python - 如何按类别为数据点着色

Question

我根据 2021 年世界幸福报告数据创建了以下一系列散点图/回归，以说明 4 个不同特征与慷慨度之间的相关性。

在数据框中，第二列 (:,1) 有一个分类属性，表示地理区域、ei、西欧、北美等。

我想为“区域指标”分配颜色，所以在图表上你也可以看到一些关于地理方面的信息，因为国家名称太多了（总共 149 个点）。

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import style 

fig, ((ax1, ax2), (ax3, ax4)) =plt.subplots(2, 2)


# Importing the dataset
pd.set_option('display.float_format','{:.4f}'.format)
df = pd.read_csv('whr.csv')

X = df.iloc[:,7].values
y = df.iloc[:,10].values
X = X.reshape(-1,1)

A = df.iloc[:,6].values
b = df.iloc[:,10].values
A = A.reshape(-1,1)

C = df.iloc[:,8].values
d = df.iloc[:,10].values
C = C.reshape(-1,1)


E = df.iloc[:,11].values
f = df.iloc[:,10].values
E = E.reshape(-1,1)


from sklearn.linear_model import LinearRegression

regressor = LinearRegression()
regressor.fit(X, y)

regressor2 = LinearRegression()
regressor2.fit(A, b)

regressor3 = LinearRegression()
regressor3.fit(C, d)

regressor4 = LinearRegression()
regressor4.fit(E, f)


#axes 
generosity = df['Generosity']
social_support =df['Social support']
logged_gdp=df['Logged GDP per capita']
life_expectancy=df['Healthy life expectancy']
perception_of_corruption=df['Perceptions of corruption']


ax1.scatter(social_support,generosity, marker="+")
ax1.set_title('Social Support')
ax1.set_xlabel('Social Support')
ax1.set_ylabel('Generosity')
ax1.plot(X, regressor.predict(X), color = '#4E47E6')

ax2.scatter(logged_gdp,generosity, marker="+")
ax2.set_title('Logged GDP')
ax2.set_xlabel('Logged GDP')
ax2.set_ylabel('Generosity')
ax2.plot(A, regressor2.predict(A), color = '#4E47E6')

ax3.scatter(life_expectancy,generosity, marker="+")
ax3.set_title('Life Expectancy')
ax3.set_xlabel('Life Expectancy')
ax3.set_ylabel('Generosity')
ax3.plot(C, regressor3.predict(C), color = '#4E47E6')


ax4.scatter(perception_of_corruption,generosity, marker="+")
ax4.set_title('Perception of Corruption')
ax4.set_xlabel('Perception of Corruption')
ax4.set_ylabel('Generosity')
ax4.plot(E, regressor4.predict(E), color = '#4E47E6')

fig.suptitle('What Impacts Generosity Around the World?', x=.525, y=.98, horizontalalignment='center', verticalalignment='top', fontsize = 15)
fig.tight_layout()
plt.scatter.markers=('+')



plt.show()
fig.savefig('Generosity.png', dpi=300)

,Country name,Regional indicator,Ladder score,Standard error of ladder score,upperwhisker,lowerwhisker,Logged GDP per capita,Social support,Healthy life expectancy,Freedom to make life choices,Generosity,Perceptions of corruption,Ladder score in Dystopia,Explained by: Log GDP per capita,Explained by: Social support,Explained by: Healthy life expectancy,Explained by: Freedom to make life choices,Explained by: Generosity,Explained by: Perceptions of corruption,Dystopia + residual
0,Finland,Western Europe,7.842,0.032,7.904,7.78,10.775,0.954,72.0,0.949,-0.098,0.186,2.43,1.446,1.106,0.741,0.691,0.124,0.481,3.253
1,Denmark,Western Europe,7.62,0.035,7.687,7.552,10.933,0.954,72.7,0.946,0.03,0.179,2.43,1.502,1.108,0.763,0.686,0.208,0.485,2.868
2,Switzerland,Western Europe,7.571,0.036,7.643,7.5,11.117,0.942,74.4,0.919,0.025,0.292,2.43,1.566,1.079,0.816,0.653,0.204,0.413,2.839
3,Iceland,Western Europe,7.554,0.059,7.67,7.438,10.878,0.983,73.0,0.955,0.16,0.673,2.43,1.482,1.172,0.772,0.698,0.293,0.17,2.967
4,Netherlands,Western Europe,7.464,0.027,7.518,7.41,10.932,0.942,72.4,0.913,0.175,0.338,2.43,1.501,1.079,0.753,0.647,0.302,0.384,2.798
5,Norway,Western Europe,7.392,0.035,7.462,7.323,11.053,0.954,73.3,0.96,0.093,0.27,2.43,1.543,1.108,0.782,0.703,0.249,0.427,2.58
6,Sweden,Western Europe,7.363,0.036,7.433,7.293,10.867,0.934,72.7,0.945,0.086,0.237,2.43,1.478,1.062,0.763,0.685,0.244,0.448,2.683
7,Luxembourg,Western Europe,7.324,0.037,7.396,7.252,11.647,0.908,72.6,0.907,-0.034,0.386,2.43,1.751,1.003,0.76,0.639,0.166,0.353,2.653
8,New Zealand,North America and ANZ,7.277,0.04,7.355,7.198,10.643,0.948,73.4,0.929,0.134,0.242,2.43,1.4,1.094,0.785,0.665,0.276,0.445,2.612
9,Austria,Western Europe,7.268,0.036,7.337,7.198,10.906,0.934,73.3,0.908,0.042,0.481,2.43,1.492,1.062,0.782,0.64,0.215,0.292,2.784
10,Australia,North America and ANZ,7.183,0.041,7.265,7.102,10.796,0.94,73.9,0.914,0.159,0.442,2.43,1.453,1.076,0.801,0.647,0.291,0.317,2.598
11,Israel,Middle East and North Africa,7.157,0.034,7.224,7.09,10.575,0.939,73.503,0.8,0.031,0.753,2.43,1.376,1.074,0.788,0.509,0.208,0.119,3.083
12,Germany,Western Europe,7.155,0.04,7.232,7.077,10.873,0.903,72.5,0.875,0.011,0.46,2.43,1.48,0.993,0.757,0.6,0.195,0.306,2.824
13,Canada,North America and ANZ,7.103,0.042,7.185,7.021,10.776,0.926,73.8,0.915,0.089,0.415,2.43,1.447,1.044,0.798,0.648,0.246,0.335,2.585
14,Ireland,Western Europe,7.085,0.04,7.164,7.006,11.342,0.947,72.4,0.879,0.077,0.363,2.43,1.644,1.092,0.753,0.606,0.238,0.367,2.384
15,Costa Rica,Latin America and Caribbean,7.069,0.056,7.179,6.96,9.88,0.891,71.4,0.934,-0.126,0.809,2.43,1.134,0.966,0.722,0.673,0.105,0.083,3.387
16,United Kingdom,Western Europe,7.064,0.038,7.138,6.99,10.707,0.934,72.5,0.859,0.233,0.459,2.43,1.423,1.062,0.757,0.58,0.34,0.306,2.596
17,Czech Republic,Central and Eastern Europe,6.965,0.049,7.062,6.868,10.556,0.947,70.807,0.858,-0.208,0.868,2.43,1.37,1.09,0.703,0.58,0.052,0.046,3.124
18,United States,North America and ANZ,6.951,0.049,7.047,6.856,11.023,0.92,68.2,0.837,0.098,0.698,2.43,1.533,1.03,0.621,0.554,0.252,0.154,2.807
19,Belgium,Western Europe,6.834,0.034,6.901,6.767,10.823,0.906,72.199,0.783,-0.153,0.646,2.43,1.463,0.998,0.747,0.489,0.088,0.187,2.862
20,France,Western Europe,6.69,0.037,6.762,6.618,10.704,0.942,74.0,0.822,-0.147,0.571,2.43,1.421,1.081,0.804,0.536,0.092,0.235,2.521
21,Bahrain,Middle East and North Africa,6.647,0.068,6.779,6.514,10.669,0.862,69.495,0.925,0.089,0.722,2.43,1.409,0.899,0.662,0.661,0.246,0.139,2.631
22,Malta,Western Europe,6.602,0.044,6.688,6.516,10.674,0.931,72.2,0.927,0.133,0.653,2.43,1.411,1.055,0.747,0.664,0.275,0.183,2.268
23,Taiwan Province of China,East Asia,6.584,0.038,6.659,6.51,10.871,0.898,69.6,0.784,-0.07,0.721,2.43,1.48,0.982,0.665,0.49,0.142,0.139,2.687
24,United Arab Emirates,Middle East and North Africa,6.561,0.039,6.637,6.484,11.085,0.844,67.333,0.932,0.074,0.589,2.43,1.555,0.86,0.594,0.67,0.236,0.223,2.422

标签： pythonpandasmatplotlibseaborn

Answer 1

• 最简单的解决方案是选择所需的列并将它们重塑为带有.melt.
• 然后使用sns.lmplot和的组合sns.regplot
  • hue可用于根据区域指定颜色，但这也会导致每个单独的回归线，而不是所有数据点的回归线，因此没有显示回归线.lmplot，而是为每个轴单独绘制.regplot.
  • seaborn是 matplotlib 的高级 API
• 使用pandas 1.2.5,seaborn 0.11.1和matplotlib 3.4.2.
• 此实现将代码从 58 行减少到 13 行。

import pandas as pd
import seaborn as sns

# given dataframe df
data = {'Country name': ['Finland', 'Denmark', 'Switzerland', 'Iceland', 'Netherlands', 'Norway', 'Sweden', 'Luxembourg', 'New Zealand', 'Austria', 'Australia', 'Israel', 'Germany', 'Canada', 'Ireland', 'Costa Rica', 'United Kingdom', 'Czech Republic', 'United States', 'Belgium', 'France', 'Bahrain', 'Malta', 'Taiwan Province of China', 'United Arab Emirates'], 'Generosity': [-0.098, 0.03, 0.025, 0.16, 0.175, 0.093, 0.086, -0.034, 0.134, 0.042, 0.159, 0.031, 0.011, 0.089, 0.077, -0.126, 0.233, -0.208, 0.098, -0.153, -0.147, 0.089, 0.133, -0.07, 0.074], 'Regional indicator': ['Western Europe', 'Western Europe', 'Western Europe', 'Western Europe', 'Western Europe', 'Western Europe', 'Western Europe', 'Western Europe', 'North America and ANZ', 'Western Europe', 'North America and ANZ', 'Middle East and North Africa', 'Western Europe', 'North America and ANZ', 'Western Europe', 'Latin America and Caribbean', 'Western Europe', 'Central and Eastern Europe', 'North America and ANZ', 'Western Europe', 'Western Europe', 'Middle East and North Africa', 'Western Europe', 'East Asia', 'Middle East and North Africa'], 'Social support': [0.954, 0.954, 0.942, 0.983, 0.942, 0.954, 0.934, 0.908, 0.948, 0.934, 0.94, 0.939, 0.903, 0.926, 0.947, 0.891, 0.934, 0.947, 0.92, 0.906, 0.942, 0.862, 0.931, 0.898, 0.844], 'Logged GDP per capita': [10.775, 10.933, 11.117, 10.878, 10.932, 11.053, 10.867, 11.647, 10.643, 10.906, 10.796, 10.575, 10.873, 10.776, 11.342, 9.88, 10.707, 10.556, 11.023, 10.823, 10.704, 10.669, 10.674, 10.871, 11.085], 'Healthy life expectancy': [72.0, 72.7, 74.4, 73.0, 72.4, 73.3, 72.7, 72.6, 73.4, 73.3, 73.9, 73.503, 72.5, 73.8, 72.4, 71.4, 72.5, 70.807, 68.2, 72.199, 74.0, 69.495, 72.2, 69.6, 67.333], 'Perceptions of corruption': [0.186, 0.179, 0.292, 0.673, 0.338, 0.27, 0.237, 0.386, 0.242, 0.481, 0.442, 0.753, 0.46, 0.415, 0.363, 0.809, 0.459, 0.868, 0.698, 0.646, 0.571, 0.722, 0.653, 0.721, 0.589]}
df = pd.DataFrame(data)

# columns to be used as value variables
cols = ['Social support', 'Logged GDP per capita', 'Healthy life expectancy', 'Perceptions of corruption']

# melt the desired columns from dataframe df
dfm = df.melt(id_vars=['Generosity', 'Regional indicator'], value_vars=cols)

# plot the points with color in a FacetGrid
p = sns.lmplot(data=dfm, col='variable', col_wrap=2, col_order=cols, x='value', y='Generosity', hue='Regional indicator', sharey=False, sharex=False, fit_reg=False)

# use regplot to plot the regression line for all points
for i, col in enumerate(cols):
    sns.regplot(x=col, y='Generosity', data=df, scatter=False, ax=p.axes[i], ci=False)

# add plot formatting
p.set_titles(row_template='{row_name}', col_template='{col_name}')  # shorten the column names
p.fig.suptitle("What Impacts Generosity Around the World?", size=16)
p.fig.subplots_adjust(hspace=.2, wspace=0.2, top=0.9)  # add spacing between plots

p.savefig('Generosity.png', dpi=300)