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How to fully interpret the Pyecharts dynamic chart, I believe that many inexperienced people are at a loss about it. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

1. Brief introduction to pyecharts

Pyecharts is based on the python encapsulation of Baidu open source chart component echarts. Support all commonly used chart components, unlike the Matlibplot chart library: pyecharts supports dynamic interactive display, which is especially useful when viewing complex data charts.

Pip install pyecharts2.pyecharts is easy to use

Pyecharts supports commonly used basic graphics display, bar chart, line chart, pie chart, scatter chart, thermal map, funnel chart, radar chart, box chart, map, etc., as well as dashboard and tree chart display.

From pyecharts.charts import Bar,Line from pyecharts import options as opts from pyecharts.globals import ThemeType line = (Line (init_opts=opts.InitOpts (theme=ThemeType.LIGHT, width='1000px',height='300px')) .add _ xaxis (["shirt", "cardigan", "chiffon shirt", "trousers", "high heels", "socks"]) .add _ yaxis ("merchant A", [5, 20, 36, 10, 75) 90]) .add _ yaxis ("merchant B", [15,6,45,20,35,66]) .set _ global_opts (title_opts=opts.TitleOpts (title= "main title", subtitle= "subtitle"), datazoom_opts=opts.DataZoomOpts (is_show=True) .set _ series_opts (label_opts=opts.LabelOpts (is_show=True) line.render ('test.html') line.render_notebook ()

As can be seen from the simple examples above, the use of pyecharts includes:

The initialization configuration of the icon type (Line) itself, such as theme, size

Load data: such as loading x-axis data, loading y-axis data (can be multiple)

Set global configuration, such as title, area zoom datazoom, toolbox, etc.

Set series configuration items, such as labels, lines, scale text display, etc.

Icon shows: render is saved as a html file, and if it is jupyter notebook, it is displayed in notebook directly through render_notebook.

3. Common configuration u

In pyecharts, operations such as chart appearance display are configured in the corresponding option, including axes, legends, data labels, gridlines, chart styles / colors, different series, and so on.

InitOpts: initial configuration of each chart type

Set_global_opts: global appearance configuration

Set_series_opts: series configuration

In order to facilitate you and yourself, the following is a common combination, usually visualization is enough, quick collection.

InitOpts: theme, length and width, animation effects

DataZoomOpts: area shrinking, which is especially useful for a lot of data, such as one-day time series data, you can drag to view global and local data (you can set whether to explicitly or draggable type_= "inside")

Title configuration TitleOpts: explain what this chart shows, must-have

Legend configuration LegendOpts: explain the different data items in the chart (this legend is clickable, you can view the data of a legend separately, which is useful)

Prompt box to configure TooltipOpts: display the data of a specific point in the legend

X-axis and y-axis title description AxisOpts

Coordinate scale adjustment: especially suitable for more scale instructions, can display angle transformation, etc.

Markpoint/markline: a special mark for a chart to highlight the section and mark the distinguishing line

From pyecharts.charts import Bar,Line from pyecharts import options as opts from pyecharts.globals import ThemeType bar = (Bar (init_opts=opts.InitOpts (theme=ThemeType.LIGHT, width='1000px', height='300px', animation_opts=opts.AnimationOpts (animation_delay=1000)) Animation_easing= "elasticOut")) .add _ xaxis (["shirt", "cardigan", "chiffon", "trousers", "high heels", "socks"]) .add _ yaxis ("merchant A", [5, 20, 36, 10, 75, 90]) .add _ yaxis ("merchant B", [15, 6, 45, 20) 35, 66]) .set _ global_opts (title_opts=opts.TitleOpts (title= "main title", subtitle= "subtitle"), toolbox_opts=opts.ToolboxOpts (is_show=False), # datazoom_opts=opts.DataZoomOpts (is_show=True) datazoom_opts= [opts.DataZoomOpts (), opts.DataZoomOpts (type_= "inside")] Legend_opts=opts.LegendOpts (type_= "scroll", pos_left= "50%", orient= "vertical"), xaxis_opts=opts.AxisOpts (axislabel_opts=opts.LabelOpts (rotate=-15), name= "I am X axis"), yaxis_opts=opts.AxisOpts (name= "I am Y axis", axislabel_opts=opts.LabelOpts (formatter= "{value} / month")) Tooltip_opts=opts.TooltipOpts (trigger= "axis", axis_pointer_type= "cross"),) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False), markpoint_opts=opts.MarkPointOpts (data= [opts.MarkPointItem (type_= "max", name= "maximum")) Opts.MarkPointItem (type_= "min", name= "minimum"), opts.MarkPointItem (type_= "average", name= "average"),]),) # line.render ('test.html') bar.render_notebook ()

4. Common use of combined charts

Common combination charts are as follows:

The combination of different chart types, such as bar chart and line chart, is combined in one chart (double y-axis), which mainly depends on the difference and correlation of different indicators from the same point of view; it is realized through overlap in pyecharts.

From pyecharts import options as opts from pyecharts.charts import Bar, Line from pyecharts.faker import Faker 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 = [2.0,5.9,9.0,26.4,28.7,18.8,6.0,2.3] v2 = [2.0,5.9,9.0,26.4,28.7,70.7,6.0,2.3] v3 = [2.0,5.9,9.0,26.4,28.7,70.7,6.0,2.3] v3 = [2.0,5.9,9.0,26.4,28. 2.2,3.3,4.5,6.3,10.2,20.3,23.4,23.0,16.5,12.0,6.2] bar = (Bar (init_opts=opts.InitOpts (width= "680px", height= "300px")) .add _ xaxis (Faker.months) .add _ yaxis ("evaporation", v1) .add _ yaxis ("precipitation") V2) .extend _ axis (yaxis=opts.AxisOpts (axislabel_opts=opts.LabelOpts (formatter= "{value} °C"), interval=5)) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False)) .set _ global_opts (title_opts=opts.TitleOpts (title= "Overlap-bar+line"), yaxis_opts=opts.AxisOpts (axislabel_opts=opts.LabelOpts (formatter= "{value} ml") Tooltip_opts=opts.TooltipOpts (trigger= "axis", axis_pointer_type= "cross"),) line = Line (). Add_xaxis (Faker.months). Add_yaxis ("average temperature", v3, yaxis_index=1) bar.overlap (line) bar.render_notebook ()

In terms of implementation

.extend _ axis adds an ordinate

When setting the axis coordinates of the added line chart, set the yaxis_index index to correspond to the front ordinate

Then two superimposed overlap bar.overlap (line)

Multiple icons are combined in GRID, mainly for comparison; in pyecharts, they are implemented through grid components

From pyecharts import options as opts from pyecharts.charts import Bar, Grid, Line from pyecharts.faker import Faker bar = (Bar () .add _ xaxis (Faker.choose ()) .add _ yaxis ("merchant A", Faker.values ()) .add _ yaxis ("merchant B") Faker.values () .set _ global_opts (title_opts=opts.TitleOpts (title= "Grid-Bar")) line = (Line () .add _ xaxis (Faker.choose ()) .add _ yaxis ("merchant A", Faker.values ()) .add _ yaxis ("merchant B", Faker.values ()) .set _ global_opts (title_opts=opts.TitleOpts (title= "Grid-Line", pos_top= "48%")) Legend_opts=opts.LegendOpts (pos_top= "48%"),) grid = (Grid (init_opts=opts.InitOpts (width= "680px", height= "500px")) .add (bar, grid_opts=opts.GridOpts (pos_bottom= "60%")) .add (line, grid_opts=opts.GridOpts (pos_top= "60%")) grid.render_notebook ()

From the perspective of implementation

Put all kinds of graphics into it mainly through Grid

The position of each chart is set by GridOpts, up and down, left and right

It should be noted that the title and legend of the chart in grid need to specify the relative position according to the location (this is a bit troublesome, multi-tuning)

5. Map usage

Maps can be used to show the geographical distribution of data, and it is also a common visual display component. Pyecharts is implemented through the Map class. The details need to be paid attention to:

Map supports different maptype, such as Chinese map china (provincial) china-cities (municipal), world map world, as well as maps of Chinese provinces and cities and countries around the world. See github pyecharts/datasets/map_filename.json

The data format of map is (geographic location, value), such as [['Guangdong', 76], ['Beijing', 58]]

You can check the key points through visualmap_opts.

From pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker C1 = (Map () .add ("merchant A", [list (z) for z in zip (Faker.guangdong_city, Faker.values ())], "Guangdong") .set _ global_opts (title_opts=opts.TitleOpts (title= "Map- Guangdong Map")) Visualmap_opts=opts.VisualMapOpts () c2 = (Map () .add ("merchant A", [list (z) for z in zip (Faker.provinces, Faker.values ())], "china") .set _ global_opts (title_opts=opts.TitleOpts (title= "Map-VisualMap)"), visualmap_opts=opts.VisualMapOpts (max_=200) )) # c1.render_notebook () c2.render_notebook ()

6. Use of characteristic charts

When learning pyecharts, I see some interesting (dynamic display) components, such as dynamically showing changes in chart data over time. Here's an introduction.

Timeline: timeline Multicast Multicast first declares a Timeline and add the chart to Timeline according to the time order in which it is displayed. You can cycle through the play button to display the chart in chronological order.

From pyecharts import options as opts from pyecharts.charts import Pie, Timeline from pyecharts.faker import Faker attr = Faker.choose () tl = Timeline () for i in range (2015, 2020): pie = (Pie () .add ("merchant A", [list (z) for z in zip (attr, Faker.values ())], rosetype= "radius", radius= ["30%" Set _ global_opts (title_opts=opts.TitleOpts ("annual turnover of a store {}" .format (I)) tl.add (pie, "{} year" .format (I)) tl.render_notebook ()

Instrument panel

From pyecharts import options as opts from pyecharts.charts import Gauge c = (Gauge () .add (", [(" completion rate ", 30.6)], radius=" 70% ", axisline_opts=opts.AxisLineOpts (color= [(0.3," # 67e0e3 "), (0.7," # 37a2da "), (1," # fd666d ")], width=30) Title_label_opts=opts.LabelOpts (font_size=20, color= "blue", font_family= "Microsoft YaHei"),) .set _ global_opts (title_opts=opts.TitleOpts (title= "Gauge- basic example"), legend_opts=opts.LegendOpts (is_show=False),) c.render_notebook ()

7. A list of other charts

From the example above, maps, bars, line charts, pie charts, and dashboards have been shown. Here are more diagrams provided by pyecharts

Radar map Radar

Tree Tree

Thermal map heatMap

Calendar chart Calendar

Scatter plot Scatter

3D picture Bar3D

Box diagram Boxplot

The python dynamic chart display component pyecharts based on echarts introduced by the editor, besides providing many commonly used charts, the most important thing is to support dynamic operation of data. The summary is as follows:

All pyecharts image property settings are set through opts, with chart initial properties / global properties / series properties.

This article provides commonly used configurations, which are enough to be used. You are welcome to take them away. See commonly used configurations.

Pyecharts supports multiple chart combinations, such as line chart and bar chart overlap, and multiple chart grid presentation

Pyecharts easy to use map, you can display maps of Chinese provinces and cities and countries around the world. Please prepare the data according to [location, value].

Timeline can rotate your charts according to time.

After reading the above, have you mastered how to fully interpret the Pyecharts dynamic chart? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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