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"""

最后

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