def v_tradecost(self, start=None, end=yesterdayobj(), **vkwds):
'''
visualization giving the average cost line together with netvalue line
:param vkwds: keywords options for line.add()
:returns: pyecharts.line
'''
funddata = []
costdata = []
pprice = self.aim.price[self.aim.price['date'] <= end]
if start is not None:
pprice = pprice[pprice['date'] >= start]
for _, row in pprice.iterrows():
date = row['date']
funddata.append([date, row['netvalue']])
if (date - self.cftable.iloc[0].date).days >= 0:
cost = self.unitcost(date)
costdata.append([date, cost])
line = Line()
line.add('fundvalue', [1 for _ in range(len(funddata))], funddata,
**vkwds)
line.add('average_cost', [1 for _ in range(len(costdata))],
costdata,
is_datazoom_show=True,
xaxis_type="time",
**vkwds)
return line
def print_results_echarts(df_hist_tmp, df_real_tmp, df_pred_tmp):
print(df_pred_tmp)
line = Line("Model results")
line.add("Historic",
df_hist_tmp.index.date,
df_hist_tmp[target_col[0]].values,
mark_point=["average"])
line.add("Prediction",
df_pred_tmp.index.date,
df_pred_tmp["prediction"],
mark_line=["max", "average"])
# line.show_config()
return line
def grid(self):
line = Line('折线图', width=1200)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add('最高气温', attr, [11, 12, 14, 11, 12, 15, 16], mark_point=['max', 'min'], \
mark_line=['average'])
line.add('最低气温', attr, [1, -1, 2, 5, 2, 3, 0], mark_point=['max', 'min'], \
mark_line=['average'], legend_pos='20%')
attr = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']
v1 = [11, 12, 14, 10, 11, 10]
pie = Pie('饼图', title_pos='55%')
pie.add('', attr, v1, radius=[45, 65], center=[65, 50], legend_pos='80%', \
legend_orient='vertical')
grid = Grid()
grid.add(line, grid_right='55%')
grid.add(pie, grid_left='60%')
def v_netvalue(self, end=yesterdayobj(), benchmark=True, **vkwds):
'''
visulaization on netvalue curve
:param vkwds: parameters for the pyecharts options in line.add(), eg. yaxis_min=0.7
'''
a, b = self.comparison(end)
xdata = [1 for _ in range(len(a))]
ydata = [[row['date'], row['netvalue']] for _, row in a.iterrows()]
ydata2 = [[row['date'], row['netvalue']] for _, row in b.iterrows()]
line = Line()
line.add('algorithm', xdata, ydata, is_datazoom_show=True, xaxis_type="time", **vkwds)
if benchmark is True:
line.add('benchmark', xdata, ydata2, is_datazoom_show=True, xaxis_type="time", **vkwds)
return line
def DrawLine(city_names):
line = Line('城市气温折线图')
attrs1 = []
attrs2 = []
values1 = []
values2 = []
for cn in city_names:
data = qr.get_data(cn)
attrs1.append(cn)
attrs2.append(cn)
values1.append(data['Htemperature'])
values2.append(data['Ltemperature'])
line.add("最高气温", attrs1, values1, is_smooth=True, mark_point=["average"])
line.add("最低气温", attrs2, values2, is_smooth=True, mark_point=["average", "max"])
line.render('weatherLine.html')
def v_netvalue(self, end=yesterdayobj(), **vkwds):
'''
起点对齐归一的，各参考基金或指数的净值比较可视化
:param end: string or object of date, the end date of the line
:param vkwds: pyechart line.add() options
:returns: pyecharts.Line object
'''
partprice = self.totprice[self.totprice['date'] <= end]
xdata = [1 for _ in range(len(partprice))]
ydatas = []
for fund in self.fundobjs:
ydata = [[row['date'], row[fund.code]] for _, row in partprice.iterrows()]
ydatas.append(ydata)
line = Line()
for i, fund in enumerate(self.fundobjs):
line.add(fund.name, xdata, ydatas[i], is_datazoom_show=True, xaxis_type="time", **vkwds)
return line
def v_techindex(self, end=yesterdayobj(), col=None, **vkwds):
'''
visualization on netvalue curve and specified indicators
:param end: date string or obj, the end date of the figure
:param col: list, list of strings for price col name, eg.['MA5','BBI']
remember generate these indicators before the visualization
:param vkwds: keywords option for pyecharts.Line().add(). eg, you may need is_symbol_show=False
to hide the symbols on lines
'''
partprice = self.price[self.price['date'] <= end]
xdata = [1 for _ in range(len(partprice))]
netvaldata = [[row['date'], row['netvalue']] for _, row in partprice.iterrows()]
line = Line()
line.add('netvalue', xdata, netvaldata, is_datazoom_show=True, xaxis_type="time", **vkwds)
if col is not None:
for ind in col:
inddata = [[row['date'], row[ind]] for _, row in partprice.iterrows()]
line.add(ind, xdata, inddata, is_datazoom_show=True, xaxis_type="time", **vkwds)
return line
def v_totvalue(self, end=yesterdayobj(), **vkwds):
'''
visualization on the total values daily change of the aim
'''
valuedata = []
partp = self.aim.price[
self.aim.price['date'] >= self.cftable.iloc[0].date]
partp = partp[partp['date'] <= end]
for i, row in partp.iterrows():
date = row['date']
valuedata.append(
[date,
self.briefdailyreport(date).get('currentvalue', 0)])
line = Line()
line.add('totvalue', [1 for _ in range(len(valuedata))],
valuedata,
is_datazoom_show=True,
xaxis_type="time",
**vkwds)
return line
def kline_plot(self, ktype=0):
df = self.cal_hadata()
#画K线图数据
date = df.index.strftime('%Y%m%d').tolist()
if ktype == 0:
k_value = df[['adjopen', 'adjclose', 'adjlow', 'adjhigh']].values
else:
k_value = df[['ha_open', 'ha_close', 'ha_low', 'ha_high']].values
#引入pyecharts画图使用的是0.5.11版本，新版命令需要重写
kline = Kline(self.name + '行情走势')
kline.add('日K线图',
date,
k_value,
is_datazoom_show=True,
is_splitline_show=False)
#加入5、20日均线
df['ma20'] = df.adjclose.rolling(20).mean()
df['ma5'] = df.adjclose.rolling(5).mean()
line = Line()
v0 = df['ma5'].round(2).tolist()
v = df['ma20'].round(2).tolist()
line.add('5日均线', date, v0, is_symbol_show=False, line_width=2)
line.add('20日均线', date, v, is_symbol_show=False, line_width=2)
#成交量
bar = Bar()
bar.add('成交量',
date,
df['vol'],
tooltip_tragger='axis',
is_legend_show=False,
is_yaxis_show=False,
yaxis_max=5 * max(df['vol']))
overlap = Overlap()
overlap.add(kline)
overlap.add(line, )
overlap.add(bar, yaxis_index=1, is_add_yaxis=True)
return overlap
def generate_chart(self):
if os.path.exists(self.outputDir+"chart/"):
pass
else:
os.mkdir(self.outputDir+"chart/")
line=Line("检测目标分析图")
t1=['time','car','person','moto']
if os.path.exists(self.outputDir+"csv/count.csv"):
with open(self.outputDir+"csv/count.csv",'r') as f:
reader=csv.reader(f)
result=np.array(list(reader))
for i in range(len(result[0])):
if i==0:
continue
line.add(t1[i],result[0:,0],result[0:,i])
line.render(self.outputDir+'chart/count.html')
t2=['time','CarIn','CarOut']
line2=Line("流量分析图")
if os.path.exists(self.outputDir+"csv/flow.csv"):
with open(self.outputDir+"csv/flow.csv",'r') as f:
reader=csv.reader(f)
result=np.array(list(reader))
for i in range(len(result[0])):
if i==0:
continue
line2.add(t2[i],result[0:,0],result[0:,i])
line2.render(self.outputDir+"chart/flow.html")
pie=Pie("速度区间分析图")
if os.path.exists(self.outputDir+"csv/speed.csv"):
with open(self.outputDir+"csv/speed.csv",'r') as f:
reader=csv.reader(f)
result=np.array(list(reader))
pie.add("速度区间",["[5km/h,10km/h)","[10km/h,15km/h)","[15km/h,20km/h)","[20km/h,25km/h)","[25km/h,30km/h)","[30km/h,40 km/h)","[40km/h,50 km/h)","[50km/h,+infinity)"],result[-1])
pie.render(self.outputDir+"chart/speed.html")
self.saveIllegal()
def line():
attr = [
'教师', '教授', '副教授', '博导', '硕导', '国家级奖项', '省部级奖项', '院士', '荣誉学者', '专利'
]
v1 = [100, 20, 15, 50, 40, 200, 200, 4, 5, 100]
v2 = [150, 30, 40, 50, 30, 250, 200, 1, 2, 110]
line = Line(width=1834, height=400)
line.add(
'北京大学',
attr,
v1,
mark_point=['average', 'max', 'min'], # 标注点：平均值，最大值，最小值
mark_point_symbol='diamond', # 标注点：钻石形状
mark_point_textcolor='#40ff27') # 标注点：标注文本颜色
line.add(
'清华大学',
attr,
v2,
mark_point=['average', 'max', 'min'],
mark_point_symbol='arrow',
xaxis_name_size=20,
yaxis_name_size=20,
)
return line
# is_label_show=True,
)
pie1.render('评分.html')
#%%
datas['dates'] = datas.date.apply(lambda x:pd.Timestamp(x).date())
datas['time'] = datas.date.apply(lambda x:pd.Timestamp(x).time().hour)
num_date = datas.author.groupby(datas['dates']).count()
# 评论数时间分布
chart = Line("评论数时间分布")
chart.use_theme('dark')
chart.add( '评论数时间分布',num_date.index, num_date.values, is_fill=True, line_opacity=0.2,
area_opacity=0.4, symbol=None)
chart.render('评论时间分布.html')
# 好评字数分布
datalikes = datas.loc[datas.likes>5]
datalikes['num'] = datalikes.content.apply(lambda x:len(x))
chart = Scatter("likes")
chart.use_theme('dark')
chart.add('likes', np.log(datalikes.likes), datalikes.num, is_visualmap=True,
xaxis_name = 'log(评论字数)',
)
chart.render('好评字数分布.html')
from pyecharts.charts import Line
fruits = ['0-20', '20-50', '50-100', '100-200', '200以上']
#实例数目--一共
shop1_sales = [607, 711, 1038, 3451, 3876, 15409]
shop2_sales = [4888, 7023, 3989, 5873, 8876, 6409]
line = Line('折线图')
line.add('扩充前A', fruits, shop1_sales, mark_point=['max'])
line.add('扩充后B', fruits, shop2_sales, mark_point=['min'])
line.show_config()
line.render('./折线图.html')
ascending=False).to_frame().reset_index(),
data.groupby(['所在区'])['总价(万元)'].mean().round(0).to_frame().reset_index(),
on=['所在区'])
bar = Bar("北京各区二手房价格")
bar.add(
"单价(元/平米)",
zone_price['所在区'],
zone_price['单价(元/平米)'],
is_label_show=True,
label_pos='inside',
label_text_color='#000',
is_toolbox_show=False,
)
line = Line()
line.add(
"总价(万元)",
zone_price['所在区'],
zone_price['总价(万元)'],
is_label_show=True,
is_toolbox_show=False,
)
overlap = Overlap(height=400, width=900)
overlap.add(bar)
overlap.add(
line,
yaxis_index=1,
is_add_yaxis=True,
)
overlap
# encoding: utf-8
from opendatatools import aqi
from pyecharts.charts import Line
import pandas as pd
if __name__ == '__main__':
df_aqi = aqi.get_daily_aqi_onecity('北京市')
df_aqi.set_index('date', inplace=True)
df_aqi.sort_index(ascending=True, inplace=True)
df_aqi = df_aqi[df_aqi.index >= "2018-01-01"]
axis_x = df_aqi.index
axis_y = df_aqi['aqi']
line = Line("北京AQI趋势图")
line.add("aqi curve for beijing", axis_x, axis_y, mark_point=["average"])
line.render("aqi_bj_curve.html")
LOGGER = logging.getLogger(__name__)
if __name__ == '__main__':
df = pd.read_csv('C:/git/machine_learning/machine_learning/data/资料01_商铺数据(2)(9).csv')
df1 = df[df['comment'].str.contains('条')]
df1['comment'] = df1['comment'].str.split(' ').str[0]
df1['comment'] = df1['comment'].astype('int')
df1 = df[df['price'].str.contains('￥')]
df1['price'] = df1['price'].str.split('￥').str[-1]
df1['price'] = df1['price'].astype('float')
df1 = df1[df1['price'] > 50]
import tushare as ts
df = ts.get_day_all()
df = df.round(2)
df2 = ts.get_hist_data('600848')
x = df2.index.tolist()[:20]
y = df2[['open', 'close', 'low', 'high']]
y1 = df2['open'].iloc[:20]
ma5 = df2['ma5']
ma10 = df2['ma10']
ma20 = df2['ma20']
line = Line("我的第一个图表", "这里是副标题", width="800px", height="400px")
line.add('开盘价', x, y1)
line.render('C:/git/machine_learning/machine_learning/data/1.html')
"Express", "Home", "Johnny Depp", "Lena Dunham", "Lewis Hamilton", "KXAN",
"Mary Ellen Mark", "Farrah Abraham", "Rita Ora", "Serena Williams",
"NCAA baseball tournament", "Point Break"
]
value = [
10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112, 965, 847, 582, 555,
550, 462, 366, 360, 282, 273, 265
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[20, 100])
wordcloud.render(r".\my_first_Ciyun.html")
"""组合图"""
line = Line("折线图", width=1200)
attr = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图", title_pos="55%")
pie.add("",
attr,
v1,
radius=[45, 65],
center=[65, 50],
legend_pos="80%",
print('钱塘新区二手房最高价格：%.2f元/平方米' % max_price)
print("钱塘新区二手房最低价格：%.2f元/平方米" % min_price)
print("钱塘新区二手房平均价格：%.2f元/平方米" % mid_price)
houselist.append('%.3f' % mid_price)
print(houselist)
house = [float(i) for i in houselist]
print(house)
house1 = Sort("地铁")
house2 = Sort("大学")
house3 = Sort("车")
time = get_date_list("2020-02-14", "2020-03-18")
a = Line("钱江新区房价变化图")
a.add("平均价格",
time,
house,
mark_line=["average"],
mark_point=["max", "min"],
xaxis_name="日期",
yaxis_name="价格",
yaxis_name_gap=60)
a.add("地铁房",
time,
house1,
mark_point=["max", "min"],
xaxis_name="日期",
yaxis_name="价格",
yaxis_name_gap=60)
a.add("学区房",
time,
house2,
mark_point=["max", "min"],
xaxis_name="日期",
def create_line(self, v):
line = Line(TITLE_TEXT, TITLE_SUBTEXT)
line.add("商家", ATTR, v, is_smooth=True, mark_line=["max", "average"])
snippet = TRANSLATOR.translate(line.options)
options = snippet.as_snippet()
return options
