from pyspark import SparkConf, SparkContext
from pyspark.sql import HiveContext,SparkSession
from sklearn.linear_model import LinearRegression
from sklearn import linear_model
from pyspark.sql.types import *
from pyspark.sql.functions import pandas_udf, PandasUDFType
import pandas as pd
import numpy as np
from davidyu_cfg import *
from functions.LinearReg import LinearReg
from pyspark.sql.functions import *

spark = SparkSession \
    .builder \
    .appName("davidyu") \
    .enableHiveSupport() \
    .getOrCreate()

sc=spark.sparkContext
sc.setLogLevel("ERROR")


def stock_basic_info():
    sql1 = """
        select * from stock.stock_index
    """
    df1 = spark.sql(sql1)
    return df1


bb893f62906ac516ce637afe71e202f295e29f77
<mask token>


def stock_basic_info():
    sql1 = """
        select * from stock.stock_index
    """
    df1 = spark.sql(sql1)
    return df1

<mask token>
sc.setLogLevel('ERROR')


def stock_basic_info():
    sql1 = """
        select * from stock.stock_index
    """
    df1 = spark.sql(sql1)
    return df1

<mask token>
spark = SparkSession.builder.appName('davidyu').enableHiveSupport(
    ).getOrCreate()
sc = spark.sparkContext
sc.setLogLevel('ERROR')


def stock_basic_info():
    sql1 = """
        select * from stock.stock_index
    """
    df1 = spark.sql(sql1)
    return df1

from pyspark import SparkConf, SparkContext
from pyspark.sql import HiveContext, SparkSession
from sklearn.linear_model import LinearRegression
from sklearn import linear_model
from pyspark.sql.types import *
from pyspark.sql.functions import pandas_udf, PandasUDFType
import pandas as pd
import numpy as np
from davidyu_cfg import *
from functions.LinearReg import LinearReg
from pyspark.sql.functions import *
spark = SparkSession.builder.appName('davidyu').enableHiveSupport(
    ).getOrCreate()
sc = spark.sparkContext
sc.setLogLevel('ERROR')


def stock_basic_info():
    sql1 = """
        select * from stock.stock_index
    """
    df1 = spark.sql(sql1)
    return df1

from pyspark import SparkConf, SparkContext
from pyspark.sql import HiveContext,SparkSession
from sklearn.linear_model import LinearRegression
from sklearn import linear_model
from pyspark.sql.types import *
from pyspark.sql.functions import pandas_udf, PandasUDFType
import pandas as pd
import numpy as np
from davidyu_cfg import *
from functions.LinearReg import LinearReg
from pyspark.sql.functions import *

spark = SparkSession \
    .builder \
    .appName("davidyu") \
    .enableHiveSupport() \
    .getOrCreate()

sc=spark.sparkContext
sc.setLogLevel("ERROR")


def stock_basic_info():
    sql1 = """
        select * from stock.stock_index
    """
    df1 = spark.sql(sql1)
    return df1

