Python import_or_none示例

示例#1

文件： conftest.py 项目： pzal8746/featuretools

def spark_session():
    sql = import_or_none('pyspark.sql')
    if sql:
        spark = sql.SparkSession.builder \
            .master('local[2]') \
            .config("spark.driver.extraJavaOptions", "-Dio.netty.tryReflectionSetAccessible=True") \
            .config("spark.sql.shuffle.partitions", "2") \
            .getOrCreate()

        return spark

示例#2

def spark_session():
    sql = import_or_none("pyspark.sql")
    if sql:
        spark = (
            sql.SparkSession.builder.master("local[2]")
            .config(
                "spark.driver.extraJavaOptions",
                "-Dio.netty.tryReflectionSetAccessible=True",
            )
            .config("spark.sql.shuffle.partitions", "2")
            .config("spark.driver.bindAddress", "127.0.0.1")
            .getOrCreate()
        )

        return spark

示例#3

from datetime import datetime

import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from woodwork.exceptions import TypeConversionError
from woodwork.logical_types import (Boolean, Categorical, Datetime, Double,
                                    Integer, NaturalLanguage)

from featuretools.entityset.entityset import LTI_COLUMN_NAME, EntitySet
from featuretools.tests.testing_utils import to_pandas
from featuretools.utils.gen_utils import Library, import_or_none

ks = import_or_none('databricks.koalas')


def test_empty_es():
    es = EntitySet('es')
    assert es.id == 'es'
    assert es.dataframe_dict == {}
    assert es.relationships == []
    assert es.time_type is None


@pytest.fixture
def pd_df():
    return pd.DataFrame({
        'id': [0, 1, 2],
        'category': ['a', 'b', 'c']
    }).astype({'category': 'category'})

示例#4

文件： test_last_time_index.py 项目： RomaKoks/featuretools

from datetime import datetime

import pandas as pd
import pytest
from dask import dataframe as dd
from woodwork.logical_types import Categorical, Datetime, Integer

from featuretools.entityset.entityset import LTI_COLUMN_NAME
from featuretools.tests.testing_utils import to_pandas
from featuretools.utils.gen_utils import Library, import_or_none

ps = import_or_none("pyspark.pandas")


@pytest.fixture
def values_es(es):
    es.normalize_dataframe(
        "log",
        "values",
        "value",
        make_time_index=True,
        new_dataframe_time_index="value_time",
    )
    return es


@pytest.fixture
def true_values_lti():
    true_values_lti = pd.Series(
        [
            datetime(2011, 4, 10, 10, 41, 0),

示例#5

文件： test_gen_utils.py 项目： RomaKoks/featuretools

def test_import_or_none():
    math = import_or_none("math")
    assert math.ceil(0.1) == 1

    bad_lib = import_or_none("_featuretools")
    assert bad_lib is None

示例#6

文件： test_gen_utils.py 项目： mikewcasale/featuretools

def test_import_or_none():
    math = import_or_none('math')
    assert math.ceil(0.1) == 1

    bad_lib = import_or_none('_featuretools')
    assert bad_lib is None