pandas的基本用法(八)——数据的绘制

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pandas的基本用法(八)——数据的绘制

文章作者：Tyan
博客：noahsnail.com  |  CSDN  |  简书

本文主要是关于pandas的一些数据的绘制。

Demo 1

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 定义Series
data = pd.Series(np.random.randn(1000), index = np.arange(1000))
print data
# 累加数据
data = data.cumsum()
# 绘制数据
data.plot()
# 显示数据
plt.show()
  • Data
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0      1.548346
1      0.572742
2      1.104705
3      1.548704
4      0.287301
5     -0.656314
6     -0.202801
7     -0.196791
8      0.096911
9     -0.113942
10    -0.883673
11     1.501955
12    -0.711122
13     0.142911
14    -0.941701
15     1.522272
16     0.610483
17    -0.219638
18     1.745611
19     0.031911
20     0.889913
21     0.771158
22    -0.586174
23     0.873769
24    -2.186166
25     1.643832
26     0.522218
27    -0.301171
28    -1.128542
29     0.085811
         ...   
970   -0.823560
971    0.828570
972    0.344901
973   -1.700792
974   -0.458375
975    0.846068
976    1.054396
977   -0.338136
978    1.039985
979    0.132224
980   -0.152097
981    1.034157
982   -0.950993
983    1.934781
984    0.301666
985   -0.910372
986    0.606312
987    1.562350
988    0.979057
989    0.262618
990    0.105402
991    0.352259
992    0.462557
993   -0.686371
994    1.125795
995   -1.202305
996   -0.879454
997    0.479948
998   -0.058433
999    1.150558
dtype: float64
  • Image

Image

Demo 2

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# 定义DataFrame
data = pd.DataFrame(np.random.randn(1000, 4), index = np.arange(1000), columns = list('ABCD'))
print data
# 累加数据
data = data.cumsum()
# print data
# 绘制数据
data.plot()
# 显示数据
plt.show()
  • Data
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            A         B         C         D
0    0.188169 -0.410177 -0.035167 -0.632530
1    1.902968 -0.253942  0.116262 -0.409900
2   -0.477557 -0.544720  0.475352 -0.763175
3   -0.131545  0.276950 -0.309663 -1.704675
4   -0.497051 -0.786458  0.142589 -1.658723
5   -1.219892 -2.844160  0.923590  1.463719
6   -0.729045 -1.040011 -0.453982 -0.589323
7    1.235946 -0.616109 -0.160319 -1.101710
8    0.064108 -0.880624  0.291627 -0.481524
9    1.178941 -0.812158 -0.440956  0.017456
10   0.246466  1.173672 -1.010398  0.493644
11   0.228121 -0.715523  0.287755 -0.227716
12   0.435218 -1.112818  1.938080 -0.348133
13  -1.154960  0.090186 -0.365532 -0.513318
14   1.061165 -0.040768 -0.994464  1.183172
15   0.138335  0.690717  0.485866 -0.014977
16   0.938048  0.251487  0.009421 -0.809593
17  -1.480628 -0.270541  0.882366 -1.808014
18  -1.122170  0.791330 -1.122514 -1.248467
19   0.736545  1.094979 -0.926841 -0.223580
20   0.439745  0.505928 -0.425728  0.306738
21   0.117386 -3.699946  0.050963 -1.166935
22  -1.433574  0.311665  2.226888 -1.139630
23   1.641118 -0.198970 -0.240798  0.720337
24   0.722513  1.714796 -0.542274  0.443971
25   0.154177  0.701450  0.832888 -1.898574
26  -0.713805 -1.184821 -0.531134  0.068217
27   0.694963 -0.318380  1.437368  0.213080
28   0.331043  1.892780 -0.256899 -1.189912
29  -0.247650  1.601953 -1.695998 -1.001989
..        ...       ...       ...       ...
970  1.096683  0.796003  0.258615 -1.275517
971 -1.302741  1.864113 -0.753244 -0.035786
972 -0.259019  0.760312 -1.273606  0.896497
973 -0.060886  1.100344  2.051858 -0.898953
974  0.058918  0.123978 -0.534120  1.256028
975 -0.813877 -0.344310 -1.149161  0.768660
976 -0.234716 -1.039258  0.592899  0.662823
977  0.353870 -0.536609 -1.078631  1.716869
978 -2.455930 -0.022458  1.159104  1.597242
979 -1.318595 -0.716460  1.254460 -2.477972
980 -0.655070 -1.299694  0.442306  0.685829
981 -0.242390  0.495463 -0.746983  1.224797
982 -0.452496 -0.961725 -0.795946  1.296465
983 -0.118532  0.136311 -0.311137 -0.205128
984 -0.395279  0.646056  1.757899  0.089445
985  1.459979  0.024268 -0.294394  1.992585
986  0.915223 -0.313486  0.873132 -1.046711
987 -1.483945  0.520361  0.728229  1.279807
988  1.496952  0.793115 -0.717488 -0.367732
989 -0.913652 -1.891394 -0.692108 -0.478300
990 -1.384200  0.167642  0.077620  0.741487
991 -0.895972 -0.393196 -0.694417 -1.110403
992  1.045946 -0.618238  1.229456  0.467488
993 -0.199291 -0.199487  1.714675  0.371975
994  0.653998  0.548682  0.598073 -0.668729
995 -0.522661  1.547213  0.684786  0.991293
996 -0.682826  1.844690 -0.577090  0.440919
997 -0.935643 -0.264333  1.067578  0.677179
998  0.957670 -1.024795  0.607110 -0.475680
999 -0.854264 -0.680246 -0.166721 -0.394088

[1000 rows x 4 columns]
  • Image

Image

Demo 3

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# 绘制散点图
ax = data.plot.scatter(x = 'A', y = 'B', color = 'DarkBlue')
data.plot.scatter(x = 'A', y = 'C', color = 'DarkGreen', ax = ax)
plt.show()
  • Image

Image

参考资料

  1. https://www.youtube.com/user/MorvanZhou
如果有收获，可以请我喝杯咖啡！