如何在tensorflowpython中读取.xls文件

Question

我在阅读xls时遇到了一个很大的问题。文件到我的机器学习项目。我需要提取的数据保存在.xls文件中，我找不到任何可以轻松提取到tensorflow数据集模型的选项，有人能帮忙吗？在

链接到此数据： “http://archive.ics.uci.edu/ml/machine-learning-databases/00192/BreastTissue.xls”

Answer 1

尝试使用熊猫模块：

import pandas as pd

In [24]: df = pd.read_excel(r'D:\download\BreastTissue.xls', sheet_name='Data')

In [25]: df
Out[25]:
     Case # Class           I0     PA500       HFS           DA           Area        A/DA      Max IP          DR            P
0         1   car   524.794072  0.187448  0.032114   228.800228    6843.598481   29.910803   60.204880  220.737212   556.828334
1         2   car   330.000000  0.226893  0.265290   121.154201    3163.239472   26.109202   69.717361   99.084964   400.225776
2         3   car   551.879287  0.232478  0.063530   264.804935   11888.391827   44.894903   77.793297  253.785300   656.769449
3         4   car   380.000000  0.240855  0.286234   137.640111    5402.171180   39.248524   88.758446  105.198568   493.701814
4         5   car   362.831266  0.200713  0.244346   124.912559    3290.462446   26.342127   69.389389  103.866552   424.796503
5         6   car   389.872978  0.150098  0.097738   118.625814    2475.557078   20.868620   49.757149  107.686164   429.385788
6         7   car   290.455141  0.144164  0.053058    74.635067    1189.545213   15.938154   35.703331   65.541324   330.267293
7         8   car   275.677393  0.153938  0.187797    91.527893    1756.234837   19.187974   39.305183   82.658682   331.588302
8         9   car   470.000000  0.213105  0.225497   184.590057    8185.360837   44.343455   84.482483  164.122511   603.315715
9        10   car   423.000000  0.219562  0.261799   172.371241    6108.106297   35.435762   79.056351  153.172903   558.274515
..      ...   ...          ...       ...       ...          ...            ...         ...         ...         ...          ...
96       97   adi  1650.000000  0.047647  0.043284   274.426177    5824.895192   21.225727   81.239571  262.125656  1603.070348
97       98   adi  2800.000000  0.083078  0.184307   583.259257   31388.652882   53.815953  298.582977  501.038494  2896.582483
98       99   adi  2329.840138  0.066148  0.353255   377.253368   25369.039925   67.246689  336.075165  171.387227  2686.435346
99      100   adi  2400.000000  0.084125  0.220610   596.041956   37939.255571   63.651988  261.348175  535.689409  2447.772353
100     101   adi  2000.000000  0.067195  0.124267   330.271646   15381.097687   46.571051  169.197983  283.639564  2063.073212
101     102   adi  2000.000000  0.106989  0.105418   520.222649   40087.920984   77.059161  204.090347  478.517223  2088.648870
102     103   adi  2600.000000  0.200538  0.208043  1063.441427  174480.476218  164.071543  418.687286  977.552367  2664.583623
103     104   adi  1600.000000  0.071908 -0.066323   436.943603   12655.342135   28.963331  103.732704  432.129749  1475.371534
104     105   adi  2300.000000  0.045029  0.136834   185.446044    5086.292497   27.427344  178.691742   49.593290  2480.592151
105     106   adi  2600.000000  0.069988  0.048869   745.474369   39845.773698   53.450226  154.122604  729.368395  2545.419744

[106 rows x 11 columns]

In [26]: df.dtypes
Out[26]:
Case #      int64
Class      object
I0        float64
PA500     float64
HFS       float64
DA        float64
Area      float64
A/DA      float64
Max IP    float64
DR        float64
P         float64
dtype: object

In [27]: df.shape
Out[27]: (106, 11)