pandas datafram写入后.csv中缺少值

Question

我有一个.csv文件：

20376.65,22398.29,4.8,0.0,1.0,2394.0,6.1,89.1,0.0,4.027,9.377,0.33,0.28,0.36,51364.0,426372.0,888388.0,0.0,2040696.0,57.1,21.75,25.27,0.0,452.0,1046524.0,1046524.0,1
7048.842,8421.754,1.44,0.0,1.0,2394.0,29.14,69.5,0.0,4.027,9.377,0.33,0.28,0.36,51437.6,426964.0,684084.0,0.0,2040696.0,57.1,12.15,14.254,3.2,568.8,1046524.0,1046524.0,1
3716.89,4927.62,0.12,0.0,1.0,2394.0,26.58,73.32,0.0,4.027,9.377,0.586,1.056,3.544,51456.0,427112.0,633008.0,0.0,2040696.0,57.1,9.75,11.5,4.0,598.0,1046524.0,1046524.0,1
3716.89,4927.62,0.0,0.0,1.0,2394.0,17.653333333,82.346666667,0.0,4.027,9.377,0.84066666667,1.796,5.9346666667,51487.2,427268.0,481781.6,0.0,2040696.0,57.1,9.75,11.5,4.0,598.0,1046524.0,1046524.0,1
3716.89,4927.62,0.0,0.0,1.0,2394.0,16.6,83.4,0.0,4.027,9.377,0.87,1.88,6.18,51492.0,427292.0,458516.0,0.0,2040696.0,57.1,9.75,11.5,4.0,598.0,1046524.0,1046524.0,1

我正在使用pandas dataframe对其进行规范化，但在.csv文件中缺少值：

^{pr2}$

我的代码：

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)

密码有什么问题吗？为什么.csv文件中缺少值？在

Answer 1

你得到很多NaN，因为用0除以0。见broadcasting behaviour。更好的解释是here。在

我使用您之前的question中的代码，因为我认为使用df.ix[:, 1:-1]进行切片是不必要的。在用切片进行规格化后，我得到空的DataFrame。在

import pandas as pd
import numpy as np
import io

temp=u"""20376.65,22398.29,4.8,0.0,1.0,2394.0,6.1,89.1,0.0,4.027,9.377,0.33,0.28,0.36,51364.0,426372.0,888388.0,0.0,2040696.0,57.1,21.75,25.27,0.0,452.0,1046524.0,1046524.0,1
7048.842,8421.754,1.44,0.0,1.0,2394.0,29.14,69.5,0.0,4.027,9.377,0.33,0.28,0.36,51437.6,426964.0,684084.0,0.0,2040696.0,57.1,12.15,14.254,3.2,568.8,1046524.0,1046524.0,1
3716.89,4927.62,0.12,0.0,1.0,2394.0,26.58,73.32,0.0,4.027,9.377,0.586,1.056,3.544,51456.0,427112.0,633008.0,0.0,2040696.0,57.1,9.75,11.5,4.0,598.0,1046524.0,1046524.0,1
3716.89,4927.62,0.0,0.0,1.0,2394.0,17.653333333,82.346666667,0.0,4.027,9.377,0.84066666667,1.796,5.9346666667,51487.2,427268.0,481781.6,0.0,2040696.0,57.1,9.75,11.5,4.0,598.0,1046524.0,1046524.0,1
3716.89,4927.62,0.0,0.0,1.0,2394.0,16.6,83.4,0.0,4.027,9.377,0.87,1.88,6.18,51492.0,427292.0,458516.0,0.0,2040696.0,57.1,9.75,11.5,4.0,598.0,1046524.0,1046524.0,1"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp),index_col=None, header=None)
#print df

^{pr2}$

#difference, you get 0
print (df.max() - df.min())
0    16659.76
1    17470.67
2        4.80
3        0.00
4        0.00
dtype: float64

print df - df.mean()
            0           1      2  3  4
0  12661.4176  13277.7092  3.528  0  0
1   -666.3904   -698.8268  0.168  0  0
2  -3998.3424  -4192.9608 -1.152  0  0
3  -3998.3424  -4192.9608 -1.272  0  0
4  -3998.3424  -4192.9608 -1.272  0  0

#you get NaN, because divide columns 3 and 4 filled 0 to difference with index 3,4 filled 0
df_norm = (df - df.mean()) / (df.max() - df.min())
print df_norm
      0     1      2   3   4
0  0.76  0.76  0.735 NaN NaN
1 -0.04 -0.04  0.035 NaN NaN
2 -0.24 -0.24 -0.240 NaN NaN
3 -0.24 -0.24 -0.265 NaN NaN
4 -0.24 -0.24 -0.265 NaN NaN

最后，如果生成^{}，则从NaN""获取，因为参数na_rep具有默认值""：

print df_norm.to_csv(index=False, header=False, na_rep="")
0.76,0.76,0.735,,
-0.04,-0.04,0.035,,
-0.24,-0.24,-0.24,,
-0.24,-0.24,-0.265,,
-0.24,-0.24,-0.265,,

如果更改na_rep的值：

#change na_rep to * for testing
print df_norm.to_csv(index=False, header=False, na_rep="*")
0.76,0.76,0.735,*,*
-0.04,-0.04,0.035,*,*
-0.24,-0.24,-0.24,*,*
-0.24,-0.24,-0.265,*,*
-0.24,-0.24,-0.265,*,*