Matplotlib 中的按列值着色 [英] Color by Column Values in Matplotlib

问题描述

在 R 中使用 ggplot2 库时，我最喜欢的一个方面是能够轻松指定美学.我可以快速制作散点图并应用与特定列相关联的颜色，我希望能够使用 python/pandas/matplotlib 执行此操作.我想知道是否有任何方便的函数供人们使用 Pandas 数据框和 Matplotlib 将颜色映射到值?

##ggplot 散点图示例，带有 R 数据框，`df`，由 col3 着色ggplot(data = df, aes(x=col1, y=col2, color=col3)) + geom_point()##pandas 数据框的理想情况，'df'，颜色由 col3 选择df.plot(x=col1,y=col2,color=col3)

感谢您的回复，但我想包含一个示例数据框来阐明我的要求.两列包含数字数据，第三列是分类变量.我正在考虑的脚本将根据此值分配颜色.

np.random.seed(250)df = pd.DataFrame({'Height': np.append(np.random.normal(6, 0.25, size=5), np.random.normal(5.4, 0.25, size=5)),'权重':np.append(np.random.normal(180, 20, size=5), np.random.normal(140, 20, size=5)),性别":[男"、男"、男"、男"、男"、女"、女"、女"、女"、女"]})身高体重性别0 5.824970 159.210508 男1 5.780403 180.294943 男2 6.318295 199.142201 男3 5.617211 157.813278 男4 6.340892 191.849944 男5 5.625131 139.588467 女6 4.950479 146.711220 女7 5.617245 121.571890 女8 5.556821 141.536028 女9 5.714171 134.396203 女

解决方案

导入和数据

import numpy进口大熊猫导入 matplotlib.pyplot 作为 plt进口seabornseaborn.set(style='ticks')numpy.random.seed(0)N = 37_genders= ['女性'、'男性'、'非二进制'、'无响应']df = 熊猫.DataFrame({'高度(厘米)':numpy.random.uniform(低=130，高=200，大小=N)，'重量(公斤)':numpy.random.uniform(低=30，高=100，大小=N)，'性别':numpy.random.choice(_genders, size=N)})

2021 年 8 月更新

• 对于seaborn 0.11.0，建议使用新的图形级别函数，例如

  旧答案

  在这种情况下，我会直接使用 matplotlib.

  将 numpy 导入为 np导入 matplotlib.pyplot 作为 plt将熊猫导入为 pddef dfScatter(df, xcol='Height', ycol='Weight', catcol='Gender'):图, ax = plt.subplots()类别 = np.unique(df[catcol])颜色 = np.linspace(0, 1, len(categories))colordict = dict(zip(类别，颜色))df[颜色"] = df[catcol].apply(lambda x: colordict[x])ax.scatter(df[xcol], df[ycol], c=df.Color)返回无花果如果 1:df = pd.DataFrame({'Height':np.random.normal(size=10),'重量':np.random.normal(大小= 10)，性别":[男"、男"、未知"、男"、男"、女性"、没有回应"、未知"、女性"、女性"]})图 = dfScatter(df)fig.savefig('fig1.png')

  这给了我:

  据我所知，颜色列可以是任何与 matplotlib 兼容的颜色(RBGA 元组、HTML 名称、十六进制值等).

  我无法获得除数值以外的任何东西来处理颜色图.

  One of my favorite aspects of using the ggplot2 library in R is the ability to easily specify aesthetics. I can quickly make a scatterplot and apply color associated with a specific column and I would love to be able to do this with python/pandas/matplotlib. I'm wondering if there are there any convenience functions that people use to map colors to values using pandas dataframes and Matplotlib?

  ##ggplot scatterplot example with R dataframe, `df`, colored by col3
  ggplot(data = df, aes(x=col1, y=col2, color=col3)) + geom_point()
  
  ##ideal situation with pandas dataframe, 'df', where colors are chosen by col3
  df.plot(x=col1,y=col2,color=col3)
  

  EDIT: Thank you for your responses but I want to include a sample dataframe to clarify what I am asking. Two columns contain numerical data and the third is a categorical variable. The script I am thinking of will assign colors based on this value.

  np.random.seed(250)
  df = pd.DataFrame({'Height': np.append(np.random.normal(6, 0.25, size=5), np.random.normal(5.4, 0.25, size=5)),
                     'Weight': np.append(np.random.normal(180, 20, size=5), np.random.normal(140, 20, size=5)),
                     'Gender': ["Male","Male","Male","Male","Male",
                                "Female","Female","Female","Female","Female"]})
  
       Height      Weight  Gender
  0  5.824970  159.210508    Male
  1  5.780403  180.294943    Male
  2  6.318295  199.142201    Male
  3  5.617211  157.813278    Male
  4  6.340892  191.849944    Male
  5  5.625131  139.588467  Female
  6  4.950479  146.711220  Female
  7  5.617245  121.571890  Female
  8  5.556821  141.536028  Female
  9  5.714171  134.396203  Female
  

  解决方案

  Imports and Data

  import numpy 
  import pandas
  import matplotlib.pyplot as plt
  import seaborn
  seaborn.set(style='ticks')
  
  numpy.random.seed(0)
  N = 37
  _genders= ['Female', 'Male', 'Non-binary', 'No Response']
  df = pandas.DataFrame({
      'Height (cm)': numpy.random.uniform(low=130, high=200, size=N),
      'Weight (kg)': numpy.random.uniform(low=30, high=100, size=N),
      'Gender': numpy.random.choice(_genders, size=N)
  })
  

  Update August 2021

  • With seaborn 0.11.0, it's recommended to use new figure level functions like seaborn.relplot than to use FacetGrid directly.

  seaborn.relplot(data=df, x='Weight (kg)', y='Height (cm)', hue='Gender', hue_order=_genders, aspect=1.61)
  plt.show()
  

  Update October 2015

  Seaborn handles this use-case splendidly:

  • Map matplotlib.pyplot.scatter onto a seaborn.FacetGrid

  fg = seaborn.FacetGrid(data=df, hue='Gender', hue_order=_genders, aspect=1.61)
  fg.map(plt.scatter, 'Weight (kg)', 'Height (cm)').add_legend()
  

  Which immediately outputs:

  Old Answer

  In this case, I would use matplotlib directly.

  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  def dfScatter(df, xcol='Height', ycol='Weight', catcol='Gender'):
      fig, ax = plt.subplots()
      categories = np.unique(df[catcol])
      colors = np.linspace(0, 1, len(categories))
      colordict = dict(zip(categories, colors))  
  
      df["Color"] = df[catcol].apply(lambda x: colordict[x])
      ax.scatter(df[xcol], df[ycol], c=df.Color)
      return fig
  
  if 1:
      df = pd.DataFrame({'Height':np.random.normal(size=10),
                         'Weight':np.random.normal(size=10),
                         'Gender': ["Male","Male","Unknown","Male","Male",
                                    "Female","Did not respond","Unknown","Female","Female"]})    
      fig = dfScatter(df)
      fig.savefig('fig1.png')
  

  And that gives me:

  As far as I know, that color column can be any matplotlib compatible color (RBGA tuples, HTML names, hex values, etc).

  I'm having trouble getting anything but numerical values to work with the colormaps.

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