在python中使用熊猫规范化.csv标记的文件
我正在规范化.csv（带标签），并遵循此链接上给出的答案： 所以，我的问题是如何保留标签和规范化数据 csv文件：在python中使用熊猫规范化.csv标记的文件,python,csv,pandas,Python,Csv,Pandas,我正在规范化.csv（带标签），并遵循此链接上给出的答案： 所以，我的问题是如何保留标签和规范化数据 csv文件： 20376.65 22398.29 4.8 0 1 2394 6.1 89.1 0 4.027 9.377 0.33 0.28 0.36 51364 426372 888388 0 2040696 57.1 21.75 25.27 0 452 1046524 1046524 1 7048.
20376.65 22398.29 4.8 0 1 2394 6.1 89.1 0 4.027 9.377 0.33 0.28 0.36 51364 426372 888388 0 2040696 57.1 21.75 25.27 0 452 1046524 1046524 1
7048.842 8421.754 1.44 0 1 2394 29.14 69.5 0 4.027 9.377 0.33 0.28 0.36 51437.6 426964 684084 0 2040696 57.1 12.15 14.254 3.2 568.8 1046524 1046524 1
3716.89 4927.62 0.12 0 1 2394 26.58 73.32 0 4.027 9.377 0.586 1.056 3.544 51456 427112 633008 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
3716.89 4927.62 0 0 1 2394 17.653333333 82.346666667 0 4.027 9.377 0.8406666667 1.796 5.9346666667 51487.2 427268 481781.6 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
3716.89 4927.62 0 0 1 2394 16.6 83.4 0 4.027 9.377 0.87 1.88 6.18 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
3716.89 4927.62 0 0 1 2394 7.16 92.84 0 4.027 9.377 1.038 2.352 7.212 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
32592.516 2902.4973333 0 0 1 2394 29.326666667 70.673333333 0 4.027 9.377 1.08 2.47 7.47 51495.466667 427687.2 335095.73333 0 2040696 57.1 30.610666667 12.626666667 3.1333333333 642.2 1046524 1046524 1
37034.92 2590.94 0 0 1 2394 39.34 60.66 0 4.0252666667 9.377 1.08 2.47 7.47 51496 427748 316108 0 2040696 57.1 33.82 12.8 3 649 1046524 1046524 1
37034.92 2590.94 0 0 1 2394 40.3 59.7 0 4.025 9.377 1.08 2.47 7.47 51496 427748 316108 0 2040696 57.1 33.82 12.8 3 649 1046524 1046524 1
14433.264 2672.884 0.16 0 1 2394 27.18 72.66 0 4.025 9.377 1.08 2.47 7.47 51508.8 427978.4 599868 0 2040696 57.1 19.316 12.312 3 649 1046524 1046524 1
7048.842 8421.754 1.44 0 1 2394 29.14 69.5 0 4.027 9.377 0.33 0.28 0.36 51437.6 426964 684084 0 2040696 57.1 12.15 14.254 3.2 568.8 1046524 1046524 0
3716.89 4927.62 0.12 0 1 2394 26.58 73.32 0 4.027 9.377 0.586 1.056 3.544 51456 427112 633008 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
3716.89 4927.62 0 0 1 2394 17.653333333 82.346666667 0 4.027 9.377 0.8406666667 1.796 5.9346666667 51487.2 427268 481781.6 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
3716.89 4927.62 0 0 1 2394 16.6 83.4 0 4.027 9.377 0.87 1.88 6.18 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
3716.89 4927.62 0 0 1 2394 7.16 92.84 0 4.027 9.377 1.038 2.352 7.212 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
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import pandas as pd
df = pd.read_csv('pooja.csv')
df_norm = (df - df.mean()) / (df.max() - df.min())
df_norm.to_csv('example.csv')
import pandas as pd
df = pd.read_csv('pooja.csv',index_col=False)
df_norm = (df.ix[:, 1:-1] - df.ix[:, 1:-1].mean()) / (df.ix[:, 1:-1].max() - df.ix[:, 1:-1].min())
rslt = pd.concat([df_norm, df.ix[:,-1]], axis=1)
rslt.to_csv('example.csv',index=False,header=False)
20376.65 22398.29 4.8 0 1 2394 6.1 89.1 0 4.027 9.377 0.33 0.28 0.36 51364 426372 888388 0 2040696 57.1 21.75 25.27 0 452 1046524 1046524 1
7048.842 8421.754 1.44 0 1 2394 29.14 69.5 0 4.027 9.377 0.33 0.28 0.36 51437.6 426964 684084 0 2040696 57.1 12.15 14.254 3.2 568.8 1046524 1046524 1
3716.89 4927.62 0.12 0 1 2394 26.58 73.32 0 4.027 9.377 0.586 1.056 3.544 51456 427112 633008 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
3716.89 4927.62 0 0 1 2394 17.653333333 82.346666667 0 4.027 9.377 0.8406666667 1.796 5.9346666667 51487.2 427268 481781.6 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
3716.89 4927.62 0 0 1 2394 16.6 83.4 0 4.027 9.377 0.87 1.88 6.18 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
3716.89 4927.62 0 0 1 2394 7.16 92.84 0 4.027 9.377 1.038 2.352 7.212 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 1
32592.516 2902.4973333 0 0 1 2394 29.326666667 70.673333333 0 4.027 9.377 1.08 2.47 7.47 51495.466667 427687.2 335095.73333 0 2040696 57.1 30.610666667 12.626666667 3.1333333333 642.2 1046524 1046524 1
37034.92 2590.94 0 0 1 2394 39.34 60.66 0 4.0252666667 9.377 1.08 2.47 7.47 51496 427748 316108 0 2040696 57.1 33.82 12.8 3 649 1046524 1046524 1
37034.92 2590.94 0 0 1 2394 40.3 59.7 0 4.025 9.377 1.08 2.47 7.47 51496 427748 316108 0 2040696 57.1 33.82 12.8 3 649 1046524 1046524 1
14433.264 2672.884 0.16 0 1 2394 27.18 72.66 0 4.025 9.377 1.08 2.47 7.47 51508.8 427978.4 599868 0 2040696 57.1 19.316 12.312 3 649 1046524 1046524 1
7048.842 8421.754 1.44 0 1 2394 29.14 69.5 0 4.027 9.377 0.33 0.28 0.36 51437.6 426964 684084 0 2040696 57.1 12.15 14.254 3.2 568.8 1046524 1046524 0
3716.89 4927.62 0.12 0 1 2394 26.58 73.32 0 4.027 9.377 0.586 1.056 3.544 51456 427112 633008 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
3716.89 4927.62 0 0 1 2394 17.653333333 82.346666667 0 4.027 9.377 0.8406666667 1.796 5.9346666667 51487.2 427268 481781.6 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
3716.89 4927.62 0 0 1 2394 16.6 83.4 0 4.027 9.377 0.87 1.88 6.18 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
3716.89 4927.62 0 0 1 2394 7.16 92.84 0 4.027 9.377 1.038 2.352 7.212 51492 427292 458516 0 2040696 57.1 9.75 11.5 4 598 1046524 1046524 0
32592.516 2902.4973333 0 0 1 2394 29.326666667 70.673333333 0 4.027 9.377 1.08 2.47 7.47 51495.466667 427687.2 335095.73333 0 2040696 57.1 30.610666667 12.626666667 3.1333333333 642.2 1046524 1046524 0
37034.92 2590.94 0 0 1 2394 39.34 60.66 0 4.0252666667 9.377 1.08 2.47 7.47 51496 427748 316108 0 2040696 57.1 33.82 12.8 3 649 1046524 1046524 0
37034.92 2590.94 0 0 1 2394 40.3 59.7 0 4.025 9.377 1.08 2.47 7.47 51496 427748 316108 0 2040696 57.1 33.82 12.8 3 649 1046524 1046524 0
14433.264 2672.884 0.16 0 1 2394 27.18 72.66 0 4.025 9.377 1.08 2.47 7.47 51508.8 427978.4 599868 0 2040696 57.1 19.316 12.312 3 649 1046524 1046524 0
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0
df_norm = (df.ix[:, 0:-1] - df.ix[:, 0:-1].mean()) / (df.ix[:, 0:-1].max() - df.ix[:, 0:-1].min())
标签rslt = pd.concat([df_norm, df.ix[:, -1]], axis=1)
让我们看看你已经尝试了什么，失败了什么。如果您能发布一个预期输出的示例，这也会很有帮助。那么您的标题发生了什么？我希望这是一个问题，只是在这个样本数据？谢谢！它给了我保留的.csv标签，但为什么我在.csv中得到空条目现在我已经把更新的代码和输出放在了问题中。希望这对理解我的问题有用。：）