def test_boxplot_item_base(fake_writer):
x_axis = ["expr1", "expr2"]
v1 = [
[850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],
[960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],
]
v2 = [
[890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],
[890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],
]
c = Boxplot()
series_a = [
opts.BoxplotItem(name=x_axis[0], value=d) for d in c.prepare_data(v1)
]
series_b = [
opts.BoxplotItem(name=x_axis[1], value=d) for d in c.prepare_data(v2)
]
c.add_xaxis(xaxis_data=x_axis).add_yaxis("A", series_a).add_yaxis(
"B", series_b)
c.render()
_, content = fake_writer.call_args[0]
assert_equal(c.theme, "white")
assert_equal(c.renderer, "canvas")
def paintBoxPlot(datadict, xtitle, ytitle, title):
graph = Boxplot(init_opts=opts.InitOpts(page_title=title))
graph.set_global_opts(
title_opts=opts.TitleOpts(title=title),
yaxis_opts=opts.AxisOpts(name='Accuracy')
)
graph.add_xaxis([xtitle])
for key, value in sorted(datadict.items()):
# 为了画图的标准性，小于5人的年级图片将不会被加入盒图
if len(value) > 4:
graph.add_yaxis(key, graph.prepare_data([value]))
graph.render('buctoj2_boxplot.html')
def test_boxpolt_base():
v1 = [
[850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],
[960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],
]
v2 = [
[890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],
[890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],
]
c = Boxplot()
c.add_xaxis(["expr1", "expr2"]).add_yaxis("A", c.prepare_data(v1)).add_yaxis(
"B", c.prepare_data(v2)
)
assert c.theme == "white"
assert c.renderer == "canvas"
c.render("render.html")
def test_boxpolt_base(fake_writer):
v1 = [
[850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],
[960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],
]
v2 = [
[890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],
[890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],
]
c = Boxplot()
c.add_xaxis(["expr1", "expr2"]).add_yaxis("A",
c.prepare_data(v1)).add_yaxis(
"B", c.prepare_data(v2))
c.render()
_, content = fake_writer.call_args[0]
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
def salary(self):
sql = 'SELECT workYear,replace(salary,\'k\',\'\') s FROM jobs group by workYear,salary order by workYear'
results = self.query(sql)
sum = {}
for r in results:
rs = r[1].split('-')
a = sum.get(r[0], [])
a.extend(rs)
sum[r[0]] = a
for k in sum:
numbers = list(map(int, sum[k]))
v = list(set(numbers))
sum[k] = v
print(list(sum.values()))
c = Boxplot()
c.add_xaxis(list(sum.keys()))
c.add_yaxis("薪资与工作经验", c.prepare_data(list(sum.values())))
c.set_global_opts(title_opts=opts.TitleOpts(title="薪资与工作经验"))
c.render("拉勾薪资.html")
dom1, dom2, dom3 = [], [], []
list1 = []
for date, AQI in zip(data['Date'], data['AQI']):
year = date.split('-')[0]
if year in ['2015', '2016']:
dom1.append(AQI)
elif year in ['2017', '2018']:
dom2.append(AQI)
elif year in ['2019', '2020']:
dom3.append(AQI)
x = ['2015-2016年', '2017-2018年', '2019-2020年']
y = [dom1, dom2, dom3]
boxplot = Boxplot()
boxplot.add_xaxis(x)
boxplot.add_yaxis('Delhi', boxplot.prepare_data(y))
boxplot.set_global_opts(title_opts=opts.TitleOpts(
title='2015-2020印度德里AQI箱型图',
pos_left='center',
),
legend_opts=opts.LegendOpts(
pos_left='left',
orient='vertical',
))
boxplot.render('boxplot_AQI.html')
slope = "{:0.2f}".format(slope)
if slope <= str(0.1):
list_5.append(df['height'][i])
if str(0.1) < slope <= str(0.5):
list_10.append(df['height'][i])
if str(0.5) < slope <= str(0.9):
list_15.append(df['height'][i])
if str(0.9) < slope <= str(1.3):
list_20.append(df['height'][i])
if slope > str(1.3):
list_30.append(df['height'][i])
sum_list = []
sum_list.append(list_5)
sum_list.append(list_10)
sum_list.append(list_15)
sum_list.append(list_20)
sum_list.append(list_30)
c = Boxplot()
c.add_xaxis(["0-0.1", "0.1-0.5", "0.5-0.9", "0.9-1.3", "1.3以上"])
c.add_yaxis("ATLAS Elevation-Airborne LiDAR Elevation(m)",
c.prepare_data(sum_list))
c.set_global_opts(
title_opts=opts.TitleOpts(title="All data"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
)
c.render("boxplot.html")
from pyecharts import options as opts
from pyecharts.charts import Boxplot
v1 = [
[850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],
[960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],
]
v2 = [
[890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],
[890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],
]
c = Boxplot()
c.add_xaxis(["expr1", "expr2"])
c.add_yaxis("A", c.prepare_data(v1))
c.add_yaxis("B", c.prepare_data(v2))
c.set_global_opts(title_opts=opts.TitleOpts(title="BoxPlot-基本示例"))
c.render("boxplot_base.html")
# 箱型图绘制
from pyecharts import options as opts
from pyecharts.charts import Boxplot
v1 = [
list(look_support_money['看一看']),
list(look_support_money['点赞']),
list(look_support_money['赞赏'])
]
c = Boxplot(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE, chart_id=4))
c.add_xaxis(["看一看", "点赞", '赞赏'])
c.add_yaxis("", c.prepare_data(v1))
c.set_global_opts(title_opts=opts.TitleOpts(title="看一看,点赞,赞赏分布"))
c.render("../output/看一看,点赞,赞赏.html")
c.render_notebook()
# ## 文章类型占比
# In[95]:
kind = article['文章类型'].value_counts()
# 绘制玫瑰图
from pyecharts import options as opts
from pyecharts.charts import Pie
v = ['爬虫', '其他', '数据分析', '爬虫+数据分析', 'Python 脚本程序', '可视化', '爬虫+可视化']
c = (Pie(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE, chart_id=5)).add(
"",
list1 = []
for date, AQI in zip(data['Date'], data['PM2.5']):
year = date.split('-')[0]
if year in ['2015', '2016']:
dom1.append(AQI)
elif year in ['2017', '2018']:
dom2.append(AQI)
elif year in ['2019', '2020']:
dom3.append(AQI)
x = ['2015-2016年', '2017-2018年', '2019-2020年']
y = [dom1, dom2, dom3]
boxplot = Boxplot()
boxplot.add_xaxis(x)
boxplot.add_yaxis('Delhi', boxplot.prepare_data(y))
boxplot.set_global_opts(
title_opts=opts.TitleOpts(
title='2015-2020印度德里PM2.5箱型图',
pos_left='center',
),
legend_opts=opts.LegendOpts(
pos_left='left',
orient='vertical',
)
)
boxplot.render('boxplot_PM2.5.html')
