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本次实验选择编程类工具Pyecharts。
在Pycharm中新建python项目，并引入pyecharts，openpyxl，datetime等必要包。
创建main.py，写入以下内容。
#!/usr/bin/env python
# coding:utf-8
"""
Name : main.py
Author : F
Time : 2020/10/15 20:09
Desc : 数据可视化实验五
"""
import openpyxl
from pyecharts.charts import HeatMap, Parallel
import pyecharts.globals as globals
from pyecharts import options as opts
from pyecharts.charts import Pie
globals._WarningControl.ShowWarning = False # 关闭pyecharts给出的警告
def getClassifyData():
filename = "classifyday1.xlsx"
ws = openpyxl.load_workbook(filename)['classifyday1']
classifyData = []
for i in range(2, ws.max_row + 1):
row = str(i)
classifyData.append([ws['A' + row].value, ws['B' + row].value])
return classifyData
# 从表格读取数据
def getRoomData():
filename = "time_allocate_day1.xlsx"
ws = openpyxl.load_workbook(filename)['time_allocate_day1']
roomData, roomName = [], []
for room in range(24):
roomName.append(
opts.ParallelAxisOpts(dim=room, name=ws[chr(room + 65) + '1'].value, min_='dataMin', max_='dataMax'))
for i in range(2, ws.max_row + 1):
row, person = str(i), []
for room in range(24):
person.append(ws[chr(room + 65) + row].value)
roomData.append(person)
return roomData, roomName
# 不同人员平行坐标系图
def drawRoomDataParallel():
roomData, roomName = getRoomData()
c = (
Parallel(init_opts=opts.InitOpts(width="2000px", height="1000px")).add_schema(
roomName).add(series_name="人员分类图",
data=roomData,
linestyle_opts=opts.LineStyleOpts(color={'type': 'linear', 'x': 0,
'y': 0,
'x2': 0,
'y2': 1,
'colorStops': [{
'offset': 0, 'color': 'red'
}, {
'offset': 1,
'color': 'blue'
}],
'global': False
}, opacity=0.5)
).set_global_opts(title_opts=opts.TitleOpts(title="数据可视化实验五"))
)
c.render("不同人员平行坐标系图.html")
def genHeatBaseData():
x, y = [], []
for i in range(100):
fmt = str("{:0>2d}".format(i))
x.append(fmt)
y.append('1' + fmt)
return x, y
def classifyHeatMap(xAxisData: list, yAxisData: list, data: list):
HeatMap(init_opts=opts.InitOpts(width="2000px", height="2000px")).add_xaxis(xaxis_data=xAxisData).add_yaxis(
series_name="classifyHeatMap",
yaxis_data=yAxisData,
value=data,
label_opts=opts.LabelOpts(
is_show=False, color="#fff", position="bottom", horizontal_align="50%"
),
).set_series_opts().set_global_opts(
legend_opts=opts.LegendOpts(is_show=True),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
axislabel_opts=opts.LabelOpts(
interval=0
)
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
visualmap_opts=opts.VisualMapOpts(
orient="horizontal", pos_left="center", is_piecewise=True,
pieces=[
{"value": 2, "label": 'waiter'},
{"value": 4, "label": 'vip'},
{"value": 6, "label": 'participant'},
{"value": 8, "label": 'meeting'},
{"value": 10, "label": 'reporter'},
]
),
).render("classifyHeatMap.html")
def serializeClassifyData(data: list):
job = {'waiter': 2, 'vip': 4, 'participant': 6, 'meeting': 8, 'reporter': 10}
sData = {}
for person in data:
if not sData.__contains__(str(person[0])[0:3]):
sData[str(person[0])[0:3]] = {}
sData[str(person[0])[0:3]][str(person[0])[3:5]] = job[person[1]]
return sData
def genClassifyHeatMapData(data: dict):
rdata = [] # data中的每个列表内容依次是横坐标 纵坐标 值
for k, v in data.items():
for sk, sv in v.items():
rdata.append([int(sk), int(k) - 100, sv])
return rdata
def drawClassifyHeatMap():
classifyData = getClassifyData()
serializedClassifyData = serializeClassifyData(classifyData)
xAxisValue, yAxisValue = genHeatBaseData()
classifyHeatMapData = genClassifyHeatMapData(serializedClassifyData)
classifyHeatMap(xAxisValue, yAxisValue, classifyHeatMapData)
# 计算每种人去了每个房间的总和
def getRoomData1():
classifyData = getClassifyData()
serializedClassifyData = serializeClassifyData(classifyData)
filename = "time_allocate_day1.xlsx"
ws = openpyxl.load_workbook(filename)['time_allocate_day1']
roomData = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
for i in range(2, ws.max_row + 1):
row = str(i)
for room in range(6):
roomData[((serializedClassifyData[str(ws['A' + row].value)[0:3]][
str(ws['A' + row].value)[3:5]]) // 2) - 1][room] += ws[chr(103 + room) + row].value
# roomData=[[0, 0, 0, 0, 60831, 234033], [89477, 1235993, 30267, 0, 60746, 0], [78857, 0, 30304, 0, 3632303, 0],
# [633270, 0, 410705, 0, 0, 0], [3233, 1550, 2401, 119402, 0, 0]]
return roomData
# 计算每个房间都被多少人呆过的总和
def getRoomData2(roomData: list):
roomData2 = [[], [], [], [], [], []]
for i in roomData:
k = 0
for v in i:
roomData2[k].append(v)
k += 1
# roomData2=[[0, 89477, 78857, 633270, 3233], [0, 1235993, 0, 0, 1550], [0, 30267, 30304, 410705, 2401],[0, 0, 0,
# 0, 119402], [60831, 60746, 3632303, 0, 0], [234033, 0, 0, 0, 0]]
return roomData2
def drawRoomData2Pie():
roomData2 = getRoomData2(getRoomData1())
jobs = ['waiter', 'vip', 'participant', 'meeting', 'reporter']
newData = [[], [], [], [], [], []]
k = 0
for room in roomData2:
i = 0
for job in room:
newData[k].append([jobs[i], job])
i += 1
k += 1
k = 0
pieCenter = [["20%", "30%"], ["55%", "30%"], ["85%", "30%"], ["20%", "70%"], ["55%", "70%"], ["85%", "70%"]]
pie = Pie()
for data in newData:
pie.add("Room" + str(k + 1), data, center=pieCenter[k], radius=[40, 70])
k += 1
pie.set_global_opts(
title_opts=opts.TitleOpts(title="Room1-6各人员访问量"),
legend_opts=opts.LegendOpts(is_show=False),
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}", is_show=True))
pie.render("roomTime.html")
if __name__ == '__main__':
# drawClassifyHeatMap() # 热力图是在已知人员分类的情况下画出来的，但是第一问的前提是不知道人员分类
# 用平行坐标系画图
drawRoomDataParallel()
# 1:休息区 2:嘉宾休息区 3:休息区 4:记者区 5:黑客竞赛现场 6：工作人员休息区
drawRoomData2Pie()
代码流程：
人员分类平行坐标系如下所示，可以通过此图观察分辨某人职位
上图从左到右从上到下依次是Room1-6的房间人员访问量。
对上图进行分析：
所以结论为：Room1:休息区 Room2:嘉宾休息区 Room3:休息区 Room4:记者区 Room5:黑客竞赛现场 Room6：工作人员休息区
数据可视化–实验五：高维非空间数据可视化实验完成，两项实验结果均符合要求，实验成功。