def render_dataset():
with st.echo("below"):
options = {
"legend": {},
"tooltip": {},
"dataset": {
"source": [
["product", "2015", "2016", "2017"],
["Matcha Latte", 43.3, 85.8, 93.7],
["Milk Tea", 83.1, 73.4, 55.1],
["Cheese Cocoa", 86.4, 65.2, 82.5],
["Walnut Brownie", 72.4, 53.9, 39.1],
]
},
"xAxis": {"type": "category"},
"yAxis": {},
"series": [{"type": "bar"}, {"type": "bar"}, {"type": "bar"}],
}
st_echarts(options, renderer="svg")
def render_liquid():
with st.echo("below"):
options = {
"series": [
{
"type": "liquidFill",
"data": [0.5, 0.4, 0.3],
"color": ["red", "#0f0", "rgb(0, 0, 255)"],
"itemStyle": {"opacity": 0.6},
"emphasis": {"itemStyle": {"opacity": 0.9}},
}
]
}
st_echarts(options)
c = (
Liquid()
.add("lq", [0.6, 0.7])
.set_global_opts(title_opts=opts.TitleOpts(title="Liquid-基本示例"))
)
st_pyecharts(c)
def render_basic_line():
with st.echo("below"):
options = {
"xAxis": {
"type": "category",
"data": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
},
"yAxis": {"type": "value"},
"series": [
{"data": [820, 932, 901, 934, 1290, 1330, 1320], "type": "line"}
],
}
st_echarts(
options=options,
height="400px",
)
st_echarts(
options=options,
height="400px",
theme="dark",
)
def render_event():
with st.echo("below"):
st.markdown("Click on chart elements")
options = {
"xAxis": {
"data": [
"shirt", "cardign", "chiffon shirt", "pants", "heels",
"socks"
]
},
"yAxis": {},
"series": [{
"name": "sales",
"type": "bar",
"data": [5, 20, 36, 10, 10, 20]
}],
}
events = {
"click": "function(params, echarts) {alert('click detection');}"
}
st_echarts(options, events=events)
def bar_status_completion(planning_summary):
df = planning_summary[planning_summary["status_completion"] != "On Progress"]
counting = df["status_completion"].value_counts()
xaxis = counting.index.tolist()
yaxis = counting.tolist()
st_echarts({
"title": {
"text": "Planning Completion"
},
# "legend": {
# "data": ['completion']
# },
"xAxis": {
"type": "category",
"data": xaxis
},
"yAxis": { "type": "value" },
"series": [
{"data": yaxis, "type": "bar" }
],
})
def render_basic_tree():
with open("./data/flare.json", "r") as f:
data = json.loads(f.read())
for idx, _ in enumerate(data["children"]):
data["children"][idx]["collapsed"] = idx % 2 == 0
option = {
"tooltip": {"trigger": "item", "triggerOn": "mousemove"},
"series": [
{
"type": "tree",
"data": [data],
"top": "1%",
"left": "7%",
"bottom": "1%",
"right": "20%",
"symbolSize": 7,
"label": {
"position": "left",
"verticalAlign": "middle",
"align": "right",
"fontSize": 9,
},
"leaves": {
"label": {
"position": "right",
"verticalAlign": "middle",
"align": "left",
}
},
"emphasis": {"focus": "descendant"},
"expandAndCollapse": True,
"animationDuration": 550,
"animationDurationUpdate": 750,
}
],
}
st_echarts(option, height="500px")
def render_effect_scatter():
with st.echo("below"):
options = {
"xAxis": {"scale": True},
"yAxis": {"scale": True},
"series": [
{
"type": "effectScatter",
"symbolSize": 20,
"data": [[161.2, 51.6], [167.5, 59]],
},
{
"type": "scatter",
"data": [
[161.2, 51.6],
[167.5, 59.0],
[159.5, 49.2],
[157.0, 63.0],
[155.8, 53.6],
[170.0, 59.0],
[159.1, 47.6],
[166.0, 69.8],
[176.2, 66.8],
[160.2, 75.2],
[172.5, 55.2],
[170.9, 54.2],
[172.9, 62.5],
[153.4, 42.0],
[160.0, 50.0],
[176.5, 71.8],
[164.4, 55.5],
[160.7, 48.6],
[174.0, 66.4],
[163.8, 67.3],
],
},
],
}
st_echarts(options)
def render_datazoom():
with st.echo("below"):
data = [
["14.616", "7.241", "0.896"],
["3.958", "5.701", "0.955"],
["2.768", "8.971", "0.669"],
["9.051", "9.710", "0.171"],
["14.046", "4.182", "0.536"],
["12.295", "1.429", "0.962"],
["4.417", "8.167", "0.113"],
["0.492", "4.771", "0.785"],
["7.632", "2.605", "0.645"],
["14.242", "5.042", "0.368"],
]
option_js = {
"xAxis": {
"type": "value"
},
"yAxis": {
"type": "value"
},
"dataZoom": [{
"type": "slider",
"start": 10,
"end": 60
}],
"series": [{
"type":
"scatter",
"itemStyle": {
"opacity": 0.8
},
"symbolSize":
JsCode("""function (val) { return val[2] * 40; }""").js_code,
"data":
data,
}],
}
st_echarts(options=option_js)
def render_calendar_heatmap():
with st.echo("below"):
def get_virtual_data(year):
date_list = pd.date_range(
start=f"{year}-01-01", end=f"{year + 1}-01-01", freq="D"
)
return [[d.strftime("%Y-%m-%d"), randint(1, 10000)] for d in date_list]
options = {
"title": {"top": 30, "left": "center", "text": "2016年某人每天的步数"},
"tooltip": {},
"visualMap": {
"min": 0,
"max": 10000,
"type": "piecewise",
"orient": "horizontal",
"left": "center",
"top": 65,
"textStyle": {"color": "#000"},
},
"calendar": {
"top": 120,
"left": 30,
"right": 30,
"cellSize": ["auto", 13],
"range": "2016",
"itemStyle": {"borderWidth": 0.5},
"yearLabel": {"show": False},
},
"series": {
"type": "heatmap",
"coordinateSystem": "calendar",
"data": get_virtual_data(2016),
},
}
st_echarts(options)
def render_map():
with st.echo("below"):
options = {
"backgroundColor": "#404a59",
"title": {
"text": "全国主要城市空气质量",
"subtext": "data from PM25.in",
"sublink": "http://www.pm25.in",
"left": "center",
"textStyle": {"color": "#fff"},
},
"tooltip": {"trigger": "item"},
"legend": {
"orient": "vertical",
"top": "bottom",
"left": "right",
"data": ["pm2.5"],
"textStyle": {"color": "#fff"},
},
"visualMap": {
"min": 0,
"max": 300,
"splitNumber": 5,
"color": ["#d94e5d", "#eac736", "#50a3ba"],
"textStyle": {"color": "#fff"},
},
"geo": {
"map": "china",
"label": {"emphasis": {"show": False}},
"itemStyle": {
"normal": {"areaColor": "#323c48", "borderColor": "#111"},
"emphasis": {"areaColor": "#2a333d"},
},
},
}
st_echarts(options)
def render_scatter_simple():
options = {
"xAxis": {},
"yAxis": {},
"series": [{
"symbolSize":
20,
"data": [
[10.0, 8.04],
[8.07, 6.95],
[13.0, 7.58],
[9.05, 8.81],
[11.0, 8.33],
[14.0, 7.66],
[13.4, 6.81],
[10.0, 6.33],
[14.0, 8.96],
[12.5, 6.82],
[9.15, 7.20],
[11.5, 7.20],
[3.03, 4.23],
[12.2, 7.83],
[2.02, 4.47],
[1.05, 3.33],
[4.05, 4.96],
[6.03, 7.24],
[12.0, 6.26],
[12.0, 8.84],
[7.08, 5.82],
[5.02, 5.68],
],
"type":
"scatter",
}],
}
st_echarts(options=options, height="500px")
def render_scatter_single_axis():
hours = [
"12a",
"1a",
"2a",
"3a",
"4a",
"5a",
"6a",
"7a",
"8a",
"9a",
"10a",
"11a",
"12p",
"1p",
"2p",
"3p",
"4p",
"5p",
"6p",
"7p",
"8p",
"9p",
"10p",
"11p",
]
days = [
"Saturday",
"Friday",
"Thursday",
"Wednesday",
"Tuesday",
"Monday",
"Sunday",
]
data = [
[0, 0, 5],
[0, 1, 1],
[0, 2, 0],
[0, 3, 0],
[0, 4, 0],
[0, 5, 0],
[0, 6, 0],
[0, 7, 0],
[0, 8, 0],
[0, 9, 0],
[0, 10, 0],
[0, 11, 2],
[0, 12, 4],
[0, 13, 1],
[0, 14, 1],
[0, 15, 3],
[0, 16, 4],
[0, 17, 6],
[0, 18, 4],
[0, 19, 4],
[0, 20, 3],
[0, 21, 3],
[0, 22, 2],
[0, 23, 5],
[1, 0, 7],
[1, 1, 0],
[1, 2, 0],
[1, 3, 0],
[1, 4, 0],
[1, 5, 0],
[1, 6, 0],
[1, 7, 0],
[1, 8, 0],
[1, 9, 0],
[1, 10, 5],
[1, 11, 2],
[1, 12, 2],
[1, 13, 6],
[1, 14, 9],
[1, 15, 11],
[1, 16, 6],
[1, 17, 7],
[1, 18, 8],
[1, 19, 12],
[1, 20, 5],
[1, 21, 5],
[1, 22, 7],
[1, 23, 2],
[2, 0, 1],
[2, 1, 1],
[2, 2, 0],
[2, 3, 0],
[2, 4, 0],
[2, 5, 0],
[2, 6, 0],
[2, 7, 0],
[2, 8, 0],
[2, 9, 0],
[2, 10, 3],
[2, 11, 2],
[2, 12, 1],
[2, 13, 9],
[2, 14, 8],
[2, 15, 10],
[2, 16, 6],
[2, 17, 5],
[2, 18, 5],
[2, 19, 5],
[2, 20, 7],
[2, 21, 4],
[2, 22, 2],
[2, 23, 4],
[3, 0, 7],
[3, 1, 3],
[3, 2, 0],
[3, 3, 0],
[3, 4, 0],
[3, 5, 0],
[3, 6, 0],
[3, 7, 0],
[3, 8, 1],
[3, 9, 0],
[3, 10, 5],
[3, 11, 4],
[3, 12, 7],
[3, 13, 14],
[3, 14, 13],
[3, 15, 12],
[3, 16, 9],
[3, 17, 5],
[3, 18, 5],
[3, 19, 10],
[3, 20, 6],
[3, 21, 4],
[3, 22, 4],
[3, 23, 1],
[4, 0, 1],
[4, 1, 3],
[4, 2, 0],
[4, 3, 0],
[4, 4, 0],
[4, 5, 1],
[4, 6, 0],
[4, 7, 0],
[4, 8, 0],
[4, 9, 2],
[4, 10, 4],
[4, 11, 4],
[4, 12, 2],
[4, 13, 4],
[4, 14, 4],
[4, 15, 14],
[4, 16, 12],
[4, 17, 1],
[4, 18, 8],
[4, 19, 5],
[4, 20, 3],
[4, 21, 7],
[4, 22, 3],
[4, 23, 0],
[5, 0, 2],
[5, 1, 1],
[5, 2, 0],
[5, 3, 3],
[5, 4, 0],
[5, 5, 0],
[5, 6, 0],
[5, 7, 0],
[5, 8, 2],
[5, 9, 0],
[5, 10, 4],
[5, 11, 1],
[5, 12, 5],
[5, 13, 10],
[5, 14, 5],
[5, 15, 7],
[5, 16, 11],
[5, 17, 6],
[5, 18, 0],
[5, 19, 5],
[5, 20, 3],
[5, 21, 4],
[5, 22, 2],
[5, 23, 0],
[6, 0, 1],
[6, 1, 0],
[6, 2, 0],
[6, 3, 0],
[6, 4, 0],
[6, 5, 0],
[6, 6, 0],
[6, 7, 0],
[6, 8, 0],
[6, 9, 0],
[6, 10, 1],
[6, 11, 0],
[6, 12, 2],
[6, 13, 1],
[6, 14, 3],
[6, 15, 4],
[6, 16, 0],
[6, 17, 0],
[6, 18, 0],
[6, 19, 0],
[6, 20, 1],
[6, 21, 2],
[6, 22, 2],
[6, 23, 6],
]
option = {
"tooltip": {
"position": "top"
},
"title": [{
"textBaseline": "middle",
"top": f"{(idx + 0.5) * 100 / 7}%",
"text": day
} for idx, day in enumerate(days)],
"singleAxis": [{
"left": 150,
"type": "category",
"boundaryGap": False,
"data": hours,
"top": f"{(idx * 100 / 7 + 5)}%",
"height": f"{(100 / 7 - 10)}%",
"axisLabel": {
"interval": 2
},
} for idx, _ in enumerate(days)],
"series": [{
"singleAxisIndex":
idx,
"coordinateSystem":
"singleAxis",
"type":
"scatter",
"data": [],
"symbolSize":
JsCode("function(dataItem){return dataItem[1]*4}").js_code,
} for idx, _ in enumerate(days)],
}
for dataItem in data:
option["series"][dataItem[0]]["data"].append(
[dataItem[1], dataItem[2]])
st_echarts(options=option, height="600px")
st.markdown("---")
# top = st.slider('Select year of interest:',min_value=10,max_value=30,value=20)
st.markdown("### An overview")
st.image(im5, use_column_width=True)
top = 25
htmap, bar, country_list = get_overall(top)
st.plotly_chart(bar, use_container_width=True)
st.image(im6, use_column_width=True)
st.plotly_chart(htmap, use_container_width=True)
st.markdown("### Country's medal in discipline over year")
st.image(im7, use_column_width=True)
country = st.selectbox('Choose the country you want to explore:', country_list)
st.write("Performance by year of ", country)
options = get_domain_plot(country)
st_echarts(options=options, height="600px")
st.image(im4, use_column_width=True)
st.markdown("### Performance in each sports")
st.image(im8, use_column_width=True)
options_sample = get_sample()
st_echarts(options=options_sample, height="500px")
st.markdown("# The wealthier, the better?")
st.markdown("---")
st.image(im1, use_column_width=True)
gdp_years = [1992, 1994, 1998, 2002, 2006, 2010, 2014]
# the first part: gdp - rank
year = st.slider('', min_value=1994, max_value=2014, step=4)
st.sidebar.markdown("### Customize the score calculation methods here:")
def render_waterfall_chart():
options = {
"title": {
"text": "阶梯瀑布图",
"subtext": "From ExcelHome",
"sublink": "http://e.weibo.com/1341556070/Aj1J2x5a5",
},
"tooltip": {
"trigger":
"axis",
"axisPointer": {
"type": "shadow"
},
"formatter":
JsCode(
"function(params){var tar;if(params[1].value!=='-'){tar=params[1]}else{tar=params[0]}return tar.name+'<br/>'+tar.seriesName+' : '+tar.value}"
).js_code,
},
"legend": {
"data": ["支出", "收入"]
},
"grid": {
"left": "3%",
"right": "4%",
"bottom": "3%",
"containLabel": True
},
"xAxis": {
"type": "category",
"splitLine": {
"show": False
},
"data": [f"11月 {i} 日" for i in range(1, 12)],
},
"yAxis": {
"type": "value"
},
"series": [
{
"name":
"辅助",
"type":
"bar",
"stack":
"总量",
"itemStyle": {
"barBorderColor": "rgba(0,0,0,0)",
"color": "rgba(0,0,0,0)",
},
"emphasis": {
"itemStyle": {
"barBorderColor": "rgba(0,0,0,0)",
"color": "rgba(0,0,0,0)",
}
},
"data":
[0, 900, 1245, 1530, 1376, 1376, 1511, 1689, 1856, 1495, 1292],
},
{
"name": "收入",
"type": "bar",
"stack": "总量",
"label": {
"show": True,
"position": "top"
},
"data":
[900, 345, 393, "-", "-", 135, 178, 286, "-", "-", "-"],
},
{
"name": "支出",
"type": "bar",
"stack": "总量",
"label": {
"show": True,
"position": "bottom"
},
"data":
["-", "-", "-", 108, 154, "-", "-", "-", 119, 361, 203],
},
],
}
st_echarts(options=options, height="500px")
def render_stacked_horizontal_bar():
options = {
"tooltip": {
"trigger": "axis",
"axisPointer": {
"type": "shadow"
}
},
"legend": {
"data":
["Direct", "Mail Ad", "Affiliate Ad", "Video Ad", "Search Engine"]
},
"grid": {
"left": "3%",
"right": "4%",
"bottom": "3%",
"containLabel": True
},
"xAxis": {
"type": "value"
},
"yAxis": {
"type": "category",
"data": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
},
"series": [
{
"name": "Direct",
"type": "bar",
"stack": "total",
"label": {
"show": True
},
"emphasis": {
"focus": "series"
},
"data": [320, 302, 301, 334, 390, 330, 320],
},
{
"name": "Mail Ad",
"type": "bar",
"stack": "total",
"label": {
"show": True
},
"emphasis": {
"focus": "series"
},
"data": [120, 132, 101, 134, 90, 230, 210],
},
{
"name": "Affiliate Ad",
"type": "bar",
"stack": "total",
"label": {
"show": True
},
"emphasis": {
"focus": "series"
},
"data": [220, 182, 191, 234, 290, 330, 310],
},
{
"name": "Video Ad",
"type": "bar",
"stack": "total",
"label": {
"show": True
},
"emphasis": {
"focus": "series"
},
"data": [150, 212, 201, 154, 190, 330, 410],
},
{
"name": "Search Engine",
"type": "bar",
"stack": "total",
"label": {
"show": True
},
"emphasis": {
"focus": "series"
},
"data": [820, 832, 901, 934, 1290, 1330, 1320],
},
],
}
st_echarts(options=options, height="500px")
def render_wordcloud():
with st.echo("below"):
options = {
"tooltip": {},
"series": [
{
"type": "wordCloud",
"gridSize": 2,
"sizeRange": [12, 50],
"rotationRange": [-90, 90],
"shape": "pentagon",
"width": 600,
"height": 400,
"drawOutOfBound": True,
"emphasis": {
"textStyle": {"shadowBlur": 10, "shadowColor": "#333"}
},
"data": [
{
"name": "Sam S Club",
"value": 10000,
"textStyle": {"color": "black"},
"emphasis": {"textStyle": {"color": "red"}},
},
{"name": "Macys", "value": 6181},
{"name": "Amy Schumer", "value": 4386},
{"name": "Jurassic World", "value": 4055},
{"name": "Charter Communications", "value": 2467},
{"name": "Chick Fil A", "value": 2244},
{"name": "Planet Fitness", "value": 1898},
{"name": "Pitch Perfect", "value": 1484},
{"name": "Express", "value": 1112},
{"name": "Home", "value": 965},
{"name": "Johnny Depp", "value": 847},
{"name": "Lena Dunham", "value": 582},
{"name": "Lewis Hamilton", "value": 555},
{"name": "KXAN", "value": 550},
{"name": "Mary Ellen Mark", "value": 462},
{"name": "Farrah Abraham", "value": 366},
{"name": "Rita Ora", "value": 360},
{"name": "Serena Williams", "value": 282},
{"name": "NCAA baseball tournament", "value": 273},
{"name": "Point Break", "value": 265},
],
}
],
}
st_echarts(options)
data = [
("生活资源", "999"),
("供热管理", "888"),
("供气质量", "777"),
("生活用水管理", "688"),
("一次供水问题", "588"),
("交通运输", "516"),
("城市交通", "515"),
("环境保护", "483"),
("房地产管理", "462"),
("城乡建设", "449"),
("社会保障与福利", "429"),
("社会保障", "407"),
("文体与教育管理", "406"),
("公共安全", "406"),
("公交运输管理", "386"),
("出租车运营管理", "385"),
("供热管理", "375"),
("市容环卫", "355"),
("自然资源管理", "355"),
("粉尘污染", "335"),
("噪声污染", "324"),
("土地资源管理", "304"),
("物业服务与管理", "304"),
("医疗卫生", "284"),
("粉煤灰污染", "284"),
("占道", "284"),
("供热发展", "254"),
("农村土地规划管理", "254"),
("生活噪音", "253"),
("供热单位影响", "253"),
("城市供电", "223"),
("房屋质量与安全", "223"),
("大气污染", "223"),
("房屋安全", "223"),
("文化活动", "223"),
("拆迁管理", "223"),
("公共设施", "223"),
("供气质量", "223"),
("供电管理", "223"),
("燃气管理", "152"),
("教育管理", "152"),
("医疗纠纷", "152"),
("执法监督", "152"),
("设备安全", "152"),
("政务建设", "152"),
("县区、开发区", "152"),
("宏观经济", "152"),
("教育管理", "112"),
("社会保障", "112"),
("生活用水管理", "112"),
("物业服务与管理", "112"),
("分类列表", "112"),
("农业生产", "112"),
("二次供水问题", "112"),
("城市公共设施", "92"),
("拆迁政策咨询", "92"),
("物业服务", "92"),
("物业管理", "92"),
("社会保障保险管理", "92"),
("低保管理", "92"),
("文娱市场管理", "72"),
("城市交通秩序管理", "72"),
("执法争议", "72"),
("商业烟尘污染", "72"),
("占道堆放", "71"),
("地上设施", "71"),
("水质", "71"),
("无水", "71"),
("供热单位影响", "71"),
("人行道管理", "71"),
("主网原因", "71"),
("集中供热", "71"),
("客运管理", "71"),
("国有公交（大巴）管理", "71"),
("工业粉尘污染", "71"),
("治安案件", "71"),
("压力容器安全", "71"),
("身份证管理", "71"),
("群众健身", "41"),
("工业排放污染", "41"),
("破坏森林资源", "41"),
("市场收费", "41"),
("生产资金", "41"),
("生产噪声", "41"),
("农村低保", "41"),
("劳动争议", "41"),
("劳动合同争议", "41"),
("劳动报酬与福利", "41"),
("医疗事故", "21"),
("停供", "21"),
("基础教育", "21"),
("职业教育", "21"),
("物业资质管理", "21"),
("拆迁补偿", "21"),
("设施维护", "21"),
("市场外溢", "11"),
("占道经营", "11"),
("树木管理", "11"),
("农村基础设施", "11"),
("无水", "11"),
("供气质量", "11"),
("停气", "11"),
("市政府工作部门（含部门管理机构、直属单位）", "11"),
("燃气管理", "11"),
("市容环卫", "11"),
("新闻传媒", "11"),
("人才招聘", "11"),
("市场环境", "11"),
("行政事业收费", "11"),
("食品安全与卫生", "11"),
("城市交通", "11"),
("房地产开发", "11"),
("房屋配套问题", "11"),
("物业服务", "11"),
("物业管理", "11"),
("占道", "11"),
("园林绿化", "11"),
("户籍管理及身份证", "11"),
("公交运输管理", "11"),
("公路（水路）交通", "11"),
("房屋与图纸不符", "11"),
("有线电视", "11"),
("社会治安", "11"),
("林业资源", "11"),
("其他行政事业收费", "11"),
("经营性收费", "11"),
("食品安全与卫生", "11"),
("体育活动", "11"),
("有线电视安装及调试维护", "11"),
("低保管理", "11"),
("劳动争议", "11"),
("社会福利及事务", "11"),
("一次供水问题", "11"),
]
c = (
WordCloud()
.add(series_name="热点分析", data_pair=data, word_size_range=[6, 66])
.set_global_opts(
title_opts=opts.TitleOpts(
title="热点分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
)
st_pyecharts(c)
def render_nightingale_rose_diagram():
option = {
"legend": {
"top": "bottom"
},
"toolbox": {
"show": True,
"feature": {
"mark": {
"show": True
},
"dataView": {
"show": True,
"readOnly": False
},
"restore": {
"show": True
},
"saveAsImage": {
"show": True
},
},
},
"series": [{
"name":
"面积模式",
"type":
"pie",
"radius": [50, 250],
"center": ["50%", "50%"],
"roseType":
"area",
"itemStyle": {
"borderRadius": 8
},
"data": [
{
"value": 40,
"name": "rose 1"
},
{
"value": 38,
"name": "rose 2"
},
{
"value": 32,
"name": "rose 3"
},
{
"value": 30,
"name": "rose 4"
},
{
"value": 28,
"name": "rose 5"
},
{
"value": 26,
"name": "rose 6"
},
{
"value": 22,
"name": "rose 7"
},
{
"value": 18,
"name": "rose 8"
},
],
}],
}
st_echarts(
options=option,
height="600px",
)
"label": {
"position": "right",
"verticalAlign": "middle",
"align": "left",
}
},
"expandAndCollapse": True,
"animationDuration": 550,
"animationDurationUpdate": 750,
}],
}
st.markdown('### **⬇️ Interactive Tree View **')
st.markdown('*Right-click to save Tree View as .jpeg* 📷')
st_echarts(opts, height=1000)
st.markdown('### **⬇️ Table View **')
edges = edges.reset_index(drop=True)
st.dataframe(edges, width=1700, height=1000)
import matplotlib as plt
import seaborn as sns
cm = sns.light_palette("green", as_cmap=True)
#edgescoloured = edges.style.background_gradient(cmap=cm)
edgescoloured = edges.style.background_gradient(cmap='Blues')
#format_dictionary = {
def render_pie_donutradius():
options = {
"tooltip": {
"trigger": "item"
},
"legend": {
"top": "5%",
"left": "center"
},
"series": [{
"name":
"访问来源",
"type":
"pie",
"radius": ["40%", "70%"],
"avoidLabelOverlap":
False,
"itemStyle": {
"borderRadius": 10,
"borderColor": "#fff",
"borderWidth": 2,
},
"label": {
"show": False,
"position": "center"
},
"emphasis": {
"label": {
"show": True,
"fontSize": "40",
"fontWeight": "bold"
}
},
"labelLine": {
"show": False
},
"data": [
{
"value": 1048,
"name": "搜索引擎"
},
{
"value": 735,
"name": "直接访问"
},
{
"value": 580,
"name": "邮件营销"
},
{
"value": 484,
"name": "联盟广告"
},
{
"value": 300,
"name": "视频广告"
},
],
}],
}
st_echarts(
options=options,
height="500px",
)
def main():
st.sidebar.title("Stream Vis")
st.sidebar.markdown("""
Streamlit Visualization using Echart
""")
try:
df = fileUpload()
st.sidebar.markdown("""---""")
st.write(df)
option = selectVisual()
if option == "Bar":
data = form.bar(df)
if st.button('Generate Bar'):
opt = Visual.barchart(df, data['show'], data['by'],
data['andby'], data['orientation'],
data['title'])
opt['color'] = colormap_check(data['color'])
st_echarts(opt)
elif option == "Line":
data = form.line(df)
if st.button('Generate Line'):
opt = Visual.line(df, data['show'], data['by'], data['andby'],
data['title'])
opt['color'] = colormap_check(data['color'])
st_echarts(opt)
elif option == "Pie":
data = form.pie(df)
if st.button("Generate Pie"):
opt = Visual.pie(df, data['show'], data['by'], data['title'],
data['doughnut'])
opt['color'] = colormap_check(data['color'])
st_echarts(opt)
elif option == "Scatter":
data = form.scatter(df)
if st.button("Generate Scatter"):
opt = Visual.scatter(df, data['x'], data['y'], data['group'],
data['title'])
opt['color'] = colormap_check(data['color'])
st_echarts(opt)
elif option == "Box":
data = form.boxplot(df)
if st.button("Generate Box"):
opt = Visual.boxplot(df, data['show'], data['by'],
data['orientation'], data['title'])
opt['color'] = colormap_check(data['color'])
st_echarts(opt['option'])
elif option == "Heatmap":
data = form.heatmap(df)
if st.button('Generate Heatmap'):
opt = Visual.heatmap(df, data['x'], data['y'], data['value'],
data['title'])
opt['color'] = colormap_check(data['color'])
st_echarts(opt)
elif option == "Histogram":
data = form.histogram(df)
if st.button("Generate Histogram"):
opt = Visual.histogram(df, data['show'], data['title'])
opt['color'] = colormap_check(data['color'])
st_echarts(opt)
elif option == "Map":
data = form.geomap(df)
st.echo(data)
if st.button("Generate Map"):
opt = Visual.geomap(df, data["region"], data["value"],
data["map_title"], data["title"])
if data['map'] == "jakarta":
with open("lib/jakarta.json", "r") as f:
map = Map(data["map_title"], json.loads(f.read()))
elif data['map'] == "jogja":
with open("lib/jogja.json", "r") as f:
map = Map(data["map_title"], json.loads(f.read()))
st_echarts(opt, map=map)
elif option == "Radar":
data = form.radar(df)
if st.button("Generate Radar"):
opt = Visual.radar(df, data["show"], data["by"], data["title"],
data["method"])
st.echo(opt)
st_echarts(opt)
else:
if st.button("Generate"):
opts = Visual.auto_generator(df)
for x in opts:
st_echarts(x)
except:
st.markdown("""
There is no dataset
""")
def main():
st.title('Votes for women MPs - UK')
df = load_data(full_spreadsheet=False).transpose()
series_data = []
for r in df:
election_data = list(df[r])
series_data.append({
'type': 'bar',
'stack': 'total',
'label': {
'show': False
},
'emphasis': {
'focus': 'opacity'
},
'data': election_data
})
xaxis_data = list(df.index)
options = {
'grid': {
'left': '3%',
'right': '4%',
'bottom': '3%',
'containLabel': True
},
'yAxis': {
'type': 'value'
},
'xAxis': {
'type': 'category',
'axisLabel': {
'show': True,
'rotate': 45
},
'data': [1]
},
'dataZoom': [{
'show': True,
'start': 0,
'end': 50
}, {
'type': 'inside',
'start': 0,
'end': 20
}],
'series': [
{
'type': 'bar',
'stack': 'total',
'label': {
'show': True
},
'emphasis': {
'focus': 'opacity'
},
'data': []
},
]
}
options['series'] = series_data
options['xAxis']['data'] = xaxis_data
theme = {
'color': [
'#d87c7c', '#919e8b', '#d7ab82', '#6e7074', '#61a0a8', '#efa18d',
'#787464', '#cc7e63', '#724e58', '#4b565b'
],
'backgroundColor':
'rgba(254,248,239,1)',
'textStyle': {},
'title': {
'textStyle': {
'color': '#333333'
},
'subtextStyle': {
'color': '#aaaaaa'
}
},
'line': {
'itemStyle': {
'borderWidth': 1
},
'lineStyle': {
'width': 2
},
'symbolSize': 4,
'symbol': 'emptyCircle',
'smooth': False
},
'radar': {
'itemStyle': {
'borderWidth': 1
},
'lineStyle': {
'width': 2
},
'symbolSize': 4,
'symbol': 'emptyCircle',
'smooth': False
},
'bar': {
'itemStyle': {
'barBorderWidth': 0,
'barBorderColor': '#ccc'
}
},
'pie': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
}
},
'scatter': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
}
},
'boxplot': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
}
},
'parallel': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
}
},
'sankey': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
}
},
'funnel': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
}
},
'gauge': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
}
},
'candlestick': {
'itemStyle': {
'color': '#c23531',
'color0': '#314656',
'borderColor': '#c23531',
'borderColor0': '#314656',
'borderWidth': 1
}
},
'graph': {
'itemStyle': {
'borderWidth': 0,
'borderColor': '#ccc'
},
'lineStyle': {
'width': 1,
'color': '#aaa'
},
'symbolSize':
4,
'symbol':
'emptyCircle',
'smooth':
False,
'color': [
'#d87c7c', '#919e8b', '#d7ab82', '#6e7074', '#61a0a8',
'#efa18d', '#787464', '#cc7e63', '#724e58', '#4b565b'
],
'label': {
'color': '#eee'
}
},
'map': {
'itemStyle': {
'normal': {
'areaColor': '#eeeeee',
'borderColor': '#444444',
'borderWidth': 0.5
},
'emphasis': {
'areaColor': 'rgba(255,215,0,0.8)',
'borderColor': '#444444',
'borderWidth': 1
}
},
'label': {
'normal': {
'textStyle': {
'color': '#000000'
}
},
'emphasis': {
'textStyle': {
'color': 'rgb(100,0,0)'
}
}
}
},
'geo': {
'itemStyle': {
'normal': {
'areaColor': '#eeeeee',
'borderColor': '#444444',
'borderWidth': 0.5
},
'emphasis': {
'areaColor': 'rgba(255,215,0,0.8)',
'borderColor': '#444444',
'borderWidth': 1
}
},
'label': {
'normal': {
'textStyle': {
'color': '#000000'
}
},
'emphasis': {
'textStyle': {
'color': 'rgb(100,0,0)'
}
}
}
},
'categoryAxis': {
'axisLine': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisTick': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisLabel': {
'show': True,
'textStyle': {
'color': '#333'
}
},
'splitLine': {
'show': False,
'lineStyle': {
'color': ['#ccc']
}
},
'splitArea': {
'show': False,
'areaStyle': {
'color':
['rgba(250,250,250,0.3)', 'rgba(200,200,200,0.3)']
}
}
},
'valueAxis': {
'axisLine': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisTick': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisLabel': {
'show': True,
'textStyle': {
'color': '#333'
}
},
'splitLine': {
'show': True,
'lineStyle': {
'color': ['#ccc']
}
},
'splitArea': {
'show': False,
'areaStyle': {
'color':
['rgba(250,250,250,0.3)', 'rgba(200,200,200,0.3)']
}
}
},
'logAxis': {
'axisLine': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisTick': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisLabel': {
'show': True,
'textStyle': {
'color': '#333'
}
},
'splitLine': {
'show': True,
'lineStyle': {
'color': ['#ccc']
}
},
'splitArea': {
'show': False,
'areaStyle': {
'color':
['rgba(250,250,250,0.3)', 'rgba(200,200,200,0.3)']
}
}
},
'timeAxis': {
'axisLine': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisTick': {
'show': True,
'lineStyle': {
'color': '#333'
}
},
'axisLabel': {
'show': True,
'textStyle': {
'color': '#333'
}
},
'splitLine': {
'show': True,
'lineStyle': {
'color': ['#ccc']
}
},
'splitArea': {
'show': False,
'areaStyle': {
'color':
['rgba(250,250,250,0.3)', 'rgba(200,200,200,0.3)']
}
}
},
'toolbox': {
'iconStyle': {
'normal': {
'borderColor': '#999999'
},
'emphasis': {
'borderColor': '#666666'
}
}
},
'legend': {
'textStyle': {
'color': '#333333'
}
},
'tooltip': {
'axisPointer': {
'lineStyle': {
'color': '#cccccc',
'width': 1
},
'crossStyle': {
'color': '#cccccc',
'width': 1
}
}
},
'timeline': {
'lineStyle': {
'color': '#293c55',
'width': 1
},
'itemStyle': {
'normal': {
'color': '#293c55',
'borderWidth': 1
},
'emphasis': {
'color': '#a9334c'
}
},
'controlStyle': {
'normal': {
'color': '#293c55',
'borderColor': '#293c55',
'borderWidth': 0.5
},
'emphasis': {
'color': '#293c55',
'borderColor': '#293c55',
'borderWidth': 0.5
}
},
'checkpointStyle': {
'color': '#e43c59',
'borderColor': '#c23531'
},
'label': {
'normal': {
'textStyle': {
'color': '#293c55'
}
},
'emphasis': {
'textStyle': {
'color': '#293c55'
}
}
}
},
'visualMap': {
'color': ['#bf444c', '#d88273', '#f6efa6']
},
'dataZoom': {
'backgroundColor': 'rgba(47,69,84,0)',
'dataBackgroundColor': 'rgba(47,69,84,0.3)',
'fillerColor': 'rgba(167,183,204,0.4)',
'handleColor': '#a7b7cc',
'handleSize': '100%',
'textStyle': {
'color': '#333333'
}
},
'markPoint': {
'label': {
'color': '#eee'
},
'emphasis': {
'label': {
'color': '#eee'
}
}
}
}
st_echarts(options=options, height="600px", theme=theme)
df = load_data().transpose()
show_party(df)
if st.checkbox('Show raw data'):
st.subheader('Raw data')
df = load_data(full_spreadsheet=True).transpose()
st.write(df)
df = load_data().transpose()
df = set_column_labels_cut_metadata(df)
df['total'] = df.sum(axis=1)
df["total"] = pd.to_numeric(df["total"])
df.sort_values(by='total', inplace=True, ascending=False)
df['name'] = df.index
st.write(df.total)
"type": "line",
"stack": "headcount",
"label": {
"show": True,
"position": "top"
},
"areaStyle": {},
"emphasis": {
"focus": "series"
},
"data": [820, 932, 901, 934, 1290, 1330, 1320],
},
],
}
st_echarts(options=options_1, height="400px")
options_2 = {
"title": {
"text": "折线图堆叠"
},
"tooltip": {
"trigger": "axis"
},
"legend": {
"data": ["邮件营销", "联盟广告", "视频广告", "直接访问", "搜索引擎"]
},
"grid": {
"left": "3%",
"right": "4%",
"bottom": "3%",
def render_anscombe_quartet():
data = [
[
[10.0, 8.04],
[8.0, 6.95],
[13.0, 7.58],
[9.0, 8.81],
[11.0, 8.33],
[14.0, 9.96],
[6.0, 7.24],
[4.0, 4.26],
[12.0, 10.84],
[7.0, 4.82],
[5.0, 5.68],
],
[
[10.0, 9.14],
[8.0, 8.14],
[13.0, 8.74],
[9.0, 8.77],
[11.0, 9.26],
[14.0, 8.10],
[6.0, 6.13],
[4.0, 3.10],
[12.0, 9.13],
[7.0, 7.26],
[5.0, 4.74],
],
[
[10.0, 7.46],
[8.0, 6.77],
[13.0, 12.74],
[9.0, 7.11],
[11.0, 7.81],
[14.0, 8.84],
[6.0, 6.08],
[4.0, 5.39],
[12.0, 8.15],
[7.0, 6.42],
[5.0, 5.73],
],
[
[8.0, 6.58],
[8.0, 5.76],
[8.0, 7.71],
[8.0, 8.84],
[8.0, 8.47],
[8.0, 7.04],
[8.0, 5.25],
[19.0, 12.50],
[8.0, 5.56],
[8.0, 7.91],
[8.0, 6.89],
],
]
line_opt = {
"animation":
False,
"label": {
"formatter": "y = 0.5 * x + 3",
"align": "right"
},
"lineStyle": {
"type": "solid"
},
"tooltip": {
"formatter": "y = 0.5 * x + 3"
},
"data": [[{
"coord": [0, 3],
"symbol": None
}, {
"coord": [20, 13],
"symbol": None
}]],
}
option = {
"title": {
"text": "Anscombe's quartet",
"left": "center",
"top": 0
},
"grid": [
{
"left": "7%",
"top": "7%",
"width": "38%",
"height": "38%"
},
{
"right": "7%",
"top": "7%",
"width": "38%",
"height": "38%"
},
{
"left": "7%",
"bottom": "7%",
"width": "38%",
"height": "38%"
},
{
"right": "7%",
"bottom": "7%",
"width": "38%",
"height": "38%"
},
],
"tooltip": {
"formatter": "Group {a}: ({c})"
},
"xAxis": [
{
"gridIndex": 0,
"min": 0,
"max": 20
},
{
"gridIndex": 1,
"min": 0,
"max": 20
},
{
"gridIndex": 2,
"min": 0,
"max": 20
},
{
"gridIndex": 3,
"min": 0,
"max": 20
},
],
"yAxis": [
{
"gridIndex": 0,
"min": 0,
"max": 15
},
{
"gridIndex": 1,
"min": 0,
"max": 15
},
{
"gridIndex": 2,
"min": 0,
"max": 15
},
{
"gridIndex": 3,
"min": 0,
"max": 15
},
],
"series": [
{
"name": "I",
"type": "scatter",
"xAxisIndex": 0,
"yAxisIndex": 0,
"data": data[0],
"markLine": line_opt,
},
{
"name": "II",
"type": "scatter",
"xAxisIndex": 1,
"yAxisIndex": 1,
"data": data[1],
"markLine": line_opt,
},
{
"name": "III",
"type": "scatter",
"xAxisIndex": 2,
"yAxisIndex": 2,
"data": data[2],
"markLine": line_opt,
},
{
"name": "IV",
"type": "scatter",
"xAxisIndex": 3,
"yAxisIndex": 3,
"data": data[3],
"markLine": line_opt,
},
],
}
st_echarts(options=option, height="600px")
def mlp_regression_interface(dataset_df, dataset_column):
selected_features = st.sidebar.multiselect(
"Choose Features as predictor : ", list(dataset_column),
list(dataset_column))
if not selected_features:
st.sidebar.error("Please select at least one feature")
st.sidebar.write("## Method Parameter")
activation_type = ['identity', 'logistic', 'tanh', 'relu']
solver_type = ['lbfgs', 'sgd', 'adam']
hidden_layer_str = st.sidebar.text_input("Hidden layer ",
value="10,10",
max_chars=20)
hidden_layer = tuple(map(int, hidden_layer_str.split(',')))
activation = st.sidebar.selectbox("activation function ",
activation_type,
index=3)
solver = st.sidebar.selectbox("solver ", solver_type, index=2)
alpha = st.sidebar.number_input("alpha ", value=0.0001)
learning_rate = st.sidebar.number_input("learning rate ", value=0.001)
max_iter = st.sidebar.number_input("maximum iteration ",
value=200,
min_value=1,
step=1)
early_stopping = st.sidebar.checkbox("early stopping ", value=False)
not_selected_features = [
feature for feature in dataset_column
if feature not in selected_features
]
target = st.sidebar.selectbox("Choose Target column : ",
not_selected_features)
is_run = st.sidebar.button("Run")
is_feature_target = selected_features and target
if is_run and is_feature_target:
#Preprocessing
dp = DataPreparation(dataframe=dataset_df,
selected_features=selected_features,
target=target)
class_name = dp.get_class_names()
X_train, X_valid, y_train, y_valid = dp.data_modeling()
#Modeling
regre = MLP_Regression(hidden_layer_sizes=hidden_layer,
activation=activation,
solver=solver,
alpha=alpha,
learning_rate_init=learning_rate,
max_iter=max_iter,
early_stopping=early_stopping)
y_pred = regre.modeling(X_train, X_valid, y_train, y_valid)
#Metric Evaluation
rmse, mae, r2 = regre.metircs_model(y_valid, y_pred, class_name)
#Interpretation
fi_score = regre.feature_importance(X_valid, y_valid,
selected_features)
#Interface
st.write('## **Metrics**')
st.write('### **Main Metrics**')
st.write(f'RMSE Score : {rmse}')
st.write(f'MAE Score : {mae}')
st.write(f'R Squared Score : {r2}')
st.write('## **Interpretation**')
fi_plot_options = feature_importance_plot(fi_score)
st_echarts(fi_plot_options)
else:
st.sidebar.error("Please fill all method parameter")
def render_mixed_line_bar():
with st.echo("below"):
options = {
"tooltip": {
"trigger": "axis",
"axisPointer": {"type": "cross", "crossStyle": {"color": "#999"}},
},
"toolbox": {
"feature": {
"dataView": {"show": True, "readOnly": False},
"magicType": {"show": True, "type": ["line", "bar"]},
"restore": {"show": True},
"saveAsImage": {"show": True},
}
},
"legend": {"data": ["蒸发量", "降水量", "平均温度"]},
"xAxis": [
{
"type": "category",
"data": [
"1月",
"2月",
"3月",
"4月",
"5月",
"6月",
"7月",
"8月",
"9月",
"10月",
"11月",
"12月",
],
"axisPointer": {"type": "shadow"},
}
],
"yAxis": [
{
"type": "value",
"name": "水量",
"min": 0,
"max": 250,
"interval": 50,
"axisLabel": {"formatter": "{value} ml"},
},
{
"type": "value",
"name": "温度",
"min": 0,
"max": 25,
"interval": 5,
"axisLabel": {"formatter": "{value} °C"},
},
],
"series": [
{
"name": "蒸发量",
"type": "bar",
"data": [
2.0,
4.9,
7.0,
23.2,
25.6,
76.7,
135.6,
162.2,
32.6,
20.0,
6.4,
3.3,
],
},
{
"name": "降水量",
"type": "bar",
"data": [
2.6,
5.9,
9.0,
26.4,
28.7,
70.7,
175.6,
182.2,
48.7,
18.8,
6.0,
2.3,
],
},
{
"name": "平均温度",
"type": "line",
"yAxisIndex": 1,
"data": [
2.0,
2.2,
3.3,
4.5,
6.3,
10.2,
20.3,
23.4,
23.0,
16.5,
12.0,
6.2,
],
},
],
}
st_echarts(options)
def svm_regression_interface(dataset_df, dataset_column):
selected_features = st.sidebar.multiselect(
"Choose Features as predictor : ", list(dataset_column),
list(dataset_column))
if not selected_features:
st.sidebar.error("Please select at least one feature")
st.sidebar.write("## Method Parameter")
C_range = [1, 10, 100, 1000]
kernel_type = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
C = float(st.sidebar.selectbox("C ", C_range, index=0))
kernel = st.sidebar.selectbox("kernel ", kernel_type)
degree = st.sidebar.number_input("degree", min_value=1, value=3, step=1)
gamma = st.sidebar.slider("gamma ",
min_value=0.1,
max_value=1.0,
value=1.0,
step=0.01)
coef0 = st.sidebar.number_input("coef0", min_value=0, value=0)
not_selected_features = [
feature for feature in dataset_column
if feature not in selected_features
]
target = st.sidebar.selectbox("Choose Target column : ",
not_selected_features)
is_run = st.sidebar.button("Run")
is_feature_target = selected_features and target
if is_run and is_feature_target:
#Preprocessing
dp = DataPreparation(dataframe=dataset_df,
selected_features=selected_features,
target=target)
class_name = dp.get_class_names()
X_train, X_valid, y_train, y_valid = dp.data_modeling()
#Modeling
regre = SVM_Regression(C=C,
kernel=kernel,
degree=degree,
gamma=gamma,
coef0=coef0)
y_pred = regre.modeling(X_train, X_valid, y_train)
#Metric Evaluation
rmse, mae, r2 = regre.metircs_model(y_valid, y_pred, class_name)
#Interpretation
fi_score = regre.feature_importance(X_valid, y_valid,
selected_features)
#Interface
st.write('## **Metrics**')
st.write('### **Main Metrics**')
st.write(f'RMSE Score : {rmse}')
st.write(f'MAE Score : {mae}')
st.write(f'R Squared Score : {r2}')
st.write('## **Interpretation**')
fi_plot_options = feature_importance_plot(fi_score)
st_echarts(fi_plot_options)
else:
st.sidebar.error("Please fill all method parameter")
def render_themeriver():
option = {
"tooltip": {
"trigger": "axis",
"axisPointer": {
"type": "line",
"lineStyle": {"color": "rgba(0,0,0,0.2)", "width": 1, "type": "solid"},
},
},
"legend": {"data": ["DQ", "TY", "SS", "QG", "SY", "DD"]},
"singleAxis": {
"top": 50,
"bottom": 50,
"axisTick": {},
"axisLabel": {},
"type": "time",
"axisPointer": {"animation": True, "label": {"show": True}},
"splitLine": {
"show": True,
"lineStyle": {"type": "dashed", "opacity": 0.2},
},
},
"series": [
{
"type": "themeRiver",
"emphasis": {
"itemStyle": {"shadowBlur": 20, "shadowColor": "rgba(0, 0, 0, 0.8)"}
},
"data": [
["2015/11/08", 10, "DQ"],
["2015/11/09", 15, "DQ"],
["2015/11/10", 35, "DQ"],
["2015/11/11", 38, "DQ"],
["2015/11/12", 22, "DQ"],
["2015/11/13", 16, "DQ"],
["2015/11/14", 7, "DQ"],
["2015/11/15", 2, "DQ"],
["2015/11/16", 17, "DQ"],
["2015/11/17", 33, "DQ"],
["2015/11/18", 40, "DQ"],
["2015/11/19", 32, "DQ"],
["2015/11/20", 26, "DQ"],
["2015/11/21", 35, "DQ"],
["2015/11/22", 40, "DQ"],
["2015/11/23", 32, "DQ"],
["2015/11/24", 26, "DQ"],
["2015/11/25", 22, "DQ"],
["2015/11/26", 16, "DQ"],
["2015/11/27", 22, "DQ"],
["2015/11/28", 10, "DQ"],
["2015/11/08", 35, "TY"],
["2015/11/09", 36, "TY"],
["2015/11/10", 37, "TY"],
["2015/11/11", 22, "TY"],
["2015/11/12", 24, "TY"],
["2015/11/13", 26, "TY"],
["2015/11/14", 34, "TY"],
["2015/11/15", 21, "TY"],
["2015/11/16", 18, "TY"],
["2015/11/17", 45, "TY"],
["2015/11/18", 32, "TY"],
["2015/11/19", 35, "TY"],
["2015/11/20", 30, "TY"],
["2015/11/21", 28, "TY"],
["2015/11/22", 27, "TY"],
["2015/11/23", 26, "TY"],
["2015/11/24", 15, "TY"],
["2015/11/25", 30, "TY"],
["2015/11/26", 35, "TY"],
["2015/11/27", 42, "TY"],
["2015/11/28", 42, "TY"],
["2015/11/08", 21, "SS"],
["2015/11/09", 25, "SS"],
["2015/11/10", 27, "SS"],
["2015/11/11", 23, "SS"],
["2015/11/12", 24, "SS"],
["2015/11/13", 21, "SS"],
["2015/11/14", 35, "SS"],
["2015/11/15", 39, "SS"],
["2015/11/16", 40, "SS"],
["2015/11/17", 36, "SS"],
["2015/11/18", 33, "SS"],
["2015/11/19", 43, "SS"],
["2015/11/20", 40, "SS"],
["2015/11/21", 34, "SS"],
["2015/11/22", 28, "SS"],
["2015/11/23", 26, "SS"],
["2015/11/24", 37, "SS"],
["2015/11/25", 41, "SS"],
["2015/11/26", 46, "SS"],
["2015/11/27", 47, "SS"],
["2015/11/28", 41, "SS"],
["2015/11/08", 10, "QG"],
["2015/11/09", 15, "QG"],
["2015/11/10", 35, "QG"],
["2015/11/11", 38, "QG"],
["2015/11/12", 22, "QG"],
["2015/11/13", 16, "QG"],
["2015/11/14", 7, "QG"],
["2015/11/15", 2, "QG"],
["2015/11/16", 17, "QG"],
["2015/11/17", 33, "QG"],
["2015/11/18", 40, "QG"],
["2015/11/19", 32, "QG"],
["2015/11/20", 26, "QG"],
["2015/11/21", 35, "QG"],
["2015/11/22", 40, "QG"],
["2015/11/23", 32, "QG"],
["2015/11/24", 26, "QG"],
["2015/11/25", 22, "QG"],
["2015/11/26", 16, "QG"],
["2015/11/27", 22, "QG"],
["2015/11/28", 10, "QG"],
["2015/11/08", 10, "SY"],
["2015/11/09", 15, "SY"],
["2015/11/10", 35, "SY"],
["2015/11/11", 38, "SY"],
["2015/11/12", 22, "SY"],
["2015/11/13", 16, "SY"],
["2015/11/14", 7, "SY"],
["2015/11/15", 2, "SY"],
["2015/11/16", 17, "SY"],
["2015/11/17", 33, "SY"],
["2015/11/18", 40, "SY"],
["2015/11/19", 32, "SY"],
["2015/11/20", 26, "SY"],
["2015/11/21", 35, "SY"],
["2015/11/22", 4, "SY"],
["2015/11/23", 32, "SY"],
["2015/11/24", 26, "SY"],
["2015/11/25", 22, "SY"],
["2015/11/26", 16, "SY"],
["2015/11/27", 22, "SY"],
["2015/11/28", 10, "SY"],
["2015/11/08", 10, "DD"],
["2015/11/09", 15, "DD"],
["2015/11/10", 35, "DD"],
["2015/11/11", 38, "DD"],
["2015/11/12", 22, "DD"],
["2015/11/13", 16, "DD"],
["2015/11/14", 7, "DD"],
["2015/11/15", 2, "DD"],
["2015/11/16", 17, "DD"],
["2015/11/17", 33, "DD"],
["2015/11/18", 4, "DD"],
["2015/11/19", 32, "DD"],
["2015/11/20", 26, "DD"],
["2015/11/21", 35, "DD"],
["2015/11/22", 40, "DD"],
["2015/11/23", 32, "DD"],
["2015/11/24", 26, "DD"],
["2015/11/25", 22, "DD"],
["2015/11/26", 16, "DD"],
["2015/11/27", 22, "DD"],
["2015/11/28", 10, "DD"],
],
}
],
}
st_echarts(option, height="500px", key="echarts")
# Create callbacks/filter data on selection
periods_filter = periods
values_region_1 = df['AveragePurchasePrice_1'][df['Regions'] == region_1].tolist()
values_region_2 = df['AveragePurchasePrice_1'][df['Regions'] == region_2].tolist()
# Create line chart with theme
st.title(region_1 + " and " + region_2)
# Open theme
with open('theme.json') as file:
theme = json.load(file)
options = {
"xAxis": {
"type": "category",
"data": periods_filter,
"name": "Year",
},
"yAxis": {"type": "value", "name": "House Prices in EUR"},
"series": [
{"data": values_region_1, "type": "line", "name": region_1, "areaStyle": {}},
{"data": values_region_2, "type": "line", "name": region_2, "areaStyle": {}}
],
"legend": {"display": "true"}
}
st_echarts(options=options, height="400px", theme=theme,)
st.write("Source: [CBS Open data StatLine 'Existing own homes; prices, region 1995-2020'](https://opendata.cbs.nl/statline/portal.html?_la=en&_catalog=CBS&tableId=83625ENG&_theme=1102)")
def render_stacked_area():
with st.echo("below"):
options = {
"title": {"text": "堆叠区域图"},
"tooltip": {
"trigger": "axis",
"axisPointer": {
"type": "cross",
"label": {"backgroundColor": "#6a7985"},
},
},
"legend": {"data": ["邮件营销", "联盟广告", "视频广告", "直接访问", "搜索引擎"]},
"toolbox": {"feature": {"saveAsImage": {}}},
"grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
"xAxis": [
{
"type": "category",
"boundaryGap": False,
"data": ["周一", "周二", "周三", "周四", "周五", "周六", "周日"],
}
],
"yAxis": [{"type": "value"}],
"series": [
{
"name": "邮件营销",
"type": "line",
"stack": "总量",
"areaStyle": {},
"data": [120, 132, 101, 134, 90, 230, 210],
},
{
"name": "联盟广告",
"type": "line",
"stack": "总量",
"areaStyle": {},
"data": [220, 182, 191, 234, 290, 330, 310],
},
{
"name": "视频广告",
"type": "line",
"stack": "总量",
"areaStyle": {},
"data": [150, 232, 201, 154, 190, 330, 410],
},
{
"name": "直接访问",
"type": "line",
"stack": "总量",
"areaStyle": {},
"data": [320, 332, 301, 334, 390, 330, 320],
},
{
"name": "搜索引擎",
"type": "line",
"stack": "总量",
"label": {"normal": {"show": True, "position": "top"}},
"areaStyle": {},
"data": [820, 932, 901, 934, 1290, 1330, 1320],
},
],
}
st_echarts(options)
},
"data": [
{"value": code_count_Origin[0], "name": Code01},
{"value": code_count_Origin[1], "name": Code02},
{"value": code_count_Origin[2], "name": Code03},
{"value": code_count_Origin[3], "name": Code04},
]
}
]
};
c1, c2 = c.beta_columns(2)
with c1:
st.markdown('## **▼ Chart Overview **')
st_echarts(options=pie_options)
with c2:
st.markdown('## **▼ Pivot Overview **')
st.write("")
st.write("")
st.table(dfPivotFiltered)
c3, c4 = c.beta_columns(2)
with c3:
with st.beta_expander("🛠️ Code for chart", expanded=False):
