使用python中的pandas规范化.csv标记的文件 [英] Normalizing .csv labelled file using pandas in python
本文介绍了使用python中的pandas规范化.csv标记的文件的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正常化了.csv（标记为），我按照此链接上给出的答案：
所以，我的问题是如何保留标签，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
7048.842 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 571 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
输出：
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 b $ b 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
我想要0-1范围内的数据保留最后一列（标签）。
代码：
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' ）
我更新了我的代码：
import pandas as pd
df = pd.read_csv（'pooja.csv'，index_col = False）
df_norm = 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）
谢谢！
但现在我在.csv中获得空文章
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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，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 ,,, - 天道酬勤，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 ,,, - 天道酬勤，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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤，-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 ,,, - 天道酬勤， 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， ,, - 天道酬勤，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 ,,, - 天道酬勤，0.626244441365,0.17781543343，-0.481481481478,0.403158769742 ,,, 0
-0.28266568562，-0.0216049382716 ,,,, 0.0380205190103，-0.0374170187085 ,, - 0.681481483334，-inf，0.257086419753,0.25296803653 ，0.220649059748,0.340407823034,0.516561514196,0.316895965036 ,,, - inf，0.023668620842,0.000618628782377，-0.481481481478,0.403158769742 ,,,
0
任何建议..：）
解决方案>
df_norm =（df.ix [:, 0：-1] - df.ix [:, 0：-1] .mean ））/（df.ix [:, 0：-1] .max（） - df.ix [:, 0：-1] .min（））
，然后添加
标签：rslt = pd.concat（[df_norm，df.ix [:, -1]]，axis = 1）
I was normalizing the .csv (labelled) and i was following the answer given on this link:
So, my question is how do i preserve labels and normalize the data.
csv file:
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 0output i got:
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 -0.5I want the data in 0-1 range preserving the last column(label) as it is.
Code :
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')I updated my code :
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)Now i get the values in -1 to 1 range thanks!
But now i get empty entries in .csv
csv file:
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,,, 0Any suggestion .. :)
解决方案try this:
df_norm = (df.ix[:, 0:-1] - df.ix[:, 0:-1].mean()) / (df.ix[:, 0:-1].max() - df.ix[:, 0:-1].min())and then add your
labelcolumn:rslt = pd.concat([df_norm, df.ix[:, -1]], axis=1)
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