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 bar_multiple(x: list,
y: list,
name: list,
title='主标题',
subtitle='副标题') -> Bar:
"""
:param x:
:param y:
:param name:
:param title:
:param subtitle:
:return:
### doctest
>>> x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
>>> y = [[5, 20, 36, 10, 75, 90],[10, 20, 30, 40, 50, 60]]
>>> name = ['商家A', '商家B']
>>> title='主标题'
>>> subtitle='副标题'
>>> test = bar_multiple(x, y, name, title, subtitle)
>>> hasattr(test, 'render')
True
"""
if len(name) != len(y):
raise ValueError("the paramter 'name' and 'y' must be same lenth ")
bar_ = Bar()
bar_.add_xaxis(x)
try:
[bar_.add_yaxis(name[i], y[i]) for i in range(len(name))]
except:
raise
bar_.set_global_opts(
title_opts=opts.TitleOpts(title=title, subtitle=subtitle))
return bar_
def analysis_score(self):
evaluate = {}
with open(self.score_file, 'r', encoding='utf-8') as f:
for i in f.readlines():
i = re.sub(r"[A-Za-z0-9\-\:]", "", i)
i = i.strip() # 去掉每行的换行符
# 如果这个没出现过，就初始化为1
if i not in evaluate:
evaluate[i] = 1
logger.logger.debug(evaluate)
else:
# 如果已经出现过了，就在自加1
evaluate[i] += 1
logger.logger.debug(evaluate)
logger.logger.info(evaluate)
bar = Bar()
eva = []
count = []
for k, v in evaluate.items():
if k != '':
eva.append(k)
count.append(v)
bar.add_xaxis(eva) # 柱状图x轴
logger.logger.info('xaxis'+str(eva))
bar.add_yaxis("评价", count) # 柱状图y轴
logger.logger.info('yaxis'+str(count))
bar.set_global_opts(title_opts=opts.TitleOpts(title="隐秘的角落 豆瓣评分"))
# 生成可视化图表
bar.render(self.path+"\\score.html")
def do_charts():
nvshen = pd.read_csv('nvshen.csv')
nvshen.sort_values('weight_score', ascending=False, inplace=True)
bar = Bar()
count_top = nvshen['count'][0:10].values.tolist()
count_bottom = nvshen['count'][-10:-1].values.tolist()
count = [''.join(list(filter(str.isdigit, i))) for i in count_top] + \
[''.join(list(filter(str.isdigit, i))) for i in count_bottom]
name_top = nvshen['name'][0:10]
name_bottom = nvshen['name'][-10:-1]
name = name_top.values.tolist() + name_bottom.values.tolist()
score_top = nvshen["weight_score"][0:10]
score_bottom = nvshen["weight_score"][-10:-1]
score = score_top.values.tolist() + score_bottom.values.tolist()
bar.add_xaxis(name)
bar.add_yaxis("女神得分/百分制", score, gap="0%")
bar.add_yaxis("打分人数/万", count, gap="0%")
bar.set_global_opts(
title_opts=opts.TitleOpts(title="女神大会", subtitle="女神大排名-top10"),
datazoom_opts=opts.DataZoomOpts(is_show=True, orient="vertical"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)),
toolbox_opts=opts.ToolboxOpts())
bar.render('女神大排名-top10.html')
word_name = nvshen['name'].values.tolist()
word_value = nvshen["weight_score"].values.tolist()
words = [i for i in zip(word_name, word_value)]
wordcloud = WordCloud()
wordcloud.add("", words, word_size_range=[10, 40], shape='circle')
wordcloud.render("女神词云.html")
def court_pri(keys, values1):
dict = {}
for result in results:
if (result["court"] in keys) and result["court"] not in dict.keys():
dict[result["court"]] = [result["unit_Price"]]
elif (result["court"] in keys) and result["court"] in dict.keys():
dict[result["court"]].append(result["unit_Price"])
keys1 = list(dict.keys())
values = list(dict.values())
list1 = []
for i in range(len(values)):
arr_mean = np.mean(values[i])
list1.append(int(arr_mean))
# print(values)
# print(keys)
# print("长度" + str(len(keys)))
# print(list1)
# print("长度" + str(len(list1)))
b = Bar(init_opts=opts.InitOpts(width="900px", height="900px"))
b.add_xaxis(keys1)
b.add_yaxis("小区价格", list1)
# b.add_yaxis("小区房源",values1)
b.set_global_opts(title_opts=opts.TitleOpts(title="新乡市",
subtitle="各小区平均价格"),
datazoom_opts=opts.DataZoomOpts(),
toolbox_opts=opts.ToolboxOpts())
return b
def weather_show(path):
# 定义时间线标题
title = ["最高气温", "昼夜温差", "降水量"]
# 定义查询字段
field = ["high_temp", "differ_temp", "precipitation"]
# 定义标记单位
remarks = ["温度(℃)", "温度(℃)", "降水量(mm)"]
# 定义时间线对象
tl = Timeline()
for i in range(3):
# 查询数据
name, value = WeatherTrans().get_data(field[i], field[i])
# 统计表标题
bar_title = "2020年3月15日全国" + title[i] + "Top 10 城市"
# 定义柱状图对象
bar = Bar()
# 添加横坐标
bar.add_xaxis(name)
# 添加纵坐标
bar.add_yaxis(remarks[i],
value,
label_opts=opts.LabelOpts(position="right"))
# 绘制横向统计图
bar.reversal_axis()
# 添加标题
bar.set_global_opts(title_opts=opts.TitleOpts(bar_title))
# 将统计图添加到时间线中
tl.add(bar, title[i])
# 生成html文件
tl.render(path)
def draw_bar(sum_sheet):
prov = sum_sheet[0].col_values(0, start_rowx=1, end_rowx=31)
line = Bar()
x1 = []
y1 = []
y2 = []
for i in range(19):
x1.append(1997 + i)
y1.append(sum_sheet[i].cell_value(1, 1))
y2.append(sum_sheet[i].cell_value(9, 1))
line.add_xaxis(x1)
line.set_global_opts(xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(font_size=10, interval=0, rotate=45)))
line.add_yaxis("Beijing", y1)
line.add_yaxis("Shanghai", y2)
line.render("北京上海年CO2排放量变化图.html")
bar = Bar()
x2 = []
for i in range(30):
x2.append(prov[i])
y3 = sum_sheet[0].col_values(1, start_rowx=1, end_rowx=31)
y4 = sum_sheet[-1].col_values(1, start_rowx=1, end_rowx=31)
bar.add_xaxis(x2)
bar.set_global_opts(xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(font_size=10, interval=0, rotate=45)))
bar.add_yaxis("1997", y3)
bar.add_yaxis("2015", y4)
bar.render("1997与2015 各省CO2年排放量对比.html")
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 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 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 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_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 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 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 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 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_grid_bar_and_bar(title, x_list, data_list, data_list_2, data_unit_list):
# 1.先生成上方的柱状图
bar1 = Bar()
bar1.add_xaxis(x_list)
for key in data_list.keys():
bar1.add_yaxis(key, data_list[key])
bar1.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="25%", pos_top="5%"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit_list[0]
, is_scale=True))
# 2.下方柱状图
bar2 = Bar()
bar2.add_xaxis(x_list)
for key in data_list_2.keys():
bar2.add_yaxis(key, data_list_2[key])
bar2.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="25%", pos_top="55%"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit_list[1],
is_scale=True))
# 3.生成最终的组合图
c = (
Grid(init_opts=opts.InitOpts(theme=THEME_TYPE,height="700px"))
.add(bar1, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(bar2, grid_opts=opts.GridOpts(pos_top="60%"))
)
src_path = "./指标-年度-图片生成/"
html_file_name = src_path + title + ".html"
img_file_name = src_path + title + ".png"
make_snapshot(snapshot, c.render(html_file_name), img_file_name)
print(img_file_name+"生成完毕...")
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 relation_count():
"""
统计每种关系的次数
数据来源ref：https://github.com/DesertsX/gulius-projects/blob/master/3_InteractiveGraph_HongLouMeng/InteractiveGraph_HongLouMeng.json
:return:
"""
with codecs.open('relative.json', 'r', encoding='utf-8') as json_str:
json_dict = json.load(json_str)
edges = json_dict['data']['edges']
edges_df = pd.DataFrame(edges)
edges_rela = edges_df.label.value_counts()
# 所有关系
relations = edges_df.label
relations = relations.tolist()
relations = list(set(relations))
# 每种关系对应的频数
rela_counts = []
for each in relations:
rela_counts.append(int(edges_rela[each]))
# 柱状图
bar = Bar()
bar.add_xaxis(relations)
bar.add_yaxis('频次', rela_counts)
bar.set_global_opts(title_opts=opts.TitleOpts(title="红楼梦关系频次表"))
bar.render('relation_count.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 draw_balance_bar(xaxis, yaxis, title="消费统计", markline=None, width=2000) -> Bar:
"""
x = [月_日, 月_日, 月_日, ....]
y = [(title1, [num1, num2, num3, num4, ...]), (title2, [num1, num2, num3, num4, ...])]
:param xaxis: x轴
:param yaxis: y轴
:param title: 标题
:param markline: 标记辅助线
:param width: 宽
:return: Bar
"""
bar = Bar()
bar.add_xaxis(xaxis)
for name, axis in yaxis:
bar.add_yaxis(name, axis, category_gap="20%", gap="0%")
bar.set_global_opts(title_opts=opts.TitleOpts(title=title, ),
datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside")],
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='shadow'))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
if markline is not None:
bar.set_series_opts(markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(
y=markline,
name='预算')
])
)
return bar
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 bar_datazoom_slider(title='Bar-DataZoom（slider-水平）',width="1200px",x_data=['前天','昨天','今天'], \
y_data = [{'name':'商家A','values':[1,6,7]}],
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[opts.DataZoomOpts()]
) -> Bar:
'''
柱状图
y_data 几根柱子，默认1根
y_data = [{'name':'商家A','values':[1,6,7]}]
toolbox_opts 是否展示右上方的小工具，默认展示，不展示传 None
datazoom_opts 是否展示图表下方可拖动的进度条，默认展示，不展示传 None
'''
b = Bar(init_opts=opts.InitOpts(width=width))
if len(y_data) == 1:
b.add_yaxis(y_data[0]['name'], y_data[0]['values'])
if len(y_data) == 2:
b.add_yaxis(y_data[0]['name'], y_data[0]['values'])
b.add_yaxis(y_data[1]['name'], y_data[1]['values'])
b.add_xaxis(x_data)
b.set_global_opts(
title_opts=opts.TitleOpts(title=title),
datazoom_opts=datazoom_opts,
toolbox_opts=toolbox_opts,
)
return b
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 coincedence(place) -> Bar:
place = np.array(place)
foo = place[:, 1].astype(np.float)
order = np.argsort(foo)
k = 0
place1 = np.copy(place)
for i in order:
place1[k] = place[i, :]
k = k + 1
place = place1
bar = Bar()
bar.add_xaxis(list(place[:, 0]))
bar.add_yaxis("热度", list(place[:, 1]))
bar.set_global_opts(title_opts=opts.TitleOpts(title="外来旅游人口相关度"))
bar.set_global_opts(xaxis_opts=opts.AxisOpts(boundary_gap=1,
interval=0,
axislabel_opts={"rotate":
45}),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=[
opts.DataZoomOpts(range_start=0,
range_end=100,
is_zoom_lock=False)
])
#bar.set_global_opts(xaxis_opts=opts.AxisTickOpts(is_align_with_label=0))
bar.set_series_opts(label_opts=opts.LabelOpts(font_size=9))
bar.width = 1200
bar.render(path="外来旅游人口相关度.html")
return True
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 print_bar(self):
bar = Bar(init_opts=opts.InitOpts(width='1080px', height='480px'))
bar.add_xaxis(self.timedata)
# category 是同系列直接的距离，gap是不同系列之间的距离
bar.add_yaxis('治愈人数',
self.heal,
gap='100%',
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#90EE90'))
if self.name == '湖北':
bar.add_yaxis('死亡人数',
self.dead,
gap='100%',
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#696969'))
# 设置全局变量：x轴标签倾斜度，html主标题
bar.set_global_opts(datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_='inside')
],
toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(name_rotate=-15),
title_opts=opts.TitleOpts(self.name,
subtitle='数据来自inews'))
line = Line()
line.add_xaxis(self.timedata)
line.add_yaxis('确诊人数',
self.confirm,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#F08080'))
print(self.name + '.html', '已成功生成到', os.getcwd())
# 在bar的基础上画line
bar.overlap(line).render(self.name + '.html')
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 plt_feat_import(model_name, max_cols=9999, max_print=10):
from pyecharts.globals import CurrentConfig, NotebookType
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB
from pyecharts import options as opts
from pyecharts.charts import Bar
# 处理数据并排序
x_name = model_name.feature_name()
y_val = [int(x) for x in model_name.feature_importance()]
val_pairs = [x for x in zip(x_name, y_val)]
val_pairs.sort(key=lambda x: x[1])
# 设置图像属性
bar = Bar()
bar.add_xaxis([x[0] for x in val_pairs][-max_cols:])
bar.add_yaxis("特征重要度", [x[1] for x in val_pairs][-max_cols:])
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=True, position='right'))
bar.set_global_opts(
title_opts=opts.TitleOpts(title="特征重要度"),
datazoom_opts=opts.DataZoomOpts(),
)
bar.reversal_axis()
print(val_pairs[-max_print:][::-1])
return bar
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'
})
