def test_grid_add_overlap():
from pyecharts import Overlap
grid = Grid()
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar("Overlap+Grid 示例", width=1200, height=600, title_pos="40%")
bar.add("蒸发量", attr, v1)
bar.add(
"降水量",
attr,
v2,
yaxis_formatter=" ml",
yaxis_max=250,
legend_pos="85%",
legend_orient="vertical",
legend_top="45%",
)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
grid.add(overlap, grid_right="20%")
grid.render()
def ax_draw_macd(axes, kdata, n1=12, n2=26, n3=9):
"""绘制MACD
:param axes: 指定的坐标轴
:param KData kdata: KData
:param int n1: 指标 MACD 的参数1
:param int n2: 指标 MACD 的参数2
:param int n3: 指标 MACD 的参数3
"""
macd = MACD(CLOSE(kdata), n1, n2, n3)
bmacd, fmacd, smacd = macd.getResult(0), macd.getResult(1), macd.getResult(2)
text = 'MACD(%s,%s,%s) DIF:%.2f, DEA:%.2f, BAR:%.2f'%(n1,n2,n3,fmacd[-1],smacd[-1],bmacd[-1])
#axes.text(0.01,0.97, text, horizontalalignment='left', verticalalignment='top', transform=axes.transAxes)
total = len(kdata)
x_list = [i for i in range(total)]
y1_list = [round(x) if x > 0 else '-' for x in bmacd]
y2_list = [round(x) if x <= 0 else '-' for x in bmacd]
style = gcf().get_style(axes)
bar = Bar(subtitle=text, title_pos='10%', title_top='8%')
bar.add('1', x_list, y1_list, is_stack=True, is_legend_show=False, **style)
bar.add('2', x_list, y2_list, is_stack=True, is_legend_show=False, **style)
axes.add(bar)
fmacd.plot(axes=axes, line_type='dotted')
smacd.plot(axes=axes)
gcf().add_axis(axes)
return gcf()
def test_timeline_label_color():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add(
"bar",
attr,
[randint(10, 50) for _ in range(6)],
label_color=["red", "#213", "black"],
)
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
content = timeline._repr_html_()
assert '"color": [' in content
assert "red" in content
assert "#213" in content
assert "black" in content
def test_grid_top_bottom():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720)
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
line = Line("折线图示例", title_top="50%")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_top="50%",
)
grid = Grid()
grid.add(bar, grid_bottom="60%")
grid.add(line, grid_top="60%")
grid.render()
def test_bar_stack():
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", CLOTHES, clothes_v1, is_stack=True)
bar.add("商家B", CLOTHES, clothes_v2, is_stack=True)
html_content = bar._repr_html_()
assert "dataZoom" not in html_content
assert "stack_" in html_content
def create_three():
page = Page(page_title=TEST_PAGE_TITLE)
# bar
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
page.add(bar)
# scatter3D
import random
data = [
[random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)] for _ in range(80)
]
scatter3d = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3d.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3d)
# guangdong
value = [20, 190, 253, 77, 65]
attr = ['汕头市', '汕尾市', '揭阳市', '阳江市', '肇庆市']
map = Map("广东地图示例", width=1200, height=600)
map.add("", attr, value, maptype='广东', is_visualmap=True,
visual_text_color='#000')
page.add(map)
return page
def test_numpy_array():
v1 = np.array([5, 20, 36, 10, 75, 90])
bar = Bar(TITLE)
bar.add("商家A", CLOTHES, v1, is_stack=True)
html = bar.render_embed()
json_encoded_title = json.dumps(TITLE)
assert json_encoded_title in html
def test_custom_template_for_chart():
data = [{
'name': '衬衫',
'value': 5
}, {
'name': '羊毛衫',
'value': 20
}, {
'name': '雪纺衫',
'value': 36
}]
configure(echarts_template_dir='.')
online()
data1 = {'衬衫': '34', '羊毛衫': 45, '雪纺衫': 40}
names, values = Bar.cast(data)
names1, values1 = Bar.cast(data1)
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", names, values, is_stack=True)
bar.add("商家B", names1, values1, is_stack=True)
bar.render(path='new_version_bar.html')
with codecs.open('new_version_bar.html', 'r', 'utf-8') as f:
actual_content = f.read()
assert "</html>" in actual_content
def test_grid_properties():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720)
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
line = Line("折线图示例", title_top="50%")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_top="50%",
)
grid = Grid(width=1024, height=768)
grid.add(bar, grid_bottom="60%")
grid.add(line, grid_top="60%")
eq_(grid.width, 1024)
eq_(grid.height, 768)
assert (
("echarts" in bar.js_dependencies)
or ("echarts.min" in bar.js_dependencies)
)
def create_a_bar(title):
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar(title)
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
return bar
def test_grid_four_direction():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add("最高气温", WEEK, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", WEEK, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], mark_line=["average"],
legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_top="50%", legend_pos="20%")
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
grid.render()
def test_bar_rotate_label():
days = ["{}天".format(i) for i in range(20)]
days_v1 = [random.randint(1, 20) for _ in range(20)]
bar = Bar("坐标轴标签旋转示例")
bar.add("", days, days_v1, xaxis_interval=0, xaxis_rotate=30,
yaxis_rotate=30)
assert "stack_" not in bar._repr_html_()
def test_bar_datazoom_undefined():
days = ["{}天".format(i) for i in range(30)]
days_v1 = [random.randint(1, 30) for _ in range(30)]
bar = Bar("Bar - datazoom 默认 示例")
bar.add("", days, days_v1, is_label_show=True, is_datazoom_show=True)
html_content = bar._repr_html_()
assert "dataZoom" in html_content
assert ': "slider"' in html_content
assert ': "inside"' not in html_content
def test_bar_datazoom_inside():
days = ["{}天".format(i) for i in range(30)]
days_v1 = [random.randint(1, 30) for _ in range(30)]
bar = Bar("Bar - datazoom - inside 示例")
bar.add("", days, days_v1, is_datazoom_show=True,
datazoom_type='inside', datazoom_range=[10, 25])
html_content = bar._repr_html_()
assert "dataZoom" in html_content
assert ': "inside"' in html_content
assert ': "slider"' not in html_content
def create_demo_bar(chart_id_demo=None):
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True)
if chart_id_demo:
bar._chart_id = chart_id_demo
return bar
def test_bar_waterfall():
months = ["{}月".format(i) for i in range(1, 8)]
months_v1 = [0, 100, 200, 300, 400, 220, 250]
months_v2 = [1000, 800, 600, 500, 450, 400, 300]
bar = Bar("瀑布图示例")
bar.add("", months, months_v1, label_color=['rgba(0,0,0,0)'],
is_stack=True)
bar.add("月份", months, months_v2, is_label_show=True, is_stack=True,
label_pos='inside')
bar.show_config()
bar.render()
def test_numpy_array():
import numpy as np
title = "柱状图数据堆叠示例"
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = np.array([5, 20, 36, 10, 75, 90])
bar = Bar(title)
bar.add("商家A", attr, v1, is_stack=True)
html = bar.render_embed()
json_encoded_title = json.dumps(title)
assert json_encoded_title in html
def draw_data_echart(select_results):
list_name = []
list_count = []
for select_list in select_results:
list_name.append(select_list[0])
list_count.append(int(select_list[2]))
#print ("select name: %s, select count: %d" % (select_list[0], int(select_list[2])))
bar = Bar("read weekly", "download count")
bar.use_theme('light')
bar.add("book download count", list_name, list_count, is_more_utils = True, is_label_show = True, is_datazoom_show = True)
bar.render("downloadcount.html")
def test_chart_properties():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图-数据堆叠示例", width=900, height=500)
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
eq_(len(bar.chart_id), UUID_HEX_LENGTH)
eq_(bar.width, 900)
eq_(bar.height, 500)
assert ('echarts' in bar.js_dependencies) or \
('echarts.min' in bar.js_dependencies)
def test_overlap_bar_line():
attr = ["A", "B", "C", "D", "E", "F"]
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line-Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def generic_formatter_t_est(**keywords):
attr = ["Jan", "Feb"]
v1 = [2.0, 4.9]
bar = Bar("Bar chart", "precipitation and evaporation one year")
bar.add(
"precipitation",
attr,
v1,
mark_line=["average"],
mark_point=["max", "min"],
**keywords
)
bar.render()
def get_bar(item_name,item_name_list,item_num_list):
subtitle = "微信公众号：大数据前沿"
bar = Bar(item_name,page_title = item_name,title_text_size=30,title_pos='center',\
subtitle = subtitle,subtitle_text_size = 25)
bar.add("", item_name_list, item_num_list,title_pos='center', xaxis_interval=0,xaxis_rotate=27,\
xaxis_label_textsize = 20,yaxis_label_textsize = 20,yaxis_name_pos='end',yaxis_pos = "%50")
bar.show_config()
grid = Grid(width=1300,height= 800)
grid.add(bar,grid_top = "13%",grid_bottom = "23%",grid_left = "15%",grid_right = "15%")
out_file_name = './analyse/'+item_name+'.html'
grid.render(out_file_name)
def test_overlap_bar_line():
attr = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line-Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def test_timeline_bar_line():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add("bar", attr, [randint(10, 50) for _ in range(6)])
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
bar_2 = Bar("3 月份数据", "数据纯属虚构")
bar_2.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_2 = Line()
line_2.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_2 = Overlap()
overlap_2.add(bar_2)
overlap_2.add(line_2)
bar_3 = Bar("4 月份数据", "数据纯属虚构")
bar_3.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_3 = Line()
line_3.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_3 = Overlap()
overlap_3.add(bar_3)
overlap_3.add(line_3)
bar_4 = Bar("5 月份数据", "数据纯属虚构")
bar_4.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_4 = Line()
line_4.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_4 = Overlap()
overlap_4.add(bar_4)
overlap_4.add(line_4)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
timeline.add(overlap_2, "3 月")
timeline.add(overlap_3, "4 月")
timeline.add(overlap_4, "5 月")
timeline.render()
def test_embed_option():
# bar_0
title = "柱状图数据堆叠示例"
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar(title)
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True)
html = bar.render_embed()
json_encoded_title = json.dumps(title)
assert json_encoded_title in html
assert "<html>" not in html
assert "<body>" not in html
def test_pandas_dataframe():
title = "bar chart"
index = pd.date_range("3/8/2017", periods=6, freq="M")
df1 = pd.DataFrame(np.random.randn(6), index=index)
df2 = pd.DataFrame(np.random.randn(6), index=index)
dtvalue1 = [i[0] for i in df1.values]
dtvalue2 = [i[0] for i in df2.values]
_index = [i for i in df1.index.format()]
bar = Bar(title, "Profit and loss situation")
bar.add("profit", _index, dtvalue1)
bar.add("loss", _index, dtvalue2)
html = bar.render_embed()
assert title in html
def test_bar_datazoom_both():
days = ["{}天".format(i) for i in range(30)]
days_v1 = [random.randint(1, 30) for _ in range(30)]
bar = Bar("Bar - datazoom - both 示例")
bar.add(
"",
days,
days_v1,
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[10, 25],
is_toolbox_show=False,
)
html_content = bar._repr_html_()
assert "dataZoom" in html_content
assert ': "inside"' in html_content
assert ': "slider"' in html_content
def test_overlap_two_yaxis():
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(width=1200, height=600)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render()
def test_grid_heatmap_bar():
data = [[i, j, random.randint(0, 50)]
for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例", height=700)
heatmap.add("热力图直角坐标系", X_TIME, Y_WEEK, data, is_visualmap=True,
visual_top="45%", visual_text_color="#000",
visual_orient='horizontal')
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", title_top="52%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_top="50%")
grid = Grid()
grid.add(heatmap, grid_bottom="60%")
grid.add(bar, grid_top="60%")
grid.render()
def draw_citys_pic(csv_file):
page = Page(csv_file+":评论城市分析")
info = count_city(csv_file)
geo = Geo("","Ctipsy原创",title_pos="center", width=1200,height=600, background_color='#404a59', title_color="#fff")
while True: # 二次筛选，和pyecharts支持的城市库进行匹配，如果报错则删除该城市对应的统计
try:
attr, val = geo.cast(info)
geo.add("", attr, val, visual_range=[0, 300], visual_text_color="#fff", is_geo_effect_show=False,
is_piecewise=True, visual_split_number=6, symbol_size=15, is_visualmap=True)
except ValueError as e:
e = str(e)
e = e.split("No coordinate is specified for ")[1] # 获取不支持的城市名称
info.pop(e)
else:
break
info = sorted(info.items(), key=lambda x: x[1], reverse=False) # list排序
# print(info)
info = dict(info) #list转dict
# print(info)
attr, val = [], []
for key in info:
attr.append(key)
val.append(info[key])
geo1 = Geo("", "评论城市分布", title_pos="center", width=1200, height=600,
background_color='#404a59', title_color="#fff")
geo1.add("", attr, val, visual_range=[0, 300], visual_text_color="#fff", is_geo_effect_show=False,
is_piecewise=True, visual_split_number=10, symbol_size=15, is_visualmap=True, is_more_utils=True)
#geo1.render(csv_file + "_城市dotmap.html")
page.add_chart(geo1)
geo2 = Geo("", "评论来源热力图",title_pos="center", width=1200,height=600, background_color='#404a59', title_color="#fff",)
geo2.add("", attr, val, type="heatmap", is_visualmap=True, visual_range=[0, 50],visual_text_color='#fff', is_more_utils=True)
#geo2.render(csv_file+"_城市heatmap.html") # 取CSV文件名的前8位数
page.add_chart(geo2)
bar = Bar("", "评论来源排行", title_pos="center", width=1200, height=600 )
bar.add("", attr, val, is_visualmap=True, visual_range=[0, 100], visual_text_color='#fff',mark_point=["average"],mark_line=["average"],
is_more_utils=True, is_label_show=True, is_datazoom_show=True, xaxis_rotate=45)
#bar.render(csv_file+"_城市评论bar.html") # 取CSV文件名的前8位数
page.add_chart(bar)
pie = Pie("", "评论来源饼图", title_pos="right", width=1200, height=600)
pie.add("", attr, val, radius=[20, 50], label_text_color=None, is_label_show=True, legend_orient='vertical', is_more_utils=True, legend_pos='left')
#pie.render(csv_file + "_城市评论Pie.html") # 取CSV文件名的前8位数
page.add_chart(pie)
page.render(csv_file + "_城市评论分析汇总.html")
def region_distribution():
# 使用一个字典统计好友地区分布数量
province_dict = {
'北京': 0,
'上海': 0,
'天津': 0,
'重庆': 0,
'河北': 0,
'山西': 0,
'吉林': 0,
'辽宁': 0,
'黑龙江': 0,
'陕西': 0,
'甘肃': 0,
'青海': 0,
'山东': 0,
'福建': 0,
'浙江': 0,
'台湾': 0,
'河南': 0,
'湖北': 0,
'湖南': 0,
'江西': 0,
'江苏': 0,
'安徽': 0,
'广东': 0,
'海南': 0,
'四川': 0,
'贵州': 0,
'云南': 0,
'内蒙古': 0,
'新疆': 0,
'宁夏': 0,
'广西': 0,
'西藏': 0,
'香港': 0,
'澳门': 0
}
# 遍历
for user in friends:
# 判断省份是否存在，有可能是外国的，这种情况不考虑
if (user.province in province_dict):
key = user.province
province_dict[key] += 1
province = list(province_dict.keys())
values = list(province_dict.values())
# maptype='china' 只显示全国直辖市和省级，数据只能是省名和直辖市的名称
map = Map("微信好友地区分布")
map.add("",
province,
values,
visual_range=[0, 50],
maptype='china',
is_visualmap=True,
visual_text_color='#000')
map.render(path="data/好友地区分布.html")
# 对好友数最多的省份进行一进步分析
max_count_province = ''
for key, value in province_dict.items():
if (value == max(province_dict.values())):
max_count_province = key
break
# 使用一个字典统计好友地区分布数量
city_dict = {}
# 遍历
for user in friends:
if (user.province == max_count_province):
# 更新键值对
if (user.city in city_dict.keys()):
city_dict[user.city] += 1
else:
city_dict[user.city] = 1
bar = Bar(max_count_province + '中,好友地区分布')
bar.add(name='地区分布',
x_axis=[x for x in city_dict.keys()],
y_axis=[x for x in city_dict.values()])
bar.render('data/某省好友地区分布.html')
df_huanjing['环境']=df_huanjing['环境'].map(lambda x:round(x,2))
df_fuwu=df.drop(df[df['服务']==0].index,inplace=False)
df_fuwu=df_fuwu.groupby(by='城市').agg({'服务':'mean'}).reset_index()
df_fuwu=df_fuwu.sort_values(by='服务',ascending=False)[0:20]
df_fuwu['服务']=df_fuwu['服务'].map(lambda x:round(x,2))
# 柱状图
bar1=Bar('小龙虾人气城市',title_top="10px",title_pos="5%")
bar2=Bar('人均消费排名',title_top="180px",title_pos="5%")
bar3=Bar('口味评分排名',title_top="350px",title_pos="5%")
bar4=Bar('环境评分排名',title_top="520px",title_pos="5%")
bar5=Bar('服务评分排名',title_top="690px",title_pos="5%")
# is_splitline_show 水平标线 xaxis_rotate 未知 legend_top 图例离页面顶部距离
bar1.add('点评',df_dianping['城市'],df_dianping['点评'],is_splitline_show=True,xaxis_rotate=30,legend_top="10px",legend_pos="50%")
bar2.add('人均消费',df_renjun['城市'],df_renjun['人均'],is_splitline_show=True,xaxis_rotate=30,legend_top="180px",legend_pos="50%")
bar3.add('评分',df_kouwei['城市'],df_kouwei['口味'],is_splitline_show=True,xaxis_rotate=30,legend_top="350px",legend_pos="50%")
bar4.add('评分',df_huanjing['城市'],df_huanjing['环境'],is_splitline_show=True,xaxis_rotate=30,legend_top="520px",legend_pos="50%")
bar5.add('评分',df_fuwu['城市'],df_fuwu['服务'],is_splitline_show=True,xaxis_rotate=30,legend_top="690px",legend_pos="50%")
grid = Grid(height=1200,width=1280)
# grid_bottom 图表底部离页面底部距离 grid_top 图表顶部离页面顶部距离
'''100%-(grid_bottom:80 + grid_top 5)=15%即为柱状图高的占比, 即4个图占60%空间,并留一些白'''
'''先填出第一个图(80,5)与最后一个图(5,80)的位置, 然后用(80-5)/(4-1)=25为增量间距'''
# grid.add(bar1,grid_bottom="80%",grid_top="6%" ,grid_left="5%",grid_right="55%")
grid.add(bar1,grid_top="50px",grid_bottom="1050px")
grid.add(bar2,grid_top="220px",grid_bottom="880px")
grid.add(bar3,grid_top="390px",grid_bottom="710px")
grid.add(bar4,grid_top="560px",grid_bottom="540px")
grid.add(bar5,grid_top="730px",grid_bottom="370px")
from pyecharts import Bar
def RE(p):
length = len(p) - 1
for i in range(int(length / 2 + 1)):
p[i], p[length - i] = p[length - i], p[i]
return list(p)
# 1:is_stack 实现数据堆叠
# Note： 全局配置项要在最后一个 add() 上设置，否侧设置会被冲刷掉。
attr = [x for x in range(1998, 2018)]
v1 = [
139008, 138271, 137462, 136782, 136072, 135404, 134735, 134091, 133450,
132802, 132129, 131448, 130756, 129988, 129227, 128453, 127627, 126743,
125786, 124761
]
v2 = [
824828.4, 740598.7, 686449.6, 644791.1, 590422.4, 539116.5, 484753.2,
411265.2, 348498.5, 321500.5, 270844, 219028.5, 185998.9, 161415.4,
136576.3, 120480.4, 109276.2, 99066.1, 89366.5, 83817.6
]
v3 = [float(v2[i]) / float(v1[i]) for i in range(len(v1))]
# 2:使用标记点和标记线
bar = Bar("生产总值")
bar.add("平均个人", attr, RE(v3), mark_point=["average"], is_stack=False)
bar.add("总体", attr, RE(v2), mark_line=["min", "max"], is_stack=False)
bar.render("GDP.html")
def index5(request):
if request.method == 'GET':
pclass_list = models.Professionalclass.objects.all()
course_list = models.Course.objects.all()
return render(request, 'index5.html', locals())
else:
from pyecharts import Bar
courses = request.POST.getlist('courses') #['1','2','4']
pclass = request.POST.get('pclass')
ll = []
createVar = locals()
dic = {}
list2 = []
for course in courses:
dic['v' + course] = course
course_name = models.Course.objects.filter(id=course).first()
tup1 = ['v' + course, course_name.name]
list2.append(tup1)
# print(list2)
l2 = []
for key, value in dic.items():
# key=value
l2.append(key)
dic1 = {}
for course in courses:
for i in l2:
if 'v' + course == i:
grades = models.Grade.objects.filter(
course=course,
student__pclass=pclass).all().values_list('grade')
dic1[i] = grades
# print(dic1)
dic3 = {}
for i in l2:
dic3[i] = []
for j in dic1[i]:
j = int(j[0])
dic3[i].append(j)
students = models.Grade.objects.filter(student__pclass=pclass).all(
).values_list('student__name').distinct()
attr = []
for student in students:
attr.append(student[0])
# print(attr)
bar = Bar("柱状图数据堆叠示例", width=1600)
# print(dic3)
for i in l2:
for j in list2:
if j[0] == i:
dic = dic3[i]
if len(dic3[i]) != len(attr):
l = []
for k in range(len(attr)):
l.append(0)
for i in dic:
print(i)
names = models.Grade.objects.filter(
grade=i,
student__pclass=pclass,
course__name=j[1]).all().values_list(
'student__name')
for name in names:
print(name)
index = attr.index(name[0])
print(index)
l[index] = i
print(l)
dic = l
# print(attr)
# print(dic)
print('--------start')
print(len(attr)) #21
print(len(dic)) #21
print('--------start')
bar.add(j[1], attr, dic, mark_line=["average"])
# v1 = l2
# ['王芳泽', '王芳泽', '王芳泽', '李晓瑶', '李晓瑶', '李晓瑶', '张凡儿', '张凡儿', '张凡儿', '刘英豪', '刘英豪', '刘英豪', '杨思萱', '杨思萱', '杨思萱', '黄建元',
# '黄建元', '黄建元', '周翔飞', '周翔飞', '周翔飞', '王飞燕', '王飞燕', '王飞燕', '吴明杰', '吴明杰', '吴明杰', '林强', '林强', '林强', '刘勇捷', '刘勇捷', '刘勇捷',
# '黄阳', '黄阳', '黄阳', '陈静逸', '陈静逸', '陈静逸', '张安怡', '张安怡', '张安怡', '刘俊名', '刘俊名', '刘俊名', '王瑶', '王瑶', '王瑶', '李宏儒', '李宏儒',
# '李宏儒', '朱乐欣', '朱乐欣', '朱乐欣', '李清照', '李清照', '李清照', '马逸龙', '马逸龙', '马逸龙', '林美美', '林美美', '林美美']
# [87, 85, 85, 98, 72, 87, 87, 79, 88, 92, 90, 75, 85, 88, 89, 83, 86, 80, 81, 95, 74, 92, 90, 93, 91, 88, 80, 94, 72,
# 70, 88, 89, 71, 93, 80, 90, 93, 83, 92, 96, 94, 93, 72, 70, 74, 99, 97, 98, 90, 92, 80, 88, 78, 60, 78, 79, 89, 87,
# 81, 76, 82, 70, 90]
bar.render('templates/imagelist/contrast_grade.html')
return render(request, 'imagelist/contrast_grade.html')
bank = data.sheets()[1]
print(table.col_values(0))
print(bank.col_values(0))
# X轴
CLOTHES = table.col_values(0)
# 数据
clothes_pv = table.col_values(1)
clothes_uv = table.col_values(2)
clothes_ip = table.col_values(3)
# print(clothes_pv, clothes_uv, clothes_ip)
# bar = Bar("account_ekeguan_com", height=720)
bar = Bar("account_ekeguan_com", height=350)
bar.add("PV", CLOTHES, clothes_pv)
bar.add("UV", CLOTHES, clothes_uv)
bar.add("IP", CLOTHES, clothes_ip, is_stack=False, is_datazoom_show=True)
# X轴
CLOTHES = bank.col_values(0)
# 数据
clothes_pv = bank.col_values(1)
clothes_uv = bank.col_values(2)
clothes_ip = bank.col_values(3)
# print(clothes_pv, clothes_uv, clothes_ip)
line = Bar("www_ekeguan_com", title_top='1%')
line.add("PV", CLOTHES, clothes_pv)
line.add("UV", CLOTHES, clothes_uv)
line.add("IP", CLOTHES, clothes_ip, is_stack=False, is_datazoom_show=True)
def ple_edcation_of_eachcity(htmlfile):
lasa = [0.46, 5.56, 7.20, 9.47, 21.10, 36.62, 19.59] # 拉萨市
cangdu = [0.04, 0.94, 1.75, 2.36, 10.28, 39.92, 44.7] #昌都市
sanlan = [0.05, 2.41, 3.39, 5.54, 16.08, 50.99, 21.55] #山南市
rkz = [0.03, 1.68, 2.41, 3.79, 14.87, 43.88, 33.35] # 日喀则市
naqu = [0.03, 1.16, 2.29, 2.78, 8.38, 35.25, 50.11] #那曲地区
ali = [0.05, 2.73, 4.64, 5.22, 10.89, 32.54, 43.93] #阿里地区
linz = [0.14, 2.52, 3.46, 4.84, 14.26, 40.60, 34.19] #林芝地区
attr = ['研究生', '本科', '专科', '高中', '初中', '小学', '未上过学']
bar = Bar("2010年西藏各城市学历", width=1000, height=400)
bar.add("拉萨市", attr, lasa)
bar.add("昌都市", attr, cangdu)
bar.add("山南市", attr, sanlan)
bar.add("日喀则市", attr, rkz)
bar.add("那曲地区", attr, naqu)
bar.add("阿里地区", attr, ali)
bar.add("林芝地区", attr, linz)
bar.show_config()
bar.render(htmlfile)
a['sort_num_score'] = a['score'].rank(ascending=0, method='dense')
a['value'] = a.apply(lambda row: my_difference(row['sort_num_money'], row[
'sort_num_score'], len(a.index)),
axis=1)
df = a.sort_values(by="value", ascending=True)[0:9]
v1 = ["{}".format('%.2f' % abs(i * 100)) for i in df['value']]
attr = np.array(df['name'])
attr = ["{}".format(i.replace('：无限战争', '')) for i in attr]
bar = Bar("2018年叫座不叫好电影TOP10",
title_pos='center',
title_top='18',
width=800,
height=400)
bar.add("",
attr,
v1,
is_convert=True,
xaxis_min=0,
xaxis_max=4,
yaxis_label_textsize=12,
is_yaxis_boundarygap=True,
yaxis_interval=0,
is_label_show=True,
is_legend_show=False,
label_pos='right',
is_yaxis_inverse=True,
is_splitline_show=False)
bar.render("2018年叫座不叫好电影TOP100.html")
if i[tuple(i.keys())[0]] <700:#如果价格小于700，则添加进一个数组
item_shenzhen = ["深圳",tuple(i.keys())[0]]
data_shenzhen.append(item_shenzhen)
style_geo = style.add(#地图的参数设置，包括图标，位置
is_label_show=True,
line_curve=0.2,
line_opacity=0.6,
legend_text_color="#eee",
legend_pos="right",
geo_effect_symbol="plane",#图标改成了飞机
geo_effect_symbolsize=15,
label_color=['#a6c84c', '#ffa022', '#46bee9'],
label_pos="right",
label_formatter="{b}",
label_text_color="#eee",
)
geolines = GeoLines("GeoLines 示例", **style.init_style)
geolines.add("从深圳出发", data_shenzhen, **style_geo)
geolines.render(path='road.html')
from pyecharts import Bar#引入包
echart_city=[]#城市
echart_price=[]#价格
for p in data_price:#遍历数据，将城市和价格分开
if p[tuple(p.keys())[0]]<700:
echart_city.append(tuple(p.keys())[0])
echart_price.append(p[tuple(p.keys())[0]])
bar = Bar("航班价格", "从深圳出发")#标题
bar.add("城市", echart_city,echart_price)#将城市、价格数据导入
bar.render(path='bar.html')#生成
#上面是mongodb聚合统计的语句
#$group：按照给定表达式组合结果，这里的_id字段表示你要基于哪个字段来进行分组，这里的$city就表示要基于city字段来进行分组
#下面的count字段的值$sum: 1表示的是获取--满足city字段相同的这一组的数量--乘以后面给定的值(本例为1，那么就是同组的数量)。
#$sort：按照给定的字段排序结果，即按计算好的count排序，-1为降序来排列
for i in res:
print(i)
#{'_id': '成都', 'count': 2074}
all.append((i['_id'].strip(), i['count']))
attr = [i[0] for i in all[:30]] #取前三十城市的名字
v1 = [i[1] for i in all[:30]] #取前三十城市的值
print(attr)
bar = Bar('新房分布柱状图') #柱状图
bar.add('各城市新楼盘数',
attr,
v1,
is_label_show=True,
is_datazoom_show=True,
xaxis_rotate=65,
label_color=[
'#87CEEB',
])
#attr 下面的城市名
#v1 数值
#is_label_show -> bool 是否正常显示标签，默认不显示。即各柱上的数字
#is_datazoom_show -> bool 是否使用区域缩放组件，默认为 False
#xaxis_rotate -> int x 轴刻度标签旋转的角度，默认为 0，即不旋转。旋转的角度从 -90 度到 90 度。
#label_color 柱的颜色
bar.render('bar.html')
# -*- coding:utf-8 -*-
# @Author：sunaihua
import pyecharts
from pyecharts import Bar
bar = Bar("我的第一个图表", "这里是副标题")
bar.use_theme('dark') #暗色背景色
bar.add(
"服装", #注解==label
["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], #横坐标
[5, 20, 36, 10, 75, 90]) #纵坐标
bar.render('./picture1.html') #文件存储路径（默认保存当前文件路径）
line_curve=0.1, #轨迹线的弯曲度，0-1
line_opacity=0.6, #轨迹线的透明度，0-1
geo_effect_symbol='plane', #特效的图形，有circle,plane,pin等等
geo_effect_symbolsize=8, #特效图形的大小
geo_effect_color='#7FFFD4', #特效的颜色
geo_effect_traillength=0.1, #特效图形的拖尾效果，0-1
label_color=colors[i], #轨迹线的颜色，标签点的颜色，
border_color='#97FFFF', #边界的颜色
geo_normal_color='#36648B', #地图的颜色
legend_pos='left',
label_formatter='{b}')
geolines.add(
name=rdc_name[i],
data=flow,
maptype='china', #地图的类型，可以是省的地方，如'广东',也可以是地市，如'东莞'等等
geo_cities_coords=City_cords,
**style_geolines)
#发布，得到图形的html文件
geolines.render("py十仓仓网覆盖.html")
##堆叠柱状图
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
y1 = [5, 20, 36, 10, 75, 90]
y2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图数据堆叠示例")
title1 = '商家A'
title2 = '商家B'
bar.add(title1, x, y1, is_stack=True)
bar.add(title2, x, y2, is_stack=True)
bar.render('柱状图数据堆叠.html')
import pandas as pd
from pyecharts import Bar
df = pd.read_csv('Best_Actor_SR.csv',
header=None,
names=['year', 'win', 'nomination'],
encoding='gbk')
win_message = df.groupby(['win'])
win_com = win_message['win'].agg(['count'])
win_com.reset_index(inplace=True)
win_com_last = win_com.sort_index()
win_com = win_com.sort_values('count', ascending=False)[0:15]
attr = win_com['win']
v1 = win_com['count']
bar = Bar("奥斯卡最佳男配角-获奖统计",
title_pos='center',
title_top='18',
width=800,
height=400)
bar.add("",
attr,
v1,
is_stack=True,
is_label_show=True,
xaxis_rotate=30,
yaxis_force_interval=1,
yaxis_max=4)
bar.render("奥斯卡最佳男配角-获奖统计.html")
def bar_echart(request, *args, **kwargs):
template = loader.get_template(
'./node_modules/gentelella/production/display_metric_detail.html')
snap = request.GET.get('snapdate')
#snap_date = datetime.strptime(snap, '%y/%m/%d').strftime('%Y-%m-%d')
if snap:
load_profile_per_hour = list(
models.loadmetric_hour.objects.values(
"time", "redo_second", "logical_second", "physical_second",
"execs_second", "trans_second").filter(snap_date=snap).all())
space_usage = list(
models.spaceusage.objects.values(
"tablespace_name", "percent").filter(collect_time=snap).all())
print(space_usage)
# load_profile_obj = apps.get_model('ora_dual', 'loadmetric_hour')
# load_profile_field = load_profile_obj._meta.fields
# title = []
# for ind in range(len(load_profile_field)):
# title.append(load_profile_field[ind].name)
attr = []
for key, value in load_profile_per_hour[0].items():
attr.append(key)
val_usage = []
val_name = []
for idx in range(len(space_usage)):
val_name.append(space_usage[idx]['tablespace_name'])
val_usage.append(space_usage[idx]['percent'])
usage_pie = Pie("饼图-空间使用率", title_pos='center')
usage_pie.add(
"",
val_name,
val_usage,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
# pie.render()
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
for idx in range(len(load_profile_per_hour)):
val = []
for key, value in load_profile_per_hour[idx].items():
val.append(value)
bar = Bar("数据库指标", val[0])
bar.add("值/秒", attr[1:], val[1:])
timeline.add(bar, val[0])
context = dict(
snap_date=snap,
title=attr,
usage_pie=usage_pie.render_embed(),
space_usage=space_usage,
metric_data=load_profile_per_hour,
myechart=timeline.render_embed(),
# host=DEFAULT_HOST,#这句改为下面这句
host=REMOTE_HOST, # <-----修改为这个
script_list=timeline.get_js_dependencies())
return HttpResponse(template.render(context, request))
def chart(self):
bar = Bar("ebooking")
bar.add("60客户", self.percent, self.sortList, is_stack=True)
bar.render()
make_a_snapshot('render.html', './pic/%s.png' % self.name)
from pyecharts import Bar
bar=Bar('图表标题','图标副标题')
bar.use_theme('dark')
kind=["衬衫","羊毛衫","雪纺衫","裤子","高跟鞋","袜子"]
bar.add('服装',kind,[5,20,36,10,75,90],is_more_utils=True)
bar.render()
item = {
'nickname': hotcommment['user']['nickname'],
'content': hotcommment['content'],
'likedCount': hotcommment['likedCount']
}
hotcomments.append(item)
# 获取评论用户名，内容，以及对应的获赞数
content_list = [content['content'] for content in hotcomments]
nickname = [content['nickname'] for content in hotcomments]
liked_count = [content['likedCount'] for content in hotcomments]
bar = Bar("热评中点赞数示例图")
bar.add("点赞数",
nickname,
liked_count,
is_stack=True,
mark_line=["min", "max"],
mark_point=["average"])
bar.render()
from wordcloud import WordCloud
import matplotlib.pyplot as plt
content_text = " ".join(content_list)
wordcloud = WordCloud(font_path=r"D:\simhei.ttf",
max_words=200).generate(content_text)
plt.figure()
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
from pyecharts import Bar
from pyecharts_snapshot.main import make_a_snapshot
bar = Bar("我的第一个图表")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], [5, 20, 36, 10, 75, 90])
bar.add("鞋子", ["衬", "羊衫", "纺衫", "裤", "高鞋", "袜"], [5, 20, 36, 10, 75, 90],
is_more_utils=True)
bar.render('Source/2.html')
结合不同类型图表叠加画在同张图上
Overlap 类的使用：
1、引入 Overlap 类，from pyecharts import Overlap
2、实例化 Overlap 类，overlap = Overlap() ，可指定 page_title, width, height, jhost 参数。
3、使用 add() 向 overlap 中添加图
4、使用 render() 渲染生成 .html 文件
Overlap.add() 方法签名
add(chart, 图表示例
xaxis_index=0, x 坐标轴索引，默认为 0
yaxis_index=0, y 坐标轴索引，默认为 0
is_add_xaxis=False, 是否新增一个 x 坐标轴，默认为 False
is_add_yaxis=False) 是否新增一个 y 坐标轴，默认为 False
'''
# Line + Bar
from pyecharts import Overlap,Line,Bar
attr=['A', 'B', 'C', 'D', 'E', 'F']
v1=[10, 20, 30, 40, 50, 60]
v2=[38, 28, 58, 48, 78, 68]
bar=Bar('Line-Bar示例')
bar.add('bar',attr,v1)
line=Line()
line.add('line',attr,v2)
overlap=Overlap()
overlap.add(line)
overlap.add(bar)
overlap.render()
from mymodule import stats_word
import requests
from pyquery import PyQuery
import matplotlib.pyplot as plt
import numpy as np
from pyecharts import Bar
def get_article():
url = 'https://mp.weixin.qq.com/s/pLmuGoc4bZrMNl7MSoWgiA'
r = requests.get(url)
document = PyQuery(r.text)
article_str = document('#js_content').text()
result = stats_word.stats_text_cn(article_str, 10)
return result
paixu = get_article()
paixu_list = []
for paixu_tuple in paixu:
for paixu_str in paixu_tuple:
paixu_list.append(paixu_str)
name = paixu_list[::2] #取出所有名字
number = paixu_list[1::2] #取出所有对应的词频数
bar = Bar('词频输出')
bar.add('词频', name, number)
bar.show_config()
bar.render()
# -*- coding: utf-8 -*- """ @author:XuMing（[email protected]) @description: """ from __future__ import unicode_literals from pyecharts import Bar from pyecharts.conf import PyEchartsConfig from pyecharts.engine import EchartsEnvironment from pyecharts.utils import write_utf8_html_file attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] v1 = [5, 20, 36, 10, 75, 90] v2 = [10, 25, 8, 60, 20, 80] bar = Bar("柱状图数据堆叠示例") bar.add("商家A", attr, v1, is_stack=True) bar.add("商家B", attr, v2, is_stack=True) config = PyEchartsConfig(echarts_template_dir='./', jshost='https://cdn.bootcss.com/echarts/3.6.2') env = EchartsEnvironment(pyecharts_config=config) tpl = env.get_template('tpl.html') html = tpl.render(bar=bar) write_utf8_html_file('my_tpl_demo2.html', html)
'nickname': hotcomment['user']['nickname'],
'content': hotcomment['content'],
'likedCount': hotcomment['likedCount']
}
hotcomments.append(item)
#获取评论用户名，内容，以及对应的获赞数
content_list = [content['content'] for content in hotcomments]
nickname = [content['nickname'] for content in hotcomments]
liked_count = [content['likedCount'] for content in hotcomments]
#点赞数
from pyecharts import Bar #pyecharts：图表包
bar = Bar('热门中点赞数示例图')
bar.add('点赞数',
nickname,
liked_count,
is_stack=True,
mark_line=['min', 'max'],
mark_point=['average'])
bar.render()
#词云图
from wordcloud import WordCloud #WordCloud：词云包
import matplotlib.pyplot as plt #matplotlib：绘图功能包
content_text = ' '.join(content_list)
wordcloud = WordCloud(font_path=r'C:\simhei.ttf',
max_words=200).generate(content_text)
plt.figure()
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
import pandas as pd
import numpy as np
df = pd.read_excel('朝阳医院2018年销售数据.xlsx')
from datetime import datetime
df['购药时间'].dropna(inplace=True)
def to_month(s):
day=s.split()[0]
date=datetime.strptime(day,'%Y-%M-%d')
return date.strftime('%Y-%M')
df['购药时间']=df['购药时间'].apply(to_month)
grouped = df[['应收金额','销售数量']].groupby(df['购药时间'])
data = grouped.sum()
bar =Bar("朝阳医院2018年销售数据",title_pos='left',width=800) #创建一个实例对象
bar.add("销售量",data.index ,data['销售数量'], is_legend_show =True ) #是否显示顶端图例
bar.add("销售金额",data.index ,data['应收金额'],is_legend_show =True ) #是否显示顶端图例
#bar.show_config()
#bar.render('sales.html')
from pyecharts import Line,Overlap
line = Line('朝阳医院2018年销售数据',title_pos='left')
line.add('销售量',data.index ,data['销售数量'],mark_point=['average','max','min'],
mark_point_symbol='diamond',mark_point_textcolor='blue')
line.add("销售金额",data.index ,data['应收金额'],mark_point=['max'], is_smpooth=True, mark_line=['average','max','min'],
mark_point_symbol='arrow',mark_point_symbolsize=20)
overlap = Overlap()
overlap.add(line)
overlap.add(bar)
overlap.render('sales.html')
def bar_chart():
ggu = tableau_services.get_growthOfunit(**g.my_dict)
timestr = ggu['growth_of_unit']['xAxis']
timestr = [i[:-9] for i in timestr]
attr = timestr
vs = "v" + "s"
print(vs)
1 + 1 = 3
print(vs)
v1 = ggu['growth_of_unit']['series']['DEMOXYZ9'][1]
v2 = ggu['growth_of_unit']['series']['DEMOXYZ9'][2]
v3 = ggu['growth_of_unit']['series']['DEMOXYZ6'][1]
v4 = ggu['growth_of_unit']['series']['DEMOXYZ6'][2]
v5 = ggu['growth_of_unit']['series']['DEMOXYZ8'][1]
v6 = ggu['growth_of_unit']['series']['DEMOXYZ8'][2]
bar = Bar(" ")
bar.add("DEMOXYZ9",
attr,
v1,
is_convert=True,
legend_text_size=18,
is_datazoom_show=True,
datazoom_type="both")
bar.add("DEMOXYZ9",
attr,
v2,
is_convert=True,
legend_text_size=18,
is_datazoom_show=True,
datazoom_type="both")
bar.add("DEMOXYZ6",
attr,
v3,
is_convert=True,
legend_text_size=18,
is_datazoom_show=True,
datazoom_type="both")
bar.add("DEMOXYZ6",
attr,
v4,
is_convert=True,
legend_text_size=18,
is_datazoom_show=True,
datazoom_type="both")
bar.add("DEMOXYZ8",
attr,
v5,
is_convert=True,
legend_text_size=18,
is_datazoom_show=True,
datazoom_type="both")
bar.add("DEMOXYZ8",
attr,
v6,
is_convert=True,
legend_text_size=18,
is_datazoom_show=True,
datazoom_type="both")
line = Line(" ")
line.add("DEMOXYZ9",
timestr,
ggu['growth_of_unit']['series']['DEMOXYZ9'][1],
is_stack=True,
is_label_show=True,
legend_top="50%",
is_datazoom_show=True,
is_legend_show=False,
datazoom_type="both")
line.add("DEMOXYZ9",
timestr,
ggu['growth_of_unit']['series']['DEMOXYZ9'][2],
is_stack=True,
is_label_show=True,
legend_top="50%",
is_datazoom_show=True,
is_legend_show=False,
datazoom_type="both")
line.add("DEMOXYZ6",
timestr,
ggu['growth_of_unit']['series']['DEMOXYZ6'][1],
is_stack=True,
is_label_show=True,
legend_top="50%",
is_datazoom_show=True,
is_legend_show=False,
datazoom_type="both")
line.add("DEMOXYZ6",
timestr,
ggu['growth_of_unit']['series']['DEMOXYZ6'][2],
is_stack=True,
is_label_show=True,
legend_top="50%",
is_datazoom_show=True,
is_legend_show=False,
datazoom_type="both")
line.add("DEMOXYZ8",
timestr,
ggu['growth_of_unit']['series']['DEMOXYZ8'][1],
is_stack=True,
is_label_show=True,
legend_top="50%",
is_datazoom_show=True,
is_legend_show=False,
datazoom_type="both")
line.add("DEMOXYZ8",
timestr,
ggu['growth_of_unit']['series']['DEMOXYZ8'][2],
is_stack=True,
is_label_show=True,
legend_top="50%",
is_datazoom_show=True,
is_legend_show=False,
datazoom_type="both")
grid = Grid()
grid.add(bar, grid_right="50%")
grid.add(line, grid_left="60%")
return grid
datetime.datetime(2019, 1, 1))
# 数据转换
dates = df_stockload.index.strftime('%Y-%m-%d')
volume_rise = [
df_stockload.Volume[x]
if df_stockload.Close[x] > df_stockload.Open[x] else "0"
for x in range(0, len(df_stockload.index))
]
volume_drop = [
df_stockload.Volume[x]
if df_stockload.Close[x] <= df_stockload.Open[x] else "0"
for x in range(0, len(df_stockload.index))
]
bar.add("rvolume",
dates,
volume_rise,
is_stack=True,
label_color=["#218868"],
is_datazoom_show=True)
bar.add("dvolume",
dates,
volume_drop,
is_stack=True,
label_color=["#FA8072"],
is_datazoom_show=True)
bar.show_config()
bar.render(r'volume.html') #渲染图表，指定生成volume.html文件
from pyecharts import Bar
bar = Bar("我的第一个图表", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], [5, 20, 36, 10, 75, 90])
# bar.print_echarts_options() # 该行只为了打印配置项，方便调试时使用
bar.render() # 生成本地 HTML 文件
def kline_echarts(self, code=None):
def kline_formater(param):
return param.name + ':' + vars(param)
"""plot the market_data"""
if code is None:
path_name = '.' + os.sep + 'QA_' + self.type + \
'_codepackage_' + self.if_fq + '.html'
kline = Kline('CodePackage_' + self.if_fq + '_' + self.type,
width=1360,
height=700,
page_title='QUANTAXIS')
bar = Bar()
data_splits = self.splits()
for ds in data_splits:
data = []
axis = []
if ds.type[-3:] == 'day':
datetime = np.array(ds.date.map(str))
else:
datetime = np.array(ds.datetime.map(str))
ohlc = np.array(ds.data.loc[:,
['open', 'close', 'low', 'high']])
kline.add(ds.code[0],
datetime,
ohlc,
mark_point=["max", "min"],
is_datazoom_show=True,
datazoom_orient='horizontal')
return kline
else:
data = []
axis = []
ds = self.select_code(code)
data = []
#axis = []
if self.type[-3:] == 'day':
datetime = np.array(ds.date.map(str))
else:
datetime = np.array(ds.datetime.map(str))
ohlc = np.array(ds.data.loc[:, ['open', 'close', 'low', 'high']])
vol = np.array(ds.volume)
kline = Kline('{}__{}__{}'.format(code, self.if_fq, self.type),
width=1360,
height=700,
page_title='QUANTAXIS')
bar = Bar()
kline.add(
self.code,
datetime,
ohlc,
mark_point=["max", "min"],
# is_label_show=True,
is_datazoom_show=True,
is_xaxis_show=False,
# is_toolbox_show=True,
tooltip_formatter='{b}:{c}', # kline_formater,
# is_more_utils=True,
datazoom_orient='horizontal')
bar.add(self.code,
datetime,
vol,
is_datazoom_show=True,
datazoom_xaxis_index=[0, 1])
grid = Grid(width=1360, height=700, page_title='QUANTAXIS')
grid.add(bar, grid_top="80%")
grid.add(kline, grid_bottom="30%")
return grid
def draw_senti(time, num, avg, pos_avg, neg_avg, pos_prop, neg_prop, fout):
# 数据预处理
senti_sum = np.multiply(np.array(num), np.array(avg)).tolist()
pos_sum = np.multiply(np.array(num), np.array(pos_avg)).tolist()
neg_sum = np.multiply(np.array(num), np.array(neg_avg)).tolist()
neg_sum = [i * -1 for i in neg_sum]
neg_avg = [i * -1 for i in neg_avg]
# 不同倾向弹幕比例
pos_num = np.multiply(np.array(num), np.array(pos_prop)).tolist()
pos_num = np.rint(pos_num)
neg_num = np.multiply(np.array(num), np.array(neg_prop)).tolist()
neg_num = np.rint(neg_num)
all_pos_prop = sum(pos_num) / sum(num)
all_neg_prop = sum(neg_num) / sum(num)
all_mid_prop = 1 - all_neg_prop - all_pos_prop
senti_kinds = ['正面', '中性', '负面']
senti_prop = [all_pos_prop, all_mid_prop, all_neg_prop]
page = Page()
pie = Pie("各类弹幕占比",
"",
title_color='black',
title_pos='center',
width=1600)
pie.add("各类弹幕占比",
senti_kinds,
senti_prop,
is_label_show=True,
legend_pos='right')
page.add(pie)
bar = Bar("弹幕数量", "", title_color='black', title_pos='center', width=1600)
bar.add("弹幕数量",
time,
num,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("正面弹幕数量",
"",
title_color='black',
title_pos='center',
width=1600)
bar.add("正面弹幕数量",
time,
pos_num,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("负面弹幕数量",
"",
title_color='black',
title_pos='center',
width=1600)
bar.add("负面弹幕数量",
time,
neg_num,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("弹幕情感值", "", title_color='black', title_pos='center', width=1600)
bar.add("弹幕情感值",
time,
senti_sum,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("弹幕情感均值",
"",
title_color='black',
title_pos='center',
width=1600)
bar.add("弹幕情感均值",
time,
avg,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("正面弹幕情感值",
"",
title_color='black',
title_pos='center',
width=1600)
bar.add("正面弹幕情感值",
time,
pos_sum,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("正面弹幕情感均值",
"",
title_color='black',
title_pos='center',
width=1600)
bar.add("正面弹幕情感均值",
time,
pos_avg,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("负面弹幕情感值",
"",
title_color='black',
title_pos='center',
width=1600)
bar.add("负面弹幕情感值",
time,
neg_sum,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("负面弹幕情感均值",
"",
title_color='black',
title_pos='center',
width=1600)
bar.add("负面弹幕情感均值",
time,
neg_avg,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
page.render(fout)
print('echarts：', fout)
# -*- coding: utf-8 -*-
__author__ = "purelove"
__date__ = '2018/5/4 11:10'
# encoding: utf-8
import time
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
from pyecharts import Bar
bar = Bar("流行音乐", "出现频率最高的词")
bar.add("Top50", ["一个", "自己", "世界", "什么", "爱情", "不要"],
[549, 540, 481, 376, 364, 350],
is_more_utils=True)
bar.show_config()
bar.render()
is_stack=True,
xaxis_rotate=30,
yaxix_min=4.2,
mark_point=['min', 'max'],
xaxis_interval=0,
line_color='lightblue',
line_width=4,
mark_point_textcolor='black',
mark_point_color='lightblue',
is_splitline_show=False)
bar = Bar("主要城市评论数")
bar.add("城市",
attr,
v1,
is_stack=True,
xaxis_rotate=30,
yaxix_min=4.2,
xaxis_interval=0,
is_splitline_show=False)
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render('主要城市评论数_平均分.html')
#词云分析
#分词
comment = jieba.cut(str(data['comment']), cut_all=False)
wl_space_split = " ".join(comment)
#导入背景图
from pyecharts import Bar
attr = [
"T-shirt", "sweater", "georgette", "pants", "high-heeled shoes", "socks"
]
v1 = [50, 10, 80, 30, 15, 50]
v2 = [10, 90, 50, 10, 50, 10]
bar = Bar("Histogram data case")
bar.add("merchant1", attr, v1, is_stack=True)
bar.add("merchant2", attr, v2, is_stack=True)
bar.use_theme("dark")
bar.render()
