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 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 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 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 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 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 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 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 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 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 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 K_line(date, v1):
kline = Kline("比特币历史价格")
kline.add(
"日K",
date,
v1,
is_datazoom_show=True,
)
overlap = Overlap()
overlap.add(kline)
overlap.render()
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 render_add(self, path="test1.html"):
overlap = Overlap(
width=1600,
height=800,
)
# overlap.add(self.empty_line)
overlap.add(self.kline)
overlap.add(self.line, is_add_yaxis=True, yaxis_index=1)
# overlap.add(self.line, yaxis_index=0)
# overlap.add(self.kline, is_add_yaxis=True, yaxis_index=3)
overlap.render(path=path)
def Longgang(longgang,price,add):
overlap=Overlap()
print(longgang)
attr=['布吉关', '布吉大芬', '布吉水径', '坂田', '布吉街', '布吉南岭', '丹竹头', '大运新城', '横岗', '龙岗双龙', '龙岗中心城', '龙岗宝荷', '坪地', '平湖']
n1, n2, n3, n4, n5, n6,n7, n8, n9, n10, n11, n12,n13,n14 = [], [], [], [], [], [],[], [], [], [], [], [],[],[]
for i in range(len(add)):
if add[i] == longgang[0]:
n1.append(price[i])
if add[i] == longgang[1]:
n2.append(price[i])
if add[i] == longgang[2]:
n3.append(price[i])
if add[i] == longgang[3]:
n4.append(price[i])
if add[i] == longgang[4]:
n5.append(price[i])
if add[i] == longgang[5]:
n6.append(price[i])
if add[i] == longgang[6]:
n7.append(price[i])
if add[i] == longgang[7]:
n8.append(price[i])
if add[i] == longgang[8]:
n9.append(price[i])
if add[i] == longgang[9]:
n10.append(price[i])
if add[i] == longgang[10]:
n11.append(price[i])
if add[i] == longgang[11]:
n12.append(price[i])
if add[i] == longgang[12]:
n13.append(price[i])
if add[i] == longgang[13]:
n14.append(price[i])
l = [len(n1), len(n2), len(n3), len(n4), len(n5), len(n6), len(n7),
len(n8), len(n9), len(n10),len(n11),len(n12),len(n13),len(n14)]
ave = [round(numpy.mean(n1), 2), round(numpy.mean(n2), 2), round(numpy.mean(n3), 2),
round(numpy.mean(n4), 2), round(numpy.mean(n5), 2), round(numpy.mean(n6), 2),
round(numpy.mean(n7), 2), round(numpy.mean(n8), 2), round(numpy.mean(n9), 2),
round(numpy.mean(n10), 2),round(numpy.mean(n11), 2), round(numpy.mean(n12), 2), round(numpy.mean(n13), 2),round(numpy.mean(n14), 2)]
bar = Bar("龙岗区房屋数量_均价分布图", "", title_pos="left", width=1200, height=600)
bar.add("", attr, ave, is_visualmap=True, visual_text_color='#fff',
is_more_utils=True, is_label_show=True, is_datazoom_show=True, xaxis_rotate=45)
overlap.add(bar)
line = Line('龙岗区房屋数量_均价图', width=1200, height=600)
line.add('数量', attr, l, is_stack=True, mark_point=['max', 'min'], line_color='lightblue',
is_more_utils=True, visual_range=[0, 50])
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
overlap.render('龙岗区房屋数量_均价折线图.html')
pie = Pie('龙岗区房屋数量面积占比', title_pos='center', width=1200, height=600)
pie.add("", attr, l, label_text_color=None, is_label_show=True, legend_orient='vertical',
is_more_utils=True, legend_pos='left')
pie.render('龙岗区房屋数量占比Pie.html')
class Draw(object):
def __init__(self, path, ispage=True):
self.page = Page()
self.overlap = Overlap()
self.ispage = ispage
self.path = path
self.range_color = [
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
"#f46d43",
"#d73027",
"#a50026",
]
def add(self, name, xs, ys):
line = Line(name)
for k, v in ys.items():
line.add(k,
xs,
v,
is_smooth=False,
is_datazoom_show=True,
yaxis_min=min(v))
self.page.add(line)
def add3d(self, name, data):
surface3D = Scatter3D(name, width=1200, height=600)
surface3D.add(
"",
data,
is_visualmap=True,
visual_range=[-1000, 4000],
visual_range_color=self.range_color,
)
self.page.add(surface3D)
def add2y(self, name, xs, ys, yaxis_index=0):
line = Line(name)
for k, v in ys.items():
line.add(k, xs, v, is_smooth=False, is_datazoom_show=True)
self.overlap.add(line, yaxis_index=yaxis_index, is_add_yaxis=True)
def draw(self):
if self.ispage:
self.page.render(self.path)
else:
self.overlap.render(self.path)
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 histogram(title, attr_list, value_list):
bar = Bar(title)
bar.add("关键词",
attr_list,
value_list,
is_stack=True,
xaxis_rotate=30,
yaxix_min=4.2,
xaxis_interval=0,
is_splitline_show=False)
overlap = Overlap()
overlap.add(bar)
overlap.render(f'{title}.html')
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 dataGet(self):
"""获取行情"""
data = self.data
#print "data1111=", data
#data.sort_values('date', inplace=True)
ochl = data[['openPrice', 'lastPrice', 'hightPrice', 'lowPrice']]
ochl_tolist = [ochl.ix[i].tolist() for i in range(len(ochl))]
close_df = data['lastPrice']
sma_N1 = talib.SMA(np.array(close_df), self.N1)
sma_N2 = talib.SMA(np.array(close_df), self.N2)
date_df = data.index
kline = Candlestick()
# datazoom_orient='vertical' dataZoom效果加到纵坐标上
kline.add(self.title,
date_df,
ochl_tolist,
mark_point=['max', 'min'],
is_datazoom_show=True,
mark_point_symbolsize=80,
mark_line_valuedim=['highest', 'lowest'])
line = Line()
line.add(u'{}日均线'.format(self.N1),
date_df,
sma_N1,
is_fill=False,
line_opacity=0.8,
is_smooth=True,
line_color='b')
line.add(u'{}日均线'.format(self.N2),
date_df,
sma_N2,
is_fill=False,
line_opacity=0.8,
is_smooth=True,
line_color='g')
overlap = Overlap(page_title=self.title,
width=self.overWidth,
height=self.overHeight)
overlap.add(kline)
overlap.add(line)
KLine_path = r'KLine.html'
overlap.render(KLine_path)
# 自动打开HTML
#self.driver = webdriver.Firefox()
#self.driver.get(KLine_path)
#if __name__ == '__main__':
#PyechartsKLine()
def moneyflow_a_stock_all(date_start, date_end, save_path_moneyflow_ggt=''):
# '000001.SH', '上证指数'
# '399001.SZ', '深证成指'
# '399006.SZ', '创业板指'
# '399005.SZ', '中小板指'
df1 = pro.index_daily(
ts_code='000001.SH',
start_date=date_start,
end_date=date_last_trade_day).sort_index(ascending=False)
df2 = pro.index_daily(
ts_code='399001.SZ',
start_date=date_start,
end_date=date_last_trade_day).sort_index(ascending=False)
# df3 = pro.index_daily(ts_code='399006.SZ', start_date=date_start, end_date=date_last_trade_day).sort_index(ascending=False)
# df4 = pro.index_daily(ts_code='399005.SZ', start_date=date_start, end_date=date_last_trade_day).sort_index(ascending=False)
# df5 = pro.index_daily(ts_code='399300.SZ', start_date=date_start, end_date=date_last_trade_day).sort_index(ascending=False)
df0 = pd.DataFrame()
df0['amount'] = df1['amount'] + df2['amount']
df0['trade_date'] = df1['trade_date']
df0 = df0.assign(amount=df0.amount.apply(lambda x: round(x / 100000, 0)))
df1 = df1.assign(amount=df1.amount.apply(lambda x: round(x / 100000, 0)))
df2 = df2.assign(amount=df2.amount.apply(lambda x: round(x / 100000, 0)))
print(df0)
title = ' 盘面资金 - A股(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("上证", df1['trade_date'], df1['amount'], bar_category_gap='40%')
bar.add("深证", df2['trade_date'], df2['amount'], bar_category_gap='40%')
line = Line()
line.add("全市场",
df0['trade_date'],
df0['amount'],
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_a_stock_all_' + date_end + '.png'
# bar.render(path=render_path)
overlap.render(path=render_path)
# pic_zoom(render_path, save_path_tgbhotstock, 740)
print('moneyflow_ggt done: ' + render_path)
return render_path
def draw(a, b, x_list, y_list):
"""
画图
"""
s = Scatter('最小二乘法')
s.add('散点', x_list, y_list)
l = Line()
l.add('y = {a} + {b}x'.format(a=a, b=b), [x_list[0], x_list[-1]],
[a + (x_list[0] * b), a + (x_list[-1] * b)])
o = Overlap()
o.add(s)
o.add(l)
o.render('最小二乘法.html')
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_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 plot_line(self, title, keys, values, **kwargs):
if 'compos' in kwargs.keys(
) and kwargs['compos'] and 'base_line' in kwargs.keys():
chart = Overlap('风险敞口和价格', width=1360, height=700)
base_chart = kwargs['base_line']
line = Line()
line.add('', keys, values, **kwargs)
chart.add(base_chart)
chart.add(line, yaxis_index=1, is_add_yaxis=True)
self.page.add(chart)
else:
chart = Line(title, title_pos='left')
chart.add('', keys, values, **kwargs)
chart.render('{}.html'.format(title))
return chart
def test_custom_0():
attr = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [15, 25, 35, 45, 55, 65]
v3 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line - Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", v2, v3)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def size_price(size,price):
#page=Page()
overlap=Overlap()
#print(size)
k=[]
attr=['0-30','30-60','60-90','90-120','120-150','150及以上']
for i in range(len(size)):
size[i]=float(size[i][:-2])
n1,n2,n3,n4,n5,n6=[],[],[],[],[],[]
for i in range(len(size)):
if size[i]<=30:
#print(size[i])
n1.append(price[i])
#n1.append(size[i])
if 30<size[i]<=60:
n2.append(price[i])
#n2.append(size[i])
if 60< size[i] <= 90:
n3.append(price[i])
# n3.append(size[i])
if 90<size[i]<=120:
n4.append(price[i])
#n4.append(size[i])
if 120<size[i]<=150:
n5.append(price[i])
#n5.append(size[i])
if 150<size[i]:
n6.append(price[i])
#n6.append(size[i])
l=[len(n1),len(n2),len(n3),len(n4),len(n5),len(n6)]
ave=[numpy.mean(n1),numpy.mean(n2),numpy.mean(n3),numpy.mean(n4),numpy.mean(n5),numpy.mean(n6)]
bar = Bar("房屋面积_数量价位分布图", "", title_pos="left", width=1200, height=600)
bar.add("", attr,ave, is_visualmap=True, visual_text_color='#fff',
is_more_utils=True, is_label_show=True, is_datazoom_show=True, xaxis_rotate=45)
overlap.add(bar)
#page.add(bar)
#bar.render('房屋面积_数量bar.html')
line = Line('房屋面积_均价图', width=1200, height=600)
line.add('数量', attr,l, is_stack=True, mark_point=['max', 'min'],line_color='lightblue',
is_more_utils=True, visual_range=[0, 50])
#page.add(line)
overlap.add(line,is_add_yaxis=True,yaxis_index=1)
#line.render('房屋面积_均价折线图.html')
overlap.render('房屋面积_数量_均价折线图.html')
pie=Pie('面积占比', title_pos='center', width=1200, height=600)
pie.add("", attr, l, label_text_color=None, is_label_show=True, legend_orient='vertical',
is_more_utils=True, legend_pos='left')
pie.render('面积占比Pie.html')
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 overlap(data):
# line
line = Line('star compare', width=1000, height=400)
line.add("dashen star line", data['name'], data['star'], xaxis_rotate=45)
movie_star = data['movie_star']/10
line.add("movie star line", data['name'], movie_star, xaxis_rotate=45)
# bar
bar = Bar("star compare", width=1000, height=400)
bar.add("dashen star bar", data['name'], data['star'], xaxis_rotate=45, is_label_show=True, is_datazoom_show=True)
movie_star = data['movie_star'] / 10
bar.add("movie star bar", data['name'], movie_star, xaxis_rotate=45, is_label_show=True, is_datazoom_show=True)
# overlap
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def render_add(self, path="test.html"):
overlap = Overlap(
width=1600,
height=800,
)
overlap.add(self.kline)
overlap.add(self.line)
overlap.add(self.line_long_win)
overlap.add(self.line_long_loss)
overlap.add(self.line_short_win)
overlap.add(self.line_short_loss)
# overlap.add(self.kline2, is_add_yaxis=True, yaxis_index=1)
overlap.render(path)
def create_line_and_column_chart(self, city_data_frame):
city_main = city_data_frame.sort_values('count', ascending=False)[0:20]
# print(city_main)
attr = city_main['user_loc']
v1 = city_main['count']
v2 = city_main['mean']
# print(attr,v1,v2)
line = Line("主要城市评分")
line.add(
"城市",
attr,
v2,
is_stack=True,
xaxis_rotate=30,
yaxix_min=4.2,
mark_point=['min', 'max'], # 对最大最小值进行标记
xaxis_interval=0,
line_color='#2196F3',
line_width=4,
mark_point_textcolor='#eeeeee',
mark_point_color='#2196F3',
is_splitline_show=False)
bar = Bar("主要城市评论数")
bar.add("城市",
attr,
v1,
is_stack=True,
xaxis_rotate=30,
yaxix_min=4.2,
xaxis_interval=0,
mark_point=['min', 'max'],
is_splitline_show=False)
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
file_path = ''
if self.movie.platform == 0:
file_path = './' + self.movie.movie_name + '/【豆瓣】《' + self.movie.movie_name + '》主要城市评论数_平均分.html'
elif self.movie.platform == 1:
file_path = './' + self.movie.movie_name + '/【猫眼】《' + self.movie.movie_name + '》主要城市评论数_平均分.html'
else:
file_path = './' + self.movie.movie_name + '/【豆瓣+猫眼】《' + self.movie.movie_name + '》主要城市评论数_平均分.html'
overlap.render(file_path)
print('折线 + 柱形图创建完毕')
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()
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()
