def tab_fun():
line = Line().add_xaxis(Faker.choose()).add_yaxis(
series_name="xx1",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(color=Faker.rand_color())).add_yaxis(
series_name="xx2",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(
color=Faker.rand_color())).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%"),
legend_opts=opts.LegendOpts(pos_left="40%"),
)
bar = Bar().add_xaxis(Faker.choose()).add_yaxis(
series_name="柱1",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(color=Faker.rand_color())).add_yaxis(
series_name="柱2",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(
color=Faker.rand_color())).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%"),
legend_opts=opts.LegendOpts(pos_left="40%"),
)
Tab().add(chart=line, tab_name="选项卡一").add(
chart=bar, tab_name="选项卡二").render(path="D:/temp/pyecharts_tab.html")
def bar_datazoom_both() -> Bar:
a = (Bar().add_xaxis([
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018"
]).add_yaxis("Birth_rate_crude",
Birth_rate_crude,
color=Faker.rand_color()).add_yaxis(
"Primary_completion_rate_total",
Primary_completion_rate_total,
color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title=""),
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
],
))
c = (Bar().add_xaxis([
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018"
]).add_yaxis("GNI_per_capita_Atlas_method",
GNI_per_capita_Atlas_method,
color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title=""),
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
],
))
return render_template("show_pyecharts1.html",
bar_data=a.dump_options(),
bar_data2=c.dump_options())
def bar_histogram():
x = Faker.dogs + Faker.animal
xlen = len(x)
y = []
for idx, item in enumerate(x):
if idx <= xlen / 2:
bar_item = opts.BarItem(
name=item,
value=(idx + 1) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#749f83")
)
else:
bar_item = opts.BarItem(
name=item,
value=(xlen + 1 - idx) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#d48265"),
)
y.append(bar_item)
obj_bar = Bar()
obj_bar.add_xaxis(x)
#直方图就是紧挨着的系列柱category_gap=0调节系列柱之间距离，
# color=Faker.rand_color()因为上面设置了柱的颜色，这里只对图例legend的颜色起作用
obj_bar.add_yaxis("series0", y, category_gap=0,color=Faker.rand_color())
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-直方图（颜色区分）", subtitle="副标题")
)
return obj_bar
def bar_histogram_color():
x = Faker.dogs + Faker.animal
xlen = len(x)
y = []
for idx, item in enumerate(x):
if idx <= xlen / 2:
y.append(
opts.BarItem(
name=item,
value=(idx + 1) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#749f83"),
))
else:
y.append(
opts.BarItem(
name=item,
value=(xlen + 1 - idx) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#d48265"),
))
c = (Bar().add_xaxis(x).add_yaxis(
"series0", y, category_gap=0,
color=Faker.rand_color()).set_global_opts(title_opts=opts.TitleOpts(
title="Bar-直方图（颜色区分）")))
return c
def bar_datazoom_inside() -> Bar:
c = (Bar().add_xaxis(Faker.days_attrs).add_yaxis(
"商家A", Faker.days_values, color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（inside）"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
))
return c
def bar_datazoom_slider_vertical() -> Bar:
c = (Bar().add_xaxis(Faker.days_attrs).add_yaxis(
"商家A", Faker.days_values, color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider-垂直）"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
))
return c
def pie_fun():
Pie().add(
series_name="饼图名称",
# 数据格式[[key, value], [key, value], ...], 用 zip 函数将两个 list 进行组合
data_pair=[list(z) for z in zip(Faker.choose(), Faker.values())],
# 饼图半径，一个值表示圆半径，两个值表示内外圆半径
# radius="40%",
# radius=["40%", "55%"],
# 中心点的 x、y 位置
center=["50%", "50%"],
# 是否展示成南丁格尔图（玫瑰图），通过半径区分数据大小，有 radius 和 area 两种模式
# radius：扇区圆心角展现数据的百分比，半径展现数据的大小
# area：所有扇区圆心角相同，仅通过半径展现数据大小
rosetype="area",
).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c}",
color=Faker.rand_color(),
)).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%",
pos_top="10%"),
# 设置 series_name 位置
legend_opts=opts.LegendOpts(pos_left="30%", pos_top="10%"),
).render(
# 设置输出路径
path="D:/temp/pyecharts_pie.html")
def draw_bar_plays(data,user_id,title="听歌方式"):
"""
两种听歌方式
:param plays:
:param user_id:
:param title:
:return:
"""
file_name = "user_data/" + str(user_id) + "_bar_plays.html"
x = ["片段播放", "完整播放"]
color = ["#749f83", "#d48265"]
xlen = len(x)
y = []
for idx, item in enumerate(x):
y.append(
opts.BarItem(
name=item,
value=data[idx],
itemstyle_opts=opts.ItemStyleOpts(color=color[idx]),
)
)
c = (
Bar()
.add_xaxis(x)
.add_yaxis("用户："+str(user_id), y,color=Faker.rand_color())
.set_global_opts(title_opts=opts.TitleOpts(title=title),toolbox_opts=opts.ToolboxOpts())
#.render(file_name)
)
return c
def draw_bar_recoder(data,user_id,title = "用户行为统计"):
"""
用户行为统计
:param data:
:param user_id:
:param title:
:return:
"""
file_name = "user_data/" + str(user_id) + "_bar_recoder.html"
x = ["循环播放","片段播放","查看评论","点赞评论","收藏歌曲"]
color = ["#F79709","#749f83", "#d48265","#33CCCC","#82C182"]
xlen = len(x)
y = []
for idx, item in enumerate(x):
y.append(
opts.BarItem(
name=item,
value=data[idx][1],
itemstyle_opts=opts.ItemStyleOpts(color=color[idx]),
)
)
c = (
Bar()
.add_xaxis(x)
.add_yaxis("" , y, color=Faker.rand_color())
.set_global_opts(title_opts=opts.TitleOpts(title=title),
toolbox_opts=opts.ToolboxOpts()
)
# .render(file_name)
)
return c
def draw_bar_like(data,user_id,title="偏爱曲风统计"):
"""
柱状图 偏爱统计
:param data:
:param user_id:
:param title:
:return:
"""
file_name = "user_data/" + str(user_id) + "_bar_like.html"
x = ["古风", "古典", "电子", "民谣", "流行", "说唱", "摇滚"]
color = ["#C43C3C", "#7D573E", "#E6E65D", "#69DA69", "#70D4D4", "#9B7CC7", "#A2A2A2"]
xlen = len(x)
y = []
for idx, item in enumerate(x):
y.append(
opts.BarItem(
name=item,
value=data[idx][1],
itemstyle_opts=opts.ItemStyleOpts(color=color[idx]),
)
)
c = (
Bar()
.add_xaxis(x)
.add_yaxis("", y, color=Faker.rand_color())
.set_global_opts(title_opts=opts.TitleOpts(title=title),
toolbox_opts=opts.ToolboxOpts()
)
#.render(file_name)
)
return c
def action_type_bar3(session, min, max):
print(min)
print(max)
min = float(int(min))/100
max = float(int(max))/100
if not session['is_login']:
return None
user_id = session['user_id']
sql = "select discount,ROI from t_roi where discount >={} and discount < {} and merchant_id = {}".format(min, max, user_id)
cursor.execute(sql)
res = cursor.fetchall()
xs = list(map(lambda x: str(x[0]), res))
ys = list(map(lambda x: (x[1]), res))
print("xs:{}".format(xs))
print(type(ys[0]))
print("ys:{}".format(ys))
line = (
Line().add_xaxis(xs).add_yaxis("roi曲线", ys, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=Faker.rand_color()))
.set_global_opts(
title_opts=opts.TitleOpts(title=""),
yaxis_opts=opts.AxisOpts(name="roi曲线图"),
xaxis_opts=opts.AxisOpts(
name="折扣",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
).dump_options_with_quotes()
)
return [json.loads(line)]
def render_vertical_datazoom_py():
with st.echo("below"):
c = (Bar().add_xaxis(Faker.days_attrs).add_yaxis(
"商家A", Faker.days_values,
color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider-垂直）"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
))
st_pyecharts(c, height="400px")
def bar_histogram():
obj_bar = Bar()
obj_bar.add_xaxis(Faker.choose())
#直方图就是紧挨着的系列柱category_gap=0调节系列柱之间距离，color=Faker.rand_color()随机颜色
obj_bar.add_yaxis("商家A", Faker.values(),category_gap=0, color=Faker.rand_color())
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-直方图", subtitle="副标题")
)
return obj_bar
def bar_sea_place() -> Bar:
c = (
Bar()
.add_xaxis(sea_ename)
.add_yaxis("东南亚",place_sum, color=Faker.rand_color())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-东南亚各国景观/目的地数"),
datazoom_opts=[opts.DataZoomOpts()],
)
)
return c.render_embed()
def timeline(list, title, name):
attr = Faker.choose()
tl = Timeline()
for i in list:
x_list = ['每日死亡', '累计死亡', '每日确诊', '累计确诊']
y_list = []
for x in range(5, 9):
y_list.append(i[x])
histogram = (Bar().add_xaxis(x_list).add_yaxis(
name, y_list, color=Faker.rand_color()))
tl.add(histogram, "{}年".format(transform_time(i[0])))
return tl
def bar_each_place() -> Bar:
c = (
Bar()
.add_xaxis(each_country_city_name)
.add_yaxis(str(country),each_country_city_place,color=Faker.rand_color())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-"+str(country)+"各地景观/目的地数"),
datazoom_opts=[opts.DataZoomOpts()],
)
)
return c.render_embed()
def line_fun():
Line().add_xaxis(
# 添加 x 轴数据
Faker.choose()).add_yaxis(
# 添加 y 轴数据，并设置属性
series_name="xx1",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(
color=Faker.rand_color())).add_yaxis(
# 添加 y 轴数据，并设置属性
series_name="xx2",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(
color=Faker.rand_color())).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%"),
# 设置 series_name 位置
legend_opts=opts.LegendOpts(pos_left="40%"),
).render(
# 设置输出路径
path="D:/temp/pyecharts_line.html")
def CumulativeConfirmed_histogram(list, title, name):
x_list = []
y_list = []
for i in list:
x_list.append(transform_time(i[0]))
y_list.append(i[8])
histogram = (Bar().add_xaxis(x_list).add_yaxis(
'累计确诊', y_list,
color=Faker.rand_color()).set_global_opts(datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
], ).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
return histogram
def map_fun():
Map().add(
series_name="全国疫情数据",
data_pair=[list(z) for z in zip(Faker.provinces, Faker.values())],
maptype="china").set_series_opts(label_opts=opts.LabelOpts(
#formatter="{b}: {c}",
color=Faker.rand_color(), )).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%",
pos_top="10%"),
# 设置 series_name 位置
legend_opts=opts.LegendOpts(pos_left="30%", pos_top="10%"),
# Map 视觉效果
visualmap_opts=opts.VisualMapOpts(),
).render(
# 设置输出路径
path="D:/temp/pyecharts_map.html")
Geo().add_schema(maptype="china").add(
series_name="全国疫情数据",
data_pair=[list(z) for z in zip(Faker.provinces, Faker.values())],
).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c}",
color=Faker.rand_color(),
)).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%",
pos_top="10%"),
# 设置 series_name 位置
# legend_opts=opts.LegendOpts(pos_left="30%", pos_top="10%"),
# Geo 视觉效果
visualmap_opts=opts.VisualMapOpts(),
).render(
# 设置输出路径
path="D:/temp/pyecharts_geo.html")
def show_bar(id):
fix_data()
bar = Bar(init_opts=opts.InitOpts(
width='1200px', height='800px', page_title='page'))
bar.add_xaxis(fix_id)
bar.add_yaxis('线程', fix_time, stack="stack1", color=Faker.rand_color())
bar.reversal_axis()
bar.set_global_opts(title_opts=opts.TitleOpts(title="压测时间图"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值"),
]))
bar.render('压力测试{}.html'.format(id + 1))
def index21():
data_str = df5.to_html()
c = (Bar().add_xaxis([
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018"
]).add_yaxis("Primary_completion_rate_total",
Primary_completion_rate_total,
color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title=""),
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
],
))
return render_template('results1.html',
the_res=data_str,
bar_data=c.dump_options(),
the_select_region=gdp,
the_action="/tochart4",
the_title=" Primary_completion_rate_total")
def gdp2019():
data_str = df4.to_html()
d = (Bar().add_xaxis([
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018"
]).add_yaxis("GNI_per_capita_Atlas_method",
GNI_per_capita_Atlas_method,
color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title=""),
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
],
))
return render_template('results1.html',
the_res=data_str,
bar_data=d.dump_options(),
the_select_region=gdp,
the_action="/tochart3",
the_title="GNI_per_capita_Atlas_method ")
def birth_rate2019():
data_str = df3.to_html()
e = (Bar().add_xaxis([
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018"
]).add_yaxis("Birth_rate_crude",
Birth_rate_crude,
color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title=""),
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
],
))
return render_template(
'results1.html',
the_res=data_str,
bar_data=e.dump_options(),
the_title="出生率",
the_select_region=birth_rate,
the_action="/tochart2",
)
def make_relase_year_bar(self):
"""
生成各年份电影发行量柱状图
:return:
"""
# print(get_current_time() + '|-------> 正在生成 各年份电影发行量 图表...')
# 导入数据并初始化
csv_path = os.path.abspath(os.path.join(os.path.dirname("__file__"), os.path.pardir, "moviespider", "movie_info_top500.csv"))
rows = pd.read_csv(csv_path, encoding='utf-8', dtype=str)
to_drop = ['名称', '导演', '演员', '国别', '类型', '语言', '评分', '评分人数', '五星占比', '四星占比', '三星占比', '二星占比', '一星占比', '短评数',
'简介']
res = rows.drop(to_drop, axis=1)
# 数据分析
res_by = res.groupby('年份')['年份'].count().sort_values(ascending=False)
res_by2 = res_by.sort_index(ascending=False)
type(res_by2)
years = []
datas = []
for k, v in res_by2.items():
years.append(k)
datas.append(v)
# 生成图标
c = Bar()
c.add_xaxis(years)
c.add_yaxis("发行电影数量", datas, color=Faker.rand_color())
c.set_global_opts(
title_opts=opts.TitleOpts(title="电影TOP500榜单中 - 各年份电影发行量"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
)
htmlPath = os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), "analyse_data", "各年份电影发行量.html"))
pngPath = os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), "analyse_data", "各年份电影发行量.png"))
# 生成html
c.render(htmlPath)
# 生成png
# make_snapshot(snapshot, c.render(), pngPath)
# print(get_current_time() + '|-------> 已生成 各年份电影发行量 图表...')
return c
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (
Bar()
.add_xaxis(Faker.days_attrs)
.add_yaxis("商家A", Faker.days_values, color=Faker.rand_color())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider-垂直）"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
)
)
c.width = "100%"
put_html(c.render_notebook())
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (Bar().add_xaxis(Faker.days_attrs).add_yaxis(
"商家A", Faker.days_values, color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider+inside）"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")],
))
c.width = "100%"
put_html(c.render_notebook())
def bar_histogram() -> Bar:
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(), category_gap=0,
color=Faker.rand_color()).set_global_opts(title_opts=opts.TitleOpts(
title="Bar-直方图")))
return c
def action_type_bar(session, charts_type):
if not session['is_login']:
return None
user_id = session['user_id']
if charts_type == 'gmv':
cursor.execute("SELECT * FROM t_commodity_sale_info WHERE merchant_id = "+user_id+" ORDER BY CAST(month_ AS UNSIGNED INTEGER)")
res = cursor.fetchall()
xs = list(map(lambda x: x[2], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("gmv变化情况", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
title_opts=opts.TitleOpts(title="gmv变化情况"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
line = (
Line().add_xaxis(xs).add_yaxis("gmv变化曲线", ys, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=Faker.rand_color()))
.set_global_opts(
title_opts=opts.TitleOpts(title="gmv变化曲线"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
).dump_options_with_quotes()
)
return [json.loads(bar), json.loads(line)]
elif charts_type == 'gender':
cursor.execute("SELECT gender,`count` FROM t_gender_distribute WHERE merchant_id = "+user_id)
res = cursor.fetchall()
def to_text(l):
if l[0] == '0.0':
return ["女性", l[1]]
elif l[0] == '1.0':
return ["男性", l[1]]
else:
return ["未知", l[1]]
res = map(to_text, res)
print(res)
pie = (
Pie().add("用户性别分布", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
.set_global_opts(title_opts=opts.TitleOpts(title="用户性别分布"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(pie)]
elif charts_type == 'area':
cursor.execute("SELECT area_name,`count` FROM t_area_distribute WHERE merchant_id = "+user_id)
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户地域分布情况", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
title_opts=opts.TitleOpts(title="用户地域分布情况"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
pie = (
Pie().add("用户性别分布", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
# .set_global_opts(title_opts=opts.TitleOpts(title="用户地域分布"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(bar), json.loads(pie)]
elif charts_type == 'age':
cursor.execute("SELECT age_range,`count` FROM t_age_distribute WHERE merchant_id = "+user_id)
res = cursor.fetchall()
label = ["未知", "[18-]", "[18,24]", "[25,29]", "[30,34]", "[35,39]", "[40,49]", "[50，60]", "[60+]"]
xs = list(map(lambda x: label[int(float(x[0]))], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户年龄分布", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
title_opts=opts.TitleOpts(title="用户年龄分布"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
line = (
Line().add_xaxis(xs).add_yaxis("用户年龄分布", ys, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=Faker.rand_color()))
.set_global_opts(
title_opts=opts.TitleOpts(title="用户年龄分布"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
).dump_options_with_quotes()
)
return [json.loads(bar), json.loads(line)]
elif charts_type == 'add_cart':
cursor.execute("SELECT commodity_brand,`count` FROM add_to_cart_distribute_info WHERE merchant_id = "+user_id)
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户添加购物车类别分布", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
title_opts=opts.TitleOpts(title="用户添加购物车类别分布"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
pie = (
Pie().add("用户性别分布", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
# .set_global_opts(title_opts=opts.TitleOpts(title="用户地域分布"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(bar), json.loads(pie)]
elif charts_type == 'add_favorite':
cursor.execute("SELECT commodity_brand,`count` FROM add_to_favorite_distribute_info WHERE merchant_id = "+user_id)
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户添加购物车类别分布", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
title_opts=opts.TitleOpts(title="用户添加购物车类别分布"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
pie = (
Pie().add("用户性别分布", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
# .set_global_opts(title_opts=opts.TitleOpts(title="用户地域分布"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(bar), json.loads(pie)]
elif charts_type == 'purchase':
cursor.execute("SELECT commodity_brand,`count` FROM purchase_distribute_info WHERE merchant_id = "+user_id)
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户添加购物车类别分布", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
title_opts=opts.TitleOpts(title="用户添加购物车类别分布"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
pie = (
Pie().add("用户性别分布", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
# .set_global_opts(title_opts=opts.TitleOpts(title="用户地域分布"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(bar), json.loads(pie)]
elif charts_type == 'click':
cursor.execute("SELECT commodity_brand,`count` FROM click_distribute_info WHERE merchant_id = "+user_id)
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户添加购物车类别分布", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
title_opts=opts.TitleOpts(title="用户添加购物车类别分布"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
pie = (
Pie().add("用户性别分布", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
# .set_global_opts(title_opts=opts.TitleOpts(title="用户地域分布"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(bar), json.loads(pie)]
def action_type_bar2(session,charts_type):
if not session['is_login']:
return None
if charts_type == 'price_month':
cursor.execute("SELECT month_,`sum(price)` FROM yunying_all_price ORDER BY CAST(month_ AS UNSIGNED INTEGER)")
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("平台整体交易额增长情况", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
toolbox_opts=opts.ToolboxOpts(),
title_opts=opts.TitleOpts(title="平台整体交易额增长情况"),
yaxis_opts=opts.AxisOpts(name="交易总额"),
xaxis_opts=opts.AxisOpts(name="月份"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
line = (
Line().add_xaxis(xs).add_yaxis("平台整体交易额增长情况", ys, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=Faker.rand_color()))
.set_global_opts(
title_opts=opts.TitleOpts(title="平台整体交易额增长情况"),
yaxis_opts=opts.AxisOpts(name="交易总额"),
xaxis_opts=opts.AxisOpts(
name="月份",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
).dump_options_with_quotes()
)
return [json.loads(bar), json.loads(line)]
elif charts_type == 'brand':
cursor.execute("SELECT brand_id,`count(*)` FROM yunying_brand")
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户购买品牌分布情况", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
toolbox_opts=opts.ToolboxOpts(),
title_opts=opts.TitleOpts(title="用户购买品牌分布情况"),
yaxis_opts=opts.AxisOpts(name="交易商品数量"),
xaxis_opts=opts.AxisOpts(name="品牌"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
pie = (
Pie().add("用户购买品牌分布情况", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(bar), json.loads(pie)]
elif charts_type == 'category':
cursor.execute("SELECT commodity_brand,`count(*)` FROM yunying_commodity")
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: int(x[1]), res))
bar = (
Bar({"theme": ThemeType.MACARONS})
.add_xaxis(xs).add_yaxis("用户购买商品分类情况", ys)
.set_series_opts(itemstyle_opts={
"normal": {
"color": Faker.rand_color(),
"barBorderRadius": [10, 10, 10, 10],
"shadowColor": '#32ca12',
}}).set_global_opts(
toolbox_opts=opts.ToolboxOpts(),
title_opts=opts.TitleOpts(title="用户购买商品分类情况"),
# yaxis_opts=opts.AxisOpts(name="交易总额"),
# xaxis_opts=opts.AxisOpts(name="月份"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")]).dump_options_with_quotes()
)
pie = (
Pie().add("用户购买品牌分布情况", list(res),
radius=["25%", "75%"],
center=["50%", "50%"],
rosetype="radius",
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
).dump_options_with_quotes()
return [json.loads(bar), json.loads(pie)]
elif charts_type == 'laoguke':
cursor.execute("SELECT '[0,10]' AS qujian,COUNT(merchant_id) FROM laoguke WHERE 10 > `sum(all_price)/count(merchant_id)` AND `sum(all_price)/count(merchant_id)` >= 0 UNION ALL "
"SELECT '[10,100]' AS qujian,COUNT(merchant_id) FROM laoguke WHERE 100 > `sum(all_price)/count(merchant_id)`AND `sum(all_price)/count(merchant_id)` >= 10 UNION ALL "
"SELECT '[100,500]' AS qujian,COUNT(merchant_id) FROM laoguke WHERE 500 > `sum(all_price)/count(merchant_id)`AND `sum(all_price)/count(merchant_id)` >= 100 UNION ALL "
"SELECT '[500,1000]' AS qujian,COUNT(merchant_id) FROM laoguke WHERE 1000 > `sum(all_price)/count(merchant_id)`AND `sum(all_price)/count(merchant_id)` >= 500 UNION ALL "
"SELECT '[1000,2000]' AS qujian,COUNT(merchant_id) FROM laoguke WHERE 2000 > `sum(all_price)/count(merchant_id)`AND `sum(all_price)/count(merchant_id)` >= 1000 UNION ALL "
"SELECT '[2000,5000]' AS qujian,COUNT(merchant_id) FROM laoguke WHERE 5000 > `sum(all_price)/count(merchant_id)`AND `sum(all_price)/count(merchant_id)` >= 2000 UNION ALL "
"SELECT '[5000,10000]' AS qujian,COUNT(merchant_id) FROM laoguke WHERE 10000 > `sum(all_price)/count(merchant_id)`AND `sum(all_price)/count(merchant_id)` >= 5000 UNION ALL "
"SELECT '[10000+]' AS qujian,COUNT(*) FROM laoguke WHERE `sum(all_price)/count(merchant_id)` >= 10000")
res = cursor.fetchall()
xs = list(map(lambda x: x[0], res))
ys = list(map(lambda x: x[1], res))
c = (
EffectScatter()
.add_xaxis(xs)
.add_yaxis("", ys)
.set_global_opts(
title_opts=opts.TitleOpts(title="商家历史gmv分布图"),
xaxis_opts=opts.AxisOpts(name="gmv区间", splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(name="商家数量", splitline_opts=opts.SplitLineOpts(is_show=True)),
)
).dump_options_with_quotes()
line = (
Line().add_xaxis(xs).add_yaxis("商家历史gmv分布图", ys, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=Faker.rand_color()))
.set_global_opts(
title_opts=opts.TitleOpts(title=""),
yaxis_opts=opts.AxisOpts(name="商家数量"),
xaxis_opts=opts.AxisOpts(
name="gmv区间",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
).dump_options_with_quotes()
)
return [json.loads(c), json.loads(line)]
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
x = Faker.dogs + Faker.animal
xlen = len(x)
y = []
for idx, item in enumerate(x):
if idx <= xlen / 2:
y.append(
opts.BarItem(
name=item,
value=(idx + 1) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#749f83"),
))
else:
y.append(
opts.BarItem(
name=item,
value=(xlen + 1 - idx) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#d48265"),
))
c = (
Bar().add_xaxis(x)
#category_gap: 同一系列的柱间距离，默认为类目间距的 20%，可设固定值
.add_yaxis(
"series0", y, category_gap=0,
color=Faker.rand_color()).set_global_opts(title_opts=opts.TitleOpts(
title="Bar-直方图（颜色区分）")).render("bar_histogram_color.html"))