本案例以2020年美国新冠肺炎疫情数据作为数据集,以Python为编程语言,使用Spark对数据进行分析,并对分析结果进行可视化。
(1)Linux:Ubuntu 16.04
(2)Hadoop3.1.3
(3)Python: 3.7
(4)Spark: 2.4.0
# -*- coding:utf-8 -*-
# @Time : 2021/1/21 10:26
# @Author: qiuqiuqiu
# @File : TranstionTxt.py
import pandas as pd
#.csv->.txt
data = pd.read_csv('us-counties.csv')
with open('us-counties.txt','a+',encoding='utf-8') as f:
for line in data.values:
f.write((str(line[0])+'\t'+str(line[1])+'\t')+str(line[2])+'\t'+str(line[3])+'\t'+str(line[4])+'\n')
print('success')
这里采用Python作为编程语言。操作的完整实验代码存放在了analyst.py中,具体如下:
# -*- coding:utf-8 -*-
# @Time : 2021/1/21 14:30
# @Author: qiuqiuqiu
# @File : analyst.py
# 1) 统计美国截止每日的累计确诊人数和累计死亡人数。做法是以date作为分组字段,对cases和deaths字段进行汇总统计。
# 2) 统计美国每日的新增确诊人数和新增死亡人数。因为新增数=今日数-昨日数,所以考虑使用自连接,连接条件是t1.date = t2.date + 1,然后使用t1.totalCases – t2.totalCases计算该日新增。
# 3) 统计截止5.19日,美国各州的累计确诊人数和死亡人数。首先筛选出5.19日的数据,然后以state作为分组字段,对cases和deaths字段进行汇总统计。
# 4) 统计截止5.19日,美国确诊人数最多的十个州。对3)的结果DataFrame注册临时表,然后按确诊人数降序排列,并取前10个州。
# 5) 统计截止5.19日,美国死亡人数最多的十个州。对3)的结果DataFrame注册临时表,然后按死亡人数降序排列,并取前10个州。
# 6) 统计截止5.19日,美国确诊人数最少的十个州。对3)的结果DataFrame注册临时表,然后按确诊人数升序排列,并取前10个州。
# 7) 统计截止5.19日,美国死亡人数最少的十个州。对3)的结果DataFrame注册临时表,然后按死亡人数升序排列,并取前10个州
# 8) 统计截止5.19日,全美和各州的病死率。病死率 = 死亡数/确诊数,对3)的结果DataFrame注册临时表,然后按公式计算。
from pyspark import SparkConf, SparkContext
from pyspark.sql import Row
from pyspark.sql.types import *
from pyspark.sql import SparkSession
from datetime import datetime
import pyspark.sql.functions as func
def toDate(inputStr):
newStr = ""
if len(inputStr) == 8:
s1 = inputStr[0:4]
s2 = inputStr[5:6]
s3 = inputStr[7]
newStr = s1 + "-" + "0" + s2 + "-" + "0" + s3
else:
s1 = inputStr[0:4]
s2 = inputStr[5:6]
s3 = inputStr[7:]
newStr = s1 + "-" + "0" + s2 + "-" + s3
date = datetime.strptime(newStr, "%Y-%m-%d")
return date
# 主程序:
spark = SparkSession.builder.appName('PythonSparkSQLexample').getOrCreate()
fields = [StructField("date", DateType(), False), StructField("county", StringType(), False),
StructField("state", StringType(), False),
StructField("cases", IntegerType(), False), StructField("deaths", IntegerType(), False), ]
schema = StructType(fields)
rdd0 = spark.sparkContext.textFile("us-counties.txt")
rdd1 = rdd0.map(lambda x: x.split("\t")).map(lambda p: Row(toDate(p[0]), p[1], p[2], int(p[3]), int(p[4])))
shemaUsInfo = spark.createDataFrame(rdd1, schema)
shemaUsInfo.createOrReplaceTempView("usInfo")
# 1.计算每日的累计确诊病例数和死亡数
df = shemaUsInfo.groupBy("date").agg(func.sum("cases"), func.sum("deaths")).sort(shemaUsInfo["date"].asc())
# 列重命名
df1 = df.withColumnRenamed("sum(cases)", "cases").withColumnRenamed("sum(deaths)", "deaths")
# df1.repartition(1).write.json("result1.json") # 写入hdfs
df1.repartition(1).write.format('json').mode('overwrite').save('result1.json')
print("success result1")
# 注册为临时表供下一步使用
df1.createOrReplaceTempView("ustotal")
# 2.计算每日较昨日的新增确诊病例数和死亡病例数
df2 = spark.sql(
"select t1.date,t1.cases-t2.cases as caseIncrease,t1.deaths-t2.deaths as deathIncrease from ustotal t1,ustotal t2 where t1.date = date_add(t2.date,1)")
df2.sort(df2["date"].asc()).repartition(1).write.json("result2.json") # 写入hdfs
print("success result2")
# 3.统计截止5.19日 美国各州的累计确诊人数和死亡人数
df3 = spark.sql(
"select date,state,sum(cases) as totalCases,sum(deaths) as totalDeaths,round(sum(deaths)/sum(cases),4) as deathRate from usInfo where date = to_date('2020-05-19','yyyy-MM-dd') group by date,state")
df3.sort(df3["totalCases"].desc()).repartition(1).write.json("result3.json") # 写入hdfs
print("success result3")
df3.createOrReplaceTempView("eachStateInfo")
# 4.找出美国确诊最多的10个州
df4 = spark.sql("select date,state,totalCases from eachStateInfo order by totalCases desc limit 10")
df4.repartition(1).write.json("result4.json")
print("success result4")
# 5.找出美国死亡最多的10个州
df5 = spark.sql("select date,state,totalDeaths from eachStateInfo order by totalDeaths desc limit 10")
df5.repartition(1).write.json("result5.json")
print("success result5")
# 6.找出美国确诊最少的10个州
df6 = spark.sql("select date,state,totalCases from eachStateInfo order by totalCases asc limit 10")
df6.repartition(1).write.json("result6.json")
print("success result6")
# 7.找出美国死亡最少的10个州
df7 = spark.sql("select date,state,totalDeaths from eachStateInfo order by totalDeaths asc limit 10")
df7.repartition(1).write.json("result7.json")
print("success result7")
# 8.统计截止5.19全美和各州的病死率
df8 = spark.sql(
"select 1 as sign,date,'USA' as state,round(sum(totalDeaths)/sum(totalCases),4) as deathRate from eachStateInfo group by date union select 2 as sign,date,state,deathRate from eachStateInfo").cache()
df8.sort(df8["sign"].asc(), df8["deathRate"].desc()).repartition(1).write.json("result8.json")
print("success result8")
# 主程序:
spark = SparkSession.builder.appName('PythonSparkSQLexample').getOrCreate()
fields = [StructField("date", DateType(), False), StructField("county", StringType(), False),
StructField("state", StringType(), False),
StructField("cases", IntegerType(), False), StructField("deaths", IntegerType(), False), ]
schema = StructType(fields)
rdd0 = spark.sparkContext.textFile("us-counties.txt")
rdd1 = rdd0.map(lambda x: x.split("\t")).map(lambda p: Row(toDate(p[0]), p[1], p[2], int(p[3]), int(p[4])))
shemaUsInfo = spark.createDataFrame(rdd1, schema)
shemaUsInfo.createOrReplaceTempView("usInfo")
df2.sort(df2["date"].asc()).repartition(1).write.json("result2.json") # 写入hdfs
pip install pyecharts
具体可视化实现代码组织与showdata.py文件中。具体代码如下:
# -*- coding:utf-8 -*-
# @Time : 2021/1/21 16:07
# @Author: qiuqiuqiu
# @File : showdata.py
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.charts import Line
from pyecharts.components import Table
from pyecharts.charts import WordCloud
from pyecharts.charts import Pie
from pyecharts.charts import Funnel
from pyecharts.charts import Scatter
from pyecharts.charts import PictorialBar
from pyecharts.options import ComponentTitleOpts
from pyecharts.globals import SymbolType
import json
# 1.画出每日的累计确诊病例数和死亡数——>双柱状图
def drawChart_1(index):
root = "p"+str(index)+".json"
print(root)
date = []
cases = []
deaths = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
date.append(str(js['date']))
cases.append(int(js['cases']))
deaths.append(int(js['deaths']))
d = (
Bar()
.add_xaxis(date)
.add_yaxis("累计确诊人数", cases, stack="stack1")
.add_yaxis("累计死亡人数", deaths, stack="stack1")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="美国每日累计确诊和死亡人数"))
.render("pp"+str(index)+".html")
)
# 2.画出每日的新增确诊病例数和死亡数——>折线图
def drawChart_2(index):
root = "p"+str(index)+".json"
date = []
cases = []
deaths = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
date.append(str(js['date']))
cases.append(int(js['caseIncrease']))
deaths.append(int(js['deathIncrease']))
(
Line(init_opts=opts.InitOpts(width="1600px", height="800px"))
.add_xaxis(xaxis_data=date)
.add_yaxis(
series_name="新增确诊",
y_axis=cases,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值")
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="美国每日新增确诊折线图", subtitle=""),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
.render("pp"+str(index)+".html")
)
(
Line(init_opts=opts.InitOpts(width="1600px", height="800px"))
.add_xaxis(xaxis_data=date)
.add_yaxis(
series_name="新增死亡",
y_axis=deaths,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max", name="最大值")]
),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值"),
opts.MarkLineItem(symbol="none", x="90%", y="max"),
opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
]
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="美国每日新增死亡折线图", subtitle=""),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
.render("pp"+str(index)+".html")
)
# 3.画出截止5.19,美国各州累计确诊、死亡人数和病死率--->表格
def drawChart_3(index):
root = "p"+str(index)+".json"
allState = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
row = []
row.append(str(js['state']))
row.append(int(js['totalCases']))
row.append(int(js['totalDeaths']))
row.append(float(js['deathRate']))
allState.append(row)
table = Table()
headers = ["State name", "Total cases", "Total deaths", "Death rate"]
rows = allState
table.add(headers, rows)
table.set_global_opts(
title_opts=ComponentTitleOpts(title="美国各州疫情一览", subtitle="")
)
table.render("pp"+str(index)+".html")
# 4.画出美国确诊最多的10个州——>词云图
def drawChart_4(index):
root ="p"+str(index)+".json"
data = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
row = (str(js['state']), int(js['totalCases']))
data.append(row)
c = (
WordCloud()
.add("", data, word_size_range=[20, 100], shape=SymbolType.DIAMOND)
.set_global_opts(title_opts=opts.TitleOpts(title="美国各州确诊Top10"))
.render("pp"+str(index)+".html")
)
# 5.画出美国死亡最多的10个州——>象柱状图
def drawChart_5(index):
root ="p"+str(index)+".json"
state = []
totalDeath = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
state.insert(0, str(js['state']))
totalDeath.insert(0, int(js['totalDeaths']))
c = (
PictorialBar()
.add_xaxis(state)
.add_yaxis(
"",
totalDeath,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=18,
symbol_repeat="fixed",
symbol_offset=[0, 0],
is_symbol_clip=True,
symbol=SymbolType.ROUND_RECT,
)
.reversal_axis()
.set_global_opts(
title_opts=opts.TitleOpts(title="PictorialBar-美国各州死亡人数Top10"),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(opacity=0)
),
),
)
.render("pp"+str(index)+".html")
)
# 6.找出美国确诊最少的10个州——>词云图
def drawChart_6(index):
root = "p"+str(index)+".json"
data = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
row = (str(js['state']), int(js['totalCases']))
data.append(row)
c = (
WordCloud()
.add("", data, word_size_range=[100, 20], shape=SymbolType.DIAMOND)
.set_global_opts(title_opts=opts.TitleOpts(title="美国各州确诊最少的10个州"))
.render("pp"+str(index)+".html")
)
# 7.找出美国死亡最少的10个州——>漏斗图
def drawChart_7(index):
root = "p"+str(index)+".json"
data = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
data.insert(0, [str(js['state']), int(js['totalDeaths'])])
c = (
Funnel()
.add(
"State",
data,
sort_="ascending",
label_opts=opts.LabelOpts(position="inside"),
)
.set_global_opts(title_opts=opts.TitleOpts(title=""))
.render("pp"+str(index)+".html")
)
# 8.美国的病死率--->饼状图
def drawChart_8(index):
root ="p"+str(index)+".json"
values = []
with open(root, 'r') as f:
while True:
line = f.readline()
if not line: # 到 EOF,返回空字符串,则终止循环
break
js = json.loads(line)
if str(js['state']) == "USA":
values.append(["Death(%)", round(float(js['deathRate']) * 100, 2)])
values.append(["No-Death(%)", 100 - round(float(js['deathRate']) * 100, 2)])
c = (
Pie()
.add("", values)
.set_colors(["blcak", "orange"])
.set_global_opts(title_opts=opts.TitleOpts(title="全美的病死率"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
.render("pp"+str(index)+".html")
)
# 可视化主程序:
index = 1
while index < 9:
funcStr = "drawChart_" + str(index)
eval(funcStr)(index)
index += 1