为什么用pyecharts跑不出图形，在海豚人工智能与大数据实验室这个网站进行的，分析想要有可视化图

Question

为什么用pyecharts跑不出图形，在海豚人工智能与大数据实验室这个网站进行的，分析想要有可视化图

from pyspark import SparkConf,SparkContext
from pyspark.sql.types import Row
from pyspark.sql import SparkSession
import pandas as pd


spark = SparkSession.builder.appName("test").master("local").getOrCreate()
df = spark.read.csv('file:///home/dolphin/Downloads/data.csv',header=True,encoding="GBK")
df.show(5)

#df.printSchema()

from pyspark.sql.types import IntegerType
df = df.withColumn('最低年薪资',df['最低年薪资'].cast(IntegerType()))
df = df.withColumn('最高年薪资',df['最高年薪资'].cast(IntegerType()))
df = df.withColumn('平均年薪资',df['平均年薪资'].cast(IntegerType()))

#df.printSchema()

s0_10_num = df.filter(df.平均年薪资< 10).count()
s10_20_num = df.filter((df.平均年薪资>= 10) & (df.平均年薪资 < 20)).count()
s20_30_num = df.filter((df.平均年薪资 >= 20) & (df.平均年薪资 < 30)).count()
s30_40_num = df.filter((df.平均年薪资 >= 30) & (df.平均年薪资 < 40)).count()
s40_50_num = df.filter((df.平均年薪资 >= 40) & (df.平均年薪资 < 50)).count()
s50_num = df.filter(df.平均年薪资 >= 50).count()
salary = ['0~10','10~20','20~30','30~40','40~50','50+']
salary_count = [s0_10_num,s10_20_num,s20_30_num,s30_40_num,s40_50_num,s50_num]

print(salary_count)

companyind_data = df.groupBy('所属行业').count().sort('count',ascending=False).toPandas()

companysize_data = df.groupBy('公司规模').count().sort('count',ascending=False).toPandas()

companytype_data = df.groupBy('公司类型').count().sort('count',ascending=False).toPandas()

jobcity_data = df.groupBy('工作城市').count().sort('count',ascending=False).toPandas()

experience_data = df.groupBy('经验要求').count().sort('count',ascending=False).toPandas()

educational_data = df.groupBy('学历要求').count().sort('count',ascending=False).toPandas()

welfare_data = df.groupBy('提供福利').count().sort('count',ascending=False).toPandas()


def loc_salary_count(df,loc_list):
    lis = []
    for loc in loc_list:
        s0_10_num=df.filter((df.平均年薪资 >= 0) & (df.平均年薪资 < 10) & (df.工作城市 == loc)).count()
        s10_20_num=df.filter((df.平均年薪资 >= 10) & (df.平均年薪资 < 20) & (df.工作城市 == loc)).count()
        s20_30_num=df.filter((df.平均年薪资 >= 20) & (df.平均年薪资 < 30) & (df.工作城市 == loc)).count()
        s30_40_num=df.filter((df.平均年薪资 >= 30) & (df.平均年薪资 < 40) & (df.工作城市 == loc)).count()
        s40_50_num=df.filter((df.平均年薪资 >= 40) & (df.平均年薪资 < 50) & (df.工作城市 == loc)).count()
        s50_num=df.filter((df.平均年薪资 >= 50) & (df.工作城市 == loc)).count()

        lis.extend([s0_10_num,s10_20_num,s20_30_num,s30_40_num,s40_50_num,s50_num])
    return lis
loc_salary_count = loc_salary_count(df,'工作城市')

from pyecharts.charts import Line
import pyecharts.options as opts
def line_base():
    line1=(
        Line()
        .add_xaxis(price)
        .add_yaxis('fenbu',price_count)
        .set_global_opts(title_opts=opts.TitleOpts(title='zhexiantu'))
    ).render_notebook()
    return line1
line_base()