def show_overview_day2(category, method, name):
# if not _check_category(category):
# return
se = SE()
m = getattr(se, method)
_df1 = m()
df1 = _df1[[
category,
]]
_, df2 = get_line(indexs.get(category, '000001'), index=True)
df1, df2 = reindex([df1, df2])
line1 = Line()
if method in ('get_market_val', 'get_negotiable_val') \
and category in ('SZ', 'CYB', 'ZXQY'):
line1.add(name,
df1.index,
df1[category] / 100000000,
is_datazoom_show=False)
else:
line1.add(name, df1.index, df1[category], is_datazoom_show=False)
line2 = Line()
line2.add('index: %s' % indexs.get(category, '000001'),
df2.index,
df2.high,
is_datazoom_show=True)
overlap = Overlap()
overlap.add(line1)
# 新增一个 y 轴，此时 y 轴的数量为 2，第二个 y 轴的索引为 1（索引从 0 开始），所以设置 yaxis_index = 1
# 由于使用的是同一个 x 轴，所以 x 轴部分不用做出改变
overlap.add(line2, yaxis_index=1, is_add_yaxis=True)
return overlap
def gen_zwyx_type(zwlb):
qs = ZpZwyxByTypeModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zwyx_type/{zwlb}.html'
else:
path = 'zwyx_type.html'
# 当月职位月薪与公司性质
df = read_frame(qs.all())
if len(df) > 0:
page = Page()
Grid_chart1 = Timeline(width=1500, height=450, timeline_bottom=0)
Grid_chart2 = Timeline(width=1500, height=450, timeline_bottom=0)
df_group = df.groupby(['year', 'month'])
for name, group in df_group:
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('type').apply(get_echarts_all_by_zwyx_value,
'type')
# 薪资
Overlap_chart = Overlap(width=800, height=450)
bar_chart = Bar(f'{zwlb}职位月薪与公司性质')
bar_chart.add('最低薪资',
df_new['type'].tolist(),
df_new['min_zwyx'].tolist(),
is_label_show=True,
is_more_utils=True)
bar_chart.add('最高薪资',
df_new['type'].tolist(),
df_new['max_zwyx'].tolist(),
is_label_show=True,
is_more_utils=True)
line_chart = Line()
line_chart.add("平均薪资",
df_new['type'].tolist(),
[(a + b) / 2
for a, b in zip(df_new['min_zwyx'].tolist(),
df_new['max_zwyx'].tolist())],
is_label_show=True)
Overlap_chart.add(bar_chart)
Overlap_chart.add(line_chart)
Grid_chart1.add(Overlap_chart, f'{year}年{month}月')
# 职位量
chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500)
chart3.add('职位量'.format(zwlb),
df_new['type'].tolist(),
df_new['count'].tolist(),
is_label_show=True,
is_stack=True,
center=[25, 50])
chart3.add('招聘人数'.format(zwlb),
df_new['type'].tolist(),
df_new['zprs'].tolist(),
is_label_show=True,
is_stack=True,
center=[75, 50])
Grid_chart2.add(chart3, f'{year}年{month}月')
page.add(Grid_chart1)
page.add(Grid_chart2)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def loc_and_mean_age(info):
grouped = group(info, ['cityChn'])
tidy = grouped['age']
tidy_com = tidy.agg(['mean', 'count'])
tidy_com.reset_index(inplace=True)
tidy_com['mean'] = round(tidy_com['mean'], 2)
attr = tidy_com['cityChn']
num = tidy_com['count']
aver = tidy_com['mean']
line = Line("小姐姐-平均年龄")
line.add("年龄",
attr,
aver,
is_stack=True,
xaxis_rotate=30,
mark_point=['max', 'min'],
yaxis_min=26,
is_splitline_show=False)
bar = Bar("小姐姐-城区分布")
bar.add("单位：人",
attr,
num,
mark_point=['max'],
mark_line=['average'],
xaxis_rotate=30,
yaxis_min=0)
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render("北京百合小姐姐平均年龄与分布.html")
def test_grid_add_overlap():
from pyecharts import Overlap
grid = Grid()
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar("Overlap+Grid 示例", width=1200, height=600, title_pos="40%")
bar.add("蒸发量", attr, v1)
bar.add(
"降水量",
attr,
v2,
yaxis_formatter=" ml",
yaxis_max=250,
legend_pos="85%",
legend_orient="vertical",
legend_top="45%",
)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
grid.add(overlap, grid_right="20%")
grid.render()
def show_overview_day(category, method, name):
# if not _check_category(category):
index_data = services.line(indexs.get(category, '999999'),
'start',
'end',
col='high')
se_data = services.se(method[4:], category)
df1 = pd.DataFrame.from_dict(index_data)
df1.index = pd.DatetimeIndex(df1.index)
df2 = pd.DataFrame.from_dict(se_data)
df2.index = pd.DatetimeIndex(df2.index)
df1, df2 = reindex2([df1, df2])
print(df1)
print(df2)
line1 = Line()
line1.add(name, df2.index, df2[category.upper()], is_datazoom_show=False)
line2 = Line()
line2.add('index: %s' % indexs.get(category, '999999'),
df1.index,
df1['high'],
is_datazoom_show=True)
overlap = Overlap()
overlap.add(line1)
# 新增一个 y 轴，此时 y 轴的数量为 2，第二个 y 轴的索引为 1（索引从 0 开始），所以设置 yaxis_index = 1
# 由于使用的是同一个 x 轴，所以 x 轴部分不用做出改变
overlap.add(line2, yaxis_index=1, is_add_yaxis=True)
return overlap
def view(numList):
line = Line('折线图', width=2000)
atter = ['数据异常', '不及格', '及格', '良好', '优秀', '满分']
v1 = [numList[0], numList[1], numList[2], numList[3], numList[4], numList[5]]
line.add('最高成绩', atter, v1,
mark_point=['max'], # 标点最大值
mark_line=['average']) # 虚线位置是平均分
line.add('最低成绩', atter, v1,
mark_point=['min'],
legend_pos='20%')
es = EffectScatter() # 调用闪烁点
es.add('', atter, v1, effect_scale=8)
# 调用合并函数，再一个图表上输出
overlop = Overlap()
overlop.add(line)
overlop.add(es)
pie = Pie('饼图', title_pos='80%')
pie.add('', atter, v1,
radius=[60, 30], # 控制内外半径的
center=[65, 50],
legend_pos='80%',
legend_orient='vertical')
# 显示两个示例图的调用函数，不然会覆盖
grid = Grid()
grid.add(overlop, grid_right='50%')
grid.add(pie, grid_left='60%')
grid.render('abc.html')
def drawfunc(self,
A,
text,
yaxis_max=200000,
yaxis_force_interval=0.25,
width=1000):
'''坏账率绘图函数'''
bar = Bar(text, title_pos='center', width='100%')
line = Line(text, title_pos='center', width='100%')
bar.add("好客户",
A.iloc[:, 0].tolist(),
A['好客户'].tolist(),
yaxis_min=0,
yaxis_max=yaxis_max,
is_label_show=True,
label_pos='inside',
label_color=['#FFB6B9'],
legend_pos='right',
legend_orient='vertical',
is_stack=True)
bar.add("坏客户",
A.iloc[:, 0].tolist(),
A['坏账'].tolist(),
yaxis_min=0,
yaxis_max=yaxis_max,
is_label_show=True,
label_pos='inside',
label_color=['#BBDED6'],
legend_pos='right',
legend_orient='vertical',
is_stack=True)
line.add('坏账率',
A.iloc[:, 0].tolist(),
A['坏账率'].tolist(),
yaxis_min=0,
yaxis_max=1,
yaxis_force_interval=yaxis_force_interval,
is_smooth=True,
legend_pos='right',
point_symbol='circle',
legend_orient='vertical',
line_width=2,
is_label_show=True)
line.add('总体坏账率',
A.iloc[:, 0].tolist(),
A['总体坏账率'].tolist(),
yaxis_min=0,
yaxis_max=1,
yaxis_force_interval=yaxis_force_interval,
is_smooth=True,
legend_pos='right',
legend_orient='vertical',
line_width=2,
line_type='dotted')
overlap = Overlap(width='100%')
overlap.add(bar)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
grid = Grid(width=width)
grid.add(overlap, grid_right='10%')
return grid
def form_valid(self, form):
line1 = Line()
val1 = services.line(form.cleaned_data['code1'],
form.cleaned_data['start'],
form.cleaned_data['end'],
form.cleaned_data['col1'],
form.cleaned_data['is_index1'])
line1.add(services.code_str(form.cleaned_data['code1']),
list(val1.keys()), list(val1.values()))
line2 = Line()
val2 = services.line(form.cleaned_data['code2'],
form.cleaned_data['start'],
form.cleaned_data['end'],
form.cleaned_data['col2'],
form.cleaned_data['is_index2'])
line2.add(services.code_str(form.cleaned_data['code2']),
list(val2.keys()), list(val2.values()))
overlap = Overlap(
'%s-%s' % (form.cleaned_data['start'], form.cleaned_data['end']))
overlap.add(line1)
overlap.add(line2, yaxis_index=1, is_add_yaxis=True)
return show(self.request, overlap)
def get_chatrs(train, col):
data = train.groupby([col])['Attrition']
data_sum = data.sum() # 离职人数
data_mean = data.mean() # 离职率
bar = Bar(col, title_pos="45%")
bar.add('离职人数',
data_sum.index,
data_sum.values,
mark_point=['max'],
yaxis_formatter='人',
yaxis_max=200,
legend_pos="40%",
legend_orient="vertical",
legend_top="95%",
bar_category_gap='25%')
line = Line()
line.add('离职率',
data_mean.index,
data_mean.values,
mark_point=['max'],
mark_line=['average'],
yaxis_max=0.8)
overlap = Overlap(width=900, height=400)
overlap.add(bar)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
return overlap
def name_trend(name, data, gender=['M','F'], year=1920, dodge = 500):
if isinstance(gender, str):
name_data = data[(data['Name'] == name)&(data['Gender']==gender)&(data['Year']>=year)]
attr = list(name_data['Year'].values)
attr1=list(map(str,attr))
bar = Bar("名人名字的影响",width=1000, height=600)
bar.add(name, attr, list(name_data['Count'].values), mark_line=["average"], mark_point=["max", "min"],
legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)
line = Line()
line.add(name, attr1, list(name_data['Count'].values + dodge))
else:
name_data = data[(data['Name'] == name)&(data['Year']>=year)]
attr = list(range(year, 2018))
attr1=list(map(str,attr))
v1 = name_data[name_data['Gender']==gender[0]].Count.values
v2 = name_data[name_data['Gender']==gender[1]].Count.values
bar = Bar("名人名字的影响",width=1000, height=600)
bar.add(name+"男", attr, list(v1), legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)
bar.add(name+"女", attr, list(v2), legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)
line = Line()
line.add(name+"男", attr1, list(v1 + dodge))
line.add(name+"女", attr1, list(v2 + dodge))
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
page.add(overlap)
def forecastPlot(foreData, target, localPath):
"""
:param ismn_data: DataFrame. Merged dataset of ISMN
:return: html file stored in paht './plot/ismn_ts'
"""
# create a new folder: plot/ismn_ts
path = '../{}/predictionPlot'.format(localPath)
if not os.path.exists(path):
os.makedirs(path)
foreData = foreData.copy()
foreData['timeStamp'] = pd.to_datetime(foreData['timeStamp'])
for station in foreData.station.unique():
line = Line("forecast of soil mosture at %i cm" % target, station)
overlap = Overlap(width=1200, height=500)
dataset = foreData[foreData.station == station]
dataset.index = dataset.timeStamp
fore_columns = list(dataset.filter(regex='fore|actual').columns)
for forecastVar in fore_columns:
x = dataset.index
y = list(dataset.loc[:, [forecastVar]].values.reshape(-1))
line.add(forecastVar, x, y, is_datazoom_show=True)
overlap.add(line)
overlap.render(path=path +
'/{0}_forecast_{1}cm.html'.format(station, target))
def avg():
df = pd.read_csv('maoyan2.csv', error_bad_lines=False)
df.round(2)
# 根据日期统计各天的平均值，并保留两位小数
date_score_avg = df.groupby('date')['score'].mean().round(2)*2
# 根据评分数据生成柱状图
bar = Bar('评分走势图', '数据来源：不正经程序员-采集自猫眼',
title_pos='center', width=1500, height=600)
# line = Line()
# line.add('', attr, value)
bar.add('', date_score_avg.index, date_score_avg.values, is_visualmap=False, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True,
is_label_show=True, xaxis_interval=0, xaxis_rotate=30, mark_line=["average"])
overlap = Overlap()
overlap.add(bar)
# overlap.add(line)
overlap.show_config()
overlap.render(
'picture\评分走势图.html')
# 根据日期统计各天的平均值，并保留两位小数
date_positive_prob_avg = df.groupby('date')['positive_prob'].mean().round(2)
bar = Bar('评论情感指数走势图', '数据来源：不正经程序员-采集自猫眼',
title_pos='center', width=1500, height=600)
# line = Line()
# line.add('', attr, value)
bar.add('', date_positive_prob_avg.index, date_positive_prob_avg.values, is_visualmap=False, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True,
is_label_show=True, xaxis_interval=0, xaxis_rotate=30, mark_line=["average"])
overlap = Overlap()
overlap.add(bar)
# overlap.add(line)
overlap.show_config()
overlap.render(
'picture\评论情感指数走势图.html')
def na_timeline(dff,column,time_col,time_type=None):
# time_col should be format like 'yyyy-mm-dd', such as '2018-01-17'
df = dff.copy()
df['na']=df[df[column].columns.values.tolist()].T.count()
df['na'] = df['na'].map(lambda x: 1 if x>0 else np.nan)
if time_type == 'timestamp':
df[time_col] = df[time_col].map(lambda x: datetime.datetime.fromtimestamp(x).strftime("%Y-%m-%d"))
elif time_type == 'time_str':
df[time_col] = df[time_col].map(lambda x: datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S").strftime("%Y-%m-%d"))
else:
df[time_col] = df[time_col].map(lambda x: x.strftime("%Y-%m-%d"))
attr = df[[time_col]+column].groupby(time_col).size().index.tolist()
v1 = df[[time_col]+column+['na']].groupby(time_col).size().values.tolist()
v2 = (np.round(100-df[[time_col]+['na']].groupby(time_col).count()['na'].values.tolist()/df[[time_col]+column+['na']].groupby(time_col).size()*100)).values.tolist()
bar = Bar(width=1200, height=600,title='NA Timeline')
bar.add("数量", attr, v1,is_stack=True,is_datazoom_show=True,datazoom_type='both',datazoom_range=[0,100],
label_color=['#0081FF','#FF007C'],is_visualmap=True, visual_type='size',visual_range=[0,100],
visual_range_size=[10,10],is_yaxislabel_align=False,visual_dimension=1,tooltip_text_color='#000000',
label_emphasis_textcolor='#000000',is_more_utils=True)
line = Line()
line.add('缺失率', attr, v2, yaxis_formatter="%", yaxis_min=0,yaxis_max=100,label_emphasis_textcolor='#000000',
mark_line=['average'],line_width=3)
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
return overlap
def MACD(self):
dif, dea, macd = self.__macd()
overlap = Overlap()
macdBar = Bar()
macdBar.add("",
self.__date,
macd,
is_datazoom_show=True,
is_visualmap=True,
is_piecewise=True,
pieces=[
{
'min': 0,
'max': 300,
'color': "#f47920"
},
{
'min': -300,
'max': 0,
'color': "#f6f5ec"
},
])
macdline = Line()
macdline.add("", self.__date, dif, is_smooth=False)
macdline.add("", self.__date, dea, is_smooth=False)
overlap.add(macdBar)
overlap.add(macdline)
return overlap
def plot_days_to_trend(video_df, save_filename):
"""
使用 pyecharts 统计视频发布后上榜的天数
:param
- video_df:
- save_filename:
"""
video_df['diff'] = (video_df['trending_date'] - video_df['publish_time']).dt.days
days_df = video_df['diff'].value_counts()
# 观察视频发布后2个月的情况
days_df = days_df[(days_df.index >= 0) & (days_df.index <= 60)]
days_df = days_df.sort_index()
bar = Bar('视频发布后2个月的情况')
bar.add(
'柱状图', days_df.index.tolist(), days_df.values.tolist(),
is_datazoom_show=True, # 启用数据缩放功能
datazoom_range=[0, 50] # 百分比范围
)
line = Line()
line.add('折线图', days_df.index.tolist(), days_df.values.tolist())
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render(os.path.join(config.output_path, save_filename))
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[['open','close', 'low','high']].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.close.rolling(20).mean()
df['ma5']=df.close.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 xuqiu_diqu_mon(conn, width, height, mon):
sql_xuqiu_diqu = "select name,count,mj from kanban_xuqiu_diqu where year(date_sub(create_date,interval 1 month))='%s'" \
"and month(date_sub(create_date,interval 1 month))='%s' order by mj desc"%(str(mon)[:4],str(mon)[-2:])
data_xuqiu_diqu = pd.read_sql(sql_xuqiu_diqu, conn)
xuqiu_diqu_name = list(data_xuqiu_diqu.iloc[:, 0])
xuqiu_diqu_mj = list(data_xuqiu_diqu.iloc[:, 2])
xuqiu_diqu_count = list(data_xuqiu_diqu.iloc[:, 1])
xuqiu_diqu_title = str(mon)[:4] + '年' + str(mon)[-2:] + '月土流网土地需求区域分布'
xuqiu_diqu_bar = Bar(xuqiu_diqu_title)
xuqiu_diqu_bar.add("面积（亩）",
xuqiu_diqu_name,
xuqiu_diqu_mj,
xaxis_interval=0,
xaxis_rotate=90)
xuqiu_diqu_line = Line()
xuqiu_diqu_line.add("宗数",
xuqiu_diqu_name,
xuqiu_diqu_count,
xaxis_interval=0,
xaxis_rotate=90)
xuqiu_diqu_overlap = Overlap(height=height, width=width)
xuqiu_diqu_overlap.add(xuqiu_diqu_bar)
xuqiu_diqu_overlap.add(xuqiu_diqu_line, yaxis_index=1, is_add_yaxis=True)
xuqiu_diqu_overlap.render()
return xuqiu_diqu_overlap
def assets_curve(dates, sum):
xs = dates
ys = sum
xs_date = [x.date() for x in xs]
line = Line("盈利走势图")
line.add("盈利",
xs_date,
sum,
is_smooth=True,
mark_line=["min", "max", "average"],
mark_point=[
'min', 'max', 'average', {
"coord": [xs_date[-1], ys[-1]],
"name": "now"
}
],
is_datazoom_show=True,
xaxis_name="时间",
yaxis_name="元",
tooltip_trigger="axis")
overlap = Overlap(width=1200, height=600)
# 默认不新增 x y 轴，并且 x y 轴的索引都为 0
overlap.add(line)
overlap.render('profit.html')
def brush(data):
data = hist_sum(data)
kline = Kline()
kline.add(
'Kline',
data.index,
data.loc[:, ['open', 'close', 'low', 'high']].values,
mark_line=['max', 'min'],
mark_line_valuedim=['highest', 'lowest'],
is_datazoom_show=True,
datazoom_xaxis_index=[0, 1],
)
brush = Line()
brush.add(
'Brush',
data.index,
data.endpoint.values,
)
overlap = Overlap()
overlap.add(kline)
overlap.add(brush)
macd = Bar()
macd.add(
'MACD',
data.index,
data.hist_sum.values,
)
page = Page()
page.add(overlap)
page.add(macd)
return page
def generate_score_tabale():
city_main = city_com.sort_values('count', ascending=False)[0:20]
attr = city_main['city']
v1 = city_main['count']
v2 = city_main['mean']
line = Line("主要城市平均评分")
line.add("主要城市平均评分",
attr,
v2,
is_stack=True,
xaxis_rotate=50,
yaxis_min=4.0,
mark_point=['min', 'max'],
xaxis_interval=0,
line_color='lightblue',
line_width=4,
mark_point_textcolor='yellow',
mark_point_color='lightblue',
is_splitline_show=False)
bar = Bar("主要城市评论数及评分")
bar.add("主要城市评论数",
attr,
v1,
is_stack=True,
xaxis_rotate=50,
yaxis_min=0,
xaxis_interval=0,
is_splitline_show=False)
overlap = Overlap()
# 默认不新增 x y 轴，并且 x y 轴的索引都为 0
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render('resources/city_score.html')
def strategy():
form = request.form
getStock = form.get('stock')
stock = getStock if getStock else "usTSLA"
getCircle = form.get('circle')
circle = getCircle if getCircle else "120"
buyStrategy = BuyTrendStrategy(stock, int(circle), 1000000)
closeDayList, closeValueList = buyStrategy.get_close_line()
buyDayList, buyValueList = buyStrategy.get_buy_point()
closeLine = Line("趋势购买策略", width="400", height="300")
closeLine.add(stock, closeDayList, closeValueList)
buyES = EffectScatter("购买点", width="400", height="300")
buyES.add("购买点",
buyDayList,
buyValueList,
symbol_size=8,
effect_scale=3,
effect_period=3,
symbol="triangle")
overlap = Overlap()
overlap.add(closeLine)
overlap.add(buyES)
return render_template(
"strategy.html",
strategyEchart=overlap.render_embed(),
host=REMOTE_HOST,
script_list=buyES.get_js_dependencies(),
)
class PlotBarWithLine:
def __init__(self):
self.data = {{'label': []}, }
self.titles = ''
self.indexes = []
self.bar = Bar()
self.line = Line()
self.overlap = Overlap()
self.script_list = self.overlap.get_js_dependencies()
def plot_bar_line(self, dat, tit, ind):
self.data = dat
self.titles = tit
self.indexes = ind
self.bar = Bar(self.titles)
for label in self.data.keys():
label_bar = '{}_B'.format(label)
label_line = '{}_L'.format(label)
self.bar.add(label_bar, self.indexes,
self.data[label],
is_label_show=True)
self.line.add(label_line, self.indexes,
self.data[label],
is_label_show=True,
mark_point=['max', 'min'])
self.overlap.add(self.bar)
self.overlap.add(self.line)
return self.overlap.render_embed()
def missing_check(df,columns,miss_rate=0.8,handling=None):
temp = pd.DataFrame(df[columns].isnull().sum())
temp = temp.reset_index().rename(columns={'index':'feature',0:'missing'})
temp = temp.sort_values('missing',ascending=True)
temp['missing_rate'] = np.round(temp['missing']/df.shape[0],2)*100
temp = temp[temp['missing_rate']>miss_rate]
attr = temp.feature.values.tolist()
v1 = temp.missing.values.tolist()
v2 = temp.missing_rate.values.tolist()
bar = Bar()
bar.add('缺失量',attr,v1,is_stack=True,is_datazoom_show=True,datazoom_type='both',datazoom_range=[0,100],
label_color=['#0081FF','#FF007C'],is_visualmap=True, visual_type='size',visual_range=[0,100],
visual_range_size=[10,10],is_yaxislabel_align=False,visual_dimension=1,tooltip_text_color='#000000',
label_emphasis_textcolor='#000000',is_more_utils=True,yaxis_rotate=45,label_emphasis_pos='right',
is_convert=True)
line=Line()
line.add('缺失率', attr, v2, yaxis_formatter="%", yaxis_min=0,yaxis_max=100,label_emphasis_textcolor='#000000',line_width=3,
label_emphasis_pos='right',is_convert=True)
overlap = Overlap(width=1000,height =np.round(temp.shape[0]/2)*30)
overlap.add(bar)
overlap.add(line, xaxis_index=1, is_add_xaxis=True)
if handling == None:
return overlap
elif handling == 'drop':
return temp['feature'].values.tolist()
def test_grid_add_overlap():
from pyecharts import Overlap
grid = Grid()
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar("Overlap+Grid 示例", width=1200, height=600, title_pos="40%")
bar.add("蒸发量", attr, v1)
bar.add(
"降水量",
attr,
v2,
yaxis_formatter=" ml",
yaxis_max=250,
legend_pos="85%",
legend_orient="vertical",
legend_top="45%",
)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
grid.add(overlap, grid_right="20%")
grid.render()
def moneyflow_lgt(date_start, date_end, df, save_path_moneyflow_lgt):
df = df.assign(hgt=df.hgt.apply(lambda x: round(x / 100, 2)))
df = df.assign(sgt=df.sgt.apply(lambda x: round(x / 100, 2)))
df = df.assign(
north_money=df.north_money.apply(lambda x: round(x / 100, 2)))
title = ' 资金流向 - 陆股通(亿元)'
subtitle = ' GZH: 摸鱼大佬'
bar = Bar(title,
subtitle,
title_pos=0.1,
subtitle_text_size=15,
subtitle_color='#aa8')
# bar.use_theme("macarons")
bar.add("沪股通", df['trade_date'], df['hgt'], bar_category_gap='40%')
bar.add("深股通", df['trade_date'], df['sgt'], bar_category_gap='40%')
line = Line()
line.add("北上资金",
df['trade_date'],
df['north_money'],
mark_line=['average'],
mark_point=['min', 'max'])
overlap = Overlap()
overlap.add(bar)
overlap.add(line, is_add_xaxis=False) #是否新增一个 x 坐标轴，默认为 False
render_path = save_path + 'moneyflow_lgt_' + date_end + '.png'
# bar.render(path=render_path)
overlap.render(path=render_path)
# pic_zoom(render_path, save_path_tgbhotstock, 740)
print('moneyflow_lgt done: ' + render_path)
return render_path
def name_trend(name, data, gender=['M','F'], year=1920, dodge = 500):
if isinstance(gender, str):
name_data = data[(data['Name'] == name)&(data['Gender']==gender)&(data['Year']>=year)]
attr = list(name_data['Year'].values)
bar = Bar(name)
bar.add("", attr, list(name_data['Count'].values), mark_line=["average"], mark_point=["max", "min"],
legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)
line = Line()
line.add("", attr, list(name_data['Count'].values + dodge))
else:
name_data = data[(data['Name'] == name)&(data['Year']>=year)]
attr = list(range(year, 2018))
v1 = name_data[name_data['Gender']==gender[0]].Count.values
v2 = name_data[name_data['Gender']==gender[1]].Count.values
bar = Bar(name)
bar.add("男", attr, list(v1), legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)
bar.add("女", attr, list(v2), legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)
line = Line()
line.add("", attr, list(v1 + dodge))
line.add("", attr, list(v2 + dodge))
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
return overlap.render(str(name)+str(gender)+str(dodge)+'.html')
def example1(time_list, base_list, net_list, close_list):
overlap = Overlap(
width=1600,
height=800,
)
line = Line()
line.add(
"BTC基准线",
time_list,
base_list,
is_datazoom_show=True,
is_datazoom_extra_show=True,
is_legend_show=True,
# is_label_show=True,
is_more_utils=True,
is_xaxislabel_align=True,
is_yaxislabel_align=True,
line_color="#7CFC00",
line_width=3,
tooltip_axispointer_type="cross",
# mark_point=["max", "min"]
)
line.add(
"净值",
time_list,
net_list,
is_datazoom_show=True,
is_datazoom_extra_show=True,
is_legend_show=True,
# is_label_show=True,
is_more_utils=True,
is_xaxislabel_align=True,
is_yaxislabel_align=True,
line_color="#00ff00",
line_width=5,
tooltip_axispointer_type="cross",
# mark_point=["max", "min"]
)
line2 = Line()
line2.add(
"BTC价格",
time_list,
close_list,
is_datazoom_show=True,
is_datazoom_extra_show=True,
is_legend_show=True,
# is_label_show=True,
is_more_utils=True,
is_xaxislabel_align=True,
is_yaxislabel_align=True,
line_color="#696969",
line_width=3,
# mark_point=["max", "min"]
)
overlap.add(line)
overlap.add(line2, is_add_yaxis=True, yaxis_index=1)
overlap.render()
def show_charts(error_data, last_time):
charts_data = error_data.copy()
charts_data.pop("null_name")
codes = [v[0] for k, v in charts_data.items() if len(v) != 0]
print(codes)
if len(codes) > 0:
page = Page()
limit = minutes.index(last_time) + 1
mins = [str(x)[:-2] + ":" + str(x)[-2:] for x in minutes[:limit]]
for code in codes:
print(code)
pipeline4 = [{
"$unwind": "$data"
}, {
"$match": {
"_id": code
}
}, {
"$limit": limit
}]
stks_data = table.aggregate(pipeline4)
price = []
avg_px = []
chg_pct = []
for stk in stks_data:
price.append(round(stk["data"]["current_px"], 2))
avg_px.append(round(stk["data"]["avg_px"], 2))
chg_pct.append(stk["data"]["chg_pct"])
last_value = min(price + avg_px)
line = Line(code)
line.add("cur_price",
mins,
price,
yaxis_min=last_value,
tooltip_axispointer_type='cross',
is_smooth=True)
line.add("avg_price",
mins,
avg_px,
yaxis_min=last_value,
tooltip_axispointer_type='cross',
is_smooth=True)
line1 = Line()
line1.add("chg_pct",
mins,
chg_pct,
tooltip_axispointer_type='cross',
is_smooth=True,
yaxis_formatter="%")
overlap = Overlap()
overlap.add(line)
overlap.add(line1, yaxis_index=1, is_add_yaxis=True)
page.add(line)
page.render(u"html\\" + str(last_time) + ".html")
def overlap_container():
overlap = Overlap(page_title="Overlap Container")
stack_bar, line = StackBar.stack_bar()
overlap.add(stack_bar)
overlap.add(line)
return overlap
def view(code, start_, end_, days):
'''
使用pyecharts展示数据
:param code: 股票代码
:param start_: 开始时间（包含）格式为'2018-01-01'
:param end_: 结束时间（包含）格式为'2018-01-01'
:param days: 设置求多少天内波动率
:return:
'''
th = get_data_from_db(code, start_, end_)
# 中间有数据缺失的情况，因为股票在工作日才会交易，非工作日的交易数据是缺失的，我们要填充上去
l = len(th)
start = th.iloc[0:1].index.tolist()[0]
end = th.iloc[l - 1:l].index.tolist()[0]
idx = pd.date_range(start=start, end=end)
# 接着我们使用reindex函数将缺失数据补全
# 数据补全的规则是，价格数据用前一个交易日的数据来填充，但是交易量需要填充为0
data = th.reindex(idx)
zvalues = data.loc[~(data.volume > 0)].loc[:, ['volume']]
data.update(zvalues.fillna(0))
# pad/ffill：用前一个非缺失值去填充该缺失值
# backfill/bfill：用下一个非缺失值填充该缺失值
data.fillna(method='ffill', inplace=True) #
# 根据分组索引来分组，days为分组时间长度
group_index = gen_item_group_index(len(data), days)
data['group_index'] = group_index
group = data.groupby('group_index').agg({
'volume': 'sum',
'low': 'min',
'high': _high_price
})
# 添加每个分组起始日期
data_col = pd.DataFrame({'group_index': group_index, 'date': idx})
group['date'] = data_col.groupby('group_index').agg('first')
# 添加波动率
group['ripples_radio'] = group.high / group.low
attr = [str(x.strftime('%Y-%m-%d')) for x in group.date]
v1 = [round(x, 2) for x in group.ripples_radio.tolist()]
v2 = [round(x / 10000, 2) for x in group.volume.tolist()]
bar = Bar(width=1200, height=600)
bar.add("波动率", attr, v1)
line = Line()
line.add("成交量", attr, v2, yaxis_formatter=" 万手")
overlap = Overlap()
# 默认不新增 x y 轴，并且 x y 轴的索引都为 0
overlap.add(bar)
# 新增一个 y 轴，此时 y 轴的数量为 2，第二个 y 轴的索引为 1（索引从 0 开始），所以设置 yaxis_index = 1
# 由于使用的是同一个 x 轴，所以 x 轴部分不用做出改变
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render('股票[{0}]{1}天波动率和成交量图.html'.format(code, days))
def combinate(base, other, xIndex=0, yIndex=0, addX=False, addY=False):
nt = Overlap()
nt.add(base)
nt.add(other,
xaxis_index=xIndex,
yaxis_index=yIndex,
is_add_xaxis=addX,
is_add_yaxis=addY)
return nt
def test_line_es():
v1 = [5, 20, 36, 10, 10, 100]
line = Line("line-EffectScatter 示例")
line.add("", CLOTHES, v1, is_random=True)
es = EffectScatter()
es.add("", CLOTHES, v1, effect_scale=8)
overlap = Overlap()
overlap.add(line)
overlap.add(es)
overlap.render()
def test_overlap_bar_line():
attr = ["A", "B", "C", "D", "E", "F"]
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line-Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def test_overlap_bar_line():
attr = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line-Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def test_timeline_label_color():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add(
"bar",
attr,
[randint(10, 50) for _ in range(6)],
label_color=["red", "#213", "black"],
)
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
content = timeline._repr_html_()
assert '"color": [' in content
assert "red" in content
assert "#213" in content
assert "black" in content
def test_overlap_kline_line():
import random
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
attr = ["2017/7/{}".format(i + 1) for i in range(31)]
kline = Kline("Kline-Line 示例")
kline.add("日K", attr, v1)
line_1 = Line()
line_1.add("line-1", attr, [random.randint(2400, 2500) for _ in range(31)])
line_2 = Line()
line_2.add("line-2", attr, [random.randint(2400, 2500) for _ in range(31)])
overlap = Overlap()
overlap.add(kline)
overlap.add(line_1)
overlap.add(line_2)
overlap.render()
def test_overlap_two_yaxis():
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(width=1200, height=600)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render()
def test_overlap_es_scatter():
v1 = [10, 20, 30, 40, 50, 60]
v2 = [30, 30, 30, 30, 30, 30]
v3 = [50, 50, 50, 50, 50, 50]
v4 = [10, 10, 10, 10, 10, 10]
es = EffectScatter("Scatter-EffectScatter 示例")
es.add("es", v1, v2)
scatter = Scatter()
scatter.add("scatter", v1, v3)
es_1 = EffectScatter()
es_1.add("es_1", v1, v4, symbol='pin', effect_scale=5)
overlap = Overlap()
overlap.add(es)
overlap.add(scatter)
overlap.add(es_1)
overlap.render()
quantity = []
for i in sorted(genres.items(), key=lambda item:sum(item[1]['boxoffice_num']), reverse=True):
styles.append(i[0])
boxoffice.append(round(sum(genres[i[0]]['boxoffice_num']), 2))
avg_boxoffice.append(round(np.mean(genres[i[0]]['boxoffice_num']), 2))
quantity.append(len(genres[i[0]]['boxoffice_num']))
grid = Grid()
bar1 = Bar(title='类型-票房') # ,title_pos='40%'
bar2 = Bar()
bar1.add('总票房',styles, boxoffice, xaxis_name='类型', yaxis_name='总票房（亿）', yaxis_name_gap=40, is_toolbox_show=False)
bar2.add('平均票房', styles, avg_boxoffice, xaxis_name='类型', yaxis_name='平均票房（亿）', is_toolbox_show=False)
overlap = Overlap(width=1200, height=600)
overlap.add(bar1)
overlap.add(bar2, is_add_yaxis=True, yaxis_index=1)
grid.add(overlap, grid_right="20%")
grid#.render('类型-票房.html')
# 上图为2018年不同类型电影的总票房和平均票房。由于一部电影一般会有多个类型，因此在计算票房时，分别考虑了该电影的每个类型。以‘红海行动’为例，‘红海行动’为动作片、战争片，因此在动作片和战争片中，都分别考虑了‘红海行动’的票房。
#
# 可以看出，就总票房而言，动作片总票房最高，喜剧片紧随其后。而就平均票房而言，科幻和战争片的平均票房较高，这可能是由于这些类型的电影制作用了更大的成本，制作较好，因此也回收了更多的票房。
# In[16]:
genres = {}
other_list = ['武侠', '悬疑', '儿童', '历史', '家庭', '古装', '传记', '纪录片', '音乐', '歌舞', '恐怖', '灾难', '运动', '同性', '西部', '戏曲']
def graphpage(items,startdate,enddate,option,width1, height1): #labels:复权ork线or分笔 option:hfq, qfq or 15, 30, D, etc
page = Page()
for i in items:#generate numbers of graphs according to numbers of queries in treewidget
j = re.split("-",i)
if len(j)==3:
a = generateline(j[1],j[2],startdate,enddate,option)#stock number, Type, startdate, enddate, 30 or 15 or days
if a is None:
continue
time = [d[0] for d in a]#get time from returned dictionary
if j[2]!="Kline":
if len(a[0])==4 and a[0][2]=="bar": #for 分笔data
overlap = Overlap()
form = [e[1] for e in a]
bar = Bar(j[0] + "-" + j[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(j[0] + "-" + j[2], time, form, yaxis_min = "dataMin",yaxis_max = "dataMax",is_datazoom_show = True, datazoom_type = "slider")
overlap.add(bar)
line = Line(j[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in a]
line.add(j[0] + "price", time, price, yaxis_min = "dataMin",yaxis_max = "dataMax", is_datazoom_show = True, datazoom_type = "slider",
yaxis_type="value")
overlap.add(line,yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
if len(a[0])==5 and a[0][3]=="pie":
overlap = Overlap()
timeline = Timeline(is_auto_play=False, timeline_bottom=0) #zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in a]
valuearray = [d[1] for d in a]
quarter = [e[2] for e in a]
num = a[0][4]
for x in range(0, num / 10):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(j[0]+"-"+"前十股东".decode("utf-8"),width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(j[0]+"-"+"前十股东".decode("utf-8"), names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter = "{b}: {c}\n{d}%")
# print list
# print names
# print quarterarray
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
return
#need more statement
else:
form = [e[1] for e in a]#for not分笔 data
line = Line(j[0] + "-" + j[2], width=width1*10/11, height=(height1*10/11)/len(items))
line.add(j[0] + "-" + j[2], time, form, is_datazoom_show=True, datazoom_type="slider",yaxis_min="dataMin",yaxis_max="dataMax")
page.add(line)
else:
overlap = Overlap()#for k线
close = zip(*a)[2]
candle = [[x[1], x[2], x[3], x[4]] for x in a]
candlestick = Kline(j[0] + "-" + j[2], width=width1*10/11, height = (height1*10/11) / len(items))
candlestick.add(j[0], time, candle, is_datazoom_show=True, datazoom_type="slider",yaxis_interval = 1)
overlap.add(candlestick)
if len(close)>10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(j[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close)>20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(j[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close)>30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(j[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:
for k in range(1, len(j)/3):#if graphs are combined
j[3*k-1] = re.sub("\n&","",j[3*k-1])
sizearray=[]
#if j[1] != "Candlestick"
layout = Overlap()
for i in xrange(0, len(j),3):
array = j[i:i +3]
b = generateline(array[1],array[2],startdate,enddate,option)
if b is None:
continue
btime = [d[0] for d in b]
if array[2] != "Kline":
if len(b[0])==4 and b[0][2]=="bar":
form = [e[1] for e in b]
bar = Bar(array[0] + "-" + array[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(array[0] + "-" + array[2], btime, form, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_max="dataMax")
layout.add(bar)
line = Line(array[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in b]
line.add(array[0] + "price", btime, price, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_type="value")
layout.add(line, yaxis_index=1, is_add_yaxis=True)
else:
line = Line(array[0] + "-" + array[2],width=width1*10/11, height=(height1*10/11) / len(items))
line.add(array[0]+"-"+array[2], btime, b, is_datazoom_show=True, yaxis_max = "dataMax", yaxis_min = "dataMin",datazoom_type="slider")
layout.add(line)
else:
candle = [[x[1], x[2], x[3], x[4]] for x in b]
candlestick = Kline(array[0] + "-" + array[1], width=width1*10/11,
height=(height1*10/11) / len(items))
candlestick.add(array[0], btime, candle, is_datazoom_show=True, datazoom_type=["slider"])
#if i == 0:
close = zip(*b)[2]
if len(close)>10:
ma10 = calculateMa(close, 10)
line4 = Line(title_color="#C0C0C0")
line4.add(array[0] + "-" + "MA10", btime, ma10)
layout.add(line4)
if len(close)>20:
ma20 = calculateMa(close, 20)
line5 = Line(title_color="#C0C0C0")
line5.add(array[0] + "-" + "MA20", btime, ma20)
layout.add(line5)
if len(close)>30:
ma30 = calculateMa(close, 30)
line6 = Line(title_color="#C0C0C0")
line6.add(array[0] + "-" + "MA30", btime, ma30)
layout.add(line6)
layout.add(candlestick)
page.add(layout)
page.render()
# In[239]:
from pyecharts import Bar, Line, Overlap, Grid
years = [str(i) for i in range(2008, 2018)]
net_sales = data.loc['净销售额'].values
net_income = data.loc['净利润'].values
bar = Bar("盈利能力")
bar.add("净销售额", years, net_sales)
bar.add("净利润", years, net_income, bar_category_gap=25, yaxis_name='百万美元', yaxis_name_gap=60)
gross = data.loc['毛利率'].values
line = Line()
line.add("毛利率", years, gross, line_width=3)
ol = Overlap()
ol.add(bar)
ol.add(line, is_add_yaxis=True, yaxis_index=1)
ol
# In[241]:
assets = data.loc['总资产'].values
cash = data.loc['现金'].values
bar = Bar("财务状况")
bar.add("总资产", years, assets)
bar.add("现金", years, cash, bar_category_gap=25, yaxis_name='百万美元', yaxis_name_gap=60)
bar
def test_page_grid_timeline_overlap():
# Grid
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add(
"es",
[11, 11, 15, 13, 12, 13, 10],
[1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%",
)
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
# Timeline
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构", height=720, width=1200)
bar_5.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add(
"冬季",
CLOTHES,
[randint(10, 100) for _ in range(6)],
is_legend_show=True,
)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
timeline.add(bar_2, "2013 年")
timeline.add(bar_3, "2014 年")
timeline.add(bar_4, "2015 年")
timeline.add(bar_5, "2016 年")
# Overlap
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(height=720, width=1200)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page = Page()
page.add(grid)
page.add(timeline)
page.add(overlap)
page.render()
def graphpage(labels,mode_combo,startdate,enddate,optInterval,width1, height1):
#optInterval='D/W/M' labels
startdate = startdate.replace("/","-")#convert to tushare readable date
enddate = enddate.replace("/","-")
page = Page()
for label in labels: # generate numbers of graphs according to numbers of queries in treewidget
label1 = re.split("-", label)
overlap = Overlap()
if label1[2]!="分笔" and label1[2]!="季度饼图": #"K线" "复权" "历史分钟"
if mode_combo == "复权":
#if optInterval == "qfq" or optInterval == "hfq":#复权
array = ts.get_h_data(label1[1], start=startdate, end=enddate, autype="qfq")
array = array.sort_index()
time = array.index.format() # 日期正是index
elif mode_combo == "K线":
#elif optInterval.isalnum() :#历史K线
array = ts.get_k_data(label1[1], start=startdate, end=enddate, ktype=optInterval)
time = array['date'].tolist() # array.date
elif mode_combo == "历史分钟":
array_bfr = ts.get_tick_data(label1[1], date=startdate)
array_bfr.sort_values("time")
a = startdate + " " + array_bfr["time"]
array_bfr["time"] = a
array_bfr["time"] = pd.to_datetime(a)
array_bfr = array_bfr.set_index("time")
if label1[2] != "Volume" and label1[2] != "Amount":
price_df = array_bfr["price"].resample(optInterval).ohlc() # resample重新取样,选出ohlc=open/high/low，提取除价格之外另外4列.close,tushare是ohcl
price_df = price_df.dropna()
array = price_df
time = price_df.index.format()
elif label1[2] == "Volume":
vols = array_bfr["volume"].resample(optInterval).sum() #resample重新取样，累加间隔内数值 relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
array = vol_df
time = vol_df.index.format()
else:
amounts = array_bfr["amount"].resample(optInterval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
array = amount_df
time = amount_df.index.format()
#绘图方法
if label1[2] == 'Kline':
re_array = array[['open', 'close', 'high', 'low']]
data_li = list(row.tolist() for index, row in re_array.iterrows()) # data_list = list(re_array.as_matrix())
close = array['close'].tolist()
kline = Kline(label1[0] + "-" + optInterval, width=width1*10/11, height = (height1*10/11) / len(labels))
kline.add(label1[0], time, data_li, is_datazoom_show=True, datazoom_type="slider", yaxis_interval=1)
overlap.add(kline)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(label1[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(label1[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(label1[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:#When label1[2]==open/close/volume
if label1[2] == 'Open':
list_aft = array['open'].tolist()
elif label1[2] == 'Close':
list_aft = close
elif label1[2] == 'High':
list_aft = array['high'].tolist()
elif label1[2] == 'Low':
list_aft = array['low'].tolist()
elif label1[2] == 'Volume':#volume
list_aft = array['volume'].tolist()
else:#amount
list_aft = array['amount'].tolist()
line = Line(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
line.add(label1[0] + "-" + label1[2], time, list_aft, is_datazoom_show=True, yaxis_max="dataMax",
yaxis_min="dataMin", datazoom_type="slider")
overlap.add(line)
page.add(overlap)
elif label1[2]=="分笔":
array = ts.get_tick_data(label1[1], date=startdate)
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = list(zip(date, amount, flag, price))
form = [e[1] for e in returnarray]
time = [d[0] for d in returnarray]
bar = Bar(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
bar.add(label1[0] + "-" + label1[2], time, form, is_datazoom_show=True, datazoom_type="slider", yaxis_min="dataMin", yaxis_max="dataMax")
overlap.add(bar)
line = Line(label1[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
price = [e[3] for e in returnarray]
line.add(label1[0] + "price", time, price, yaxis_min="dataMin", yaxis_max="dataMax", is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
elif label1[2]=="季度饼图":
datestr = startdate.split("-")
thisyear = datestr[0]
df2 = ts.top10_holders(code=label1[1], gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in range(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in range(0, len(value), 10)]
quarterarray = [quarter[k:k + 10] for k in range(0, len(quarter), 10)]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = list(zip(namearray, valuearray, quarterarray, flag, num))
timeline = Timeline(is_auto_play=False, timeline_bottom=0) # zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in returnarray]
valuearray = [d[1] for d in returnarray]
quarter = [e[2] for e in returnarray]
num = returnarray[0][4]
for x in range(0, int(num / 10)):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(label1[0] + "-" + "前十股东", width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(label1[0] + "-" + "前十股东", names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter="{b}: {c}\n{d}%")
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
page.render()
def test_timeline_bar_line():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add("bar", attr, [randint(10, 50) for _ in range(6)])
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
bar_2 = Bar("3 月份数据", "数据纯属虚构")
bar_2.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_2 = Line()
line_2.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_2 = Overlap()
overlap_2.add(bar_2)
overlap_2.add(line_2)
bar_3 = Bar("4 月份数据", "数据纯属虚构")
bar_3.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_3 = Line()
line_3.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_3 = Overlap()
overlap_3.add(bar_3)
overlap_3.add(line_3)
bar_4 = Bar("5 月份数据", "数据纯属虚构")
bar_4.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_4 = Line()
line_4.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_4 = Overlap()
overlap_4.add(bar_4)
overlap_4.add(line_4)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
timeline.add(overlap_2, "3 月")
timeline.add(overlap_3, "4 月")
timeline.add(overlap_4, "5 月")
timeline.render()
import numpy as np
arr = np.array(data)
# In[57]:
from pyecharts import Bar, Line, Overlap
bar = Bar("历年报考人数与录取率")
bar.add("报考人数",arr[::-1,0] , arr[::-1,1])
bar.add("录取人数",arr[::-1,0] , arr[::-1,2])
line = Line()
line.add("录取率",arr[::-1,0] , arr[::-1,3]*10, line_width=3, line_color='green')
ol = Overlap()
ol.add(bar)
ol.add(line)
ol
# In[112]:
data_born = [[124761, 123626, 122389, 121121, 119850, 118517, 117171, 115823, 114333, 112704, 111026, 109300, 107507, 105851, 104357, 103008, 101654, 100072, 98705, 97542, 96259, 94974, 93717, 92420, 90859, 89211, 87177, 85229, 82992, 80671, 78534, 76368, 74542, 72538, 70499, 69172, 67296, 65859, 66207, 67207, 65994],
[14.64, 15.64, 16.57, 16.98, 17.12, 17.70, 18.09, 18.24, 19.68, 21.06, 21.58, 22.37, 23.33, 22.43, 21.04, 19.90, 20.19, 22.28, 20.91, 18.21, 17.82, 18.25, 19.03, 20.01, 23.13, 24.95, 28.07, 29.92, 30.74, 33.59, 34.25, 35.75, 34.12, 35.21, 38.00, 39.34, 43.60, 37.22, 18.13, 20.86, 24.78]]
arr_born = np.array(data_born)
list_born = list((arr_born[0]*arr_born[1]/1000)[::-1])
list_born
# In[113]:
