def get_bar():
poets = Poet.query.order_by(Poet.num.desc()).limit(10)
attr_poet = []
num_poet = []
for poet in poets:
attr_poet.append(poet.name)
num_poet.append(poet.num)
bar = Bar("作诗数前十名诗人")
bar.add("", attr_poet, num_poet, is_label_show=True, center=[50,50])
return bar
def plot(t_list, sensor1_list, sensor2_list, title_str, htmlName):
from pyecharts.charts.basic_charts.bar import Bar #导入相应包
from pyecharts import options as opts
from pyecharts.options import DataZoomOpts
bar = Bar() #生成对象，title为柱状图标题
#is_stack=True表示将数据堆叠，is_label_show=True表示显示对应数值
bar.add_xaxis(t_list)
bar.add_yaxis("sensor1触发次数", sensor1_list)
bar.add_yaxis("sensor2触发次数", sensor2_list)
bar.set_global_opts(
title_opts=opts.TitleOpts(title=title_str),
datazoom_opts=[DataZoomOpts(is_show=True, type_="slider")])
bar.render(htmlName)
def plot_pages():
"""
Pages可以将多张图表按顺序展示在一张网页中，适合制作图形化报表。
Pages中的图表可以是Grid,Overlap或Timeline.
使用Pages来绘制组合图
子图是自己本身的宽高
:return:html
"""
# 1.首先绘制一个柱状图
# 初始化数据来源
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
y1 = [5, 20, 36, 10, 75]
y2 = [10, 25, 8, 60, 20]
# 初始化柱状图
bar = Bar(init_opts=opts.InitOpts(width='600px', height='400px'))
# x轴数据
bar.add_xaxis(xaxis_data=x)
# y轴数据
bar.add_yaxis(series_name="商家A", y_axis=y1)
bar.add_yaxis(series_name="商家B", y_axis=y2)
# 设置配置
bar.set_global_opts(title_opts=opts.TitleOpts(title='柱状图:商家货物销量'))
# 2.绘制一个折线图
# 初始化数据来源
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
y1 = [5, 20, 36, 10, 75]
y2 = [10, 25, 8, 60, 20]
# 初始化折线图
line = Line(init_opts=opts.InitOpts(width="500px", height="200px"))
# x轴数据
line.add_xaxis(xaxis_data=x)
# y轴数据
line.add_yaxis(series_name='商家A', y_axis=y1)
line.add_yaxis(series_name='商家B', y_axis=y2)
# 设置配置
line.set_global_opts(title_opts=opts.TitleOpts(title='折线图:商家货物销量'))
# 初始化pages
pages = Page(page_title='多图', layout=opts.PageLayoutOpts())
# 添加图形系列
pages.add(bar, line, line)
# 输出pages
pages.render('组合图pages示范图.html')
def plot_grid():
"""
使用Grid来绘制组合图
子图的宽高是自适应画板(Grid)的宽高
子图的位置用grid_opts中pos选项来调节:
当两个子图为pos_left与pos_right时,此时为左右排列
当两个子图为pos_top与pos_bottom时,此时为上下排列
:return: html
"""
# 1.首先绘制一个柱状图
# 初始化数据来源
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
y1 = [5, 20, 36, 10, 75]
y2 = [10, 25, 8, 60, 20]
# 初始化柱状图
bar = Bar(init_opts=opts.InitOpts(width='200px', height='100px'))
# x轴数据
bar.add_xaxis(xaxis_data=x)
# y轴数据
bar.add_yaxis(series_name="商家A", y_axis=y1)
bar.add_yaxis(series_name="商家B", y_axis=y2)
# 设置配置
bar.set_global_opts(title_opts=opts.TitleOpts(title='柱状图:商家货物销量'))
# 2.绘制一个折线图
# 初始化数据来源
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
y1 = [5, 20, 36, 10, 75]
y2 = [10, 25, 8, 60, 20]
# 初始化折线图
line = Line(init_opts=opts.InitOpts(width="100px", height="100px"))
# x轴数据
line.add_xaxis(xaxis_data=x)
# y轴数据
line.add_yaxis(series_name='商家A', y_axis=y1)
line.add_yaxis(series_name='商家B', y_axis=y2)
# 设置配置
line.set_global_opts(
title_opts=opts.TitleOpts(title='折线图:商家货物销量', pos_top='50%'))
# 初始化组合图:Grid
grid = Grid(init_opts=opts.InitOpts(width='600px', height='400px'))
# 利用grid_bottom,grid_top,grid_left,grid_right四个参数控制子图的相对位置
grid.add(bar, grid_opts=opts.GridOpts(pos_right='60%'))
grid.add(line, grid_opts=opts.GridOpts(pos_left='60%'))
grid.render('组合图Grid示范图.html')
def plot_timeline():
"""
Timeline可以将多个图表制作成动画。
:return:
"""
# 初始化多组数据
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
year = 5
start_year = 2018
num = len(attr)
# 初始化时间线图形
timeline = Timeline(init_opts=opts.InitOpts(width='600px', height='400px'))
# 时间轴添加配置项
timeline.add_schema(is_auto_play=False,
is_loop_play=True,
is_timeline_show=True,
control_position='right',
itemstyle_opts=opts.ItemStyleOpts(color='blue',
opacity=0.8),
play_interval=1000)
for i in range(5):
year_sales1 = [randint(10, 100) for _ in range(num)]
year_sales2 = [randint(200, 500) for _ in range(num)]
# 初始化
bar_temp = Bar(init_opts=opts.InitOpts())
# 加载数据
bar_temp.add_xaxis(xaxis_data=attr)
bar_temp.add_yaxis(series_name=f'{start_year+i}年净销售额',
y_axis=year_sales1)
bar_temp.add_yaxis(series_name=f'{start_year+i}年实际销售额',
y_axis=year_sales2)
# bar配置项
bar_temp.set_global_opts(title_opts=opts.TitleOpts(
title=f'{start_year + i} 销售额情况'))
# 时间轮播图添加图形
timeline.add(bar_temp, f'{start_year + i}年营业额')
# 输出时间线轮播图
timeline.render('组合图时间线轮播图.html')
def plot_bar():
"""
绘制柱状图:
Bar
:return:
"""
# 初始化数据来源
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
y1 = [5, 20, 36, 10, 75]
y2 = [10, 25, 8, 60, 20]
# 初始化柱状图
bar = Bar(init_opts=opts.InitOpts(width='1000px', height='600px'))
# x轴数据
bar.add_xaxis(xaxis_data=x)
# y轴数据
bar.add_yaxis(series_name="商家A", y_axis=y1)
bar.add_yaxis(series_name="商家B", y_axis=y2)
# 导出绘图html文件，可直接用浏览器打开
bar.render('柱状图示范.html')
df = df[df.price == '暂无']
# # 去除重复行
# df = df.drop_duplicates()
print("have value row number is {0}".format(len(df.index)))
####################################################
# 最贵的小区排名
####################################################
df.sort_values("price", ascending=False, inplace=True)
num = 5
print(df.head(num))
# city = df["city_ch"][0]
city = 'Shanghai'
xqs = df["xiaoqu"][0:num]
prices = df["price"][0:num]
bar = Bar("{0}小区均价".format(city))
bar.add("小区均价前{0}名".format(num),
xqs,
prices,
is_stack=True,
is_label_show=True,
xaxis_interval=0,
xaxis_rotate=45)
bar.render(path="xiaoqu.html")
####################################################
# 区县均价排名
####################################################
district_df = df.groupby('district').mean()
district_df = district_df.round(0)
district_df.sort_values("price", ascending=False, inplace=True)
}, {
'>=85分,<90分': [85, 90]
}, {
'>=90分,<95分': [90, 95]
}, {
'>=95分,<100分': [95, 100]
}]
score_range = {
k: sum(i[1] for i in score_counter
if not isinstance(i[0], str) and i[0] < j[1] and i[0] >= j[0])
for kv in step_range for k, j in kv.items()
}
page = Page()
bar_company = (Bar(init_opts=opts.InitOpts(
theme=ThemeType.LIGHT)).add_xaxis(list(cc.keys())).add_yaxis(
"人数", list(cc.values())).set_global_opts(title_opts=opts.TitleOpts(
title="紧缺人才名单公司分布柱状图",
subtitle="company",
)))
page.add(bar_company)
pie_company = (Pie().add("", comp_counter).set_colors(
["blue", "green", "yellow", "red", "pink", "orange",
"purple"]).set_global_opts(
title_opts=opts.TitleOpts(title="紧缺人才名单公司分布饼图", ),
legend_opts=opts.LegendOpts(pos_top="bottom")).set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))
page.add(pie_company)
bar_area = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis(
list(dd.keys())).add_yaxis("人数", list(
dd.values())).set_global_opts(title_opts=opts.TitleOpts(
title="紧缺人才名单区域分布柱状图",