def genPiePicture(df_score):
# 导入Pie组件，用于生成饼图
from pyecharts import Pie
# 设定五个等级的星级标签
attr = ["一星", "二星", "三星", "四星", "五星"]
score = df_score.groupby("评分").size() # 分组求和
# print(score)
value = [
score.iloc[0] + score.iloc[1] + score.iloc[2], # 0,0.5,1分均定为一星
score.iloc[3] + score.iloc[4], # 1.5，2分均定位二星
score.iloc[5] + score.iloc[6], # 2.5,3分均定为三星
score.iloc[7] + score.iloc[8], # 3.5,4分均定为四星
score.iloc[9] + score.iloc[10], # 4.5，5分均定为五星
]
# 利用Pie模块，数据可视化
pie = Pie('《流浪地球》评星比例', title_pos='left', width=1000, height=500)
pie.use_theme("dark")
pie.add("评分",
attr,
value,
center=[60, 60],
radius=[25, 75],
rosetype='raea',
is_legend_show=True,
is_label_show=True)
pie.render('评星.html')
return pie
def Length_of_sente():
with open('./Love_words.txt','r', encoding='utf-8') as f:
lines = f.readlines()
data = pd.Series(np.array([len(line) for line in lines]))
print(len(data))
print(data.describe())
bins = [i for i in range(0,201,20)]
lengths = pd.cut(np.array([len(line) for line in lines]),bins=bins)
# print(len(lengths))
c = Counter(lengths)
lengths = [i[0] for i in c.most_common(10)]
lengths_num = [i[1] for i in c.most_common(10)]
# print(words)
# print(words_num)
print(lengths)
print(lengths_num)
print(c.most_common())
pie = Pie('句长统计', title_pos='best')
pie.use_theme('dark')
# pie.add(
# '句长统计',
# lengths,
# lengths_num,
# legend_pos='best',
# is_legend_show=False,
# is_label_show=True
# )
pie.render('句长.html')
def show840Pie(dictData, renderAlone):
"""
840饼图，待完善
dictData:{'A':2,'B':3,...}
renderAlone:是否单独渲染
"""
pie = Pie('840刷新报表',
'平均值饼图',
title_pos='center',
width=1600,
height=900,
page_title='840刷新报表')
x_axis = tuple(dictData.keys())
y_axis = tuple(dictData.values())
pie.add('840刷新报表',
x_axis,
y_axis,
legend_orient="vertical",
legend_pos='left',
rosetype='radius',
radius=[30, 70],
is_more_utils=True,
is_label_show=True)
# 其他主题：vintage,macarons,infographic,shine，roma
pie.use_theme('vintage')
if renderAlone:
pie.render('840-pie-{}.html'.format(time.strftime('%Y%m%d%H%M%S')))
else:
return pie
def day_loan_income():
'''生成日放款收入图表'''
# X轴label
attr = ["产品A", "产品B", "产品C", "产品D", "产品E", "产品F"]
# 图表数据
v1 = list(np.random.randint(200, 1000, 6) / 10)
# 生成图表实例
chart = Pie(
"今日收入(单位:万)",
"今日总收入为%f万" % np.sum(v1),
background_color=BACKGROUND_COLOR,
width="100%")
# 使用暗色主题
chart.use_theme('dark')
# 载入默认设置并差异化更新
chart_kwargs = DEFAULT_KWARGS
chart_kwargs.update({})
# 添加数据
chart.add("收入", attr, v1, **chart_kwargs)
return chart
def analysisFrequency_Pie():
# 使用cursor()方法获取操作游标
conn = getConnection()
cursor = conn.cursor()
try:
# 执行SQL语句
cursor.execute('CALL Proc_HitTimes()')
# 获取所有记录列表
results = cursor.fetchall()
textList = []
valueList = []
for row in results:
textList.append(row['OpenNumber'])
valueList.append(row['OpenNumberCount'])
pie = Pie()
pie.use_theme('dark')
pie.add("命中次数",
textList,
valueList,
'55%', ['50%', '60%'],
roseType='area',
is_label_show=True)
pie.render()
except Exception as queryError:
print(queryError)
finally:
conn.close()
def drawPie(title, data, savepath='./results'):
if not os.path.exists(savepath):
os.mkdir(savepath)
pie = Pie(title, title_pos='center')
pie.use_theme('westeros')
attrs = [i for i, j in data.items()]
values = [j for i, j in data.items()]
pie.add('', attrs, values, is_label_show=True, legend_orient="vertical", legend_pos="left", radius=[30, 75], rosetype="area")
pie.render(os.path.join(savepath, '%s.html' % title))
def analysisSex(self):
title = 'Gender Analysis'
data = {'Male': 0, 'Female': 0, 'Confidential': 0}
pie = Pie(title, title_pos='center')
pie.use_theme('westeros')
for each in self.friends_info.get('sex'):
if each == 0:
data['Confidential'] += 1
elif each == 1:
data['Male'] += 1
elif each == 2:
data['Female'] += 1
attrs = [i for i, j in data.items()]
values = [j for i, j in data.items()]
pie.add('',
attrs,
values,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
radius=[30, 75],
rosetype="area")
pie.render(os.path.join(self.savedir, '%s.html' % title))
def chart_view03():
# 从mongo中获取数据
(x, y) = BidNotice.get_records_group_by_notice_type()
# 设置bar图形的基本属性
pie = Pie("公告类型分析图", "From: cmccb2b")
pie.use_theme('light')
pie.add("公告类型", x, y, is_stack=False) # 从Mongo读取的数据存放在这里
javascript_snippet = TRANSLATOR.translate(pie.options) # 将bar的属性设置翻译为js脚本
# pie.print_echarts_options() # 该行只为了打印配置项，方便调试时使用
return render_template(
"pyecharts.html", # 自定义，位于templates/的模版文件
chart_id=pie.chart_id, # 默认设置，
host=REMOTE_HOST, # 常量定义，存放js文件的url地址
renderer=pie.renderer, # 默认设置，
my_width="100%", # 默认设置，定义图表的宽度
my_height=600, # 默认设置，定义图表的高度
custom_function=javascript_snippet.
function_snippet, # 默认设置，保存图片的方法，似乎基于node.js
options=javascript_snippet.option_snippet, # 默认设置，
script_list=pie.get_js_dependencies(), # 默认设置，需要动态加载的js文件
)
tokens=[token for token in cun if token not in stops]
d={}
for i in tokens:
d[i] = d.get(i,0) + 1
count_list = sorted(d.items(), key=lambda x:x[1], reverse=True)
count_list = count_list[:20]
keyword_list = [k[0] for k in count_list]
value_list = [k[1] for k in count_list]
attr = keyword_list
v1 = value_list
#v2 = [19, 21, 32, 20, 20, 33]
pie =Pie(background_color='#404a59')
pie.use_theme('dark')
#pie.add("学历", attr, v1, center=[25, 50], is_random=True, radius=[20, 60], rosetype='radius')
pie.add("学历", attr, v1, is_random=True, radius=[20, 60], is_legend_show=False, is_label_show=True)
pie.show_config()
#pie.render(r"D:\BI大屏\学历需求饼图.html")
page.add_chart(pie,name="pie")
#=============================================================================
# 工作经验折线图
import pandas as pd
from pyecharts import Line
#=============================================================================
#导入文本
a = []
txt1 = pd.read_excel(r'D:\BI大屏\拉勾网.xlsx')
def display(self):
self.radioButton.setChecked(True)
conn = pymysql.connect(host='127.0.0.1',
user='******',
passwd='714511',
db='qinwenrui',
port=3306,
charset='utf8mb4')
year = self.comboBox_2.currentText().replace(' ', '')
month = self.comboBox_4.currentText().replace(' ', '') #输入
sql = 'SELECT * FROM `dianying`;'
df = pd.read_sql(sql, conn)
if year != '所有年份':
a = df[(df.年份 == year) & (df.月份 == int(month))]
else:
a = df[(df.月份 == int(month))]
M = []
TYPE = a[['题材', '票房']]
c = TYPE[(TYPE.题材 == '犯罪')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '喜剧')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '剧情')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '纪录片')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '奇幻')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '动作')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '冒险')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '悬疑')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '动画')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '战争')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '家庭')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '科幻')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
c = TYPE[(TYPE.题材 == '历史')]
money = 0
for k in c.index.tolist():
money = money + c.loc[k].values[1]
M.append(money)
#TYPE.loc[rows]获取信息
tp = [
'犯罪', '喜剧', '剧情', '纪录片', '奇幻', '动作', '冒险', '悬疑', '动画', '战争', '家庭',
'科幻', '历史'
]
pie = Pie('{}年{}月题材票房占比'.format(year, month),
title_pos='center',
width=907,
height=619) #h=625
pie._option['animation'] = False
pie.use_theme('dark')
pie.add('',
tp,
M,
center=[50, 50],
radius=[0, 60],
is_label_show=True,
legend_orient='vertical',
legend_pos='left')
pie.render('./graph/monthtype.html')
self.widget.load(QUrl.fromLocalFile('/graph/monthtype.html'))
def draw_pic(data, title, type_):
title = title + '成绩分布图'
list_1 = [0, 0, 0, 0, 0]
for i in data:
if i >= 0 and i < 60:
list_1[0] += 1
elif i >= 60 and i < 70:
list_1[1] += 1
elif i >= 70 and i < 80:
list_1[2] += 1
elif i >= 80 and i < 90:
list_1[3] += 1
elif i >= 90 and i < 100:
list_1[4] += 1
attr = ['60以下', '60~70', '70~80', '80~90', '90~100']
if type_ == 'pie':
pie = Pie(title=title, height=582, width=880)
pie.use_theme('dark')
pie.add("人数",
attr,
list_1,
is_more_utils=True,
radius=[40, 75],
is_legend_show=True,
is_label_show=True,
rosetype='area')
pie.render()
elif type_ == 'bar':
bar = Bar(title=title, height=582, width=880)
bar.use_theme('dark')
bar.add("人数",
attr,
list_1,
is_more_utils=True,
mark_line=['average'],
mark_point=['min', 'max'])
bar.render()
elif type_ == 'bar3D' or type_ == 'bar3d':
dot_set = [(0, 0), (1, 0), (2, 0), (3, 0), (4, 0)]
dot_set = [[x[0], x[1]] for x in dot_set]
for i in range(5):
dot_set[i].append(list_1[i])
max_ = 1.4 * max(list_1)
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
bar = Bar3D(title, height=582, width=880)
# bar.use_theme('dark')
# for x in dot_set:
# print(x[2])
bar.add("", ['低于60分', '60~70', '70~80', '80~90', '90~100'], [0],
[[x[0], x[1], x[2]] for x in dot_set],
is_visualmap=True,
visual_range=[0, max_],
grid3d_width=100,
visual_range_color=range_color,
grid3d_depth=25,
is_more_utils=True)
bar.render()
def draw_bar(self, dbName, begDate=None):
Beg, End = self.get_date_begin_end()
if begDate == None:
begDate = Beg[0]
maxvalue = get_days(begDate, End[0])
data1 = self.__get_draw_bar_data_core__(dbName, begDate, ('<', 50))
dataBase = self.__get_draw_bar_data_core__(dbName, begDate, ('<', 100))
data3 = self.__get_draw_bar_data_core__(dbName, begDate, ('<', 150),
('>=', 100)) #轻度
data4 = self.__get_draw_bar_data_core__(dbName, begDate, ('<', 200),
('>=', 150))
data5 = self.__get_draw_bar_data_core__(dbName, begDate, ('<', 300),
('>=', 200))
data6 = self.__get_draw_bar_data_core__(dbName, begDate, ('>=', 300))
count = max(len(data1), len(dataBase), len(data3), len(data4),
len(data5), len(data6))
# print(len(data1),len(dataBase),len(data3),len(data4),len(data5),len(data6))
valueBase, nameBase = self.__get_value_name_bar__(dataBase, count)
def __re_arrange(count, nameBase, dataInput):
valuetemp = count * [0]
idx = 0
for item in nameBase:
for i in dataInput:
if item == i[1]:
valuetemp[idx] = i[0]
break
idx = idx + 1
return valuetemp
def label_formatter(params):
return params.name + ' ' + str(params.value) + '%'
grid = Grid(**style2.init_style)
grid.use_theme("dark")
bar = Bar("", **style1.init_style)
value1 = __re_arrange(count, nameBase, data1)
bar.add("优", nameBase[:-1], value1[:-1], is_stack=True)
# list(map(lambda x: x[0]-x[1], zip(valueBase, valuetemp)))
bar.add("良",
nameBase[:-1],
list(
map(lambda x: x[0] - x[1] - x[2],
zip(valueBase[:-1], value1[:-1], count * [3]))),
is_stack=True)
value3 = __re_arrange(count, nameBase, data3)
bar.add("轻度污染", nameBase[:-1], value3[:-1], is_stack=True)
value4 = __re_arrange(count, nameBase, data4)
bar.add("中度污染", nameBase[:-1], value4[:-1], is_stack=True)
value5 = __re_arrange(count, nameBase, data5)
bar.add("重度污染", nameBase[:-1], value5[:-1], is_stack=True)
value6 = __re_arrange(count, nameBase, data6)
bar.add(
"严重污染",
nameBase[:-1],
value6[:-1],
is_stack=True,
# yaxis_name="天",
# yaxis_name_size='16',
# yaxis_name_gap='25',
# yaxis_name_pos='top',
xaxis_type="category",
xaxis_interval=0,
yaxis_interval=0,
xaxis_pos='top',
yaxis_max=maxvalue,
# is_label_show=True,
#bar_category_gap="30%",
xaxis_label_textsize=12,
yaxis_label_textsize=12,
xaxis_rotate=90,
yaxis_rotate=90,
# label_formatter=label_formatter,
is_legend_show=True,
legend_pos='91%',
legend_top='center',
legend_orient='horizontal',
label_pos='bottom',
label_text_size=16,
label_color=[
'#43CE17', '#EFDC31', '#FFAA00', '#FF401A', '#D20040',
'#9C0A4E'
])
idxList = [str(x) for x in range(1, len(valueBase))]
line = Line()
meanList = len(idxList) * [sum(valueBase[:-1]) / len(idxList)]
line.add("",
idxList,
meanList,
line_width=3,
symbol=None,
line_color='#0078D7',
mark_line=["average"])
line.add("",
idxList,
valueBase[:-1],
is_smooth=True,
symbol=None,
line_width=3,
xaxis_label_textsize=16,
xaxis_rotate=90,
line_color='#FF401A')
overlap = Overlap()
overlap.add(bar)
overlap.add(line, is_add_xaxis=True, xaxis_index=1)
grid.add(overlap, grid_top="21%")
grid.render()
grid.render("最优城市排行榜.jpeg")
#低于均值城市的饼状图
lowThanMeanDic = {}
for v in nameBase[-101:-1]:
# if v < meanList[0]:
key = v.split('-')[1]
if key in lowThanMeanDic.keys():
lowThanMeanDic[key] = lowThanMeanDic[key] + 1
else:
lowThanMeanDic[key] = 1
data = self.__sort_by_value__(lowThanMeanDic)
value, name = self.__get_value_name_bar__(data, len(data))
pie = Pie()
pie.use_theme("dark")
pie.add("",
name,
value,
is_legend_show=False,
is_label_show=True,
label_text_size=10)
pie.render("最污百城省份分布.jpeg")