def bar(self,
title=None,
attr=None,
b1name=None,
b2name=None,
b1=None,
b2=None):
"""
柱状图
title:数据可视化的标题(字符串)
attr:名称列表[列表]
b1name:图表上b1数据对应的名称
b2name:图表上b2数据对应的名称
:param b1: 名称attr列表对应的数据[列表]
:param b2: 名称attr列表对应的数据[列表]
:return: 生成一个柱状图
"""
b1 = b1
b2 = b2
bar = Bar(title)
bar.add(b1name, attr, b1, is_stack=True, is_more_utils=True) # 显示工具
bar.add(b2name, attr, b2, is_stack=True)
# bar.render()
self.page.add(bar)
self.page.render()
def create_bar(self, v):
bar = Bar(TITLE_TEXT, TITLE_SUBTEXT)
bar.add('商家1', ATTR, v, is_more_utils=True)
bar.add('商家2', ATTR, v, is_more_utils=True)
snippet = TRANSLATOR.translate(bar.options)
options = snippet.as_snippet()
return options
def main():
urls = [
"http://www.weather.com.cn/textFC/hb.shtml",
"http://www.weather.com.cn/textFC/db.shtml",
"http://www.weather.com.cn/textFC/hd.shtml",
"http://www.weather.com.cn/textFC/hz.shtml",
"http://www.weather.com.cn/textFC/hn.shtml",
"http://www.weather.com.cn/textFC/xb.shtml",
"http://www.weather.com.cn/textFC/xn.shtml",
"http://www.weather.com.cn/textFC/gat.shtml"
]
for url in urls:
parse_page(url)
# 数据分析
# 根据最低气温进行排序
# def sorr_key(data):
# min_temp = data["min_temp"]
# return min_temp
ALL_DATA.sort(key=lambda data:data["min_temp"])
data = ALL_DATA[0:10]
cities = list(map(lambda x:x['city'],data))
temps = list(map(lambda x:x['min_temp'],data))
chart = Bar("中国天气最低温度排行榜")
chart.add('',cities,temps)
chart.render('temperature.html')
def zhu(request):
# // 导入柱状图 - Bar
# // 设置行名
columns = [
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct",
"Nov", "Dec"
]
# // 设置数据
data1 = [
2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3
]
data2 = [
2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3
]
# // 设置柱状图的主标题与副标题
bar = Bar("柱状图", "一年的降水量与蒸发量")
# // 添加柱状图的数据及配置项
bar.add("降水量",
columns,
data1,
mark_line=["average"],
mark_point=["max", "min"])
bar.add("蒸发量",
columns,
data2,
mark_line=["average"],
mark_point=["max", "min"])
# // 生成本地文件（默认为.html文件）
# bar.render()
return render(request, bar.render)
def get_echarts_instance(self, *args, **kwargs):
bar = Bar("我的第一个图表", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
[5, 20, 36, 10, 75, 90])
abc = PurchaseStatusModel.objects.raw(
"select ps.id, ps.quantity, subq.status, subq.created_at from (select pid, status, created_at from (select purchase_id as pid, status, created_at, row_Number() over (Partition by purchase_id order by (case when status = 'dispatched' then 1 when status = 'delivered' then 2 end )) as rn from purchase_purchasestatusmodel) as sqb group by 1,2,3, sqb.rn having rn = max(rn)) as subq inner join purchase_purchasemodel as ps on ps.id = pid"
)
return bar
def group_common_in():
# 获取所有活跃的群聊
groups = bot.groups()
# 每个好友与你相同的群聊个数
dict_common_in = {}
# 遍历所有好友，第0个为你自己，所以去掉
for x in friends[1:]:
# 依次在每个群聊中搜索
for y in groups:
# x在y中
if (x in y):
# 获取微信名称
name = x.nick_name
# 判断是否有备注，有的话就使用备注
if (x.remark_name and x.remark_name != ''):
name = x.remark_name
# 增加计数
if (name in dict_common_in.keys()):
dict_common_in[name] += 1
else:
dict_common_in[name] = 1
# 从dict_common_in结果中取出前n大个数据
n = 0
if (len(dict_common_in) > 5):
n = 6
elif (len(dict_common_in) > 4):
n = 5
elif (len(dict_common_in) > 3):
n = 4
elif (len(dict_common_in) > 2):
n = 3
elif (len(dict_common_in) > 1):
n = 2
elif (len(dict_common_in) > 0):
n = 1
# 排序，并转化为list
sort_list = sorted(dict_common_in.items(),
key=lambda item: item[1],
reverse=True)
# 取出前n大的值
sort_list = sort_list[:n]
bar = Bar('共同所在群聊分析')
bar.add(name='',
x_axis=[x[0] for x in sort_list],
y_axis=[x[1] for x in sort_list],
legend_orient="vertical",
legend_pos="left")
bar.render('data/共同所在群聊分析.html')
def create_door(door):
"""
生成柱状图
"""
attr = [j for j in door['city'][:3]]
v1 = [j for j in door['line'][:3]]
bar = Bar("地铁站最爱用“门”命名的城市",
title_pos='center',
title_top='18',
width=800,
height=400)
bar.add("", attr, v1, is_stack=True, is_label_show=True, yaxis_max=40)
bar.render("地铁站最爱用门命名的城市.html")
def create_word(word_message):
"""
生成柱状图
"""
attr = [j[0] for j in word_message]
v1 = [j[1] for j in word_message]
bar = Bar("中国地铁站最爱用的字",
title_pos='center',
title_top='18',
width=800,
height=400)
bar.add("", attr, v1, is_stack=True, is_label_show=True)
bar.render("中国地铁站最爱用的字.html")
def draw_bar(all_list):
print("开始绘图")
attr = ["海沧", "湖里", "集美", "思明", "翔安", "同安"]
v0 = all_list[0]
v1 = all_list[1]
v2 = all_list[2]
v3 = all_list[3]
bar = Bar("厦门市租房租金概况")
bar.add("最小值", attr, v0, is_stack=True)
bar.add("最大值", attr, v1, is_stack=True)
bar.add("平均值", attr, v2, is_stack=True)
bar.add("中位数", attr, v3, is_stack=True)
bar.render()
print("结束绘图")
def DrawBar(city_names):
bar = Bar("城市气温柱状图")
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'])
bar.add("最高气温", attrs1, values1, mark_point=["average"])
bar.add("最低气温", attrs2, values2, mark_point=["min", "max"])
bar.render('weatherBar.html')
def create_pie(self, v=None):
from pyecharts import options as opts
from pyecharts.charts import Bar
print('create_pie')
bar = Bar("test", 'TITLE_SUBTEXT')
bar.add('商家1', ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
['12', '50', '20', '25', '10'],
is_more_utils=True)
bar.add('商家2', ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
['12', '50', '20', '25', '10'],
is_more_utils=True)
snippet = TRANSLATOR.translate(bar.options)
options = snippet.as_snippet()
return options
def get_html():
def label_formatter(params):
return params.data + '分'
v1 = [] #学号
attr = [] #总消费
bar = Bar('testBar_color', 'Theme', page_title='人均消费排名')
# 注意 label_color的属性值为 list(列表)
bar.add("test_X",
attr,
v1,
is_label_show=True,
label_color=['#5AB5FF'],
label_text_color='#3367FF',
label_formatter=label_formatter)
bar.render()
def vtradevolume(cftable, freq='D', bar_category_gap='35%', **vkwds):
'''
aid function on visualization of trade summary
:param cftable: cftable (pandas.DataFrame) with at least date and cash columns
:param freq: one character string, frequency label, now supporting D for date,
W for week and M for month, namely the trade volume is shown based on the time unit
:param vkwds: keyword argument for pyecharts Bar.add()
:returns: the Bar object
'''
if freq == 'D':
selldata = [[row['date'], row['cash']]
for _, row in cftable.iterrows() if row['cash'] > 0]
buydata = [[row['date'], row['cash']] for _, row in cftable.iterrows()
if row['cash'] < 0]
elif freq == 'W':
cfmerge = cftable.groupby(
[cftable['date'].dt.year, cftable['date'].dt.week])['cash'].sum()
selldata = [[dt.datetime.strptime(str(a) + '4', '(%Y, %W)%w'), b] \
for a, b in cfmerge.iteritems() if b > 0]
buydata = [[dt.datetime.strptime(str(a) + '4', '(%Y, %W)%w'), b] \
for a, b in cfmerge.iteritems() if b < 0]
elif freq == 'M':
cfmerge = cftable.groupby(
[cftable['date'].dt.year, cftable['date'].dt.month])['cash'].sum()
selldata = [[dt.datetime.strptime(str(a) + '15', '(%Y, %m)%d'), b] \
for a, b in cfmerge.iteritems() if b > 0]
buydata = [[dt.datetime.strptime(str(a) + '15', '(%Y, %m)%d'), b] \
for a, b in cfmerge.iteritems() if b < 0]
else:
raise Exception('no such freq tag supporting')
bar = Bar()
bar.add('买入', [0 for _ in range(len(buydata))],
buydata,
xaxis_type='time',
bar_category_gap=bar_category_gap)
bar.add('卖出', [0 for _ in range(len(selldata))],
selldata,
xaxis_type='time',
is_datazoom_show=True,
bar_category_gap=bar_category_gap,
**vkwds)
bar
return bar
def create_line(df):
"""
生成城市地铁线路数量分布情况
"""
title_len = df['line']
bins = [0, 5, 10, 15, 20, 25]
level = ['0-5', '5-10', '10-15', '15-20', '20以上']
len_stage = pd.cut(title_len, bins=bins,
labels=level).value_counts().sort_index()
# 生成柱状图
attr = len_stage.index
v1 = len_stage.values
bar = Bar("各城市地铁线路数量分布",
title_pos='center',
title_top='18',
width=800,
height=400)
bar.add("", attr, v1, is_stack=True, is_label_show=True)
bar.render("各城市地铁线路数量分布.html")
def create_year_bar():
bar = Bar("专利年份分布", width=1200, height=600)
year_dicts = {}
for date in Patents.select(Patents.request_date).dicts():
if date.get('request_date', '') != '':
date_time = datetime.strptime(date.get('request_date', ''),
'%Y.%m.%d')
year_patents = year_dicts.get(date_time.year, 0)
year_patents += 1
year_dicts.__setitem__(date_time.year, year_patents)
year_list = []
count_list = []
for year, counts in year_dicts.items():
year_list.append(year)
year_list.sort()
for year in year_list:
count_list.append(year_dicts.get(year))
bar.add('申请', year_list, count_list)
year_dicts = {}
for date in Patents.select(Patents.publish_date).dicts():
if date.get('publish_date', '') != '':
date_time = datetime.strptime(date.get('publish_date', ''),
'%Y.%m.%d')
year_patents = year_dicts.get(date_time.year, 0)
year_patents += 1
year_dicts.__setitem__(date_time.year, year_patents)
year_list = []
count_list = []
for year, counts in year_dicts.items():
year_list.append(year)
year_list.sort()
for year in year_list:
count_list.append(year_dicts.get(year))
bar.add('公告', year_list, count_list)
return bar
def analyze_special_friends():
# 星标好友(很重要的人), 不让他看我的朋友圈的好友, 不看他朋友圈的好友, 消息置顶好友, 陌生人
star_friends, hide_my_post_friends, hide_his_post_friends, sticky_on_top_friends, stranger_friends = 0, 0, 0, 0, 0
for user in friends:
# 星标好友为1,为0表示非星标,不存在星标选项的为陌生人
if ('StarFriend' in (user.raw).keys()):
if ((user.raw)['StarFriend'] == 1):
star_friends += 1
else:
stranger_friends += 1
# 好友类型及权限：1和3好友，259和33027不让他看我的朋友圈，65539和65537和66051不看他的朋友圈，65795两项设置全禁止, 73731陌生人
if ((user.raw)['ContactFlag'] in [259, 33027, 65795]):
hide_my_post_friends += 1
if ((user.raw)['ContactFlag'] in [66051, 65537, 65539, 65795]):
hide_his_post_friends += 1
# 消息置顶好友为2051
if ((user.raw)['ContactFlag'] in [2051]):
sticky_on_top_friends += 1
# 陌生人
if ((user.raw)['ContactFlag'] in [73731]):
stranger_friends += 1
bar = Bar('特殊好友分析')
bar.add(name='',
x_axis=['星标', '不让他看我朋友圈', '不看他朋友圈', '消息置顶', '陌生人'],
y_axis=[
star_friends, hide_my_post_friends, hide_his_post_friends,
sticky_on_top_friends, stranger_friends
],
legend_orient="vertical",
legend_pos="left")
bar.render('data/特殊好友分析.html')
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 main():
urls = {
"http://www.weather.com.cn/textFC/hb.shtml",
"http://www.weather.com.cn/textFC/db.shtml",
"http://www.weather.com.cn/textFC/hz.shtml",
"http://www.weather.com.cn/textFC/hn.shtml",
"http://www.weather.com.cn/textFC/xb.shtml",
"http://www.weather.com.cn/textFC/xn.shtml",
"http://www.weather.com.cn/textFC/gat.shtml"
}
for url in urls:
parse_page(url)
# 分析数据
# 根据最低气温排序
ALL_DATA.sort(key=lambda data: data['min_temp'])
data = ALL_DATA[0:20]
cities = list(map(lambda x: x['city'], data))
temps = list(map(lambda x: x['min_temp'], data))
chart = Bar("中国天气最低气温排行榜")
chart.add('', cities, temps)
chart.render("temperature.html")
def main():
urls = [
'http://www.weather.com.cn/textFC/hb.shtml'
# 'http://www.weather.com.cn/textFC/hn.shtml',
# 'http://www.weather.com.cn/textFC/hz.shtml',
# 'http://www.weather.com.cn/textFC/hb.shtml',
# 'http://www.weather.com.cn/textFC/xb.shtml',
# 'http://www.weather.com.cn/textFC/gat.shtml',
]
for url in urls:
parse_page(url)
ALL_DATA.sort(key=lambda data: data['temp_low'])
cities = []
for city_temp in ALL_DATA:
city = city_temp['city']
cities.append(city)
cities = list(map(lambda x: x['city'], ALL_DATA))
temp = list(map(lambda x: x['temp_low'], ALL_DATA))
print(cities)
print(temp)
chart = Bar("天气")
chart.add('城市', cities, temp)
chart.render('test.html')
def region_distribution():
# 使用一个字典统计好友地区分布数量
province_dict = {
'北京': 0,
'上海': 0,
'天津': 0,
'重庆': 0,
'河北': 0,
'山西': 0,
'吉林': 0,
'辽宁': 0,
'黑龙江': 0,
'陕西': 0,
'甘肃': 0,
'青海': 0,
'山东': 0,
'福建': 0,
'浙江': 0,
'台湾': 0,
'河南': 0,
'湖北': 0,
'湖南': 0,
'江西': 0,
'江苏': 0,
'安徽': 0,
'广东': 0,
'海南': 0,
'四川': 0,
'贵州': 0,
'云南': 0,
'内蒙古': 0,
'新疆': 0,
'宁夏': 0,
'广西': 0,
'西藏': 0,
'香港': 0,
'澳门': 0
}
# 遍历
for user in friends:
# 判断省份是否存在，有可能是外国的，这种情况不考虑
if (user.province in province_dict):
key = user.province
province_dict[key] += 1
province = list(province_dict.keys())
values = list(province_dict.values())
# maptype='china' 只显示全国直辖市和省级，数据只能是省名和直辖市的名称
map = Map("微信好友地区分布")
map.add("",
province,
values,
visual_range=[0, 50],
maptype='china',
is_visualmap=True,
visual_text_color='#000')
map.render(path="data/好友地区分布.html")
# 对好友数最多的省份进行一进步分析
max_count_province = ''
for key, value in province_dict.items():
if (value == max(province_dict.values())):
max_count_province = key
break
# 使用一个字典统计好友地区分布数量
city_dict = {}
# 遍历
for user in friends:
if (user.province == max_count_province):
# 更新键值对
if (user.city in city_dict.keys()):
city_dict[user.city] += 1
else:
city_dict[user.city] = 1
bar = Bar(max_count_province + '中,好友地区分布')
bar.add(name='地区分布',
x_axis=[x for x in city_dict.keys()],
y_axis=[x for x in city_dict.values()])
bar.render('data/某省好友地区分布.html')
def createCharts(self):
"""生成图表"""
# 读取数据,格式：[{"北京", 10}, {"上海",10}]
data = self.readCityNum()
# 1 热点图
geo1 = Geo("观众位置分布热点图",
"数据来源：猫眼，Fly采集",
page_title="#FFF",
title_pos="center",
width="100%",
height=600,
bg_color="#404A59")
attr1, value1 = geo1.cast(data)
geo1.add("",
attr1,
value1,
type="heatmap",
visual_range=[0, 1000],
visual_text_color="#FFF",
symbol_size=15,
is_visualmap=True,
is_piecewise=False,
visual_split_number=10)
geo1.render("files/观众位置热点图.html")
# 2 位置图
geo2 = Geo("观众位置分布",
"数据来源：猫眼，Fly采集",
title_color="#FFF",
title_pos="center",
width="100%",
height=600,
background_color="#404A59")
attr2, value2 = geo1.cast(data)
geo2.add("",
attr2,
value2,
visual_range=[0, 1000],
visual_text_color="#FFF",
symbol_size=15,
is_visualmap=True,
is_piecewise=False,
visual_split_number=10)
geo2.render("files/无名之辈-观众位置图.html")
# 3、top20 柱状图
data_top20 = data[:20]
bar = Bar("《无名之辈》观众来源排行 TOP20",
"数据来源：猫眼，Fly采集",
title_pos="center",
width="100%",
height=600)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 3500],
visual_text_color="#FFF",
is_more_utils=True,
is_label_show=True)
bar.render("files/无名之辈-观众来源top20.html")
print("图表生成完成")
zufang = db.get_collection('zufang_sh') # 连接到集合zufang
mon_data = zufang.find() # 查询这个集合下的所有记录
lianjia_df = pd.DataFrame(list(mon_data))
zone_price = pd.merge(
data.groupby(['所在区'])['单价(元/平米)'].mean().round(0).sort_values(
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)
def tiaoxing(names, prices):
bar = Bar("笔记本电脑价格图", "X-电脑名，Y-价格")
bar.add("笔记本电脑", names, prices)
bar.show_config()
bar.render("D:\\scrapy\\jingdong\\prices.html")
df = df[df.price > 0]
# # 去除重复行
# df = df.drop_duplicates()
print("row number is {0}".format(len(df.index)))
####################################################
# 最贵的小区排名
####################################################
df.sort_values("price", ascending=False, inplace=True)
num = 5
print(df.head(num))
city = df["city_ch"][0]
xqs = df["xiaoqu"][0:num]
prices = df["price"][0:num]
bar = Bar("{0}小区均价".format(city))
bar.add("小区均价前{0}名".format(num), xqs, prices, is_stack=True, is_label_show=True, xaxis_interval=0, xaxis_rotate=45)
bar.render(path="xiaoqu.html")
####################################################
# 区县均价排名
####################################################
district_df = df.groupby('district').mean()
district_df = district_df.round(0)
district_df.sort_values("price", ascending=False, inplace=True)
print(district_df)
districts = district_df.index.tolist()
prices = district_df["price"]
bar = Bar("{0}区县均价".format(city))
bar.add("区县均价排名", districts, prices, is_stack=True, is_label_show=True, xaxis_interval=0, xaxis_rotate=45)
bar.render(path="district.html")
import numpy
import pyecharts
from pyecharts.charts import Bar
from pyecharts_snapshot.main import make_a_snapshot #for creating output in forms of PNG and PDF
attr = ["V40", "V60", "V90", "S90", "S60", "XC40", "XC60", "XC90", "Polestar1"]
v1 = [500, 200, 360, 1000, 750, 300, 600, 900, 100]
v2 = [300, 400, 160, 400, 1250, 100, 1000, 1800, 200]
bar = Bar({"width": "900px"})
bar.add("Dealer A", attr, v1, is_stack=True)
bar.add("Dealer B", attr, v2, is_stack=True)
bar.render() # output html file in default file path where python lives in
make_a_snapshot('render.html', 'snapshot.png') # output png
make_a_snapshot('render.html', 'snapshot.pdf') # output PDF
from pyecharts.charts import Bar, Pie, Scatter, Gauge, HeatMap, Funnel, WordCloud, Line, Grid, Map, Liquid
import random
import os, sys
os.chdir(sys.path[0]) # 改变目录
author = 'damao'
"""柱状图"""
skill = ['Q', 'W', 'E', 'R']
var1 = [110, 60, 5, 200]
var2 = [90, 100, 300, 500]
bar = Bar("英雄联盟", "League of Legends")
bar.add("无极剑圣", skill, var1, s_more_utils=True)
bar.add("诡术妖姬", skill, var2, is_more_utils=True)
bar.show_config()
bar.render(r".\my_first_Bar.html")
"""饼图"""
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "鞋子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("各产品销售情况")
pie.add("", attr, v1, is_label_show=True)
pie.render(r".\my_first_Pie.html")
"""环形图"""
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "鞋子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图—圆环图示例", title_pos="center")
pie.add("",
attr,
v1,
radius=[40, 75],
label_text_color=None,
#导入柱状图-Bar
from pyecharts.charts import Bar
#设置行名
columns = [
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct",
"Nov", "Dec"
]
#设置数据
data1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
data2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
#设置柱状图的主标题与副标题
bar = Bar("柱状图", "一年的降水量与蒸发量")
#添加柱状图的数据及配置项
bar.add("降水量",
columns,
data1,
mark_line=["average"],
mark_point=["max", "min"])
bar.add("蒸发量",
columns,
data2,
mark_line=["average"],
mark_point=["max", "min"])
#生成本地文件（默认为.html文件）
bar.render()
# print(random.randrange(1,4))
# print(random.randint(1,3))
# print(random.uniform(1,2))
# print(random.sample(a,3))
# random.shuffle(a)
# print(a)
# print(time.time())
# print(time.localtime())
# print(time.localtime(time.time()))
# coding:utf-8
#导入读取Excel的库
import xlrd
#导入需要读取Excel表格的路径
data = xlrd.open_workbook(r'E:\test.xlsx')
table = data.sheets()[0]
y = ''
#将列的值存入字符串
y = table.col_values(2) #读取列的值
#导入pyechats库
# from pyecharts import Bar
from pyecharts.charts import Bar
import numpy as np
t = np.linspace(1, 296, len(y)) #等间隔取值
bar = Bar("文章阅读量展示") #主副标题
bar.add("博客文章阅读量折线图展示", t, y, is_more_utils=True) #标题
bar.show_config() #展示HTML源代码
bar.render(
r"C:/Users/ASUS/Desktop/txt1/bokezhexiantu.html") #保存到本地bokezhexiantu.html
# 多图表page
import pyecharts.options as opts
from pyecharts.charts import Bar
# 多page
from pyecharts.charts import Page,Bar,Funnel
page=Page()
bar=Bar("商品销量","各季度的商品销量")
bar.add("2018Q3",["轴承","弹簧","齿轮","导轨","丝杠"],[25,23,17,14,17])
bar.add("2018Q4",["轴承","弹簧","齿轮","导轨","丝杠"],[23,21,19,19,13])
page.add_chart(bar,name="bar")
funnel=Funnel("订单转化效率","今日用户的订单转化效率")
funnel.add("",["访问","搜索","点击","加购","订单"],[100.00,78.12,35.74,17.17,2.62])
page.add_chart(funnel,name="funnel")
page.render("page.html")
