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import pandas as pd
import numpy as np
UsageError: unrecognized arguments: 设置这行代码，显示
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df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('2020-1-1',periods=10),columns=list('ABCD'))
df
A B C D
2020-01-01 0.846958 -0.092453 1.439972 -1.736005
2020-01-02 0.199984 -0.822618 0.756459 -0.566921
2020-01-03 -0.400146 0.505496 -0.306564 0.920308
2020-01-04 0.222298 -0.985005 1.126557 2.711075
2020-01-05 1.952021 1.096278 -0.085026 0.335684
2020-01-06 -1.359681 1.493068 -0.736807 -0.846511
2020-01-07 -0.837022 -1.017107 -0.444694 0.689624
2020-01-08 0.097225 1.996268 0.703147 -0.461194
2020-01-09 0.749375 0.003991 -0.871616 0.287275
2020-01-10 -0.733558 0.575336 1.087097 0.201447
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%matplotlib inline # 设置这行代码，显示
df.plot()
UsageError: unrecognized arguments: # 设置这行代码，显示
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df = pd.DataFrame(np.random.randn(10,4),columns=['A','B','C','D'])
df
A B C D
0 -0.220509 -0.042927 -0.238487 1.672412
1 -0.164110 -0.507156 -0.403201 0.212512
2 -0.213318 0.100192 1.569447 1.140537
3 0.899244 -0.773582 0.186109 0.630794
4 -0.065581 -0.331992 0.296159 -0.477399
5 -0.681295 -0.035207 -0.843813 0.294918
6 0.447513 2.029172 -0.418954 0.435755
7 2.448048 0.931032 -0.470845 -1.186709
8 -0.318224 -1.299177 0.344508 -0.996497
9 0.230928 0.715940 -0.567065 -0.009406
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df.plot.bar()
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# 堆积条形图,h表示水平
df.plot.barh(stacked=True)
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df = pd.DataFrame({
'a':np.random.randn(100)+1,
'b':np.random.randn(100),
'c':np.random.randn(100)-1
},columns=['a','b','c'])
df
a b c
0 0.000225 -0.802291 -2.977373
1 2.643898 -0.722520 -1.057299
2 1.387954 -1.036934 -1.058767
3 0.581279 -1.228817 -1.139152
4 -0.401500 -1.500153 0.081929
... ... ... ...
95 2.895006 0.212231 -1.123560
96 0.641197 0.207960 0.833677
97 0.837922 0.228554 -0.961109
98 2.107162 -0.056751 -2.883269
99 0.586976 -1.269353 -0.343638
100 rows × 3 columns
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df.plot.hist(bins=20)# bins=20是区间显示
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df.plot.box()
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# 区域块形图,这个代码错了
# df.plot.area()
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# 散点图
df = pd.DataFrame(np.random.randn(50,4),columns=['A','B','C','D'])
df
A B C D
0 0.976834 1.149036 -0.350094 0.547278
1 1.076609 -0.729466 0.805290 0.077687
2 0.905936 -1.384177 0.945078 2.239078
3 -0.769447 -0.833319 1.633905 0.195962
4 0.337959 0.195163 0.052347 -1.759461
5 0.291865 -0.140926 -0.171821 -0.193732
6 -0.030381 1.252231 -1.371790 0.955666
7 -0.159967 -0.204076 -0.608549 1.698038
8 0.025247 -0.433548 0.546536 0.317204
9 -0.668021 0.835804 1.448863 -0.855055
10 0.869959 -0.907479 -0.353877 -0.904369
11 -0.266059 -1.525401 -0.820096 -1.532421
12 -0.573746 -0.382850 -0.173064 0.609361
13 0.499136 -0.553104 -1.271152 -0.778085
14 -0.125324 -0.910958 -0.620956 -0.634354
15 2.388082 1.657346 -1.980270 0.851881
16 1.040289 0.063811 -0.644910 0.686238
17 0.614557 0.313544 0.319014 -0.126910
18 1.762719 -2.197812 -0.644599 1.103788
19 -0.665237 0.588063 -1.395894 0.111074
20 -1.197394 -0.529851 -1.176089 -0.718828
21 0.115390 0.030522 -0.367691 0.733676
22 0.665735 -0.498446 -0.265189 -1.100315
23 0.494392 -1.982058 -0.384783 -0.372455
24 1.215364 1.043641 0.624550 0.467968
25 0.215523 0.312841 -0.060308 -0.875984
26 -1.135017 -0.063532 0.319131 -0.700542
27 0.183737 -0.076965 -0.014999 0.711829
28 1.348638 0.812489 0.489820 1.207570
29 -2.435077 -0.858729 -0.942066 0.732689
30 0.791332 -1.089636 -0.453003 -0.630916
31 0.071361 0.029469 0.051310 0.473051
32 -0.814295 0.398640 -0.185814 -0.774485
33 0.578655 -1.780421 1.203517 0.166697
34 0.430287 -0.916687 1.447872 -0.166584
35 -0.142278 -0.033319 -0.503827 1.685162
36 0.267174 -0.660718 0.592760 -1.999655
37 -0.008522 1.281695 -0.247696 -0.792215
38 1.461348 -0.716580 0.748531 -0.948239
39 -0.627528 1.069450 -0.598248 0.544610
40 -0.079064 1.758644 1.057895 0.532964
41 -0.104020 0.659945 -0.109066 -1.724713
42 -0.501239 -1.516701 1.095822 1.801034
43 0.076188 -1.364045 0.142956 -0.062114
44 -0.450091 1.413097 0.594281 -0.867741
45 -0.617244 1.112824 -0.484753 -0.348894
46 0.736782 -0.601504 0.917616 0.387469
47 -0.921364 0.194857 0.965232 0.006958
48 -0.568531 -1.979506 0.965640 -0.617862
49 -0.161154 -0.278164 0.236000 -0.181289
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df.plot.scatter(x='A',y='B')
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# 散点图
df = pd.DataFrame(3*np.random.rand(4),index=['A','B','C','D'],columns=['aa'])
df
aa
A 2.265322
B 1.600306
C 2.545557
D 2.660989
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df.plot.pie(subplots=True)
array([],
dtype=object)
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