def paint_bar(self,
x: list,
collects: list,
title: str,
mark_point: bool = False):
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
bar.add_xaxis(x)
for collect in collects:
for i, (name, unit, data) in enumerate(collect):
bar.add_yaxis(f'{name}-单位：{unit}', data, yaxis_index=i)
if i != 0:
bar.extend_axis(yaxis=opts.AxisOpts(
name='',
type_='value',
position='right',
))
bar.set_global_opts(title_opts=opts.TitleOpts(title=title,
pos_left='5%'),
legend_opts=opts.LegendOpts(pos_bottom='0'))
bar.set_series_opts(
label_opts=opts.LabelOpts(position='top'),
tooltip_opts=opts.TooltipOpts(formatter=f'{{b}}年{{a}}：{{c}}'))
if mark_point:
bar.set_series_opts(
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
],
symbol_size=80))
return bar
def workhour_get():
p = WorkhourInfo()
# 获取最近的12月
df = p.df.dropna()[-12:]
xdata = df['月份'].tolist()
# fig = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
fig = Bar()
fig.add_xaxis(xdata)
fig.add_yaxis('总投入',
df['实际投入工时'].tolist(),
label_opts=opts.LabelOpts(position='inside'))
for v in df.columns[6:]:
fig.add_yaxis(v,
df[v].tolist(),
stack='detail',
is_selected=False,
label_opts=opts.LabelOpts(is_show=False))
fig.set_global_opts(
title_opts=opts.TitleOpts(title="最近12月工时投入"),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="shadow"),
yaxis_opts=opts.AxisOpts(name='工时'),
)
#"""
df['投入总工时比率'] = (df['投入总工时比率'] * 100).round(0)
maxratio = df['投入总工时比率'].max()
maxratio = 100 if maxratio <= 100 else maxratio
ratio = df['投入总工时比率'].tolist()
line = Line()
line.add_xaxis(xdata)
line.add_yaxis('投入工时率', ratio, yaxis_index=1)
fig.extend_axis(yaxis=opts.AxisOpts(name="投入工时率",
type_="value",
min_=0,
max_=maxratio,
axislabel_opts=opts.LabelOpts(
formatter="{value} %")))
fig.overlap(line)
#"""
return fig.dump_options_with_quotes()
def bar_chart(x, y1, y2) -> Bar():
c = Bar(init_opts=opts.InitOpts(width='1200px', height='800px'))
c.add_xaxis(xaxis_data=x, )
# label_opts = opts.LabelOpts(is_show=False) 是否顯示數值
c.add_yaxis(series_name='',
yaxis_data=y1,
label_opts=opts.LabelOpts(is_show=True))
c.set_global_opts(
title_opts=opts.TitleOpts(title=''),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
datazoom_opts=opts.DataZoomOpts(type_='slider'))
c.extend_axis(yaxis=opts.AxisOpts(name='觀看次數',
type_='value',
min_=min(y2),
max_=max(y2),
interval=500000,
axislabel_opts=opts.LabelOpts(
formatter='{value}次')))
return c
def paint_bar_stack_with_line(self, x: list, children: dict, parents: dict,
sub_title: str):
for (parent_name, unit), item in children.items():
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))
bar.add_xaxis(x)
line = Line()
line.add_xaxis(x)
child_names = []
for child_name, data, overall in item:
bar.add_yaxis(child_name, data, stack='stack1')
line.add_yaxis(f'{child_name}占比', overall, yaxis_index=1)
child_names.append(child_name)
bar.add_yaxis(parent_name,
parents[parent_name],
stack='stack1',
yaxis_index=0)
bar.set_global_opts(
title_opts=opts.TitleOpts(title='，'.join(child_names),
subtitle=sub_title,
pos_left='5%'),
legend_opts=opts.LegendOpts(pos_bottom='0'))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(
formatter=f'{{b}}年{{a}}：{{c}}{unit}'),
itemstyle_opts=opts.ItemStyleOpts(opacity=0.5))
bar.extend_axis(yaxis=opts.AxisOpts(
type_='value',
name='所占比例',
min_=0,
max_=1,
position='right',
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(
opacity=1))))
bar.overlap(line)
yield bar
def echarts_stackbar(
df, # 传入数据df，应该是一个行索引为date的时间序列面板数据
df_gr=None, # 传入同比增长率df，可以没有
datatype="ABS", # 主Y轴形式是绝对值，增长率还是份额，用来确定一些标签格式，默认为绝对值
) -> Bar:
axislabel_format = "{value}" # 主Y轴默认格式
max = df[df > 0].sum(axis=1).max() # 主Y轴默认最大值
min = df[df <= 0].sum(axis=1).min() # 主Y轴默认最小值
if datatype in ["SHARE", "GR"]: # 如果主数据不是绝对值形式而是份额或增长率如何处理
df = df.multiply(100).round(2)
axislabel_format = "{value}%"
max = 100
min = 0
if df_gr is not None:
df_gr = df_gr.multiply(100).round(2) # 如果有同比增长率，原始数*100呈现
if df.empty is False:
stackbar = Bar().add_xaxis(df.index.tolist())
for i, item in enumerate(df.columns): # 预留的枚举，这个方法以后可以根据输入对象不同从单一柱状图变成堆积柱状图
stackbar.add_yaxis(
item,
df[item].values.tolist(),
stack="总量",
label_opts=opts.LabelOpts(is_show=False),
z_level=1,
)
# .add_yaxis(series_name=df.index[-5].strftime("%Y-%m"),
# yaxis_data=df_ya.values.tolist(),
# stack='总量',
# label_opts=opts.LabelOpts(is_show=False)
# )
# .add_yaxis(series_name=df.index[-1].strftime("%Y-%m")+' vs '+df.index[-5].strftime("%Y-%m"),
# yaxis_data=df_diff.values.tolist(),
# stack='总量',
# label_opts=opts.LabelOpts(is_show=False)
# )
if df_gr is not None: # 如果有同比增长率数据则加入次Y轴
stackbar.extend_axis(
yaxis=opts.AxisOpts(
name=df_gr.columns.astype("str")[0],
type_="value",
axislabel_opts=opts.LabelOpts(formatter="{value}%"),
)
)
stackbar.set_global_opts(
legend_opts=opts.LegendOpts(pos_top="5%", pos_left="10%", pos_right="60%"),
# toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross",),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=True,
axislabel_opts=opts.LabelOpts(rotate=90), # x轴标签方向rotate有时能解决拥挤显示不全的问题
splitline_opts=opts.SplitLineOpts(
is_show=False,
linestyle_opts=opts.LineStyleOpts(type_="dotted", opacity=0.5,),
),
),
yaxis_opts=opts.AxisOpts(
max_=max,
min_=min,
type_="value",
axislabel_opts=opts.LabelOpts(formatter=axislabel_format),
# axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(type_="dotted", opacity=0.5,),
),
),
)
if df_gr is not None:
line = (
Line()
.add_xaxis(xaxis_data=df_gr.index.tolist())
.add_yaxis(
series_name=df_gr.columns.astype("str")[0],
yaxis_index=1,
y_axis=df_gr.values.tolist(),
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3),
symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(
border_width=1, border_color="", border_color0="white"
),
z_level=2,
)
)
else:
stackbar = Bar()
if df_gr is not None:
return stackbar.overlap(line) # 如果有次坐标轴最后要用overlap方法组合
else:
return stackbar
# 添加折线
# line = Line()
# line.add_xaxis(zhongqiu_city)
# line.add_yaxis("", tem_data)
# line.add_yaxis("", weather_data)
# pictorialbar.overlap(line)
pictorialbar.render_notebook()
# 在双轴图中查看
bar = Bar()
bar.add_xaxis(zhongqiu_city)
bar.add_yaxis("", weather_data)
bar.extend_axis(
yaxis=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} °C"), interval=5
)
)
bar.set_global_opts(
title_opts=opts.TitleOpts(title="中秋节省会城市降雨和温度情况"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30), is_show=True),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(opacity=0)
),
axislabel_opts=opts.LabelOpts(formatter="{value}% 降水概率")
),
datazoom_opts=opts.DataZoomOpts()
)
bar.set_series_opts(itemstyle_opts={
def generate_bar_html(rows, title):
xx1 = []
xx2 = []
yy1 = []
yy2 = []
count = 0
for row in rows:
count += 1
bond_code = row[0]
bond_code = trade_utils.rebuild_bond_code(bond_code)
if count <= 20:
xx1.append(row[1].replace('转债', ''))
yy1.append({'value': row[2], 'bond_code': bond_code, 'price': row[3], 'premium': row[4], 'name': row[1]})
else:
xx2.append(row[1].replace('转债', ''))
yy2.append({'value': row[2], 'bond_code': bond_code, 'price': row[3], 'premium': row[4], 'name': row[1]})
max_value = 0
size = len(yy1)
for i in range(size):
if yy1[i]['value'] + abs(yy2[i]['value']) > max_value:
max_value = yy1[i]['value'] + abs(yy2[i]['value'])
max_value = round(max_value * 1.1, 2) + 1
chart_id = str(abs(hash(title)))
bar = Bar(init_opts=opts.InitOpts(height='700px', width='1424px', theme=ThemeType.SHINE, chart_id=chart_id))
view_utils.add_popwin_js_code(bar, chart_id)
# 底部x轴
bar.add_xaxis(xx1)
# 顶部x轴
bar.extend_axis(
xaxis_data=xx2,
xaxis=opts.AxisOpts(
type_="category",
position='top',
axislabel_opts=opts.LabelOpts(
rotate=-60,
)
),
)
# 右侧y轴
bar.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
position="right",
name='跌幅(%)',
name_gap=45,
name_location='middle',
min_=-max_value,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(
is_show=True,
)
),
axislabel_opts=opts.LabelOpts(formatter="{value}%"),
)
)
# 添加涨的柱状图
bar.add_yaxis("涨", yy1,
bar_width=25,
category_gap='1%',
gap='1%',
label_opts=opts.LabelOpts(
position="top",
formatter=JsCode(
"function(x){return x.data['value'] +'%';}"
)
),
)
# 添加跌的柱状图
bar.add_yaxis("跌",
yy2,
bar_width=25,
yaxis_index=1,
label_opts=opts.LabelOpts(
position="bottom",
formatter=JsCode(
"function(x){return x.data['value'] +'%';}"
)
),
)
# bar.reversal_axis()
bar.set_series_opts(
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"function(x){return x.data['value']>0?'#c23531':'#1d953f'}"
)
)
)
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="=========" + title + "=========",
pos_left='center',
pos_top='-1px',
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode("function (params){return '名 称: ' + params.data['name'] + '<br/>' + '价 格: ' + params.data['price'] + '元<br/>' + '溢价率: ' + params.data['premium']}")
),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
# data=None,
# type_='category',
# name_gap=0,
# name_rotate=90,
axislabel_opts=opts.LabelOpts(
rotate=-60,
),
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
is_on_zero=True,
# symbol=['none', 'arrow']
)
),
yaxis_opts=opts.AxisOpts(
type_='value',
name='涨幅(%)',
# name_rotate=90,
name_gap=35,
name_location='middle',
# min_=0,
max_=max_value,
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}%'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False,
# symbol=['none', 'arrow']
)
),
)
bar_html = bar.render_embed('template.html', html_utils.env)
return bar_html
series_name='治愈数',
yaxis_index=2,
yaxis_data=cured_num,
color='green',
)
# 设置额外的坐标轴
bar.extend_axis(
# 配置新的纵轴
yaxis=opts.AxisOpts(
type_='value', # value 表示数据轴 category 表示类目轴 time 表示时间轴
name='死亡数',
is_show=True, # 展示
min_=0,
max_=700, # 刻度轴的最大值
position='right', # 显示位置
# offset=60, # 偏离位置
# 轴线配置
axisline_opts=opts.AxisLineOpts(
is_show=True, # 显示轴线
# 设置轴线风格
linestyle_opts=opts.LineStyleOpts(color='blue')),
# 设置对应的标签， ｛｝ 数据
axislabel_opts=opts.LabelOpts(formatter='{value}')))
bar.extend_axis(
# 配置新的纵轴
yaxis=opts.AxisOpts(
type_='value', # value 表示数据轴 category 表示类目轴 time 表示时间轴
name='治愈数',
is_show=True, # 展示
min_=0,
def create_mixed_line_and_bar(type, title, x_list, data_list, data_unit1, data_unit2 = None):
"""
:param type: 1-折线用于表示变化率and单柱状图+单折线 2-折线用于表示变化率and多柱状图+多折线
3-折线表示占比and单柱状图+单折线 4-折线表示占比and多柱状图+多折线
5-折线用于描述另一维度指标（此时data_list中最后一个k-v为折线数据）
:param title: 图表名称
:param x_list: 横坐标内容为时间序列，纵坐标高度为多个(同级)指标的总和，具体各个指标的数值、比例以不同颜色标出
:param data_list: 字典{key1:value1,key2:value2...}，构成柱状图的多个不同类型的bar
:param data_unit1: 第一根y轴的数值单位
:param data_unit2: 第二根y轴的数值单位
:return:
"""
c = Bar(init_opts=opts.InitOpts(height="600px",width="900px",theme=ThemeType.MACARONS))
c.add_xaxis(x_list)
if type == 1: # 因为有overlap，所以需要自适应调整刻度范围，是的bar和line都能显示出来
all_rate_list = []
for key in data_list.keys(): # 单柱状图只有一个bar
change_rate_list = [0]
c.add_yaxis(key, data_list[key], bar_width="20%",label_opts=opts.LabelOpts(is_show=True))
for i in range(1,len(data_list[key])):
if data_list[key][i-1] == 0 or data_list[key][i-1] is None or data_list[key][i] is None: # 注意分母不为0
change_rate_list.append(None)
else:
change_rate_list.append(round((data_list[key][i]-data_list[key][i-1])/data_list[key][i-1]*100,2))
all_rate_list.append(change_rate_list)
c.extend_axis(yaxis=opts.AxisOpts(
name="变化率",
type_="value",
min_=int(round((getMinRate(all_rate_list)-(getMaxRate(all_rate_list)-getMinRate(all_rate_list)))/10)*10),
max_=int(round((getMaxRate(all_rate_list)/10)*10)),
interval=10,
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
))
line = (
Line()
.add_xaxis(xaxis_data=x_list)
.add_yaxis(
series_name="增长率",
yaxis_index=1,
y_axis=all_rate_list[0],
label_opts=opts.LabelOpts(is_show=True, formatter=JsCode(
"function(x){console.log(x);return Number(x.data[1]).toFixed(2) + '%';}"
)),
)
)
c.set_global_opts(title_opts=opts.TitleOpts(title=title,# subtitle="数据来源国家统计局",
# pos_left="center",pos_bottom="0px",
# item_gap=2,
# subtitle_textstyle_opts=opts.TextStyleOpts(color="black")
),
legend_opts=opts.LegendOpts(pos_left="right"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit1,
# name_location="end",
# name_gap=5,
# # name_rotate=45,
min_=0, # 基本上指标数据都是为正的
max_=int(round(getMaxData(data_list)/10)*10)*2,
interval=10,
))
c.overlap(line)
elif type == 2:
count = len(data_list.keys())
all_rate_list = []
keys_list = []
for key in data_list.keys():
keys_list.append(key)
change_rate_list = [0]
c.add_yaxis(key, data_list[key], label_opts=opts.LabelOpts(is_show=True))
for i in range(1,len(data_list[key])):
if data_list[key][i-1] == 0 or data_list[key][i-1] is None or data_list[key][i] is None: # 注意分母不为0
change_rate_list.append(None)
else:
change_rate_list.append(round((data_list[key][i]-data_list[key][i-1])/data_list[key][i-1]*100,2))
all_rate_list.append(change_rate_list)
c.extend_axis(yaxis=opts.AxisOpts(
name="变化率",
type_="value",
min_=int(
round((getMinRate(all_rate_list) - (getMaxRate(all_rate_list) - getMinRate(all_rate_list))) / 10) * 10),
max_=int(round((getMaxRate(all_rate_list) / 10) * 10)),
interval=10,
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
))
all_line = []
for i in range(count):
line = (
Line()
.add_xaxis(xaxis_data=x_list)
.add_yaxis(
series_name=keys_list[i] + "增长率",
yaxis_index=1,
y_axis=all_rate_list[i],
label_opts=opts.LabelOpts(is_show=True, formatter=JsCode(
"function(x){console.log(x);return Number(x.data[1]).toFixed(2) + '%';}"
)),
)
)
all_line.append(line)
c.set_global_opts(title_opts=opts.TitleOpts(title=title, # subtitle="数据来源国家统计局",
pos_left="center",pos_bottom="0px",
# item_gap=2,
# subtitle_textstyle_opts=opts.TextStyleOpts(color="black")
),
legend_opts=opts.LegendOpts(pos_left="right"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit1,
# name_location="end",
# name_gap=5,
# # name_rotate=45,
min_=0,
max_=int(round(getMaxData(data_list)/10)*10)*2,
interval=10,
))
for line in all_line:
c.overlap(line)
src_path = "./指标-年度-图片生成/"
html_file_name = src_path + title + ".html"
img_file_name = src_path + title + ".png"
make_snapshot(snapshot, c.render(html_file_name), img_file_name)
print(img_file_name+":生成完毕...")
# from pyecharts.charts import Bar
# columns = ['衡阳', '长沙', '株洲', '湘潭', '邵阳', '岳阳', '常德', '张家界', '益阳','娄底','郴州','永州','怀化','湘西']
# # #//设置数据
# data1 = [48,242,80,36,102,156,82,5,60,76,39,44,40,8]
# data2 = [5,20,6,5,2,7,6,9,4,3,10,9,4,4]
# #
# #//设置柱状图的主标题与副标题
# bar = Bar()
# #//添加柱状图的数据及配置项
# bar.add("累计感染", columns, data1, mark_line=["average"], mark_point=["max", "min"])
# bar.add("累计死亡", columns, data2, mark_line=["average"], mark_point=["max", "min"])
# #//生成本地文件（默认为.html文件）
# bar.render("柱状图.html")
from pyecharts.charts import Bar
bar = Bar()
bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
#bar.set_series_opts()
bar.add_yaxis("商家A", [5.5, 20, 36, 10, 75, 90])
bar.extend_axis(15)
#bar.add_yaxis("商家B", [5, 20, 36, 10, 75, 90])
#bar.add_yaxis("商家B", [5, 20, 36, 10, 75, 90])
#bar.set_global_opts(opts.TitleOpts(title= "主标题", subtitle = "副标题"))
bar.render()
