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This article mainly explains "how to draw scatter plots with Python". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let Xiaobian take you to learn "how to draw scatter plots with Python"!

You think the scatterplot looks like this:

In fact, scatter plots can also grow like this:

bubble chart

Big Wild Goose Flying South

Does it look tall and beautiful? Let's take you to learn how to draw beautiful scatter plots with pyecharts.

1. The most basic scatterplot

from pyecharts import options as opts from pyecharts.charts import Scatter x=['January',' February','March',' April','May',' June','July',' August','September'] y=[8,5,3,4,8,2,2,5,1] c = ( Scatter() .add_xaxis(x) .add_yaxis("", y) .set_global_opts(title_opts=opts.TitleOpts(title="base") ))c.render_notebook()

This is the most basic scatterplot, just input horizontal and vertical two lists can output graphics

II. Scatter plot showing dividing lines

from pyecharts import options as opts from pyecharts.charts import Scatter x=['January',' February','March',' April','May',' June','July',' August','September'] y=[8,5,3,4,8,2,2,5,1] c = ( Scatter() .add_xaxis(x) .add_yaxis("", y) .set_global_opts( title_opts=opts.TitleOpts(title="base"), xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)), yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)) ))c.render_notebook()

You can set horizontal and vertical dividing lines according to your own needs

III. Setting Multidimensional Data

from pyecharts import options as opts from pyecharts.charts import Scatter from pyecharts.commons.utils import JsCode x=['January',' February','March',' April','May',' June','July',' August','September'] y=[8,5,3,4,8,2,2,5,1] c = ( Scatter() .add_xaxis(x) .add_yaxis("", y, label_opts=opts.LabelOpts( formatter=JsCode( "function(params){return params.value[1] +' : '+ params.name;}" ) ),) .set_global_opts( title_opts=opts.TitleOpts(title="Scatter-Multidimensional Data"), visualmap_opts=opts.VisualMapOpts( type_="color", max_=8, dimension=1 ), ))c.render_notebook()

Mainly through the insertion of js code to achieve

IV. Bubble Chart

By segmenting the dot plot, setting different colors, and setting the radius of the dots to change with the data, the scatter plot becomes a bubble plot.

from pyecharts import options as opts from pyecharts.charts import Scatter x=['January',' February','March',' April','May',' June','July',' August','September'] y=[8,5,3,4,8,2,2,5,1] c = ( Scatter() .add_xaxis(x) .add_yaxis("", y) .set_global_opts( title_opts=opts.TitleOpts(title="Bubble Plot"), visualmap_opts=opts.VisualMapOpts( range_opacity=0.45, type_="size", max_=10, is_piecewise=True, dimension=0, pieces=[ {"lte": 2, "color": "green"}, {"gt": 2, "lte": 4, "color": "red"}, {"gt": 4, "lte": 6, "color": "yellow"}, {"gt": 6, "lte": 8, "color": "red"}, {"gt": 8, "color": "green"}, ], pos_right=0, pos_bottom=100) ))c.render_notebook()

Parameter introduction:

range_opacity: set transparency type: size, use shape size to represent data size

is_piecewise: whether to segment pieces: specific segmentation range

V. Set the shape, size and color of scattered points

By changing the shape, size, and color of the dots, you can make the dots look like geese flying south.

from pyecharts import options as opts from pyecharts.charts import Scatter x=['January',' February','March',' April','May',' June','July',' August','September'] y=[8,5,3,4,8,2,2,5,1] c = ( Scatter() .add_xaxis(x) .add_yaxis(series_name="", y_axis=y, symbol='arrow', symbol_size=[40,20], symbol_rotate=-45) .set_global_opts( title_opts=opts.TitleOpts(title="base"), visualmap_opts=opts.VisualMapOpts(max_=10), ))c.render_notebook() Key parameters: symbol: set shape symbol_size: set shape size ymbol_rotate: set shape angle

Key parameters:

symbol: set shape

symbol_size: Set the shape size

ymbol_rotate: Set the shape angle

At this point, I believe everyone has a deeper understanding of "how to draw scatter plots with Python," so let's actually operate it! Here is the website, more related content can enter the relevant channels for inquiry, pay attention to us, continue to learn!

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