def drawThreeD(data, mode, time):
bar3d = Bar3D()
temp = list(arrangeByTime(data).get(time).keys())
res = []
data = arrangeByChannel(data) # 按频道分
news = dict()
for i in data.keys():
news[i] = arrangeByTime(data.get(i)).get(time)
value = dict()
comment_channel = dict()
for i in news.keys():
comment_channel[i] = dict()
value[i] = dict()
for j in news[i].keys():
comment_channel[i][j] = divideComment(news[i].get(j))
value[i] = getMood(comment_channel[i], mode)
for i in value.keys():
if i in ['js', 'jilin', 'video', 'live']:
continue
if len(value.get(i)) == 0:
continue
for j in temp:
if j in value.get(i).keys():
res.append((tran(i), j, value.get(i).get(j)))
else:
res.append((tran(i), j, None))
bar3d.add(
"",
[[d[1], d[0], d[2]] for d in res],
).set_global_opts(
visualmap_opts=options.VisualMapOpts(max_=1),
title_opts=options.TitleOpts(title="时间、维度与新浪评论情感总图"),
)
return bar3d
def bar3d_base() -> Bar3D:
data = read_do()
data_tip = [
"使用总能耗 [kW]", "发电 [kW]", "洗碗机 [kW]", "炉子 1 [kW]", "房屋整体能耗 [kW]",
"冰箱 [kW]", "酒窖 [kW]", "车库门 [kW]"
]
data_year = [
2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
2018, 2019
]
c = (Bar3D().add("",
data,
xaxis3d_opts=opts.Axis3DOpts(data_tip,
type_="category",
max_=8),
yaxis3d_opts=opts.Axis3DOpts(data_year, max_=2020),
zaxis3d_opts=opts.Axis3DOpts(type_="value", max_=1),
grid3d_opts=opts.Grid3DOpts(
width="180", height="50")).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=1),
title_opts=opts.TitleOpts(title="部分能耗图"),
))
return c
def draw_bar3D(cls, title: str, data: pd.DataFrame) -> Bar3D:
"""
根据df内容绘制3D柱状图
:param title: 标题
:param data: 包含三轴数据的dataframe index为x轴 column为Y轴 value为z轴
:return:
"""
data_list = []
index_list = data.index.tolist()
column_list = data.columns.tolist()
# 获取dataframe最大最小值
min_data = data.min().min()
max_data = data.max().max()
# 遍历dataframe，准备待操作数组
for i in range(len(index_list)):
for j in range(len(column_list)):
# 记录 XYZ
temp_list = [index_list[i], column_list[j], data.iloc[i, j]]
# print(i,j,index_list[i],column_list[j])
data_list.append(temp_list)
c = (
Bar3D(init_opts=opts.InitOpts(
width=DEFAULT_WIDTH,
animation_opts=opts.AnimationOpts(
animation_delay=200,
animation_easing="bounceOut"), # 增加启动动效
)).add(
series_name=title,
data=data_list,
xaxis3d_opts=opts.Axis3DOpts(type_="category",
data=index_list),
yaxis3d_opts=opts.Axis3DOpts(type_="category",
data=column_list),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=True)).
set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0%"),
toolbox_opts=opts.ToolboxOpts(), # 显示工具箱
tooltip_opts=opts.TooltipOpts(is_show=True),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, type_="none"), # 指针移动时显示所有数值
legend_opts=opts.LegendOpts(
is_show=True,
selected_mode="multiple",
# pos_bottom="0%",
# pos_right="0%",
# orient="vertical",
), # 显示图例说明
# datazoom_opts=[
# opts.DataZoomOpts(
# range_start=0, range_end=100, orient="vertical", pos_left="2%"
# ),
# opts.DataZoomOpts(range_start=0, range_end=100, orient="horizontal"),
# ], # 增加缩放配置横纵轴都支持缩放
visualmap_opts=opts.VisualMapOpts(max_=max_data, min_=min_data)
# visualmap_opts=opts.VisualMapOpts(type_="color", max_=1, min_=-1),
))
return c
def creat_pivot_chart(pivot, index, column, agg, value):
index_list = pivot.index.tolist()
column_list = pivot.columns.tolist()
if pivot.max().tolist():
visualmap_max = max(pivot.max().tolist())
range_text = ['最大值', '最小值']
else:
visualmap_max = 1
range_text = ['无', '数据']
# print(pivot.max())
data = [(i, j, int(pivot.iloc[i, j])) for i in range(len(index_list))
for j in range(len(column_list))]
c = (Bar3D().add(
series_name=agg + '(' + value + ')',
data=data,
shading="lambert",
xaxis3d_opts=opts.Axis3DOpts(index_list, type_="category", name=index),
yaxis3d_opts=opts.Axis3DOpts(column_list,
type_="category",
name=column),
zaxis3d_opts=opts.Axis3DOpts(type_="value",
name=agg + '(' + value + ')'),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=visualmap_max,
range_text=range_text),
title_opts=opts.TitleOpts(title="数据透视三维图"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=opts.DataZoomOpts(type_="inside")))
return c
def bar3d_base():
data = [[i, j, random.randint(0, 20)] for i in Faker.clock for j in Faker.week]
bar3d = Bar3D(init_opts=opts.InitOpts(width='1100px', height='600px'))
bar3d.add(" ", data)
bar3d.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="Bar_3D-基本实例")
)
bar3d.render("./bar_3d_base.html")
def bar3d():
data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(12)]
c = (Bar3D().add(
'', [[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=op.Axis3DOpts(Faker.color, type_='category'),
yaxis3d_opts=op.Axis3DOpts(Faker.week_en, type_='category'),
zaxis3d_opts=op.Axis3DOpts(type_='value')).set_global_opts(
visualmap_opts=op.VisualMapOpts(max_=20),
title_opts={'text': '三维'}))
return c
def bar3d_html(data):
raw = pd.read_csv(data, header=0, index_col=0, sep=None, engine='python')
data = [(i, j, raw.loc[j, i]) for i in raw.columns for j in raw.index]
c = (Bar3D().add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(raw.index, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(raw.columns, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=20), ))
c.render('bar3d.html')
def test_bar3d_base():
data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
c = (Bar3D().add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=20)))
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
c.render()
def bar3d_base() -> Bar3D:
data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
c = (Bar3D().add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="Bar3D-基本示例"),
))
return c
def bar3d_base() -> Bar3D:
data = [(i, j, random.randint(0, 1)) for i in range(6)
for j in range(24)]
c = (Bar3D().add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(city_nms_top10, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(city_nums_top10, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="Bar3D-Python工程师"),
))
return c
def bar3d_base() -> Bar3D:
data = [(i, j, int(results[i][j])) for i in range(11) for j in range(11)]
c = (Bar3D().add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(xlist, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(ylist, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=1100),
title_opts=opts.TitleOpts(title="Bar3D-Boston crash accident account"),
))
return c
def bar3d_stack():
x_data = y_data = list(range(10))
bar3d = Bar3D()
for _ in range(10):
bar3d.add(
"",
generate_date(),
shading="lambert",
xaxis3d_opts=opts.Axis3DOpts(data=x_data, type_="value"),
yaxis3d_opts=opts.Axis3DOpts(data=y_data, type_="value"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
)
bar3d.set_global_opts(title_opts=opts.TitleOpts("Bar3D-堆叠柱状图示例"))
bar3d.set_series_opts(**{"stack": "stack"})
return bar3d
def init_bar3d_base(self):
from example.commons import Faker
from pyecharts import options as opts
from pyecharts.charts import Bar3D
c = (Bar3D().add(
"",
[],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title=""),
))
return c
def draw():
areas = [
'0-50', '50-100', '100-150', '150-200', '200-250', '250-300',
'300-350', '350+'
]
dic = {}
with open("清洗后城市二手房数据.csv", 'r', encoding='utf8') as f:
reader = csv.reader(f)
data = list(reader)
Bar3D().add(
series_name="",
data=[[item[0], item[4], item[6]] for item in data],
xaxis3d_opts=opts.Axis3DOpts(name='城市', type_='category'),
yaxis3d_opts=opts.Axis3DOpts(name='总价'),
zaxis3d_opts=opts.Axis3DOpts(name='面积')).render("bar3D.html")
def article_day_3d():
today = datetime.datetime.strptime('2019-12-31', '%Y-%m-%d')
date_list = [[(today - datetime.timedelta(days=i)).strftime('%Y-%m-%d'),
(today - datetime.timedelta(days=i)).isocalendar()[1],
(today - datetime.timedelta(days=i)).isocalendar()[2]]
for i in range(365)]
date_list.reverse()
data = [[
date[1], date[2],
WechatArticle.query.filter(
and_(
extract('year', WechatArticle.publish_time) == str(
date[0]).split('-')[0],
extract('month', WechatArticle.publish_time) == str(
date[0]).split('-')[1],
extract('day', WechatArticle.publish_time) == str(
date[0]).split('-')[2],
)).count()
] for date in date_list]
c = (Bar3D().add(
"",
[[d[0], d[1] - 1, d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(data=[i for i in range(1, 53)],
type_="category",
name='一年周期',
name_gap=30),
yaxis3d_opts=opts.Axis3DOpts(data=Faker.week_en,
type_="category",
name='week'),
zaxis3d_opts=opts.Axis3DOpts(type_="value", name='每天数量'),
grid3d_opts=opts.Grid3DOpts(width=300,
height=120,
depth=120,
rotate_speed=10,
is_rotate=True),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=210,
range_color=Faker.visual_color),
title_opts=opts.TitleOpts(title="2019公众号文章发布统计",
pos_top='10%',
pos_left='43%'),
legend_opts=opts.LegendOpts(item_gap=20, item_width=20),
))
c.render(path='./static/year/article_day_3d.html')
print('生成3D文章发布统计')
return c
def plot_asset_distribution():
months = [
"1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月",
"12月"
]
years = [str(x.year) for x in YearDB.objects.all()]
years = years[::-1]
data = []
for idx1, month in enumerate(months):
for idx2, year in enumerate(years):
data.append([idx1, idx2, 0])
for each in IncomDB.objects.all():
each_year = each.Year.year
each_month = each.monthly
if each_year in years and each_month in months:
idx = years.index(
each_year) + months.index(each_month) * len(years)
data[idx] = [
months.index(each_month),
years.index(each_year),
float(each.actual_balance)
]
bar_3d = (Bar3D(
init_opts=opts.InitOpts(width="1400px", height="700px")).add(
series_name="存款",
data=data,
xaxis3d_opts=opts.Axis3DOpts(type_="category", data=months),
yaxis3d_opts=opts.Axis3DOpts(type_="category", data=years),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=100000,
range_color=[
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
"#f46d43",
"#d73027",
"#a50026",
])))
abs = os.path.dirname(__file__)
bar_3d.render(abs + "/static/analysis.html")
def bar3d_base() -> Bar3D:
import random
from example.commons import Faker
from pyecharts import options as opts
from pyecharts.charts import Bar3D
data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
c = (Bar3D().add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title=""),
))
return c
def bar3d_base() -> Bar3D:
# data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
# print([[d[1], d[0], d[2]] for d in data])
c = (
Bar3D(init_opts=opts.InitOpts(width='1200px', height='1000px'))
.add(
"",
data,
xaxis3d_opts=opts.Axis3DOpts(date1, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(start_uids, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
)
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=500),
title_opts=opts.TitleOpts(title="情感分析3D"),
)
)
return c
def gzu_major_satisfied():
data1 = analysis_data.gzu_satisfied()
data1 = data1.iloc[:, :5]
data = [(i, j, data1.iloc[i, j + 1]) for i in range(data1.shape[0])
for j in range(data1.shape[1] - 1)]
c = (Bar3D().add(
"",
[[d[0], d[1], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts([x[0] for x in data1.values],
type_="category"),
yaxis3d_opts=opts.Axis3DOpts(['综合满意度', '办学条件满意度', '教学质量满意度', '就业满意度'],
type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(min_=0, max_=2),
title_opts=opts.TitleOpts(title="贵州大学专业满意度"),
))
return c
def echart_plot_3d(data):
from pyecharts.charts import Bar3D
from pyecharts import options as opts
print(data.tail())
df = data[['s_Time', '未来=月终收益', '预测值']].values.tolist()
(Bar3D(init_opts=opts.InitOpts(width="1600px", height="800px")).add(
"",
df,
xaxis3d_opts=opts.Axis3DOpts(type_="category"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=100000),
title_opts=opts.TitleOpts(title="predict"),
).render("predict.html"))
def draw_bar3D(self):
from pyecharts.faker import Faker
from pyecharts import options as opts
data = [(i, j, random.randint(0, 12)) for i in range(6)
for j in range(24)]
c = (Bar3D(init_opts=opt.InitOpts(js_host="js2/")).add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="Bar3D-基本示例"),
).render('bar3d1.html'))
print(c)
return c
def plot(self):
self.chart = Bar3D(init_opts=opts.InitOpts(page_title=self.title))
self.chart.add(
self.zaxis_name,
[[d[1], d[0], d[2]] for d in self.heat_map],
xaxis3d_opts=opts.Axis3DOpts(self.data_y,
type_="category",
name=self.yaxis_name,
name_gap=25),
yaxis3d_opts=opts.Axis3DOpts(self.data_x,
type_="category",
name=self.xaxis_name,
name_gap=25),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
)
self.chart.set_global_opts(title_opts=opts.TitleOpts(title=self.title),
visualmap_opts=opts.VisualMapOpts(
max_=self.max_value,
min_=self.min_value))
return self
def test_bar3d_stack(fake_writer):
data1 = [(i, j, random.randint(0, 12)) for i in range(6)
for j in range(24)]
data2 = [(i, j, random.randint(13, 20)) for i in range(6)
for j in range(24)]
c = (Bar3D().add(
"1",
[[d[1], d[0], d[2]] for d in data1],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).add(
"2",
[[d[1], d[0], d[2]] for d in data2],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(
max_=20)).set_series_opts(**{"stack": "stack"}))
c.render()
_, content = fake_writer.call_args[0]
assert_in("stack", content)
def draw():
code = []
for ch in ["A", "B", "C", "D", "E"]:
for i in range(1, 11):
code.append(ch + "-" + str(i))
k_value = ["recall@1", "recall@5", "recall@10"]
recall_value = []
plot_data = []
for i in range(len(code)):
for j in range(len(k_value)):
plot_data.append((code[i], k_value[j], random.uniform(0, 1)))
c = (Bar3D().add(
"Sample",
plot_data,
xaxis3d_opts=opts.Axis3DOpts(code, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(k_value, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=1),
title_opts=opts.TitleOpts(title="50 Sample Recall"),
))
return c
def bar_3d_fun():
# 三维数据，定义 x、y 长度，z 数值随机
data = [(i, j, random.randint(1, 20)) for i in range(7) for j in range(10)]
print(len(data), data)
Bar3D().add(
# 添加 x、y、z 轴数据，并设置属性
series_name="",
data=data,
xaxis3d_opts=opts.Axis3DOpts(Faker.choose(), type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.choose(), type_="category"),
zaxis3d_opts=opts.Axis3DOpts(Faker.values(), type_="value"),
# 添加旋转效果
grid3d_opts=opts.Grid3DOpts(width=100,
depth=100,
rotate_speed=150,
is_rotate=True),
).set_global_opts(
# 设置图表属性
# 视觉配置
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="主标题", subtitle="副标题")).render(
# 设置输出路径
path="D:/temp/pyecharts_bar3d.html")
hours = ["厚度1", "厚度2", "厚度3", "厚度4", "厚度5", "厚度6", "厚度7", "厚度8", "厚度9", "平均厚度"]
days = ["1", "2", "3", "4", "5", "6", "7"]
data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
data = [[d[1], d[0], d[2]] for d in data]
c = (Bar3D(init_opts=opts.InitOpts(width="900px", height="600px")).add(
series_name="",
data=data,
xaxis3d_opts=opts.Axis3DOpts(type_="category", data=hours),
yaxis3d_opts=opts.Axis3DOpts(type_="category", data=days),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(title_opts=opts.TitleOpts("标准3D柱状图"),
visualmap_opts=opts.VisualMapOpts(
max_=20,
range_color=[
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
"#f46d43",
"#d73027",
"#a50026",
],
)).render("标准3D柱状图.html"))
c.render_notebook()
Bar3D(init_opts = opts.InitOpts(width = "1600px", height = "800px"))
.add(
series_name = "",
data = data,
xaxis3d_opts = opts.Axis3DOpts(type_ = "category", data = Elec_kinds_X,
name = "Power type (left : XJ & right : ZJ)"),
yaxis3d_opts = opts.Axis3DOpts(type_ = "category", data = Indicator_Y, name = "Indicator type",name_gap = 30),
zaxis3d_opts = opts.Axis3DOpts(type_ = "value", name = "Points"),
# label_opts = opts.LabelOpts(is_show = True)
# 数据标签显示
)
.set_global_opts(
visualmap_opts = opts.VisualMapOpts(
max_ = 0.12,
range_color = [
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
"#f46d43",
"#d73027",
"#a50026",
],
)
)
.render("endpoint1.html")
# In[147]:
# vis
import random
from pyecharts import options as opts
from pyecharts.charts import Bar3D
from pyecharts.faker import Faker
data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
bar3d = (
Bar3D().add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="Bar3D-基本示例"),
)
#.render("bar3d_base.html")
)
bar3d.render_notebook()
# ## EffectScatter
# In[116]:
# vis
from pyecharts import options as opts
def to_map_china(self, area, variate, value, update_time):
# 显示标识栏的颜色分段表示
pieces = [
{
"max": 99999999,
"min": 10000,
"label": '>10000',
"color": '#E64546'
},
{
"max": 9999,
"min": 1000,
"label": '1000-9999',
"color": '#F57567'
},
{
"max": 999,
"min": 100,
"label": '100-999',
"color": '#FF9985'
},
{
"max": 99,
"min": 10,
"label": '10-99',
"color": '#FFC4B3'
},
{
"max": 9,
"min": 1,
"label": '1-9',
"color": '#FFE5DB'
},
{
"max": 0,
"min": 0,
"label": '0',
"color": '#FFFFFF'
},
]
data_tip = [
'累计确诊', '死亡', '治愈', '现有确诊', '累计确诊增量', '死亡增量', '治愈增量', '现有确诊增量'
]
data_area = [
'西藏', '澳门', '青海', '台湾', '香港', '贵州', '吉林', '新疆', '宁夏', '内蒙古', '甘肃',
'天津', '山西', '辽宁', '黑龙江', '海南', '河北', '陕西', '云南', '广西', '福建', '上海',
'北京', '江苏', '四川', '山东', '江西', '重庆', '安徽', '湖南', '河南', '广东', '浙江',
'湖北'
]
# 绘制地图
map = (
Map(init_opts=opts.InitOpts(width='800px',
height='600px')) # 初始化配置项，设置地图大小
.add("累计确诊人数", [
list(z) for z in zip(area, variate)
], "china").set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情地图分布",
subtitle='截止%s 中国疫情分布情况' %
(update_time),
pos_left='center',
pos_top='30px'), #TitleOpts：标题设置
visualmap_opts=opts.VisualMapOpts(max_=200,
is_piecewise=True,
pieces=pieces),
# # max_：设置 visualMapPiecewise 所有取值区间中的最大值，is_piecewise设置数据是否连续，split_number设置为分段数，pices可自定义数据分段
).render("中国疫情地图.html") # 展示提取后的效果
)
# 绘制地理坐标图
geo = (
Geo().add_schema(maptype="china") #地图类型
.add(
"累计确诊人数",
[list(z) for z in zip(area, variate)],
type_=ChartType.EFFECT_SCATTER,
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)) # label_opts:标签配置项设置，is_show：是否显示视觉映射配置
.set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情地图分布",
subtitle='截止%s 中国疫情分布情况' %
(update_time),
pos_left='center',
pos_top='30px'),
visualmap_opts=opts.VisualMapOpts(max_=200,
is_piecewise=True,
pieces=pieces),
).render("中国疫情新增情况.html"))
# 绘制折线图
line = (
Line().add_xaxis(area) #添加x轴数值
.add_yaxis("累计确诊人数", variate) #添加y轴名称、数值
.add_yaxis("累计治愈人数", value).set_global_opts(
title_opts=opts.TitleOpts(title="国内疫情情况"),
xaxis_opts=opts.AxisOpts(axislabel_opts={
"interval": "0",
"rotate": "45"
}), #设置x轴数值分割间隔为0，且旋转45°
yaxis_opts=opts.AxisOpts(
type_='log',
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
) #设置y轴数据类型为“log”，凸显分割线，不会强制包含零刻度
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #标签设置项
.render("全国累计趋势折线图.html"))
# 绘制柱状图
bar = (
Bar().add_xaxis(area).add_yaxis(
"累计确诊人数", variate,
stack='stack1') #若y轴设置项“stack”为同一个值，则显示为堆叠型柱状，反之，成分散型
# .add_yaxis("累计治愈人数",value,stack='stack2')
# .add_yaxis("累计死亡人数", value, stack='stack1')
.add_yaxis("累计治愈人数", value, stack='stack1').set_global_opts(
title_opts=opts.TitleOpts(title="全国累计确诊治愈情况"),
xaxis_opts=opts.AxisOpts(axislabel_opts={
"interval": "0",
"rotate": "45"
}),
yaxis_opts=opts.AxisOpts(
type_='log',
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
)).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).render("全国累计确诊治愈.html"))
# 绘制饼状图
pie = (
Pie().add(
"",
[list(z) for z in zip(area, variate)],
radius=["40%", "75%", "log"], #设置圆环大小及数据类型
).set_global_opts(
title_opts=opts.TitleOpts(title="国内疫情情况"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"),
#图例配置项：图例列表的布局朝向为水平，图例组件离容器上侧的距离为15%，图例组件离容器左侧的距离为相对于容器高宽的20%
).set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}")) #设置标签形式
.render("全国累计趋势饼状图.html"))
# 绘制3D柱状图
bar3d = (
Bar3D().add("全国指标",
data,
label_opts=opts.LabelOpts(position='left'),
yaxis3d_opts=opts.Axis3DOpts(data_area),
xaxis3d_opts=opts.Axis3DOpts(
data_tip,
type_="category",
max_=8,
interval=0,
),
zaxis3d_opts=opts.Axis3DOpts(type_="value", min_=0),
grid3d_opts=opts.Grid3DOpts(width="600",
height="100",
is_rotate=True)
#设置三维笛卡尔坐标系组件在三维场景中的宽度，高度，以及是否自动旋转
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True, pieces=pieces),
title_opts=opts.TitleOpts(title="疫情指标"),
).render("疫情指标.html"))
from pyecharts.charts import Bar3D
from pyecharts.faker import Faker #虚构的数据
from pyecharts import options as opts #配置
from pyecharts.charts import Bar #导入bar图
from pyecharts.globals import ThemeType
import random
data = [(i, j, random.randint(0, 12)) for i in range(24) for j in range(24)]
bar3d = Bar3D()
bar3d.add(
"",
data,
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
)
bar3d.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="Bar3D-基本示例"),
)
bar3d.render()