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e_plot.py
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from pyecharts.commons.utils import JsCode
from pyecharts.charts import Grid, Bar, Map, Pie,Tab
from pyecharts.components import Table
from pyecharts.options import ComponentTitleOpts
import pyecharts.options as opts
from pyecharts.charts import WordCloud
import pandas as pd
import numpy as np
import jieba
import jieba.posseg as pseg
jieba.enable_paddle()
import jieba.analyse
import seaborn as sns
from Bonds.pendemic_bonds.bonds_analysis import *
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
fig_height = "600px"
fig_width = "1250px"
pixel_ratio = 1
def issuer_industry_plot(data):
name = data.columns[0]
data = data.sort_values(by=name)
x_data = data.index
value = np.round(data[name],2).tolist()
bar = (
Bar()
.add_xaxis(
xaxis_data=x_data.tolist(),
)
.add_yaxis(
series_name="",
yaxis_data=value,
yaxis_index=0,
color="#36648B",
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=name,
position="left",
# offset=80,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()
),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
type_="category",
name="行业",
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
title_opts=opts.TitleOpts(subtitle="数据来源：WIND"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
)
)
pie = (Pie(init_opts=opts.InitOpts()).add(
series_name="",
data_pair=[list(z) for z in zip(x_data, (data['占比']*100).round(2).tolist())],
radius=["25%", "35%"],
center=["30%","35%"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False,pos_left="legft", orient="vertical")) \
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{b}: {c}%"
),
label_opts=opts.LabelOpts(formatter="{b}: {c}%"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
))
grid_chart = (
Grid(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10%", pos_top="10%"
),
)
.add(pie, grid_opts=opts.GridOpts())
)
return grid_chart
def province_analysis_plot(data):
def format_data(data):
data['占比'] = (data['占比'] * 100).round(4)
_ = data.reset_index().values
data = list(zip([d[0] for d in _], _.tolist()))
data = [list(d) for d in data]
return data
data = format_data(data)
min_data, max_data = (
min([d[1][1] for d in data]),
max([d[1][1] for d in data]),
)
map_chart = (
Map(init_opts=opts.InitOpts(
width=fig_width, height=fig_height,
page_title="中国各省疫情防控债发行情况"))
.add(
series_name="",
data_pair=data,
# label_opts=opts.LabelOpts(is_show=False,
# # formatter="{b}:{@占比}"
# ),
is_map_symbol_show=False,
).set_series_opts(
label_opts=opts.LabelOpts(is_show=True,
formatter=JsCode("""
function(params){
if (typeof(params.data) == 'undefined') {
return params.value[2];
} else {
return params.data.name + params.data.value[2] + '%' ;
}
}"""
))
)
.set_global_opts(
title_opts=opts.TitleOpts(
# title="中国各省疫情防控债发行情况",
subtitle="数据来源：WIND",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"
),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"""function(params) {
if ('value' in params.data) {
return params.data.value[0] + '<br/>占比：' + params.data.value[2]+'%<br/>数量：'+params.data.value[1];
}
}"""
),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=1,
pos_left="10",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
)
)
return map_chart
def rate_histogram_plot(data,bins = 10):
m, bins = np.histogram(data.dropna(), bins)
m = m.astype(float)
s = m.sum()
x = bins.round(2).astype(str).tolist() + [""]
bar = (
Bar(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=bins.round(2).tolist(),
)
.extend_axis(xaxis_data=x,
xaxis=opts.AxisOpts(
position="bottom",
is_show=True,
# ,
is_scale = True,
boundary_gap = False,
# type_="value",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
on_zero_axis_index = 0,
is_on_zero=False, linestyle_opts=opts.LineStyleOpts()
),
axispointer_opts=opts.AxisPointerOpts(
is_show=False, label=opts.LabelOpts()
),
),
)
.add_yaxis(
xaxis_index =0,
series_name="",
yaxis_data=m.astype(float).tolist(),
# yaxis_index=0,
# color=colors[1],
category_gap=0,
gap =5,
itemstyle_opts=opts.ItemStyleOpts(border_color=None),
label_opts=opts.LabelOpts(
formatter=JsCode(
"""function(params) {
return params.data + '(' + ((Math.round(params.data / %s *10000)/100)) + '%s)' ;
}"""%(s,"%")
)
),
color="#36648B",
)
.set_global_opts(
title_opts=opts.TitleOpts(subtitle="数据来源:WIND"),
yaxis_opts=opts.AxisOpts(
type_="value",
name="频数",
position="left",
# offset=80,
axisline_opts=opts.AxisLineOpts(
is_on_zero=True,linestyle_opts=opts.LineStyleOpts()
),
axislabel_opts=opts.LabelOpts(
# # if ('value' in params.data) {
# }
# formatter="{value}"
formatter = JsCode(
"""function(params) {
return (params);
}"""
)
),
),
xaxis_opts=opts.AxisOpts(
is_show=False
),
tooltip_opts=opts.TooltipOpts(is_show=False),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
))
return bar
def word_clouds(weights):
w = WordCloud(init_opts=opts.InitOpts(width=fig_width, height=fig_height)).add(series_name="热点分析",
data_pair=weights,
word_size_range=[6, 66],
shape="circle",
)\
.set_global_opts(
title_opts=opts.TitleOpts(
title="", title_textstyle_opts=opts.TextStyleOpts(font_size=23),subtitle="数据来源：WIND"
),
tooltip_opts=opts.TooltipOpts(is_show=True),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
)
return w
def table_base(data):
data = data.reset_index().round(4)
headers = data.columns.tolist()
cont = data.values.tolist()
table = Table().add(headers, cont).set_global_opts(
title_opts=ComponentTitleOpts(title="", subtitle="数据来源:WIND")
).set_global_opts()
return table
def pie_plot(data):
_p_data = data.copy()
pie = (Pie(init_opts=opts.InitOpts(width=fig_width, height=fig_height)).add(
series_name="占比",
data_pair=[list(z) for z in zip(_p_data.index, (_p_data.values).round(2).tolist())],
radius=["30%", "50%"],
# center=["30%","35%"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=True,pos_left="2%", orient="vertical",pos_top="10%"),
title_opts=opts.TitleOpts(title="",subtitle="数据来源：WIND"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
) \
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
label_opts=opts.LabelOpts(formatter= "{b}: {c}（{d}%）")
))
return pie
# pie_plot(ranks_analysis).render("mm.html")
def daily_issue_analysis(bonds_data):
g = bonds_data.groupby('发行起始日期↑')
count = g.apply(len).rename("发行数量")
quantity = g['发行总额\r\n[单位] 亿元'].sum().rename("发行总额")
merged_data = pd.concat([count,quantity],axis=1).sort_index()
merged_data.index.name = "发行日期"
cummulative = merged_data.cumsum()
cummulative = cummulative.rename(columns = lambda x:"累计%s"%x)
return merged_data,cummulative
def daily_issue_plot(daily,cummulative):
import pyecharts.options as opts
from pyecharts.charts import Bar, Line
# color = ["#36648B","tomato"]
daily = daily.round(2)
cummulative = cummulative.round(2)
x_data = daily.index.astype(str).tolist()
bar = (
Bar(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="每日发行数量",
yaxis_index=2,
yaxis_data=daily['发行数量'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="每日发行总额",
yaxis_index=3,
yaxis_data=daily['发行总额'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
name="数量",
type_="value",
# min_=0,
# max_=250,
# interval=50,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
)
)
line = (
Line(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="累计发行数量",
yaxis_index=0,
y_axis=cummulative['累计发行数量'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name="累计发行总额",
yaxis_index=1,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(width=2),
y_axis=cummulative['累计发行总额'].tolist(),
label_opts=opts.LegendOpts(is_show=False),
)
.extend_axis(yaxis=opts.AxisOpts(
name= "总额",
type_="value",
position='right',
offset=60,
axislabel_opts=opts.LabelOpts(formatter="{value}亿",position="right"),
),).extend_axis(
yaxis=opts.AxisOpts(
name="数量",
type_="value",
position="left",
offset=0,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
name="总额",
type_="value",
position="left",
offset=60,
axislabel_opts=opts.LabelOpts(formatter="{value}亿"),
)
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#E8E8E8")
),
),
yaxis_opts=opts.AxisOpts(
name = "数量",
type_="value",
position="right",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=1)
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color='#E8E8E8')
),
),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
),
)
)
line.overlap(bar)
return line
def plot_all(data):
bonds_data = raw_data_process(data,rate_columns_names="票面利率(发行时)\r\n[单位] %")
bonds_data['省份'] = bonds_data['省份'].fillna("其他")
coupon_rate = coupon_rate_summary(bonds_data,group_name="Wind债券二级分类")
province_analysis = province_data_format(category_analysis(bonds_data, "省份")).round(4)
industry_analysis = category_analysis(bonds_data, "所属申万行业名称\r\n[行业级别] 一级行业")
province_analysis_2 = province_data_format(category_analysis(bonds_data, "省份",counts=False)).round(4)
industry_analysis_2 = category_analysis(bonds_data, "所属申万行业名称\r\n[行业级别] 一级行业",counts=False)
ranks_analysis = rank(bonds_data)
hotspot_analysis = usage_analysis(bonds_data)
daily, cummulative = daily_issue_analysis(bonds_data)
tc,tq = expireTime(bonds_data)
tab = Tab(page_title="疫情防控债统计分析")
tab.add(table_base(coupon_rate), "利率统计")
tab.add(rate_histogram_plot(bonds_data['票面利率(%)'].replace("--", np.nan).astype(np.float)), "利率分布直方图")
tab.add(province_analysis_plot(province_analysis), "发行省份统计（数量）")
tab.add(province_analysis_plot(province_analysis_2),"发行省份统计（金额）")
tab.add(issuer_industry_plot(industry_analysis), "发行行业统计（数量）")
tab.add(issuer_industry_plot(industry_analysis_2),"发行行业统计（金额）")
tab.add(word_clouds(hotspot_analysis), "资金用途热点")
tab.add(pie_plot(ranks_analysis.drop(["已公布评级","合计"])), "评级统计")
tab.add(pie_plot(tc),"债券期限统计（数量）")
tab.add(pie_plot(tq),"债券期限统计（金额）")
tab.add(daily_issue_plot(daily,cummulative),"每日发行状况统计")
# table
province_analysis['占比'] = (province_analysis['占比'].round(2)).astype(str)+"%"
province_analysis = province_analysis.sort_values(by='数量',ascending=False).astype(str)
tab.add(table_base(province_analysis),"发行省份统计表（数量）")
province_analysis_2['占比'] = (province_analysis_2['占比'].round(2)).astype(str)+"%"
province_analysis_2 = province_analysis_2.sort_values(by='金额(亿)',ascending=False).astype(str)
tab.add(table_base(province_analysis_2),"发行省份统计表（金额）")
industry_analysis.index.name = "行业(申万)"
industry_analysis["占比"] = (industry_analysis['占比']*100).round(2).astype('str')+"%"
industry_analysis = industry_analysis.sort_values(by='数量',ascending=False)
tab.add(table_base(industry_analysis),"发行行业统计表（数量）")
industry_analysis_2.index.name = "行业(申万)"
industry_analysis_2["占比"] = (industry_analysis_2['占比']*100).round(2).astype('str')+"%"
industry_analysis_2 = industry_analysis_2.sort_values(by='金额(亿)',ascending=False)
tab.add(table_base(industry_analysis_2),"发行行业统计表（金额）")
hotspot_analysis_df = pd.DataFrame(hotspot_analysis).rename(columns={0:"关键词",1:"权重"}).set_index("关键词").round(4)
tab.add(table_base(hotspot_analysis_df),"热点统计表")
tab.add(table_base(ranks_analysis),"债券评级统计表")
t = pd.concat([tc.rename("数量"),tq.rename("总金额(亿)")],axis=1).round(2)
tab.add(table_base(t),"债券期限统计表")
df = pd.concat([daily,cummulative],axis=1).round(2)
df.index = df.index.date.astype(str)
df.index.name = "日期"
tab.add(table_base(df),"每日发行状况统计表")
return tab
def expireTime(bonds_data):
ex_time = bonds_data['债券期限(年)\r\n[单位] 年'].rename("期限")
quantity = bonds_data['发行总额\r\n[单位] 亿元'].rename("金额")
time_lists = [0,30/360,90/360,180/360,1,2,3,5,np.inf]
labels = ['1个月以内',"1-3个月","3-6个月","6个月-1年","1-2年","2-3年","3-5年",'5年以上']
all_ls = []
for i,d in enumerate(time_lists[1:]):
all_ls.append(pd.Series(labels[i],index= ex_time.where((ex_time<=d)&(ex_time>time_lists[i])).dropna().index.tolist()))
all_ls = pd.concat(all_ls)
_ = pd.concat([all_ls.rename("期限"),quantity],axis=1)
counts = _.groupby(['期限']).apply(len)
quantity = _.groupby("期限").apply(lambda x:x['金额'].sum())
index = ['1个月以内','1-3个月','3-6个月','6个月-1年','1-2年','2-3年','3-5年','5年以上']
return counts.reindex(index),quantity.reindex(index)
# rate_histogram_plot(bonds_data['票面利率(%)'].replace("--", np.nan).astype(np.float))
# _ = rate_histogram_plot(data,bins = 10)
# _.render("ll.html")