Python实现平行坐标图的两种方法小结

Z时代
2024-01-10
分类：综合

平行坐标图，一种数据可视化的方式。以多个垂直平行的坐标轴表示多个维度，以维度上的刻度表示在该属性上对应值，相连而得的一个折线表示一个样本，以不同颜色区分类别。

但是很可惜，才疏学浅，没办法在Python里实现不同颜色来区分不同的类别。如果对此比较在意的大神可以不要往下看了。。。。。。。。。

上图是一个基于iris数据集所画的一个平行坐标图。

隔开隔开.......................................隔开隔开

不多扯了，下面正式上代码

方法一、基于pyecharts第三方包来实现


from pyecharts import Parallel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
data = pd.read_csv('iris.csv')
data_1 = np.array(data[['Sepal_length', 'Sepal_width', 'Petal_length', 'Petal_width']]).tolist()
schema = ['Sepal_length', 'Sepal_width', 'Petal_length', 'Petal_width']
parallel = Parallel('iris平行坐标图')
parallel.config(schema)
parallel.add('dasfd',data_1,is_random = True)
parallel

可惜，这样子run出来的恰好结果就是上图，没办法实现不同类别用不同颜色来区分。实在不得不说是一个令人超级不爽的一个地方，劳资都想咋了电脑当时，哈哈哈哈。。。

在这里多扯两句啊，pyecharts这个包还真的是特么的好用啊，各种图都能实现，感兴趣的朋友不妨装个来耍耍

方法二、基于pandas来实现

what？pandas？这把绝世好剑不是用来处理一些数据的吗？什么时候还具有画图的功能了，lz你没猫饼吧？

说实话，lz当时也没想到pandas能用来画图，而且是画平行坐标图。下面就是代码了：


import matplotlib.pyplot as plt
import pandas as pd
from pandas.tools.plotting import parallel_coordinates
data = pd.read_csv('iris.csv')
data_1 =data[['Species','Sepal_length', 'Sepal_width', 'Petal_length', 'Petal_width']]
parallel_coordinates(data_1,'Species')
plt.legend(loc='upper center', bbox_to_anchor=(0.5,-0.1),ncol=3,fancybox=True,shadow=True)
plt.show()

run一下，就可以得到下图了

不难看出，这张图是具有了不同颜色，但是每个坐标轴的刻度都是0-8啊，lz希望的是每个轴独立的啊·········

虽然lz没办法在Python里画出满意的平行坐标图，但是最后也用Echarts实现了一下（哈哈，有时候没办法了，不妨试试换个工具）

顺道附上代码吧，不然担心被人画小圈圈


// Schema:
// date,AQIindex,PM2.5,PM10,CO,NO2,SO2
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];
var schema = [
  {name: 'Sepal_length', index: 0, text: 'Sepal_length'},
  {name: 'Sepal_width', index: 1, text: 'Sepal_width'},
  {name: 'Petal_length', index: 2, text: 'Petal_length'},
  {name: 'Petal_width', index: 3, text: 'Petal_width'},
];
var lineStyle = {
  normal: {
    width: 1,
    opacity: 0.5
  }
};
option = {
  legend: {
    top: 0,
    data:['Iris-setosa','Iris-versicolor','Iris-virginica'],
    itemGap: 10
  },
  parallelAxis: [
    {dim: 0, name: schema[0].text},
    {dim: 1, name: schema[1].text},
    {dim: 2, name: schema[2].text},
  ],
  parallel: {
    left: '5%',
    right: '13%',
    bottom: '10%',
    top: '15%',
    parallelAxisDefault: {
      type: 'value',
      name: '平行坐标',
      nameLocation: 'end',
      nameGap: 20,
      nameTextStyle: {
        fontSize: 12
      }
    }
  },
  series: [
    {
      name: 'Iris-setosa',
      type: 'parallel',
      lineStyle: lineStyle,
      data: data1
    },
    {
      name: 'Iris-versicolor',
      type: 'parallel',
      lineStyle: lineStyle,
      data: data2
    },
    {
      name: 'Iris-virginica',
      type: 'parallel',
      lineStyle: lineStyle,
      data: data3
    },
  ]  
};