def pie_set_colors():
obj_pie = Pie()
obj_pie.add("",[list(z) for z in zip(Faker.choose(), Faker.values())])
obj_pie.set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"])
obj_pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
obj_pie.set_global_opts(title_opts=opts.TitleOpts(title="Pie-设置颜色",subtitle ="副标题"))
return obj_pie
def show_pie_charts(df):
score_perc = df['star'].value_counts() / df['star'].value_counts().sum(
) # 评分占比 = 评分1-5的个数(分别) / 评分总个数
score_perc = np.round(score_perc * 100, 2)
score_perc = score_perc.sort_index() # Series也可以排序
# 聚合后类型为Series，转成Dataframe
# df_score = pd.DataFrame(list(zip(score_perc.index, score_perc.values)), columns=['score', 'percentage']).sort_values('score')
pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
pie1.add(
series_name='评分占比',
data_pair=[*zip(score_perc.index, score_perc.values)
], # Series: zip打包为一个元组对象，用*解压成元组，再[]转成列表
# data_pair=[*zip(df_score['score'], df_score['percentage'])], # Dataframe
label_opts=opts.LabelOpts(formatter='{c}%'), # formatter='{c}%'百分比
radius=['35%', '70%'] # 是什么意思
)
pie1.set_global_opts(
title_opts=opts.TitleOpts(title='总体评分分布'),
legend_opts=opts.LegendOpts(orient='vertical',
pos_top='15%',
pos_left='2%'), # 图例的位置：水平/垂直
toolbox_opts=opts.ToolboxOpts()) # 右上角的工具栏，保存图片等等
pie1.set_colors(['#D7655A', '#FFAF34', '#3B7BA9', '#EF9050', '#6FB27C'])
pie1.render('charts/pie_chart.html')
def show_pie(id):
attr = ["成功（含熔断）", "失败", "限流"]
counts = [len(sync.success), len(sync.fail), sync.limit]
c = Pie(init_opts=opts.InitOpts(
width='1200px', height='800px', page_title='page'))
c.add("", [list(attr) for attr in zip(attr, counts)])
c.set_colors(["green", "red", "blue"])
c.set_global_opts(title_opts=opts.TitleOpts(title="成功失败数量饼图"))
c.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
c.render('成功失败数量饼图{}.html'.format(id + 1))
def pieChart(df):
def randomcolor(kind):
colors = []
for i in range(kind):
#colArr = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F','G','H','I','K','L','M','N']
colArr = [
'1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C',
'D', 'E', 'F'
]
color = ''
for i in range(6):
color += colArr[random.randint(0, 14)]
colors.append('#' + color)
return colors
all_columns = df.columns
type_of_plot = st.selectbox("Select Type of Plot",
["PIE1", "PIE2", "PIE3"])
columns_to_plot = st.selectbox("Select 1 column", all_columns, key='a')
columns_to_plot1 = st.selectbox("Select 1 column", all_columns, key='b')
if st.button("Generate Plot"):
#st.success("Generating Customizable Plot of {} for {}".format(type_of_plot))
if type_of_plot == 'PIE1':
cje = df[columns_to_plot].tolist()
other_var = df[columns_to_plot1].tolist()
color_series = randomcolor(len(cje))
pie = Pie()
pie.add('', [list(z) for z in zip(cje, other_var)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
pie.set_global_opts(title_opts=opts.TitleOpts(
title='Population Generale des 20 CJE de Montreal'),
legend_opts=opts.LegendOpts(is_show=False),
toolbox_opts=opts.ToolboxOpts())
pie.set_series_opts(
label_opts=opts.LabelOpts(is_show=True,
position='inside',
font_size=12,
formatter='{b}:{c}',
font_style='italic',
font_weight='bold',
font_family='Microsoft YaHei'))
pie.set_colors(color_series)
return pie
def pyechart_Pie_plot(self, filedata, para):
from pyecharts.charts import Pie
file_name = '南丁格尔玫瑰图.html'
path_plotly = self.path_dir_plotly_html + os.sep + file_name # 文件路径，前面是文件夹后面是文件名
costumeTheme = self.themedict[para['theme']]
# -----------------------------------------------------------------------
# 准备数据
df = pd.read_excel(filedata, sheet_name='sheet1')
# # 提取数据
v = df['provinces'].values.tolist()
d = df['num'].values.tolist()
color_series = df['color_series'].values.tolist()
# 降序排序
df.sort_values(by='num', ascending=False, inplace=True)
# 实例化Pie类
pie1 = Pie(init_opts=opts.InitOpts(
theme=costumeTheme, width=para['width'], height=para['height']))
# 设置颜色
pie1.set_colors(color_series)
# 添加数据，设置饼图的半径，是否展示成南丁格尔图
pie1.add("", [list(z) for z in zip(v, d)],
radius=["10%", "135%"],
center=["50%", "65%"],
rosetype="area")
# 设置全局配置项
pie1.set_global_opts(
title_opts=opts.TitleOpts(title=para["title"],
subtitle=para["subtitle"]),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color="white")))
#visualmap_opts=opts.VisualMapOpts(),
)
# 设置系列配置项
pie1.set_series_opts(
label_opts=opts.LabelOpts(
is_show=True,
position="inside",
font_size=12,
formatter="{b}:{c}天",
font_style="normal", # css的格式
font_weight="normal",
font_family="宋体"), )
# 生成html文档
pie1.render(path_plotly)
return path_plotly # 返回该HTML文件路径
def draw(list1, list2):
pie = Pie()
pie.add("", list(zip(list1, list2)))
pie.set_global_opts(
title_opts=opts.TitleOpts(title="消费者购买的HUAWEIP30颜色图例"), #标题
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="4%"),
)
pie.set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}")) #图例显示（颜色+人数），例：亮黑色：46
pie.set_colors(
["lightblue", "black", "turquoise", "orangered", "lightpink"])
pie.render(desktop + "\\jd.huaweiP30.html")
return True
def show_pie_charts():
score_perc = df['rating'].value_counts() / df['rating'].value_counts().sum(
)
score_perc = np.round(score_perc * 100, 2)
print(score_perc)
pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
pie1.add("", [*zip(score_perc.index, score_perc.values)],
radius=["35%", "70%"])
pie1.set_global_opts(title_opts=opts.TitleOpts(title='总体评分分布'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"),
toolbox_opts=opts.ToolboxOpts())
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{c}%"))
pie1.set_colors(['#D7655A', '#FFAF34', '#3B7BA9', '#EF9050', '#6FB27C'])
pie1.render()
def pie_base(data1, data2) -> Pie:
list_color = ['#990033', '#990000', '#CC0066', '#CC0033', '#CC0000', '#FF0000', 'FF00FF', 'FF1493', '#FF4500',
'#FF6347']
pie = Pie()
pie.add(
series_name="",
data_pair=[list(z) for z in zip(data1, data2)],
radius=["20%", "75%"],
rosetype="area",
label_opts=opts.LabelOpts(is_show=False),
)
pie.set_colors(list_color)
pie.set_global_opts(
title_opts=opts.TitleOpts(title="", title_textstyle_opts=(opts.TextStyleOpts(color='white'))),
legend_opts=opts.LegendOpts(is_show=False))
return pie
def Cartography():
#创建地图
map = Map( init_opts=opts.InitOpts(width="1900px", height="900px", bg_color="#d0effa", page_title="全球新冠疫情_1"))
map.add("确诊人数",[list(z) for z in zip(names_new, confirm)],is_map_symbol_show=False,
maptype="world",label_opts=opts.LabelOpts(is_show=False),itemstyle_opts=opts.ItemStyleOpts(color="rgb(98,121,146)"))#地图区域颜色
map.set_global_opts(title_opts = opts.TitleOpts(title='全球新冠疫情确诊人数'),legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=2000000, is_piecewise=True,
pieces=[
{"max": 3000000,"min": 500001,"label":">500000","color":"#460303"},
{"max": 500000, "min": 100001, "label": "100001-500000", "color": "#8A0808"},
{"max": 100000, "min": 10001, "label": "10001-100000", "color": "#B40404"},
{"max": 10000, "min": 1001, "label": "1001-10000", "color": "#DF0101"},
{"max": 1000, "min": 101, "label": "101-1000", "color": "#F78181"},
{"max": 100, "min": 1, "label": "1-100", "color": "#F5A9A9"},
{"max": 0, "min": 0, "label": "0", "color": "#fababa"},
])
)
map.render('Global_new_crown_epidemic_map.html')
#创建饼图
pie = Pie(init_opts=opts.InitOpts(width='1900px', height='900px',page_title="全球新冠疫情_2",bg_color="#fee4e7"))
# 添加数据
pie.add("", [list(z) for z in zip(country_list, dead_list)],
radius=['20%', '100%'],#设置内径外径
center=['60%', '65%'],
rosetype='area')#圆心角相同，通过半径展现数据大小#rosetype='radius'圆心角展现数据百分比，半径展现数据大小
# 设置全局配置
pie.set_global_opts(title_opts=opts.TitleOpts(title='全球新冠疫情',subtitle='死亡人数超过\n5000的国家\n (除中国)',
title_textstyle_opts=opts.TextStyleOpts(font_size=15,color= '#f40909'),
subtitle_textstyle_opts= opts.TextStyleOpts(font_size=15,color= '#8a0b0b'),
pos_right= 'center',pos_left= '57%',pos_top= '60%',pos_bottom='center'),
legend_opts=opts.LegendOpts(is_show=False))
# 设置系列配置和颜色
pie.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=13,
formatter='{b}：{c}', font_style='italic',
font_family='Microsoft YaHei'))
pie.set_colors(color_matching)
pie.render('Global_new_crown_epidemic_Rose.html')
#创建雷达图
radar = Radar(init_opts=opts.InitOpts(width='1900px',height='900px',page_title="全球新冠疫情_3",bg_color="#d1eff3"))
#由于雷达图传入的数据得为多维数据，所以这里需要做一下处理
radar_data1 = [list(dead_list)]
radar_data2 = [list(heal_list)]
radar.add_schema(
schema=[
opts.RadarIndicatorItem(name='巴西', max_=8000),
opts.RadarIndicatorItem(name='荷兰', max_=8000),
opts.RadarIndicatorItem(name='伊朗', max_=20000),
opts.RadarIndicatorItem(name='德国', max_=40000),
opts.RadarIndicatorItem(name='比利时', max_=70000),
opts.RadarIndicatorItem(name='英国', max_=80000),
opts.RadarIndicatorItem(name='法国 ', max_=110000),
opts.RadarIndicatorItem(name='西班牙', max_=150000),
opts.RadarIndicatorItem(name='意大利', max_=170000),
opts.RadarIndicatorItem(name='美国', max_=220000),
]
)
radar.add("死亡人数",radar_data1,color='blue',areastyle_opts = opts.AreaStyleOpts(opacity = 0.2,color='blue'))
radar.add("治愈人数",radar_data2,color='red',areastyle_opts=opts.AreaStyleOpts(opacity=0.3,color='red'))
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
radar.set_global_opts(title_opts=opts.TitleOpts(title="死亡人数与治愈人数对比"))
radar.render("Death_Versus_Heal.html")
'#D44C2D', '#F57A34', '#FA8F2F', '#D99D21', '#CF7B25', '#CF7B25', '#CF7B25'
]
# Create data box
df = pd.DataFrame({'provinces': provinces, 'num': num})
# Descending sort
df.sort_values(by='num', ascending=False, inplace=True)
# Extract data
v = df['provinces'].values.tolist()
d = df['num'].values.tolist()
# Instantiate the Pie class
pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
# Set color
pie1.set_colors(color_series)
# Add data, set the radius of pie chart, and show it as Nightingale chart or not
pie1.add("", [list(z) for z in zip(v, d)],
radius=["30%", "135%"],
center=["50%", "65%"],
rosetype="area")
# Set global configuration item
pie1.set_global_opts(title_opts=opts.TitleOpts(title='Rose chart example'),
legend_opts=opts.LegendOpts(is_show=False),
toolbox_opts=opts.ToolboxOpts())
# Set series configuration items
pie1.set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
position="inside",
font_size=12,
formatter="{b}:{c}day",
bg_color='white'))
pie.add('', [list(z) for z in zip(provinces, numbers)],
radius=['30%', '115%'],
center=['40%', '50%'],
rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title='广东省2020年1月-6月最高负荷平方根（兆瓦）',
pos_left='25%',
pos_top='5%'),
legend_opts=opts.LegendOpts(is_show=False,
pos_top='12%',
pos_left='12%',
orient='vertical',
align='auto'))
# pie.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12, formatter='{b}:{c}', font_family='Microsoft YaHei'))
pie.set_series_opts(label_opts=opts.LabelOpts(is_show=False,
position='inside',
font_size=12,
font_family='Microsoft YaHei'))
pie.set_colors(colors)
# pie.render('广东省2020年1月-6月最高负荷平方根（radius）.html')
make_snapshot(snapshot,
pie.render(),
"广东省2020年1月-6月最高负荷平方根（radius）.png",
pixel_ratio=13,
is_remove_html=True)
# 绘制饼图可以清晰地看出各价格区间货品销售额在总体占比情况：
data_pair = [
list(z)
for z in zip(cut_purchase.index.tolist(), cut_purchase.values.tolist())
]
# 绘制饼图
pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
pie1.add('', data_pair, radius=['35%', '60%'])
pie1.set_global_opts(title_opts=opts.TitleOpts(title='不同价格区间的销售额整体表现'),
legend_opts=opts.LegendOpts(orient='vertical',
pos_top='15%',
pos_left='2%'))
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%"))
pie1.set_colors([
'#EF9050', '#3B7BA9', '#6FB27C', '#FFAF34', '#D8BFD8', '#00BFFF', '#7FFFAA'
])
pie1.render()
# 玩一个词云图
def get_cut_words(content_series):
# 读入停用词表
stop_words = []
with open(
r"C:/Users/Maggie/Desktop/Udemy-desktop/xiaoxiangxueyuan/Python认知打卡课课程资料包/资料代码整理/数据分析/解读乐高/stop_words.txt",
'r',
encoding='utf-8') as f:
lines = f.readlines()
for line in lines:
stop_words.append(line.strip())
def foregin_city_all():
path = readPath + '/各国各地区疫情信息'
path2 = savaPath + '/各国疫情严重程度排名前十地区信息'
dwf.createFile(path2)
Filelist = [] # 将当前文件夹内的所有表名存放此列表
for home, dirs, files in os.walk(path):
for filename in files:
Filelist.append(filename)
# 判断数据是否存在
def checknan(name):
if np.any(pd.isnull(name)) == True:
name.fillna(value="0", inplace=True)
# 随机颜色生成用于制作南丁格尔玫瑰图
def randomcolor(kind):
colors = []
for i in range(kind):
colArr = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F']
color = ""
for i in range(6):
color += colArr[random.randint(0, 14)]
colors.append("#" + color)
return colors
for i in Filelist:
page1 = Page()
data = pd.read_excel(readPath + '/各国各地区疫情信息/%s' % (i), index=False)
data_sort = data.sort_values(axis=0, ascending=False, by=['confirm'])
data_message = data_sort.head(10) # 提取疫情严重程度排名前十地区的信息
n = i[:-5] # 只提取国家名，不要后缀（.xlsx）
checknan(data_message['confirm'])
checknan(data_message['heal'])
checknan(data_message['dead'])
y1_confirm = data_message['confirm']
y1_confirm = list(y1_confirm)
y1_confirm = [int(i) for i in y1_confirm]
y2_dead = data_message['dead']
y2_dead = list(y2_dead)
y2_dead = [int(i) for i in y2_dead]
y3_heal = data_message['heal']
y3_heal = list(y3_heal)
y3_heal = [int(i) for i in y3_heal]
name_list = []
for j in data_message['name']:
name_list.append(j)
x = name_list
color_series = randomcolor(len(x))
# Bars = (
# Bar(init_opts=opts.InitOpts(width='1080px',height='700px'))
# .add_xaxis(xaxis_data=x)
# .add_yaxis(series_name='确诊人数', yaxis_data=y1_confirm)
# )
#####画南丁格尔玫瑰图##########
# 画出确诊人数的图
fig = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig.add("", [list(z) for z in zip(x, y1_confirm)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig.set_global_opts(title_opts=opts.TitleOpts(title=n + '疫情严重程度排名前十地区的确诊人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12,
formatter='{b}:{c}例', font_style='italic', font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
fig.set_colors(color_series)
# 画出死亡人数的图
fig1 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig1.add("", [list(z) for z in zip(x, y2_dead)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig1.set_global_opts(title_opts=opts.TitleOpts(title=n + '疫情严重程度排名前十地区的死亡人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig1.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12,
formatter='{b}:{c}例', font_style='italic', font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
# 画出治愈人数的图
fig2 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig2.add("", [list(z) for z in zip(x, y3_heal)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig2.set_global_opts(title_opts=opts.TitleOpts(title=n + '疫情严重程度排名前十地区的治愈人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig2.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12,
formatter='{b}:{c}例', font_style='italic', font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
page1.add(fig) # 将图像加入同一页
page1.add(fig1) # 将图像加入同一页
page1.add(fig2) # 将图像加入同一页
page1.render(savaPath + '/各国疫情严重程度排名前十地区信息/%s.html' % (n), index=False)
if flag:
dwf.write_to_file(savaPath + '/各国疫情严重程度排名前十地区信息/（确诊）%s.txt' % (n),str(fig.dump_options_with_quotes()))
dwf.write_to_file(savaPath + '/各国疫情严重程度排名前十地区信息/（死亡）%s.txt' % (n),str(fig1.dump_options_with_quotes()))
dwf.write_to_file(savaPath + '/各国疫情严重程度排名前十地区信息/（治愈）%s.txt' % (n),str(fig2.dump_options_with_quotes()))
border_color="#aaa",
border_width=1,
border_radius=4,
rich={
"a": {"color": "#999", "lineHeight": 22, "align": "center"},
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {"fontSize": 16, "lineHeight": 33},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
), )
pie.set_global_opts(title_opts=opts.TitleOpts(title='职业占比'))
pie.set_colors(['red', 'orange', 'purple']) # 设置颜色
pie.render('美女职业分布.html')
)
df = df.sort_values('no_trails') #, ascending=False)
# Nightingale Rose Chart
c = df['region'].values.tolist()
d = df['no_trails'].values.tolist() #create the color_series for the rosechart
# define rosechart
rosechart = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
# set the color
color_series = [
'#802200', '#B33000', '#FF4500', '#FAA327', '#9ECB3C', '#6DBC49',
'#37B44E', '#14ADCF', '#209AC9', '#1E91CA', '#2C6BA0', '#2B55A1',
'#2D3D8E', '#44388E', '#6A368B', '#D02C2A', '#D44C2D', '#F57A34',
'#FA8F2F', '#D99D21'
]
rosechart.set_colors(color_series)
# add the data to the rosechart
rosechart.add(
"",
[list(z) for z in zip(c, d)],
radius=["20%", "95%"], # 20% inside radius，95% ourside radius
center=["30%", "60%"], # center of the chart
rosetype="area") # set the global options for the chart
rosechart.set_global_opts(
title_opts=opts.TitleOpts(title='Washington Trails',
subtitle="# of Trails by Region"),
legend_opts=opts.LegendOpts(is_show=False),
toolbox_opts=opts.ToolboxOpts()) # set the series options
rosechart.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position="inside",
font_size=12,
sex_num.drop('保密', inplace=True)
print(sex_num) #男性观众4537人，女性观众3759人
# 绘制饼图
data_pair = [
list(z) for z in zip(sex_num.index.tolist(), sex_num.values.tolist())
]
# 绘制饼图
pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
pie1.add('', data_pair, radius=['35%', '60%'])
pie1.set_global_opts(title_opts=opts.TitleOpts(title='评论用户性别占比'),
legend_opts=opts.LegendOpts(orient='vertical',
pos_top='15%',
pos_left='2%'))
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%"))
pie1.set_colors(['#EF9050', '#3B7BA9', '#6FB27C'])
pie1.render(
) #generate a file:///C:/Users/Maggie/Desktop/Udemy-desktop/xiaoxiangxueyuan/pythonCases/xiaoxiang_projects/python_in_16_days/render.html
# 这次我们需要操作的是‘设备’这一列的数据。
device_num = df.设备.value_counts(ascending=True)
print(device_num)
# 绘制一个柱状图来展示一下
# 柱形图
bar1 = Bar(init_opts=opts.InitOpts(width='1350px', height='750px'))
bar1.add_xaxis(device_num.index.tolist())
bar1.add_yaxis('',
device_num.values.tolist(),
label_opts=opts.LabelOpts(position='right'))
bar1.set_global_opts(title_opts=opts.TitleOpts(title='评论客户端分布'),
def foregin_city_all():
file = os.getcwd() # 读取当前文件路径
path = file + '\\data\\各国各地区疫情信息'
path1 = file + '\\daliy_changes_view'
isExists = os.path.exists(path1) # 判断当前目录是否有文件夹photos，如果没有则创建
if not isExists:
os.mkdir(path1) # 创建photos,用于存放生成的图像数据
path2 = file + '\\daliy_changes_view\\各国疫情严重程度排名前十地区信息'
isExists = os.path.exists(path2) # 判断当前目录是否有文件夹daliy_changes_view，如果没有则创建
if not isExists:
os.mkdir(path2) # 创建daliy_changes_view,用于存放生成的图像数据
Filelist = [] # 将当前文件夹内的所有表名存放此列表
for home, dirs, files in os.walk(path):
for filename in files:
Filelist.append(filename)
# 判断数据是否存在
def checknan(name):
if np.any(pd.isnull(name)) == True:
name.fillna(value="0", inplace=True)
# 随机颜色生成用于制作南丁格尔玫瑰图
def randomcolor(kind):
colors = []
for i in range(kind):
colArr = [
'1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C',
'D', 'E', 'F'
]
color = ""
for i in range(6):
color += colArr[random.randint(0, 14)]
colors.append("#" + color)
return colors
for i in Filelist:
data = pd.read_excel(file + '\\data\\各国各地区疫情信息\\%s' % (i), index=False)
data_sort = data.sort_values(axis=0, ascending=False, by=['confirm'])
data_message = data_sort.head(10) # 提取疫情严重程度排名前十地区的信息
n = i[:-5] # 只提取国家名，不要后缀（.xlsx）
checknan(data_message['confirm'])
checknan(data_message['heal'])
checknan(data_message['dead'])
y1_confirm = data_message['confirm']
y1_confirm = list(y1_confirm)
y1_confirm = [int(i) for i in y1_confirm]
y2_dead = data_message['dead']
y2_dead = list(y2_dead)
y2_dead = [int(i) for i in y2_dead]
y3_heal = data_message['heal']
y3_heal = list(y3_heal)
y3_heal = [int(i) for i in y3_heal]
name_list = []
for j in data_message['name']:
name_list.append(j)
x = name_list
color_series = randomcolor(len(x))
# Bars = (
# Bar(init_opts=opts.InitOpts(width='1080px',height='700px'))
# .add_xaxis(xaxis_data=x)
# .add_yaxis(series_name='确诊人数', yaxis_data=y1_confirm)
# )
page = Page() # 创建一个分页用于放所有的图像在这一分页上
#####画南丁格尔玫瑰图##########
# 画出确诊人数的图
fig = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig.add("", [list(z) for z in zip(x, y1_confirm)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig.set_global_opts(title_opts=opts.TitleOpts(title=n +
'疫情严重程度排名前十地区的确诊人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig.set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
position='inside',
font_size=12,
formatter='{b}:{c}例',
font_style='italic',
font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
fig.set_colors(color_series)
# 画出死亡人数的图
fig1 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig1.add("", [list(z) for z in zip(x, y2_dead)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig1.set_global_opts(title_opts=opts.TitleOpts(title=n +
'疫情严重程度排名前十地区的死亡人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig1.set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
position='inside',
font_size=12,
formatter='{b}:{c}例',
font_style='italic',
font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
# 画出治愈人数的图
fig2 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig2.add("", [list(z) for z in zip(x, y3_heal)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig2.set_global_opts(title_opts=opts.TitleOpts(title=n +
'疫情严重程度排名前十地区的治愈人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig2.set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
position='inside',
font_size=12,
formatter='{b}:{c}例',
font_style='italic',
font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
page.add(fig) # 将图像加入同一页
page.add(fig1) # 将图像加入同一页
page.add(fig2) # 将图像加入同一页
page.render_notebook()
page.render(file + '\\daliy_changes_view\\各国疫情严重程度排名前十地区信息\\%s.html' %
(n),
index=False)
