使用pyspark时要注意 pyspark_python 设置为python3.5 ，可以使用ml，运行环境也应该是python3.5，版本必须一致，不然会报错。

import findspark

findspark.init()

import pandas as pd

import numpy as np

import pickle

import os

os.environ["PYSPARK_PYTHON"] = "/home/q/conda/bin/python3.5"

from pyspark import SparkContext, SparkConf

from pyspark.sql import SparkSession, SQLContext

from pyspark.ml.feature import HashingTF, IDF, Tokenizer

from pyspark.ml import Pipeline

from pyspark.ml.classification import NaiveBayes

from pyspark.ml.evaluation import MulticlassClassificationEvaluator

os.environ["PYSPARK_DRIVER_PYTHON"] = "python"

# local[20]

#import jieba

#jieba.initialize()

conf = SparkConf() \

    .setAppName("NLP_Project_youming.guo") \

    .setMaster("yarn") \

    .set(‘spark.yarn.queue‘, "root.adhoc") \

    .set(‘spark.yarn.dist.files‘,

         ‘file:/home/q/spark/python/lib/pyspark.zip,file:/home/q/spark/python/lib/py4j-0.10.4-src.zip‘) \

    .setExecutorEnv(‘PYTHONPATH‘, ‘pyspark.zip:py4j-0.10.4-src.zip‘) \

    .set(‘PYSPARK_PYTHON‘, ‘/home/q/conda/bin/python3.5‘)

conf.set("spark.executor.memory", "5g")

conf.set("spark.driver.memory","10g")

conf.set("spark.executor.cores","2")

conf.set("spark.dynamicAllocation.maxExecutors","5")

conf.set("spark.driver.maxResultSize","0")

conf.set("spark.dynamicAllocation.enabled","true")

conf.set("spark.shuffle.service.enabled", "true")

conf.set("spark.shuffle.service.port", "7338")

sc = SparkContext(conf=conf)

sqlContext = SQLContext(sc)

spark = SparkSession(sc)

pyspark配置config

原文：https://www.cnblogs.com/Tw1st-Fate/p/11094344.html