正火.csv文件使用大熊貓蟒蛇

-1

我正火的.csv（標記）和我下面這個鏈接給出了答案標記文件：正火.csv文件使用大熊貓蟒蛇

所以，我的問題是我如何保存標籤並規範化數據。

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 
7048.842 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

輸出我得到：

20376.65 22398.29 4.8 0 1 2394 6.1 89.1 0.0.1 4.027 9.377 0.33 0.28 0.36 51364 426372 888388 0.0.2 2040696 57.1 21.75 25.27 0.0.3 452 1046524 1046524.0.1 1 
0 -0.2653633083 0.703280702 0.8672839506    0.0971635486 -0.1327700664  0.3185185167 -inf -0.7429135802 -0.7470319635 -0.7793509403 -0.659592177 -0.4834384858 0.565758717   -inf -0.2740463771 0.7057747653 -0.2814814815 -0.5968412303   0.5 
1 -0.3653677803 0.1040274931 -0.049382716    0.01991551 -0.0175015088  0.3185185167 -inf -0.4015802469 -0.3926940639 -0.3315309684 -0.4011652107 -0.3375394322 0.4269561864   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   0.5 
2 -0.3653677803 0.1040274931 -0.1327160494    -0.2494467914 0.2548782941  0.3185185167 -inf -0.0620246914 -0.0547945205 0.0047090691 0.0370370365 -0.1837539432 0.0159880797   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   0.5 
3 -0.3653677803 0.1040274931 -0.1327160494    -0.2812311406 0.2866626433  0.3185185167 -inf -0.0229135802 -0.0164383562 0.0392144606 0.1044527669 -0.1600946372 -0.0472377828   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   0.5 
4 -0.3653677803 0.1040274931 -0.1327160494    -0.566083283 0.5715147858  0.3185185167 -inf 0.2010864198 0.199086758 0.1843621399 0.1044527669 -0.1600946372 -0.0472377828   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   0.5 
5 0.5012988863 -0.2432863926 -0.1327160494    0.1027962181 -0.0973647154  0.3185185167 -inf 0.2570864198 0.2529680365 0.2206490597 0.1531419101 0.2294952681 -0.3826408164   -inf 0.492911108 0.1148766778 -0.3481481482 0.3183707398   0.5 
6 0.6346322197 -0.296719298 -0.1327160494    0.4049487025 -0.3995171998  -0.5481481333 -inf 0.2570864198 0.2529680365 0.2206490597 0.1606325421 0.2894321767 -0.434241283   -inf 0.6262444414 0.1778154334 -0.4814814815 0.4031587697   0.5 
7 0.6346322197 -0.296719298 -0.1327160494    0.433916717 -0.4284852142  -0.6814814833 -inf 0.2570864198 0.2529680365 0.2206490597 0.1606325421 0.2894321767 -0.434241283   -inf 0.6262444414 0.1778154334 -0.4814814815 0.4031587697   0.5 
8 -0.0437288959 -0.2826656856 -0.0216049383    0.038020519 -0.0374170187  -0.6814814833 -inf 0.2570864198 0.2529680365 0.2206490597 0.340407823 0.5165615142 0.336895965   -inf 0.0236686208 0.0006186288 -0.4814814815 0.4031587697   0.5 
9 -0.2653633083 0.703280702 0.8672839506    0.0971635486 -0.1327700664  0.3185185167 -inf -0.7429135802 -0.7470319635 -0.7793509403 -0.659592177 -0.4834384858 0.565758717   -inf -0.2740463771 0.7057747653 -0.2814814815 -0.5968412303   -0.5 
10 -0.3653677803 0.1040274931 -0.049382716    0.01991551 -0.0175015088  0.3185185167 -inf -0.4015802469 -0.3926940639 -0.3315309684 -0.4011652107 -0.3375394322 0.4269561864   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   -0.5 
11 -0.3653677803 0.1040274931 -0.1327160494    -0.2494467914 0.2548782941  0.3185185167 -inf -0.0620246914 -0.0547945205 0.0047090691 0.0370370365 -0.1837539432 0.0159880797   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   -0.5 
12 -0.3653677803 0.1040274931 -0.1327160494    -0.2812311406 0.2866626433  0.3185185167 -inf -0.0229135802 -0.0164383562 0.0392144606 0.1044527669 -0.1600946372 -0.0472377828   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   -0.5 
13 -0.3653677803 0.1040274931 -0.1327160494    -0.566083283 0.5715147858  0.3185185167 -inf 0.2010864198 0.199086758 0.1843621399 0.1044527669 -0.1600946372 -0.0472377828   -inf -0.3737555586 -0.2942252347 0.5185185185 -0.2327514547   -0.5 
14 0.5012988863 -0.2432863926 -0.1327160494    0.1027962181 -0.0973647154  0.3185185167 -inf 0.2570864198 0.2529680365 0.2206490597 0.1531419101 0.2294952681 -0.3826408164   -inf 0.492911108 0.1148766778 -0.3481481482 0.3183707398   -0.5 
15 0.6346322197 -0.296719298 -0.1327160494    0.4049487025 -0.3995171998  -0.5481481333 -inf 0.2570864198 0.2529680365 0.2206490597 0.1606325421 0.2894321767 -0.434241283   -inf 0.6262444414 0.1778154334 -0.4814814815 0.4031587697   -0.5 
16 0.6346322197 -0.296719298 -0.1327160494    0.433916717 -0.4284852142  -0.6814814833 -inf 0.2570864198 0.2529680365 0.2206490597 0.1606325421 0.2894321767 -0.434241283   -inf 0.6262444414 0.1778154334 -0.4814814815 0.4031587697   -0.5 
17 -0.0437288959 -0.2826656856 -0.0216049383    0.038020519 -0.0374170187  -0.6814814833 -inf 0.2570864198 0.2529680365 0.2206490597 0.340407823 0.5165615142 0.336895965   -inf 0.0236686208 0.0006186288 -0.4814814815 0.4031587697   -0.5

我想在0-1範圍內保留的最後一列（標籤），因爲它是數據。

代碼：

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')

我更新了我的代碼：

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)

現在我得到的值在-1到1分範圍內的感謝！

但現在我得到空條目以.csv

CSV文件：

0.703280701968,0.867283950617,,,,0.0971635485818,-0.132770066385,,0.318518516666,-inf,-0.742913580247,-0.74703196347,-0.779350940252,-0.659592176966,-0.483438485804,0.565758716954,,,-inf,-0.274046377081,0.705774765311,-0.281481481478,-0.596841230258,,,1 
    0.104027493068,-0.0493827160494,,,,0.0199155099578,-0.0175015087508,,0.318518516666,-inf,-0.401580246914,-0.392694063927,-0.331530968381,-0.401165210674,-0.337539432177,0.426956186355,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,1 
    0.104027493068,-0.132716049383,,,,-0.2494467914,0.254878294116,,0.318518516666,-inf,-0.0620246913541,-0.0547945205479,0.00470906912955,0.0370370365169,-0.183753943218,0.0159880797389,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,1 
    0.104027493068,-0.132716049383,,,,-0.281231140616,0.286662643331,,0.318518516666,-inf,-0.0229135802474,-0.0164383561644,0.0392144605923,0.104452766854,-0.160094637224,-0.0472377828174,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,1 
    0.104027493068,-0.132716049383,,,,-0.566083283042,0.571514785757,,0.318518516666,-inf,0.201086419753,0.199086757991,0.184362139917,0.104452766854,-0.160094637224,-0.0472377828174,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,1 
    -0.243286392557,-0.132716049383,,,,0.102796218075,-0.0973647153591,,0.318518516666,-inf,0.257086419753,0.25296803653,0.220649059748,0.153141910112,0.229495268139,-0.382640816358,,,-inf,0.492911108046,0.114876677802,-0.348148148178,0.318370739817,,,1 
    -0.296719298032,-0.132716049383,,,,0.404948702474,-0.399517199759,,-0.548148133334,-inf,0.257086419753,0.25296803653,0.220649059748,0.160632542135,0.289432176656,-0.434241283046,,,-inf,0.626244441365,0.17781543343,-0.481481481478,0.403158769742,,,1 
    -0.296719298032,-0.132716049383,,,,0.433916716958,-0.428485214243,,-0.681481483334,-inf,0.257086419753,0.25296803653,0.220649059748,0.160632542135,0.289432176656,-0.434241283046,,,-inf,0.626244441365,0.17781543343,-0.481481481478,0.403158769742,,,1 
    -0.28266568562,-0.0216049382716,,,,0.0380205190103,-0.0374170187085,,-0.681481483334,-inf,0.257086419753,0.25296803653,0.220649059748,0.340407823034,0.516561514196,0.336895965036,,,-inf,0.023668620842,0.000618628782377,-0.481481481478,0.403158769742,,,1 
    0.703280701968,0.867283950617,,,,0.0971635485818,-0.132770066385,,0.318518516666,-inf,-0.742913580247,-0.74703196347,-0.779350940252,-0.659592176966,-0.483438485804,0.565758716954,,,-inf,-0.274046377081,0.705774765311,-0.281481481478,-0.596841230258,,,0 
    0.104027493068,-0.0493827160494,,,,0.0199155099578,-0.0175015087508,,0.318518516666,-inf,-0.401580246914,-0.392694063927,-0.331530968381,-0.401165210674,-0.337539432177,0.426956186355,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,0 
    0.104027493068,-0.132716049383,,,,-0.2494467914,0.254878294116,,0.318518516666,-inf,-0.0620246913541,-0.0547945205479,0.00470906912955,0.0370370365169,-0.183753943218,0.0159880797389,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,0 
    0.104027493068,-0.132716049383,,,,-0.281231140616,0.286662643331,,0.318518516666,-inf,-0.0229135802474,-0.0164383561644,0.0392144605923,0.104452766854,-0.160094637224,-0.0472377828174,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,0 
    0.104027493068,-0.132716049383,,,,-0.566083283042,0.571514785757,,0.318518516666,-inf,0.201086419753,0.199086757991,0.184362139917,0.104452766854,-0.160094637224,-0.0472377828174,,,-inf,-0.373755558635,-0.294225234689,0.518518518522,-0.232751454697,,,0 
    -0.243286392557,-0.132716049383,,,,0.102796218075,-0.0973647153591,,0.318518516666,-inf,0.257086419753,0.25296803653,0.220649059748,0.153141910112,0.229495268139,-0.382640816358,,,-inf,0.492911108046,0.114876677802,-0.348148148178,0.318370739817,,,0 
    -0.296719298032,-0.132716049383,,,,0.404948702474,-0.399517199759,,-0.548148133334,-inf,0.257086419753,0.25296803653,0.220649059748,0.160632542135,0.289432176656,-0.434241283046,,,-inf,0.626244441365,0.17781543343,-0.481481481478,0.403158769742,,,0 
    -0.296719298032,-0.132716049383,,,,0.433916716958,-0.428485214243,,-0.681481483334,-inf,0.257086419753,0.25296803653,0.220649059748,0.160632542135,0.289432176656,-0.434241283046,,,-inf,0.626244441365,0.17781543343,-0.481481481478,0.403158769742,,,0 
    -0.28266568562,-0.0216049382716,,,,0.0380205190103,-0.0374170187085,,-0.681481483334,-inf,0.257086419753,0.25296803653,0.220649059748,0.340407823034,0.516561514196,0.336895965036,,,-inf,0.023668620842,0.000618628782377,-0.481481481478,0.403158769742,,, 

0

任何建議.. :)

來源

2016-03-05 Ankit G.

告訴我們什麼？你已經嘗試什麼失敗。如果您發佈預期產出的樣本，這也會很有幫助。 – MaxU

和你的頭文件發生了什麼？我希望這只是一個問題，在這個示例數據？ – MaxU

試試這個：

df_norm = (df.ix[:, 0:-1] - df.ix[:, 0:-1].mean())/(df.ix[:, 0:-1].max() - df.ix[:, 0:-1].min())

，然後添加您label柱：

rslt = pd.concat([df_norm, df.ix[:, -1]], axis=1)

來源

2016-03-05 12:28:34 MaxU

謝謝！它給了我標籤保存的.csv，但爲什麼我在.csv中得到空條目，現在我已經把更新後的代碼和輸出置於問題中。希望它對理解我的問題可能有用。 :) –

正火.csv文件使用大熊貓蟒蛇

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