matplotlib绘制简单密度图与直方图结合

import matplotlib.pyplot as plt import pandas as pd import numpy as np import matplotlib.mlab as mlab # 加载数据 data = pd.read_csv("birth-rate.csv") # 删除空数据 data.dropna(subset=['2008'], inplace=True) # print(data.head(5)) #绘制密度图 k = mlab.GaussianKDE(data['2008']) x = np.linspace(start=data['2008'].min(), stop=data['2008'].max(), num=100) plt.plot(x, k(x)) # 绘制直方图 plt.hist(x=data['2008'],bins=np.arange(data['2008'].min(), data['2008'].max(), 3),density=True, edgecolor='black') # 显示 plt.show()

结果图

数据

""" 0 11.716000 1 46.538000 2 42.875000 3 14.649000 4 13.281000 5 26.324048 6 14.004000 7 17.269000 8 15.299000 11 13.800000 12 9.326000 13 17.800000 14 34.465000 15 11.672000 16 39.395000 17 47.212000 18 21.431000 19 10.194000 20 18.017000 21 16.704000 22 9.097000 23 11.140000 24 24.698000 25 12.482000 26 27.104000 27 16.194000 28 11.208000 29 19.802000 30 21.494000 31 24.540000 32 35.422000 33 11.250000 34 10.055000 35 9.330000 36 14.942000 37 12.140000 38 34.952000 39 36.860000 40 34.509000 41 20.403000 42 32.426000 43 24.121000 44 16.677000 45 10.488000 47 11.496000 48 11.470000 49 8.312000 50 28.435000 52 11.839000 53 22.527000 54 20.759000 55 14.392480 56 13.860763 57 14.662983 58 12.611785 59 20.800000 60 24.698000 61 10.483857 62 36.983000 63 11.391000 64 11.955000 65 38.230000 66 10.857797 67 11.204000 68 20.945000 69 12.862000 71 25.256000 72 27.273000 73 12.935000 74 12.087000 75 32.355000 77 39.633000 78 36.761000 79 41.188000 80 37.971000 81 10.278000 82 19.419000 83 14.798445 84 33.009000 85 18.311000 86 17.869000 87 11.993008 88 11.300000 89 27.484000 90 39.306400 91 9.900000 92 27.643000 93 9.882000 94 18.569000 95 22.800000 96 16.906000 97 18.906000 98 31.220000 99 15.232000 100 21.500000 101 9.624000 102 16.690000 103 25.726000 104 8.700000 105 22.700000 106 38.767000 107 24.360000 108 24.733000 111 9.400000 112 19.010000 113 17.705000 114 18.771113 115 27.281000 116 15.727000 117 38.327000 118 23.299000 120 18.704826 121 34.624376 122 33.865294 123 9.900000 124 18.800000 125 20.035432 126 21.570196 127 28.942000 128 10.442000 129 11.452000 130 10.568000 131 8.204000 132 20.423000 134 12.320000 135 35.896000 136 18.709000 137 23.707650 138 18.328064 140 19.431061 141 10.898000 142 42.610000 143 10.018000 144 20.528000 145 24.054897 146 12.117000 147 18.785000 149 39.192000 150 33.587000 151 12.900000 152 40.224000 153 20.376000 154 25.081000 155 13.999705 156 27.555000 157 16.200000 158 53.536000 159 39.826000 160 24.624000 161 11.229000 162 14.910585 163 12.688000 164 25.385000 165 15.060000 166 11.735468 167 12.750821 168 21.961000 169 30.086000 170 20.640000 171 21.107000 172 24.728000 174 31.427000 175 10.872000 176 11.822378 177 13.732000 178 9.847000 179 24.625000 180 17.986000 181 12.075000 182 12.100000 183 41.132000 184 23.882067 185 23.424000 186 31.293000 187 38.435000 188 10.200000 189 30.447000 190 40.305000 191 20.235000 192 11.000000 193 44.105000 194 9.400000 195 38.455059 196 38.454670 197 32.110000 198 18.983000 199 10.609000 200 10.495000 201 11.855000 202 29.895000 203 17.800000 204 27.980000 206 45.692000 207 32.875000 208 14.520000 209 28.079000 210 21.933000 211 27.733000 212 14.830000 213 17.700000 214 18.229000 216 41.534000 217 46.151000 218 11.000000 219 17.137045 220 14.580000 221 14.300958 222 21.680535 223 17.581000 224 21.244000 225 12.000000 226 17.152000 227 30.198000 228 19.983917 229 23.508000 230 36.795000 231 22.038000 232 42.879000"""