def create_Pie_charts(data, title):
'''饼状图'''
page = Page()
style = Style(width=800, height=800)
chart = Pie(title, **style.init_style)
attr, value = chart.cast(data)
chart.add("", attr, value, is_label_show=True)
page.add(chart)
return page
def sexpietest(self):
from pyecharts import Pie
pie = Pie('性别分布饼形图', self.ftitle, height=500)
key, value = pie.cast(self.sexpiedata)
pie.add('人数',
key,
value,
is_label_show=True,
center=[50, 55],
rosetype='area')
pie.render(r'pic\性别分布饼形图.html')
print('性别饼形图输出成功')
def pietest(self):
from pyecharts import Pie
pie = Pie('头衔分布饼形图', self.ftitle, height=600, title_pos='left')
key, value = pie.cast(self.piedata)
pie.add('人数',
key,
value,
is_label_show=True,
is_legend_show=False,
radius=[30, 70])
pie.render(r'pic\头衔分布饼形图.html')
print('饼形图输出Pie测试网页')
def _education_show(self):
pie = Pie("学历统计", title_pos='center', width=1200, height=500)
attr, value = pie.cast(self.g.education_data)
pie.add("",
attr,
value,
adius=[40, 80],
title_pos='center',
is_label_show=True,
legend_orient='vertical',
legend_pos='left')
return pie.render_embed()
def fromtest(self):
from pyecharts import Pie
pie = Pie('帖子来源饼形图', self.ftitle, title_pos='center')
key, value = pie.cast(self.fromdata)
pie.add('帖子数',
key,
value,
is_label_show=True,
radius=[35, 65],
center=[50, 55],
legend_pos='left',
legend_orient='vertical')
pie.render(r'pic\帖子来源饼形图.html')
def create_profession(data, title):
page = Page()
style = Style(width=900,
height=400,
title_pos="center",
background_color='#c4ccd3')
kwargs = dict(radius=(40, 75),
label_text_color=None,
is_label_show=True,
legend_orient='her',
legend_pos='left')
profession_chart = Pie(title, **style.init_style)
attr, value = profession_chart.cast(data)
profession_chart.add("", attr, value, **kwargs)
page.add(profession_chart)
return page
itchat.auto_login(hotReload=True) #登陆微信
myFriendList = itchat.get_friends() #获取微信中的好友列表
sexCounter = {'男' : 0, '女' : 0, '未标注' : 0}
for friend in myFriendList:
if friend['Sex'] == 1:
sexCounter['男'] += 1
elif friend['Sex'] == 2:
sexCounter['女'] += 1
else:
sexCounter['未标注'] += 1
pie = Pie('你的微信好友性别比例图',width=1200, height=600, title_pos='center')
attr, value = pie.cast(sexCounter)
'''利用pyecharts的cast方法将字典列表转换成两个列表用于绘图
例：
tagsList = [{'周一':3},{'周二':2},{'周三':1}]
用cast方法获得
attr = ['周一','周二','周三']
value = [3,2,1]
'''
pie.add(
'', attr, value,
is_label_show = True,
legend_orient = 'vertical', # 图例是否垂直
legend_pos = 'left'
)
pie.render('./你的微信好友性别比例图.html') # 绘图渲染
def pricePieMaking(task_id, start_range, end_range):
client = pymongo.MongoClient("127.0.0.1", 27017)
list = []
dict = {}
list1 = []
try:
db = client.crawlData # 连接test数据库
collection = db.taobao # 访问test数据库中things集合
for m in collection.find({"task_id": task_id}): # 到时候改这里，改成根据id查找
list.append(m['now_price'])
except:
pass
finally:
client.close()
end_range = float(end_range)
start_range = float(start_range)
price_range = (end_range - start_range) / 4
price1 = str(start_range) + '~' + str(
start_range + price_range) # 1000~1250
price2 = str(start_range + price_range) + '~' + str(
start_range + price_range * 2) # 1250~1500
price3 = str(start_range + price_range * 2) + '~' + str(
start_range + price_range * 3) # 1500~1750
price4 = str(start_range + price_range * 3) + '~' + str(
end_range) # 1750~2000
dict[price1] = 0
dict[price2] = 0
dict[price3] = 0
dict[price4] = 0
# 分析数据
for index in list: # 遍历词语列表
if float(index.encode("utf-8")) > start_range and float(
index.encode("utf-8")) < start_range + price_range * 1:
dict[price1] += 1
elif float(
index.encode("utf-8")) > start_range + price_range and float(
index.encode("utf-8")) < start_range + price_range * 2:
dict[price2] += 1
elif float(index.encode(
"utf-8")) > start_range + price_range * 2 and float(
index.encode("utf-8")) < start_range + price_range * 3:
dict[price3] += 1
elif float(index.encode(
"utf-8")) > start_range + price_range * 3 and float(
index.encode("utf-8")) < end_range:
dict[price4] += 1
else: # 不在这个范围内的
continue
# print dict
for item in dict.items(): # 化成列表类型
list1.append(item[0])
list1.append(item[1])
# print list1
list2 = []
for i in range(0, len(list1), 2): # 拼成pycharts能够输入的格式,步长为2
a = (list1[i], list1[i + 1])
list2.append(a)
# 生成图表
pie = Pie(u"商品价格范围分布图")
pie.width = 1200
pie.height = 800
attr, value = pie.cast(list2)
pie.add("", attr, value, is_label_show=True)
# pie.show_config()
pie.render("static/prices/price" + task_id + ".html")
def render():
global positive_text
global negative_text
# 获取评论中所有城市
cities = []
with open('maoyan2.csv', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows[1:]:
if row.count(',') != 9:
continue
elements = row.split(',')
time = elements[0]
gender = elements[3]
city = elements[4]
comment = elements[7]
sentiment = elements[8]
score = elements[6]
if not is_float(elements[9]):
continue
if not is_float(score):
continue
if city != '': # 去掉城市名为空的值
cities.append(city)
if score != '':
scores.append(float(score) * 2)
# if float(score) * 2 > 7:
# positive_text += comment
# elif float(score) * 2 < 4:
# negative_text += comment
if gender != '':
genders.append(gender)
if time != '':
dates.append(time)
positive_prob = float(elements[9])
if positive_prob >= 0.7:
# positive_text += comment
positive_probs.append(round(positive_prob, 2))
else:
# negative_text += comment
negative_probs.append(round(positive_prob, 1))
sentiments.append(sentiment)
# print(positive_text)
# print(negative_text)
# with open("positive_text.txt","w", encoding='utf-8') as f:
# f.write(positive_text)
# with open("negative_text.txt","w", encoding='utf-8') as f:
# f.write(negative_text)
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
data = Counter(cities).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
score_data = Counter(scores).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
# 按0-10进行排序
score_data = sorted(score_data)
gender_data = Counter(genders).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
print(gender_data)
date_data = Counter(dates).most_common()
# 按日期进行排序
date_data = sorted(date_data)
# print(data)
sentiments_data = Counter(sentiments).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
positive_probs_data = Counter(positive_probs).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
positive_probs_data = sorted(positive_probs_data)
negative_probs_data = Counter(negative_probs).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
negative_probs_data = sorted(negative_probs_data)
# 定义样式
style = Style(
title_color='#fff',
title_pos='center',
width=800,
height=600,
background_color='#404a59'
)
from pyecharts import Pie
# 设置主标题与副标题，标题设置居中，设置宽度为900
pie = Pie("情感分析结果分布图", "数据来源：不正经程序员-采集自猫眼",title_pos='center',width=900)
attr, value = pie.cast(sentiments_data)
# 加入数据，设置坐标位置为【25，50】，上方的colums选项取消显示
pie.add("", attr, value ,visual_range=[0, 3500],is_legend_show=False, is_label_show=True)
# 保存图表
pie.render('picture\情感分析结果分布图.html')
# 根据正向情感分析指数生成柱状图
bar = Bar('正向情感分布', '数据来源：不正经程序员-采集自猫眼',
title_pos='center', width=1200, height=600)
attr, value = bar.cast(positive_probs_data)
bar.add('', attr, value, is_visualmap=False, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True,
is_label_show=True)
bar.render('picture\正向情感分布-柱状图.html')
# 根据负向情感分析指数生成柱状图
bar = Bar('负向情感分布', '数据来源：不正经程序员-采集自猫眼',
title_pos='center', width=1200, height=600)
attr, value = bar.cast(negative_probs_data)
bar.add('', attr, value, is_visualmap=False, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True,
is_label_show=True)
bar.render('picture\负向情感分布-柱状图.html')
# 根据城市数据生成地理坐标图
geo = Geo('观众地理分布', '数据来源：不正经程序员-采集自猫眼', **style.init_style)
attr, value = geo.cast(data)
geo.add('', attr, value, visual_range=[0, 600],
visual_text_color='#fff', symbol_size=7,
is_visualmap=True, is_piecewise=True, visual_split_number=10)
geo.render(
'picture\观众地理分布-地理坐标图.html')
# 根据城市数据生成柱状图
data_top20 = Counter(cities).most_common(20) # 返回出现次数最多的20条
bar = Bar('观众来源排行TOP20', '数据来源：不正经程序员-采集自猫眼',
title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top20)
bar.add('', attr, value, is_visualmap=False, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True,
is_label_show=True)
bar.render('picture\观众来源排行-柱状图.html')
# 根据评分数据生成柱状图
bar = Bar('《哪吒》各评分数量', '数据来源：不正经程序员-采集自猫眼',
title_pos='center', width=900, height=600)
attr, value = bar.cast(score_data)
# line = Line()
# line.add('', attr, value)
bar.add('', attr, value, is_visualmap=False, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True,
is_label_show=True)
overlap = Overlap()
overlap.add(bar)
# overlap.add(line)
overlap.show_config()
overlap.render(
'picture\评分数量-柱状图.html')
# 根据评分数据生成柱状图
bar = Bar('评价人数走势图', '数据来源：不正经程序员-采集自猫眼',
title_pos='center', width=1200, height=600)
attr, value = bar.cast(date_data)
# line = Line()
# line.add('', attr, value)
bar.add('', attr, value, is_visualmap=False, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True,
is_label_show=True)
overlap = Overlap()
overlap.add(bar)
# overlap.add(line)
overlap.show_config()
overlap.render(
'picture\评价人数走势图.html')
from pyecharts import Pie
# 设置主标题与副标题，标题设置居中，设置宽度为900
pie = Pie("观众性别分布图", "数据来源：不正经程序员-采集自猫眼",title_pos='center',width=900)
attr, value = geo.cast(gender_data)
# 加入数据，设置坐标位置为【25，50】，上方的colums选项取消显示
pie.add("", ["其他","男","女"], value ,visual_range=[0, 3500],is_legend_show=False, is_label_show=True)
# 保存图表
pie.render('picture\观众性别分布图.html')