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 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 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 2115 并不准确
if ((user.raw)['ContactFlag'] in [2051]):
sticky_on_top_friends += 1
# 陌生人 仍然是不准确的
if ((user.raw)['ContactFlag'] in [73731]):
stranger_friends += 1
bar = Bar()
bar.add_xaxis(['星标', '不让他看我朋友圈', '不看他朋友圈', '消息置顶', '陌生人'])
bar.add_yaxis('特殊好友分析', [
star_friends, hide_my_post_friends, hide_his_post_friends,
sticky_on_top_friends, stranger_friends
])
bar.render('data/特殊好友分析.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
# 根据最低气温排序
ALL_DATA.sort(key=lambda data:data['min_temp'])
print(ALL_DATA)
data = ALL_DATA[0:10]
# 取名字
cities = list(map(lambda x:x['city'],data))
# 取气温
temps = list(map(lambda x:x['min_temp'],data))
print(cities,temps)
chart = Bar()
chart.add_xaxis(cities)
chart.add_yaxis('',temps)
chart.render('./temperature.html')
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 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 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 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 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 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 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 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 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 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 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 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 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 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 make_bar_rating(filename):
# 定义各个文件名
# title = filename.split('/')[-1].split('.')[0]
title = os.path.basename(filename).split('.')[0]
target_dir = os.path.join(
os.path.abspath(os.path.dirname(os.path.dirname(__file__))),
"analyse_data", "bar_echarts_pic")
if not os.path.exists(target_dir):
os.mkdir(target_dir)
print("分析结果保存在 ", target_dir, " 文件夹下...")
htmlName = title + '.html'
pngName = title + '.png'
htmlPath = os.path.join(target_dir, htmlName)
pngPath = os.path.join(target_dir, pngName)
# 导入数据，并删除无关列
rows = pd.read_csv(filename, encoding='utf-8', dtype=str)
to_drop = ['用户', '是否看过', '评论时间', '有用数', '评论']
rows.drop(to_drop, axis=1, inplace=True)
# 数据清洗
rows = rows[rows['评分'].isin(['力荐', '推荐', '还行', '较差', '很差'])]
# 数据统计
result_rating = rows.groupby('评分')['评分'].count()
# 转换类型 pd.Series -> py.dict
values = {i: v for i, v in result_rating.items()}
# pyecharts 生成柱状图
bar = Bar()
bar.add_xaxis(['力荐', '推荐', '还行', '较差', '很差'])
bar.add_yaxis(
title,
[values['力荐'], values['推荐'], values['还行'], values['较差'], values['很差']])
bar.render(htmlPath)
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 test():
bar = Bar()
bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
# 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('charts/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)
# 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 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 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 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 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 pye_bar(x_data, y_data, ylabel):
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.CHALK))
bar.add_xaxis(x_data)
bar.add_yaxis(ylabel, y_data)
# 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()
def drawbar(arrt, value, name):
# 图表初始化配置
init_opts = opts.InitOpts(page_title=name, height="700px")
bar = Bar(init_opts=init_opts)
# 标题配置
title = opts.TitleOpts(title=name, pos_left='center')
# 图例配置
legend_opts = opts.LegendOpts(pos_top="5%", pos_left="15%")
# 工具箱配置
# feature = opts.ToolBoxFeatureOpts(save_as_image=True, restore=True, data_view=True, data_zoom=True)
# 工具箱配置
toolbox_opts = opts.ToolboxOpts(pos_top="5%", pos_right="30%")
bar.set_global_opts(
title_opts=title,
legend_opts=legend_opts,
toolbox_opts=toolbox_opts,
# 区域缩放配置项
datazoom_opts=opts.DataZoomOpts(),
)
# add_yaxis
bar.add_xaxis(arrt)
# 渐变色
bar.add_yaxis(
"",
value,
gap="0%",
category_gap="30%",
# 自定义颜色
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 1, 0,
[{
offset: 0,
color: 'rgb(39, 117, 182)'
},
{
offset: 0.5,
color: 'rgb(147, 181,207)'
},
{
offset: 1,
color: 'rgb(35, 118, 183)'
}], false)""")),
)
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值"),
opts.MarkLineItem(type_="average", name="平均值"),
]),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值"),
]),
)
bar.render('{0}.html'.format(name))
def test_bar_colors(fake_writer):
c = Bar().add_xaxis(["A", "B", "C"]).add_yaxis("series0", [1, 2, 4])
c.set_colors(["#AABBCC", "#BBCCDD", "#CCDDEE"] + c.colors)
c.render()
_, content = fake_writer.call_args[0]
assert_in("#AABBCC", content)
assert_in("#BBCCDD", content)
assert_in("#CCDDEE", content)
