is provided outside of the class because the engine object is
best to be created only once per application.
A `sqlalchemy engine url <https://docs.sqlalchemy.org/en/14/
core/engines.html#database-urls>`_
combines the user name, password, database names, etc
into a single string.
Parameters
----------
url : str
Sqlalchemy engine url.
Returns
-------
sqlalchemy engine
Examples
--------
Instantiate and use a database operator object.
>>> from dwopt import make_eng, Pg
>>> url = "postgresql://dwopt_tester:1234@localhost/dwopt_test"
>>> pg_eng = make_eng(url)
>>> pg = Pg(pg_eng)
>>> pg.iris()
>>> pg.run('select count(1) from iris')
count
0 150
"""
return sqlalchemy.create_engine(url)
def make_meta(eng, schema):
pass
def _make_iris_df():
res = [
{"v1": 5.1, "v2": 3.5, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 4.9, "v2": 3.0, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 4.7, "v2": 3.2, "v3": 1.3, "v4": 0.2, "v5": "setosa"},
{"v1": 4.6, "v2": 3.1, "v3": 1.5, "v4": 0.2, "v5": "setosa"},
{"v1": 5.0, "v2": 3.6, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 5.4, "v2": 3.9, "v3": 1.7, "v4": 0.4, "v5": "setosa"},
{"v1": 4.6, "v2": 3.4, "v3": 1.4, "v4": 0.3, "v5": "setosa"},
{"v1": 5.0, "v2": 3.4, "v3": 1.5, "v4": 0.2, "v5": "setosa"},
{"v1": 4.4, "v2": 2.9, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 4.9, "v2": 3.1, "v3": 1.5, "v4": 0.1, "v5": "setosa"},
{"v1": 5.4, "v2": 3.7, "v3": 1.5, "v4": 0.2, "v5": "setosa"},
{"v1": 4.8, "v2": 3.4, "v3": 1.6, "v4": 0.2, "v5": "setosa"},
{"v1": 4.8, "v2": 3.0, "v3": 1.4, "v4": 0.1, "v5": "setosa"},
{"v1": 4.3, "v2": 3.0, "v3": 1.1, "v4": 0.1, "v5": "setosa"},
{"v1": 5.8, "v2": 4.0, "v3": 1.2, "v4": 0.2, "v5": "setosa"},
{"v1": 5.7, "v2": 4.4, "v3": 1.5, "v4": 0.4, "v5": "setosa"},
{"v1": 5.4, "v2": 3.9, "v3": 1.3, "v4": 0.4, "v5": "setosa"},
{"v1": 5.1, "v2": 3.5, "v3": 1.4, "v4": 0.3, "v5": "setosa"},
{"v1": 5.7, "v2": 3.8, "v3": 1.7, "v4": 0.3, "v5": "setosa"},
{"v1": 5.1, "v2": 3.8, "v3": 1.5, "v4": 0.3, "v5": "setosa"},
{"v1": 5.4, "v2": 3.4, "v3": 1.7, "v4": 0.2, "v5": "setosa"},
{"v1": 5.1, "v2": 3.7, "v3": 1.5, "v4": 0.4, "v5": "setosa"},
{"v1": 4.6, "v2": 3.6, "v3": 1.0, "v4": 0.2, "v5": "setosa"},
{"v1": 5.1, "v2": 3.3, "v3": 1.7, "v4": 0.5, "v5": "setosa"},
{"v1": 4.8, "v2": 3.4, "v3": 1.9, "v4": 0.2, "v5": "setosa"},
{"v1": 5.0, "v2": 3.0, "v3": 1.6, "v4": 0.2, "v5": "setosa"},
{"v1": 5.0, "v2": 3.4, "v3": 1.6, "v4": 0.4, "v5": "setosa"},
{"v1": 5.2, "v2": 3.5, "v3": 1.5, "v4": 0.2, "v5": "setosa"},
{"v1": 5.2, "v2": 3.4, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 4.7, "v2": 3.2, "v3": 1.6, "v4": 0.2, "v5": "setosa"},
{"v1": 4.8, "v2": 3.1, "v3": 1.6, "v4": 0.2, "v5": "setosa"},
{"v1": 5.4, "v2": 3.4, "v3": 1.5, "v4": 0.4, "v5": "setosa"},
{"v1": 5.2, "v2": 4.1, "v3": 1.5, "v4": 0.1, "v5": "setosa"},
{"v1": 5.5, "v2": 4.2, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 4.9, "v2": 3.1, "v3": 1.5, "v4": 0.2, "v5": "setosa"},
{"v1": 5.0, "v2": 3.2, "v3": 1.2, "v4": 0.2, "v5": "setosa"},
{"v1": 5.5, "v2": 3.5, "v3": 1.3, "v4": 0.2, "v5": "setosa"},
{"v1": 4.9, "v2": 3.6, "v3": 1.4, "v4": 0.1, "v5": "setosa"},
{"v1": 4.4, "v2": 3.0, "v3": 1.3, "v4": 0.2, "v5": "setosa"},
{"v1": 5.1, "v2": 3.4, "v3": 1.5, "v4": 0.2, "v5": "setosa"},
{"v1": 5.0, "v2": 3.5, "v3": 1.3, "v4": 0.3, "v5": "setosa"},
{"v1": 4.5, "v2": 2.3, "v3": 1.3, "v4": 0.3, "v5": "setosa"},
{"v1": 4.4, "v2": 3.2, "v3": 1.3, "v4": 0.2, "v5": "setosa"},
{"v1": 5.0, "v2": 3.5, "v3": 1.6, "v4": 0.6, "v5": "setosa"},
{"v1": 5.1, "v2": 3.8, "v3": 1.9, "v4": 0.4, "v5": "setosa"},
{"v1": 4.8, "v2": 3.0, "v3": 1.4, "v4": 0.3, "v5": "setosa"},
{"v1": 5.1, "v2": 3.8, "v3": 1.6, "v4": 0.2, "v5": "setosa"},
{"v1": 4.6, "v2": 3.2, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 5.3, "v2": 3.7, "v3": 1.5, "v4": 0.2, "v5": "setosa"},
{"v1": 5.0, "v2": 3.3, "v3": 1.4, "v4": 0.2, "v5": "setosa"},
{"v1": 7.0, "v2": 3.2, "v3": 4.7, "v4": 1.4, "v5": "sicolor"},
{"v1": 6.4, "v2": 3.2, "v3": 4.5, "v4": 1.5, "v5": "sicolor"},
{"v1": 6.9, "v2": 3.1, "v3": 4.9, "v4": 1.5, "v5": "sicolor"},
{"v1": 5.5, "v2": 2.3, "v3": 4.0, "v4": 1.3, "v5": "sicolor"},
{"v1": 6.5, "v2": 2.8, "v3": 4.6, "v4": 1.5, "v5": "sicolor"},
{"v1": 5.7, "v2": 2.8, "v3": 4.5, "v4": 1.3, "v5": "sicolor"},
{"v1": 6.3, "v2": 3.3, "v3": 4.7, "v4": 1.6, "v5": "sicolor"},
{"v1": 4.9, "v2": 2.4, "v3": 3.3, "v4": 1.0, "v5": "sicolor"},
{"v1": 6.6, "v2": 2.9, "v3": 4.6, "v4": 1.3, "v5": "sicolor"},
{"v1": 5.2, "v2": 2.7, "v3": 3.9, "v4": 1.4, "v5": "sicolor"},
{"v1": 5.0, "v2": 2.0, "v3": 3.5, "v4": 1.0, "v5": "sicolor"},
{"v1": 5.9, "v2": 3.0, "v3": 4.2, "v4": 1.5, "v5": "sicolor"},
{"v1": 6.0, "v2": 2.2, "v3": 4.0, "v4": 1.0, "v5": "sicolor"},
{"v1": 6.1, "v2": 2.9, "v3": 4.7, "v4": 1.4, "v5": "sicolor"},
{"v1": 5.6, "v2": 2.9, "v3": 3.6, "v4": 1.3, "v5": "sicolor"},
{"v1": 6.7, "v2": 3.1, "v3": 4.4, "v4": 1.4, "v5": "sicolor"},
{"v1": 5.6, "v2": 3.0, "v3": 4.5, "v4": 1.5, "v5": "sicolor"},
{"v1": 5.8, "v2": 2.7, "v3": 4.1, "v4": 1.0, "v5": "sicolor"},
{"v1": 6.2, "v2": 2.2, "v3": 4.5, "v4": 1.5, "v5": "sicolor"},
{"v1": 5.6, "v2": 2.5, "v3": 3.9, "v4": 1.1, "v5": "sicolor"},
{"v1": 5.9, "v2": 3.2, "v3": 4.8, "v4": 1.8, "v5": "sicolor"},
{"v1": 6.1, "v2": 2.8, "v3": 4.0, "v4": 1.3, "v5": "sicolor"},
{"v1": 6.3, "v2": 2.5, "v3": 4.9, "v4": 1.5, "v5": "sicolor"},
{"v1": 6.1, "v2": 2.8, "v3": 4.7, "v4": 1.2, "v5": "sicolor"},
{"v1": 6.4, "v2": 2.9, "v3": 4.3, "v4": 1.3, "v5": "sicolor"},
{"v1": 6.6, "v2": 3.0, "v3": 4.4, "v4": 1.4, "v5": "sicolor"},
{"v1": 6.8, "v2": 2.8, "v3": 4.8, "v4": 1.4, "v5": "sicolor"},
{"v1": 6.7, "v2": 3.0, "v3": 5.0, "v4": 1.7, "v5": "sicolor"},
{"v1": 6.0, "v2": 2.9, "v3": 4.5, "v4": 1.5, "v5": "sicolor"},
{"v1": 5.7, "v2": 2.6, "v3": 3.5, "v4": 1.0, "v5": "sicolor"},
{"v1": 5.5, "v2": 2.4, "v3": 3.8, "v4": 1.1, "v5": "sicolor"},
{"v1": 5.5, "v2": 2.4, "v3": 3.7, "v4": 1.0, "v5": "sicolor"},
{"v1": 5.8, "v2": 2.7, "v3": 3.9, "v4": 1.2, "v5": "sicolor"},
{"v1": 6.0, "v2": 2.7, "v3": 5.1, "v4": 1.6, "v5": "sicolor"},
{"v1": 5.4, "v2": 3.0, "v3": 4.5, "v4": 1.5, "v5": "sicolor"},
{"v1": 6.0, "v2": 3.4, "v3": 4.5, "v4": 1.6, "v5": "sicolor"},
{"v1": 6.7, "v2": 3.1, "v3": 4.7, "v4": 1.5, "v5": "sicolor"},
{"v1": 6.3, "v2": 2.3, "v3": 4.4, "v4": 1.3, "v5": "sicolor"},
{"v1": 5.6, "v2": 3.0, "v3": 4.1, "v4": 1.3, "v5": "sicolor"},
{"v1": 5.5, "v2": 2.5, "v3": 4.0, "v4": 1.3, "v5": "sicolor"},
{"v1": 5.5, "v2": 2.6, "v3": 4.4, "v4": 1.2, "v5": "sicolor"},
{"v1": 6.1, "v2": 3.0, "v3": 4.6, "v4": 1.4, "v5": "sicolor"},
{"v1": 5.8, "v2": 2.6, "v3": 4.0, "v4": 1.2, "v5": "sicolor"},
{"v1": 5.0, "v2": 2.3, "v3": 3.3, "v4": 1.0, "v5": "sicolor"},
{"v1": 5.6, "v2": 2.7, "v3": 4.2, "v4": 1.3, "v5": "sicolor"},
{"v1": 5.7, "v2": 3.0, "v3": 4.2, "v4": 1.2, "v5": "sicolor"},
{"v1": 5.7, "v2": 2.9, "v3": 4.2, "v4": 1.3, "v5": "sicolor"},
{"v1": 6.2, "v2": 2.9, "v3": 4.3, "v4": 1.3, "v5": "sicolor"},
{"v1": 5.1, "v2": 2.5, "v3": 3.0, "v4": 1.1, "v5": "sicolor"},
{"v1": 5.7, "v2": 2.8, "v3": 4.1, "v4": 1.3, "v5": "sicolor"},
{"v1": 6.3, "v2": 3.3, "v3": 6.0, "v4": 2.5, "v5": "rginica"},
{"v1": 5.8, "v2": 2.7, "v3": 5.1, "v4": 1.9, "v5": "rginica"},
{"v1": 7.1, "v2": 3.0, "v3": 5.9, "v4": 2.1, "v5": "rginica"},
{"v1": 6.3, "v2": 2.9, "v3": 5.6, "v4": 1.8, "v5": "rginica"},
{"v1": 6.5, "v2": 3.0, "v3": 5.8, "v4": 2.2, "v5": "rginica"},
{"v1": 7.6, "v2": 3.0, "v3": 6.6, "v4": 2.1, "v5": "rginica"},
{"v1": 4.9, "v2": 2.5, "v3": 4.5, "v4": 1.7, "v5": "rginica"},
{"v1": 7.3, "v2": 2.9, "v3": 6.3, "v4": 1.8, "v5": "rginica"},
{"v1": 6.7, "v2": 2.5, "v3": 5.8, "v4": 1.8, "v5": "rginica"},
{"v1": 7.2, "v2": 3.6, "v3": 6.1, "v4": 2.5, "v5": "rginica"},
{"v1": 6.5, "v2": 3.2, "v3": 5.1, "v4": 2.0, "v5": "rginica"},
{"v1": 6.4, "v2": 2.7, "v3": 5.3, "v4": 1.9, "v5": "rginica"},
{"v1": 6.8, "v2": 3.0, "v3": 5.5, "v4": 2.1, "v5": "rginica"},
{"v1": 5.7, "v2": 2.5, "v3": 5.0, "v4": 2.0, "v5": "rginica"},
{"v1": 5.8, "v2": 2.8, "v3": 5.1, "v4": 2.4, "v5": "rginica"},
{"v1": 6.4, "v2": 3.2, "v3": 5.3, "v4": 2.3, "v5": "rginica"},
{"v1": 6.5, "v2": 3.0, "v3": 5.5, "v4": 1.8, "v5": "rginica"},
{"v1": 7.7, "v2": 3.8, "v3": 6.7, "v4": 2.2, "v5": "rginica"},
{"v1": 7.7, "v2": 2.6, "v3": 6.9, "v4": 2.3, "v5": "rginica"},
{"v1": 6.0, "v2": 2.2, "v3": 5.0, "v4": 1.5, "v5": "rginica"},
{"v1": 6.9, "v2": 3.2, "v3": 5.7, "v4": 2.3, "v5": "rginica"},
{"v1": 5.6, "v2": 2.8, "v3": 4.9, "v4": 2.0, "v5": "rginica"},
{"v1": 7.7, "v2": 2.8, "v3": 6.7, "v4": 2.0, "v5": "rginica"},
{"v1": 6.3, "v2": 2.7, "v3": 4.9, "v4": 1.8, "v5": "rginica"},
{"v1": 6.7, "v2": 3.3, "v3": 5.7, "v4": 2.1, "v5": "rginica"},
{"v1": 7.2, "v2": 3.2, "v3": 6.0, "v4": 1.8, "v5": "rginica"},
{"v1": 6.2, "v2": 2.8, "v3": 4.8, "v4": 1.8, "v5": "rginica"},
{"v1": 6.1, "v2": 3.0, "v3": 4.9, "v4": 1.8, "v5": "rginica"},
{"v1": 6.4, "v2": 2.8, "v3": 5.6, "v4": 2.1, "v5": "rginica"},
{"v1": 7.2, "v2": 3.0, "v3": 5.8, "v4": 1.6, "v5": "rginica"},
{"v1": 7.4, "v2": 2.8, "v3": 6.1, "v4": 1.9, "v5": "rginica"},
{"v1": 7.9, "v2": 3.8, "v3": 6.4, "v4": 2.0, "v5": "rginica"},
{"v1": 6.4, "v2": 2.8, "v3": 5.6, "v4": 2.2, "v5": "rginica"},
{"v1": 6.3, "v2": 2.8, "v3": 5.1, "v4": 1.5, "v5": "rginica"},
{"v1": 6.1, "v2": 2.6, "v3": 5.6, "v4": 1.4, "v5": "rginica"},
{"v1": 7.7, "v2": 3.0, "v3": 6.1, "v4": 2.3, "v5": "rginica"},
{"v1": 6.3, "v2": 3.4, "v3": 5.6, "v4": 2.4, "v5": "rginica"},
{"v1": 6.4, "v2": 3.1, "v3": 5.5, "v4": 1.8, "v5": "rginica"},
{"v1": 6.0, "v2": 3.0, "v3": 4.8, "v4": 1.8, "v5": "rginica"},
{"v1": 6.9, "v2": 3.1, "v3": 5.4, "v4": 2.1, "v5": "rginica"},
{"v1": 6.7, "v2": 3.1, "v3": 5.6, "v4": 2.4, "v5": "rginica"},
{"v1": 6.9, "v2": 3.1, "v3": 5.1, "v4": 2.3, "v5": "rginica"},
{"v1": 5.8, "v2": 2.7, "v3": 5.1, "v4": 1.9, "v5": "rginica"},
{"v1": 6.8, "v2": 3.2, "v3": 5.9, "v4": 2.3, "v5": "rginica"},
{"v1": 6.7, "v2": 3.3, "v3": 5.7, "v4": 2.5, "v5": "rginica"},
{"v1": 6.7, "v2": 3.0, "v3": 5.2, "v4": 2.3, "v5": "rginica"},
{"v1": 6.3, "v2": 2.5, "v3": 5.0, "v4": 1.9, "v5": "rginica"},
{"v1": 6.5, "v2": 3.0, "v3": 5.2, "v4": 2.0, "v5": "rginica"},
{"v1": 6.2, "v2": 3.4, "v3": 5.4, "v4": 2.3, "v5": "rginica"},
{"v1": 5.9, "v2": 3.0, "v3": 5.1, "v4": 1.8, "v5": "rginica"},
]
res = pd.DataFrame(res)
res.columns = [
"Sepal.Length",
"Sepal.Width",
"Petal.Length",
"Petal.Width",
"Species",
]
return res
def _make_mtcars_df():
res1 = [
{"v0": "Mazda RX4"},
{"v0": "Mazda RX4 Wag"},
{"v0": "Datsun 710"},
{"v0": "Hornet 4 Drive"},
{"v0": "Hornet Sportabout"},
{"v0": "Valiant"},
{"v0": "Duster 360"},
{"v0": "Merc 240D"},
{"v0": "Merc 230"},
{"v0": "Merc 280"},
{"v0": "Merc 280C"},
{"v0": "Merc 450SE"},
{"v0": "Merc 450SL"},
{"v0": "Merc 450SLC"},
{"v0": "Cadillac Fleetwood"},
{"v0": "Lincoln Continental"},
{"v0": "Chrysler Imperial"},
{"v0": "Fiat 128"},
{"v0": "Honda Civic"},
{"v0": "Toyota Corolla"},
{"v0": "Toyota Corona"},
{"v0": "Dodge Challenger"},
{"v0": "AMC Javelin"},
{"v0": "Camaro Z28"},
{"v0": "Pontiac Firebird"},
{"v0": "Fiat X1-9"},
{"v0": "Porsche 914-2"},
{"v0": "Lotus Europa"},
{"v0": "Ford Pantera L"},
{"v0": "Ferrari Dino"},
{"v0": "Maserati Bora"},
{"v0": "Volvo 142E"},
]
res2 = [
{"v1": 21.0, "v2": 6, "v3": 160.0, "v4": 110},
{"v1": 21.0, "v2": 6, "v3": 160.0, "v4": 110},
{"v1": 22.8, "v2": 4, "v3": 108.0, "v4": 93},
{"v1": 21.4, "v2": 6, "v3": 258.0, "v4": 110},
{"v1": 18.7, "v2": 8, "v3": 360.0, "v4": 175},
{"v1": 18.1, "v2": 6, "v3": 225.0, "v4": 105},
{"v1": 14.3, "v2": 8, "v3": 360.0, "v4": 245},
{"v1": 24.4, "v2": 4, "v3": 146.7, "v4": 62},
{"v1": 22.8, "v2": 4, "v3": 140.8, "v4": 95},
{"v1": 19.2, "v2": 6, "v3": 167.6, "v4": 123},
{"v1": 17.8, "v2": 6, "v3": 167.6, "v4": 123},
{"v1": 16.4, "v2": 8, "v3": 275.8, "v4": 180},
{"v1": 17.3, "v2": 8, "v3": 275.8, "v4": 180},
{"v1": 15.2, "v2": 8, "v3": 275.8, "v4": 180},
{"v1": 10.4, "v2": 8, "v3": 472.0, "v4": 205},
{"v1": 10.4, "v2": 8, "v3": 460.0, "v4": 215},
{"v1": 14.7, "v2": 8, "v3": 440.0, "v4": 230},
{"v1": 32.4, "v2": 4, "v3": 78.7, "v4": 66},
{"v1": 30.4, "v2": 4, "v3": 75.7, "v4": 52},
{"v1": 33.9, "v2": 4, "v3": 71.1, "v4": 65},
{"v1": 21.5, "v2": 4, "v3": 120.1, "v4": 97},
{"v1": 15.5, "v2": 8, "v3": 318.0, "v4": 150},
{"v1": 15.2, "v2": 8, "v3": 304.0, "v4": 150},
{"v1": 13.3, "v2": 8, "v3": 350.0, "v4": 245},
{"v1": 19.2, "v2": 8, "v3": 400.0, "v4": 175},
{"v1": 27.3, "v2": 4, "v3": 79.0, "v4": 66},
{"v1": 26.0, "v2": 4, "v3": 120.3, "v4": 91},
{"v1": 30.4, "v2": 4, "v3": 95.1, "v4": 113},
{"v1": 15.8, "v2": 8, "v3": 351.0, "v4": 264},
{"v1": 19.7, "v2": 6, "v3": 145.0, "v4": 175},
{"v1": 15.0, "v2": 8, "v3": 301.0, "v4": 335},
{"v1": 21.4, "v2": 4, "v3": 121.0, "v4": 109},
]
res3 = [
{"v5": 3.90, "v6": 2.620, "v7": 16.46, "v8": 0, "v9": 1, "w0": 4, "w1": 4},
{"v5": 3.90, "v6": 2.875, "v7": 17.02, "v8": 0, "v9": 1, "w0": 4, "w1": 4},
{"v5": 3.85, "v6": 2.320, "v7": 18.61, "v8": 1, "v9": 1, "w0": 4, "w1": 1},
{"v5": 3.08, "v6": 3.215, "v7": 19.44, "v8": 1, "v9": 0, "w0": 3, "w1": 1},
{"v5": 3.15, "v6": 3.440, "v7": 17.02, "v8": 0, "v9": 0, "w0": 3, "w1": 2},
{"v5": 2.76, "v6": 3.460, "v7": 20.22, "v8": 1, "v9": 0, "w0": 3, "w1": 1},
{"v5": 3.21, "v6": 3.570, "v7": 15.84, "v8": 0, "v9": 0, "w0": 3, "w1": 4},
{"v5": 3.69, "v6": 3.190, "v7": 20.00, "v8": 1, "v9": 0, "w0": 4, "w1": 2},
{"v5": 3.92, "v6": 3.150, "v7": 22.90, "v8": 1, "v9": 0, "w0": 4, "w1": 2},
{"v5": 3.92, "v6": 3.440, "v7": 18.30, "v8": 1, "v9": 0, "w0": 4, "w1": 4},
{"v5": 3.92, "v6": 3.440, "v7": 18.90, "v8": 1, "v9": 0, "w0": 4, "w1": 4},
{"v5": 3.07, "v6": 4.070, "v7": 17.40, "v8": 0, "v9": 0, "w0": 3, "w1": 3},
{"v5": 3.07, "v6": 3.730, "v7": 17.60, "v8": 0, "v9": 0, "w0": 3, "w1": 3},
{"v5": 3.07, "v6": 3.780, "v7": 18.00, "v8": 0, "v9": 0, "w0": 3, "w1": 3},
{"v5": 2.93, "v6": 5.250, "v7": 17.98, "v8": 0, "v9": 0, "w0": 3, "w1": 4},
{"v5": 3.00, "v6": 5.424, "v7": 17.82, "v8": 0, "v9": 0, "w0": 3, "w1": 4},
{"v5": 3.23, "v6": 5.345, "v7": 17.42, "v8": 0, "v9": 0, "w0": 3, "w1": 4},
{"v5": 4.08, "v6": 2.200, "v7": 19.47, "v8": 1, "v9": 1, "w0": 4, "w1": 1},
{"v5": 4.93, "v6": 1.615, "v7": 18.52, "v8": 1, "v9": 1, "w0": 4, "w1": 2},
{"v5": 4.22, "v6": 1.835, "v7": 19.90, "v8": 1, "v9": 1, "w0": 4, "w1": 1},
{"v5": 3.70, "v6": 2.465, "v7": 20.01, "v8": 1, "v9": 0, "w0": 3, "w1": 1},
{"v5": 2.76, "v6": 3.520, "v7": 16.87, "v8": 0, "v9": 0, "w0": 3, "w1": 2},
{"v5": 3.15, "v6": 3.435, "v7": 17.30, "v8": 0, "v9": 0, "w0": 3, "w1": 2},
{"v5": 3.73, "v6": 3.840, "v7": 15.41, "v8": 0, "v9": 0, "w0": 3, "w1": 4},
{"v5": 3.08, "v6": 3.845, "v7": 17.05, "v8": 0, "v9": 0, "w0": 3, "w1": 2},
{"v5": 4.08, "v6": 1.935, "v7": 18.90, "v8": 1, "v9": 1, "w0": 4, "w1": 1},
{"v5": 4.43, "v6": 2.140, "v7": 16.70, "v8": 0, "v9": 1, "w0": 5, "w1": 2},
{"v5": 3.77, "v6": 1.513, "v7": 16.90, "v8": 1, "v9": 1, "w0": 5, "w1": 2},
{"v5": 4.22, "v6": 3.170, "v7": 14.50, "v8": 0, "v9": 1, "w0": 5, "w1": 4},
{"v5": 3.62, "v6": 2.770, "v7": 15.50, "v8": 0, "v9": 1, "w0": 5, "w1": 6},
{"v5": 3.54, "v6": 3.570, "v7": 14.60, "v8": 0, "v9": 1, "w0": 5, "w1": 8},
{"v5": 4.11, "v6": 2.780, "v7": 18.60, "v8": 1, "v9": 1, "w0": 4, "w1": 2},
]
res = pd.DataFrame([dict(**i, **j, **k) for i, j, k in zip(res1, res2, res3)])
res.columns = [
"name",
"mpg",
"cyl",
"disp",
"hp",
"drat",
"wt",
"qsec",
"vs",
"am",
"gear",
"carb",
]
return res