03 pyechars rectangular coordinate system chart (example code + effect drawing)

Catalog

1.Bar Histogram / Bar chart

2 Line Broken line diagram

3 Scatter Scatter plot

4 EffectScatter Ripple effect scatter

5 Boxplot Box figure

6 Kline k Line graph

7 HeatMap Heat map

8 PictorialBar Image histogram

9 overlap Cascade diagram

9.1 Overlap_bar_line

9.2 Overlap_line_scatter

Faker Introduction of functions in ：

---

1.Bar Histogram / Bar chart

#  Histogram / Bar chart 
from pyecharts.charts import Bar

x_data = [" Chinese language and literature ", " mathematics ", " English ", " biological ", " Physics ", " chemical "]  # x Axis data   Data must be a string 
y_data = [114, 95, 107, 81, 85, 87]  # y Axis data   Data must be integer or decimal 
bar = Bar()  #  Initialization diagram 
bar.add_xaxis(x_data)  # x Axis 
bar.add_yaxis(' achievement ', y_data)  # y Axis 
bar.render("bar.html")  #  Rendering html file 

 

2 Line Broken line diagram

#  Broken line diagram 
from pyecharts.charts import Line

x_data = ['1 month ', '2 month ', '3 month ', '4 month ', '5 month ', '6 month ']  # x Axis data   Data must be a string 
y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y Axis data   Data must be integer or decimal 
line = Line()  #  Initialization diagram 
line.add_xaxis(x_data)  # x Axis 
line.add_yaxis(' Monthly consumption ', y_data)  # y Axis 
line.render("line.html")  #  Rendering html file 

3 Scatter Scatter plot

#  Scatter plot 
from pyecharts.charts import Scatter

x_data = ['1 month ', '2 month ', '3 month ', '4 month ', '5 month ', '6 month ']  # x Axis data 
y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y Axis data 
scatter = Scatter()  #  Initialization diagram 
scatter.add_xaxis(x_data)  # x Axis 
scatter.add_yaxis(' Monthly consumption ', y_data)  # y Axis 
scatter.render("scatter.html")  #  Rendering html file 

 

4 EffectScatter Ripple effect scatter

from pyecharts.charts import EffectScatter

x_data = ['1 month ', '2 month ', '3 month ', '4 month ', '5 month ', '6 month ']  # x Axis data 
y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y Axis data 
effectScatter = EffectScatter()
effectScatter.add_xaxis(x_data)  # x Axis 
effectScatter.add_yaxis(' Monthly consumption ', y_data)  # y Axis 
effectScatter.render("effectScatter.html")  #  Rendering html file 

 

5 Boxplot Box figure

#  Box figure 
from pyecharts.charts import Boxplot
import random

x_data = ['A', 'B', 'C', 'D', 'E', 'F']
y_data = [[random.randint(100, 200) for i in range(10)] for item in x_data] #[[164, 102, 161, 106, 144, 186, 197, 186, 196, 125], [158, 131, 143, 111, 110, 175, 109, 107, 104, 153], [163, 174, 182, 142, 134, 158, 113, 149, 176, 183], [198, 142, 182, 125, 105, 149, 171, 122, 105, 156], [146, 126, 100, 164, 149, 117, 118, 170, 138, 155], [177, 110, 119, 178, 126, 164, 131, 176, 195, 134]]

print(y_data)
Boxplot = Boxplot()
Boxplot.add_xaxis(x_data)  # x Axis 
Boxplot.add_yaxis('', Boxplot.prepare_data(y_data))  # y Axis 
Boxplot.render("Boxplot.html")  #  Rendering html file 

 

6 Kline k Line graph

# k Line graph 
from pyecharts.charts import Kline

date_list = ["2022/6/{}".format(i + 1) for i in range(30)]
y_data = [
    [2320.26, 2320.26, 2287.3, 2362.94],
    [2300, 2291.3, 2288.26, 2308.38],
    [2295.35, 2346.5, 2295.35, 2345.92],
    [2347.22, 2358.98, 2337.35, 2363.8],
    [2360.75, 2382.48, 2347.89, 2383.76],
    [2383.43, 2385.42, 2371.23, 2391.82],
    [2377.41, 2419.02, 2369.57, 2421.15],
    [2425.92, 2428.15, 2417.58, 2440.38],
    [2411, 2433.13, 2403.3, 2437.42],
    [2432.68, 2334.48, 2427.7, 2441.73],
    [2430.69, 2418.53, 2394.22, 2433.89],
    [2416.62, 2432.4, 2414.4, 2443.03],
    [2441.91, 2421.56, 2418.43, 2444.8],
    [2420.26, 2382.91, 2373.53, 2427.07],
    [2383.49, 2397.18, 2370.61, 2397.94],
    [2378.82, 2325.95, 2309.17, 2378.82],
    [2322.94, 2314.16, 2308.76, 2330.88],
    [2320.62, 2325.82, 2315.01, 2338.78],
    [2313.74, 2293.34, 2289.89, 2340.71],
    [2297.77, 2313.22, 2292.03, 2324.63],
    [2322.32, 2365.59, 2308.92, 2366.16],
    [2364.54, 2359.51, 2330.86, 2369.65],
    [2332.08, 2273.4, 2259.25, 2333.54],
    [2274.81, 2326.31, 2270.1, 2328.14],
    [2333.61, 2347.18, 2321.6, 2351.44],
    [2340.44, 2324.29, 2304.27, 2352.02],
    [2326.42, 2318.61, 2314.59, 2333.67],
    [2314.68, 2310.59, 2296.58, 2320.96],
    [2309.16, 2286.6, 2264.83, 2333.29],
    [2282.17, 2263.97, 2253.25, 2286.33],
]

kline = (Kline()
         .add_xaxis(date_list)
         .add_yaxis('', y_data)
         )

kline.render("kline.html")  #  Rendering html file 

 

7 HeatMap Heat map

#  Heat map 
from pyecharts.charts import HeatMap
import random

data = [[i, j, random.randint(0, 100)] for i in range(24) for j in range(7)]
hour_list = [str(i) for i in range(24)]
week_list = [' Sunday ', ' Monday ', ' Tuesday ', ' Wednesday ', ' Thursday ', ' Friday ', ' Saturday ']

heat = (HeatMap()
        .add_xaxis(hour_list)
        .add_yaxis("", week_list, data)
        )
heat.render("heat.html")

 

8 PictorialBar Image histogram

#  Pictogram 
from pyecharts.charts import PictorialBar

x_data = ['1 month ', '2 month ', '3 month ', '4 month ', '5 month ', '6 month ']  # x Axis data 
y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y Axis data 
pictorialBar = PictorialBar()  #  Initialization diagram 
pictorialBar.add_xaxis(x_data)  # x Axis 
pictorialBar.add_yaxis(' Monthly consumption ', y_data)  # y Axis 
pictorialBar.render("pictorialBar .html")  #  Rendering html file 

9 overlap Cascade diagram

9.1 Overlap_bar_line

#  Cascade diagram Overlap_bar_line
from pyecharts.charts import Bar
from pyecharts.charts import Line

x_data = ['1 month ', '2 month ', '3 month ', '4 month ', '5 month ', '6 month ']
y_data_bar = [1123, 1153, 1089, 1207, 1298, 1123]
y_data_line = [1300, 1200, 1100, 1400, 1500, 1200]

bar = (Bar()
       .add_xaxis(x_data)
       .add_yaxis('', y_data_bar)
       )

line = (Line()
        .add_xaxis(x_data)
        .add_yaxis('', y_data_line)
        )

overlap = bar.overlap(line)
overlap.render("overlap_bar_line .html")

 

9.2 Overlap_line_scatter

#  Cascade diagram  Overlap_line_scatter
from pyecharts import options as opts
from pyecharts.charts import Line, Scatter
from pyecharts.faker import Faker

x = Faker.choose()
line = (
    Line()
    .add_xaxis(x)
    .add_yaxis(" merchants A", Faker.values())
    .add_yaxis(" merchants B", Faker.values())
    .set_global_opts(title_opts=opts.TitleOpts(title="Overlap-line+scatter"))
)
scatter = (
    Scatter()
    .add_xaxis(x)
    .add_yaxis(" merchants A", Faker.values())
    .add_yaxis(" merchants B", Faker.values())
)
line.overlap(scatter)
line.render("overlap_line_scatter.html")

 Faker.values() Detailed explanation ： Generate 7 A random integer , this 7 A random integer is usually a combination of two and three digits

Faker Introduction of functions in ：

class _Faker:
    clothes = [" shirt ", " sweater ", " necktie ", " The trousers ", " Windbreaker ", " High heels ", " socks "]
    drinks = [" coke ", " Sprite ", " Orange Juice ", " Green tea ", " Milk tea ", " Budweiser ", " Qingdao "]
    phones = [" millet ", " samsung ", " Huawei ", " Apple ", " meizu ", "VIVO", "OPPO"]
    fruits = [" strawberry ", " Mango. ", " grapes ", " Snow pear ", " watermelon ", " lemon ", " Cherry "]
    animal = [" Hippo ", " Python ", " The tiger ", " Elephant ", " The rabbit ", " Panda ", " The lion "]
    cars = [" BMW ", " ferrari ", " Mercedes ", " audi ", " The public ", " Toyota ", " tesla "]
    dogs = [" Siberian Husky ", " Samoye ", " Poodle ", " Golden hair ", " Shepherd Dog ", " Ji Wawa ", " corgi "]
    week = [" Monday ", " Tuesday ", " Wednesday ", " Thursday ", " Friday ", " Saturday ", " Sunday "]
    week_en = "Saturday Friday Thursday Wednesday Tuesday Monday Sunday".split()
    clock = (
        "12a 1a 2a 3a 4a 5a 6a 7a 8a 9a 10a 11a 12p "
        "1p 2p 3p 4p 5p 6p 7p 8p 9p 10p 11p".split()
    )
    visual_color = [
        "#313695",
        "#4575b4",
        "#74add1",
        "#abd9e9",
        "#e0f3f8",
        "#ffffbf",
        "#fee090",
        "#fdae61",
        "#f46d43",
        "#d73027",
        "#a50026",
    ]
    months = ["{} month ".format(i) for i in range(1, 13)]
    provinces = [" guangdong ", " Beijing ", " Shanghai ", " jiangxi ", " hunan ", " Zhejiang ", " jiangsu "]
    guangdong_city = [" Shantou city ", " Shanwei cities ", " Jieyang city ", " yangjiang ", " Zhaoqing city ", " guangzhou ", " huizhou "]
    country = [
        "China",
        "Canada",
        "Brazil",
        "Russia",
        "United States",
        "Africa",
        "Germany",
    ]
    days_attrs = ["{} God ".format(i) for i in range(30)]
    days_values = [random.randint(1, 30) for _ in range(30)]