def bar_base_with_custom_background_image():
obj_bar = Bar(
init_opts=opts.InitOpts(
bg_color={
"type":"pattern",
"image":JsCode("img"),
"repeat":"no-repeat"
}
)
)
obj_bar.add_xaxis(Faker.choose())
obj_bar.add_yaxis("A", Faker.values())
obj_bar.add_yaxis("B", Faker.values())
obj_bar.set_global_opts(
title_opts = opts.TitleOpts(
title = "Bar-背景图基本示例",
subtitle = "副标题",
# 设置标题为白色
title_textstyle_opts=opts.TextStyleOpts(color="white")
)
)
obj_bar.add_js_funcs(
"var img = new Image(); img.src = 'https://s2.ax1x.com/2019/07/08/ZsS0fK.jpg';"
)
return obj_bar
def show_dlt(self):
red_balls = []
blue_balls = []
with open(self.input_file, 'r') as f:
for i in range(Config.PAGES * Config.ITEMS_PER_PAGE):
one_line_data = f.readline().strip()
# print(one_line_data)
red_balls.extend([
int(one_line_data[15 + (2 * i):15 + (2 * (i + 1))])
for i in range(5)
])
blue_balls.append(int(one_line_data[-4:-2]))
blue_balls.append(int(one_line_data[-2:]))
red_counter = Counter(red_balls)
blue_counter = Counter(blue_balls)
# print(red_balls)
# print(blue_balls)
# print(red_counter)
# print(blue_counter)
# print(red_counter.most_common())
# print(blue_counter.most_common())
red_dict = {}
blue_dict = {}
for i in red_counter.most_common():
red_dict['{}'.format(i[0])] = i[1]
for j in blue_counter.most_common():
blue_dict['{}'.format(j[0])] = j[1]
print(red_dict)
print(blue_dict)
red_list = sorted(red_counter.most_common(),
key=lambda number: number[0])
blue_list = sorted(blue_counter.most_common(),
key=lambda number: number[0])
print(blue_list)
print(red_list)
# 红球图表添加
red_bar = Bar()
red_x = ['{}'.format(str(x[0])) for x in red_list]
red_y = ['{}'.format(str(x[1])) for x in red_list]
red_bar.add_xaxis(red_x)
red_bar.add_yaxis('红色球出现的次数', red_y)
# 篮球图表添加
blue_bar = Bar()
blue_x = ['{}'.format(str(x[0])) for x in blue_list]
blue_y = ['{}'.format(str(x[1])) for x in blue_list]
blue_bar.add_xaxis(blue_x)
blue_bar.add_yaxis('蓝色球出现的次数',
blue_y,
itemstyle_opts=opts.ItemStyleOpts(color='blue'))
page = Page(page_title='大乐透数据分析', interval=3)
page.add(red_bar)
page.add(blue_bar)
page.render(self.output_file)
def plot_data(condition,title):
#distinct删除重复值
table_names = pd.read_sql(
"select name from sqlite_master where type='table' order by name",engine).values
if 'sqlite_sequence' in table_names:
table_names = table_names[:-1]
all_tables = table_names
temp = None
n = 1
for table in all_tables[:100]:
sql=f"select distinct * from '{table[0]}' where trade_date>'20190101' and "+ condition
data=pd.read_sql(sql,engine)
print(f"第{n}/{len(all_tables[:100])}:{table[0]}已筛选！")
if temp is None:
temp = data
else:
temp = pd.concat([data, temp], axis=0)
n += 1
count_=temp.groupby('trade_date')['ts_code'].count()
# print(count_)
attr=count_.index.values.tolist()
v1=count_.values.tolist()
bar=Bar()
# # bar= BAR(title)
bar.add_xaxis(attr)
bar.add_yaxis('',v1)
# # bar.add('',attr,v1,is_splitline_show=False,is_datazoom_show=True,linewidth=2)
# bar.render()
bar.render(title+'.html')
def bar(self,df,legend,title,path):
'''
柱状图
:param df: 要操作的数据
:param legend: 图例
:param title: 报表标题
:param path: 保存路径
:return:
'''
tmp = df.value_counts()
x = list(tmp.index)
y = list(tmp)
bar = Bar()
bar.add_xaxis(x)
bar.add_yaxis(legend, y, category_gap="60%")
bar.set_series_opts(itemstyle_opts={
"normal": {
"color": utils.JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'
}, {
offset: 1,
color: 'rgba(0, 77, 167, 1)'
}], false)"""),
"barBorderRadius": [30, 30, 30, 30],
"shadowColor": 'rgb(0, 160, 221)',
}})
bar.set_global_opts(title_opts=opts.TitleOpts(title=title))
bar.render(path + '/' + title + '.html')
def main():
urls = [
"http://www.weather.com.cn/textFC/hz.shtml",
"http://www.weather.com.cn/textFC/hb.shtml",
"http://www.weather.com.cn/textFC/hn.shtml",
"http://www.weather.com.cn/textFC/hd.shtml",
"http://www.weather.com.cn/textFC/db.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)
ALL_DATA.sort(key=lambda data: data['max_temp'], reverse=True)
data = ALL_DATA[0:10]
print(data)
cities = list(map(lambda x: x['city'], data))
temp_max = list(map(lambda x: x['max_temp'], data))
chart = Bar()
chart.add_xaxis(cities)
chart.add_yaxis('气温', temp_max)
chart.render('temperature.html')
def main():
urls = [
'华北地区天气预报.html',
'东北地区天气预报.html',
'华东地区天气预报.html',
'华中地区天气预报.html',
'华南地区天气预报.html',
'西北地区天气预报.html',
'西南地区天气预报.html',
'港澳台地区天气预报.html',
]
for url in urls:
parse_page(url)
# 分析数据
# 根据最低气温进行排序
ALL_DATA.sort(key=lambda data: data['min_temp'])
# print(ALL_DATA)
data = ALL_DATA[0:10]
cities = map(lambda x: x['city'], data)
min_temp = map(lambda x: x['min_temp'], data)
bar = Bar()
bar.add_xaxis(list(cities))
bar.add_yaxis("今日中国最低温度", list(min_temp))
bar.render("mycharts_local.html")
def chart_by_one_ver(by_type, pro_id, item, title, L=None):
"""
生成项目特定版本下的不同分类的bug分布图表
:param by_type: 分类
:param pro_id: 项目标识
:param L: 资源类别名称、资源bug数量组成的列表
:param item: 图表图示
:param title: 图表title
:return: html格式的图表
"""
if L is None:
L = []
result = getdata.get_issue_by_one_ver(by_type, pro_id)
for v in result:
L.append(v)
xaxis_data = []
yaxis_data = []
else:
print('L的类型必须为None，请检查')
if len(L) != 0:
xaxis_data = [x[0] for x in L]
yaxis_data = [x[1] for x in L]
v_name = [x[2] for x in L][0]
title = ''.join([title, v_name, '版本'])
bar = Bar()
bar.add_xaxis(xaxis_data)
bar.add_yaxis(item, yaxis_data)
bar.set_global_opts(title_opts=opts.TitleOpts(title=title))
if os.path.exists(os.path.join(chart_path, '{}.html'.format(title))):
os.remove(os.path.join(chart_path, '{}.html'.format(title)))
bar.render(os.path.join(chart_path, '{}.html'.format(title)))
else:
print('没有数据可展示')
def create_avg_likes(df):
# 筛选
df = df[df['videos'] > 0]
# 计算单个视频平均点赞数
df.eval('result = likes/(videos*10000)', inplace=True)
df['result'] = df['result'].round(decimals=1)
df = df.sort_values('result', ascending=False)
# 取TOP10
attr = df['name'][0:10]
v1 = ['%.1f' % (float(i)) for i in df['result'][0:10]]
# 初始化配置
bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
# 添加数据
bar.add_xaxis(list(reversed(attr.tolist())))
bar.add_yaxis("", list(reversed(v1)))
# 设置全局配置项，标题、工具箱(下载图片)、y轴分割线
bar.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V平均视频点赞数TOP10(万)",
pos_left="center",
pos_top="18"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)))
# 设置系列配置项
bar.set_series_opts(label_opts=opts.LabelOpts(
is_show=True, position="right", color="black"))
# 翻转xy轴
bar.reversal_axis()
bar.render("抖音大V平均视频点赞数TOP10(万).html")
def create_abroad(df):
# 筛选数据
df = df[(df["country"] != "中国") & (df["country"] != "") &
(df["country"] != "暂不设置") & (df["country"] != "China")]
df1 = df.copy()
# 数据替换
df1["country"] = df1["country"].str.replace("United States",
"美国").replace("大韩民国", "韩国")
# 分组计数
df_num = df1.groupby("country")["country"].agg(
count="count").reset_index().sort_values(by="count", ascending=False)
df_country = df_num[:8]["country"].values.tolist()
df_count = df_num[:8]["count"].values.tolist()
# 初始化配置
bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
bar.add_xaxis(df_country)
bar.add_yaxis("", df_count)
bar.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V国外分布TOP10",
pos_left="center",
pos_top="18"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)))
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=True, position="top", color="black"))
bar.render("抖音大V国外分布TOP10.html")
def show(x: list, y: list, title: str = "键盘键入统计"):
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.WONDERLAND))
bar.add_xaxis(x)
bar.add_yaxis("", y, category_gap="60%")
bar.set_series_opts(
itemstyle_opts={
"normal": {
"color":
JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'
}, {
offset: 1,
color: 'rgba(250, 177, 160, 1)'
}], false)"""),
"barBorderRadius": [30, 30, 30, 30],
"shadowColor":
"rgb(0, 160, 221)",
}
})
bar.set_global_opts(
title_opts=opts.TitleOpts(title=title),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")],
)
bar.load_javascript()
bar.render_notebook()
return Markup(bar.render_embed())
def city_company_scale_chart(self):
"""前10城市的企业阶段"""
city_company_scale = pd.crosstab(self.frame.city,
self.frame.company_scale,
margins=True).sort_values(
by='All', ascending=False)[:10]
city_company_scale = city_company_scale.drop('All',
axis=0).drop('All',
axis=1)
html_path = os.path.join(
create_or_get_directory(os.path.join('report', 'single')),
'city_company_scale_chart.html')
bar = Bar(init_opts=opts.InitOpts(width="60%"))
bar.add_xaxis(list(city_company_scale.index))
for i in range(len(list(city_company_scale.T.index))):
bar.add_yaxis(city_company_scale.T.index[i],
[str(v) for v in city_company_scale.T.values[i]])
bar.set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-20, color='red')),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
color='red')),
)
bar.render(html_path)
script_string = self.get_javascript_string(html_path)
self.set_report_javascript(script_string, 'city_company_scale_chart')
self.right_charts.append({
'title': '前10城市的企业阶段',
'name': 'city_company_scale_chart'
})
def generate_bar(self, x_axis, y_axis):
bar = Bar(init_opts=opts.InitOpts(width="1280px", height="720px"))
bar.add_xaxis(x_axis)
bar.add_yaxis("", y_axis)
bar.set_global_opts(xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=10)))
bar.render('bar.html')
def get_plot_base(self, _df, title_name='数据分布', subtitle_name=''):
'''
plot line & bar info
:param _df:
:return:
'''
bar = Bar()
line = Line()
bar.set_global_opts(
title_opts=opts.TitleOpts(title=title_name,
subtitle=subtitle_name),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}")),
datazoom_opts=opts.DataZoomOpts(), # 滑动条
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=-15)), # label旋转
toolbox_opts=opts.ToolboxOpts(),
)
xaxis = list(map(str, _df.index))
bar.add_xaxis(xaxis)
line.add_xaxis(xaxis)
for field in _df.columns:
field = str(field)
bar.add_yaxis(series_name=field,
yaxis_data=_df[field].to_list(),
is_selected=True)
line.add_yaxis(series_name=field + self.series_name_add,
y_axis=_df[field].to_list(),
is_symbol_show=False)
bar.overlap(line)
return bar
def generte_bar_chart(title: str,
xaxis_caption: str,
range_groups: List[IntegerRangeCounterGroup],
bar_gap: int = 4) -> Bar:
chart = Bar()
chart.set_global_opts(
title_opts=opts.TitleOpts(title=title),
yaxis_opts=opts.AxisOpts(
name='Samples',
name_textstyle_opts=opts.global_options.TextStyleOpts(
font_size=16)),
xaxis_opts=opts.AxisOpts(
name=xaxis_caption,
name_textstyle_opts=opts.global_options.TextStyleOpts(
font_size=16)))
# Data (y axis)
for range_group in range_groups:
# Add values on the yaxis for each file type.
# If the count is 0, replace it by None to avoid overlapped numbers in the chart.
chart.add_yaxis(range_group.file_type,
range_group.counts,
category_gap=bar_gap,
stack="stack1")
# Captions (x axis)
chart.add_xaxis(range_groups[0].captions)
chart.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return chart
def bar(x, y):
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))
bar.add_xaxis(x)
bar.add_yaxis("盈利", y)
bar.set_global_opts(title_opts=opts.TitleOpts(title="380平台月盈利"))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.render('../graph/4.1平台月盈利.html')
def create_likes(df):
# 排序，降序
df = df.sort_values('likes', ascending=False)
# 获取TOP10的数据
attr = df['name'][0:10]
v1 = [float('%.1f' % (float(i) / 100000000)) for i in df['likes'][0:10]]
# 初始化配置
bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
# x轴数据
bar.add_xaxis(list(reversed(attr.tolist())))
# y轴数据
bar.add_yaxis("", list(reversed(v1)))
# 设置全局配置项，标题、工具箱(下载图片)、y轴分割线
bar.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V点赞数TOP10(亿)",
pos_left="center",
pos_top="18"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)))
# 设置系列配置项，标签样式
bar.set_series_opts(label_opts=opts.LabelOpts(
is_show=True, position="right", color="black"))
bar.reversal_axis()
bar.render("抖音大V点赞数TOP10(亿).html")
def testBar(self):
bar = Bar()
bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
bar.set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况"))
bar.render()
def create_cut_likes(df):
# 将数据分段
Bins = [
0, 1000000, 5000000, 10000000, 25000000, 50000000, 100000000,
5000000000
]
Labels = [
'0-100', '100-500', '500-1000', '1000-2500', '2500-5000', '5000-10000',
'10000以上'
]
len_stage = pd.cut(df['likes'], bins=Bins,
labels=Labels).value_counts().sort_index()
# 获取数据
attr = len_stage.index.tolist()
v1 = len_stage.values.tolist()
# 生成柱状图
bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
bar.add_xaxis(attr)
bar.add_yaxis("", v1)
bar.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V点赞数分布情况(万)",
pos_left="center",
pos_top="18"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)))
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=True, position="top", color="black"))
bar.render("抖音大V点赞数分布情况(万).html")
def __plot_con_notebook(self, split_n)->pyecharts.charts.Bar:
"""
调用echarts,连续变量
:param split_n:
:return:
"""
data = self.data[~np.isnan(self.X)]
data.sort_values('X',inplace=True)
X = data['X'].values
y = data['y'].values
max_x = max(X)
min_x = min(X)
freq,edge = np.histogram(X, np.linspace(min_x, max_x, split_n+1))
freq = np.round(freq/data.shape[0] *100,2)
# print(freq)
# print(edge)
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))
bar.load_javascript()
bar.add_xaxis([('（' + str(int(edge[i])) + ' , ' + str(int((edge[i + 1]))) + ']') if i else (
'[' + str(int(edge[i])) + ' , ' + str(int((edge[i + 1]))) + ']') for i in range(len(edge) - 1)])
bar.add_yaxis(self.var_name, freq.tolist(), category_gap=1, )
bar.set_global_opts(title_opts=opts.TitleOpts(title=self.var_name),
datazoom_opts=opts.DataZoomOpts(is_show=True),
xaxis_opts=opts.AxisOpts(name_location='end', name='Groups'),
yaxis_opts=opts.AxisOpts(name_location='end', name='%'),
)
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
return bar
def draw_amount_bar(self):
"""
画出股票的成交量和
需要传入的数据是股票的["amount"
:return:
"""
# SC = "000021.SZ"
Df_s1 = Read_One_Stock(self.SC).select_col("vol", "amount")
length = Df_s1.shape[0]
Df_s1.sort_values("trade_date", inplace=True)
Df_s1.index = list(range(length))
amount = np.array(Df_s1["amount"]).tolist()
date = np.array(Df_s1["trade_date"], dtype=np.string_).tolist()
bar = Bar()
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title="K-Line of {}".format(self.SC)))
bar.add_xaxis(date)
bar.add_yaxis("Amounts",
amount,
label_opts=opts.LabelOpts(is_show=False))
bar.render("./Plots/{} Amount Bar Plot.html".format(self.SC))
def main():
regions = ['hb', 'db', 'hd', 'hz', 'hn', 'xb', 'xn', 'gat'] # gat 的源码不规范
for index, region in enumerate(regions):
# 获取url
url = URL.format(region)
html = get_url(url)
# 规则网页
parse_html(html)
# 数据可视化（数据分析）
# 根据最低气温排序
ALL_DATA.sort(key=lambda data: data['min_temp'])
data = ALL_DATA[:10] # 取前十个
# 从列表元素（字典），获取值。
# 方法1、遍历列表，获取列表的字典的值，再把这些值放入新列表；方法2、map
# 全部城市
citys = list(map(lambda x: x['city'], data))
# 全部天气
min_temps = list(map(lambda x: x['min_temp'], data))
# pyechars
bar = Bar() # 初始化
bar.add_xaxis(citys)
bar.add_yaxis("城市", min_temps)
# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件
# 也可以传入路径参数，如 bar.render("mycharts.html")
bar.render("mycharts.html")
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)
# 分析数据
# 根据最低气温进行排序
ALL_DATA.sort(key=lambda data: data['min_temp'])
# print(ALL_DATA)
data = ALL_DATA[0:10]
cities = map(lambda x: x['city'], data)
min_temp = map(lambda x: x['min_temp'], data)
bar = Bar()
bar.add_xaxis(list(cities))
bar.add_yaxis("今日中国最低温度", list(min_temp))
bar.render("mycharts.html")
def timeline_bar() -> Timeline:
allBars = []
# 共有 12 个Bar
for (months, index) in zip(yearMonth, range(0, 11)):
bar = Bar()
bar.set_global_opts(title_opts=opts.TitleOpts("实验二-每月的购买力活跃程度"))
bar.add_xaxis(['municipal'])
for z in zip(list(np.array(rowData)[:, 0]),
list(np.array(rowData)[:, index + 1]),
list(np.array(rowBrand)[:, index + 1])):
bar.add_yaxis(
series_name=z[0] + z[2], yaxis_data=[z[1]]).set_global_opts(
title_opts=opts.TitleOpts("实验四-每月的销售最高Brand-city"),
legend_opts=opts.LegendOpts(
type_='scroll',
pos_left='50%',
))
allBars.append(bar)
# 将allBars 与时间轴绑定
tl = (Timeline().add_schema(play_interval=2000, is_auto_play=True))
# bug：为什么后面的 bar 的x轴坐标会覆盖掉前面的bar呢
for (itemTime, itemBar) in zip(yearMonth, allBars):
tl.add(itemBar, itemTime)
return tl
def gzu_major_satisfied():
data1 = analysis_data.gzu_satisfied()
data1 = data1.iloc[:, :5]
print(data1)
type_sas = {
'综合满意度': 'all',
'办学条件满意度': 'facility',
'教学质量满意度': 'teaching',
'就业满意度': 'job'
}
c = Bar()
for a in type_sas:
c.add_xaxis([str(x) for x in data1['major'].values])
c.add_yaxis(a, [x for x in data1[type_sas[a]].values])
c.set_global_opts(
yaxis_opts=opts.AxisOpts(min_=3),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(font_size=10, interval=0)),
datazoom_opts=opts.DataZoomOpts())
c.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
]),
)
return c
def create_result_chart(data):
instrulist = list(set(d.instrument for d in data))
# x = ["{} P{}".format(localtime(d.upload_time).strftime("%Y-%m-%d %X"), d.platenum) for d in data]
x = ["{} P{}".format(d.upload_time.strftime("%Y-%m-%d %X"), d.platenum) for d in data]
# 均值直方图
b = Bar(init_opts=opts.InitOpts(width='1900px'))
b.add_xaxis(x)
for instru in instrulist:
mean = get_mean(data, instru)
b.add_yaxis(instru, mean)
b.set_global_opts(title_opts=opts.TitleOpts(title="结果mean图"))
# sd折线图
l = Line(init_opts=opts.InitOpts(width='1900px'))
l.add_xaxis(x)
for instru in instrulist:
sd = get_sd(data, instru)
l.add_yaxis(instru, sd, is_connect_nones=True)
l.set_global_opts(title_opts=opts.TitleOpts(title="结果sd图", pos_top='48%'))
grid = Grid(init_opts=opts.InitOpts(width='1900px'))
grid.add(b, grid_opts=opts.GridOpts(pos_bottom='60%'))
grid.add(l, grid_opts=opts.GridOpts(pos_top='60%'))
return grid
def paint_bar(self,
x: list,
collects: list,
title: str,
mark_point: bool = False):
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
bar.add_xaxis(x)
for collect in collects:
for i, (name, unit, data) in enumerate(collect):
bar.add_yaxis(f'{name}-单位：{unit}', data, yaxis_index=i)
if i != 0:
bar.extend_axis(yaxis=opts.AxisOpts(
name='',
type_='value',
position='right',
))
bar.set_global_opts(title_opts=opts.TitleOpts(title=title,
pos_left='5%'),
legend_opts=opts.LegendOpts(pos_bottom='0'))
bar.set_series_opts(
label_opts=opts.LabelOpts(position='top'),
tooltip_opts=opts.TooltipOpts(formatter=f'{{b}}年{{a}}：{{c}}'))
if mark_point:
bar.set_series_opts(
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
],
symbol_size=80))
return bar
def theme_macarons(make_new, tag):
tl = Timeline(init_opts=opts.InitOpts(
theme=ThemeType.MACARONS)) # theme=ThemeType.PURPLE_PASSION
tl.add_schema(
pos_bottom='-7px',
is_auto_play=True,
symbol_size=[20, 20],
play_interval=2000,
)
for i in range(2015, 2020):
bar = Bar()
bar.add_xaxis(list(make_new.columns))
bar.add_yaxis("",
list(make_new.loc[i, :].values),
label_opts=opts.LabelOpts(is_show=False))
bar.set_colors([
random.choice([
'blue', 'rgba(100,255,0,0.8)', '#5793f3', '#007A99', 'yellow'
])
])
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(name="", axislabel_opts={"rotate": 40}),
title_opts=opts.TitleOpts("{}-{}指数".format(i, tag)))
tl.add(bar, "{}年".format(str(i)))
return tl
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 根据最低气温排序
ALL_DATA.sort(key=lambda data: data["min_temp"]) # 通过key指定根据什么排序
# data 是ALL_DATA列表中每一行的字典数据, 根据data["min_temp"]返回值排序
data = ALL_DATA[0:10] # 最低气温排前十的城市/区
cities = list(map(lambda x: x['city'], data))
temps = list(map(lambda x: x['min_temp'], data))
chart = Bar()
# 给这个图取名
#chart.add(cities,temps,is_more_utils=True)
chart.add_xaxis(cities)
chart.add_yaxis("最低气温表", temps)
chart.render('temperature.html')
def plot_echart(df, type='Bar', title=None, theme='dark'):
'''
数据可视化
:param df: 数据表
:param type: 图像类型
:param title: 图像标题
:return: chart
'''
label_x = df.columns[0]
x = list(df[label_x])
label_y = df.columns[-1]
y = list(df[label_y])
chart = Bar()
if type == 'Bar': # 绘制柱形图
chart = Bar(opt.InitOpts(theme=theme))
x = list(df[label_x]) # x轴
y = list(df[df.columns[-1]]) # y轴
chart.add_xaxis(x)
chart.add_yaxis(label_y, y)
elif type == 'Pie': # 绘制饼图
x = df[label_x]
chart = (Pie().add(series_name=label_y,
data_pair=list(zip(list(x), y))))
# 图像设置
chart.set_global_opts(title_opts=opt.TitleOpts(title=title),
toolbox_opts=opt.ToolboxOpts(is_show=True),
legend_opts=opt.LegendOpts(pos_bottom=1))
return chart
def bar_histogram():
x = Faker.dogs + Faker.animal
xlen = len(x)
y = []
for idx, item in enumerate(x):
if idx <= xlen / 2:
bar_item = opts.BarItem(
name=item,
value=(idx + 1) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#749f83")
)
else:
bar_item = opts.BarItem(
name=item,
value=(xlen + 1 - idx) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#d48265"),
)
y.append(bar_item)
obj_bar = Bar()
obj_bar.add_xaxis(x)
#直方图就是紧挨着的系列柱category_gap=0调节系列柱之间距离，
# color=Faker.rand_color()因为上面设置了柱的颜色，这里只对图例legend的颜色起作用
obj_bar.add_yaxis("series0", y, category_gap=0,color=Faker.rand_color())
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-直方图（颜色区分）", subtitle="副标题")
)
return obj_bar
