replacing the column values with zero-CodePudding

I have 3 columns of data. I want the entire data to be replaced by zero, except for the 5-10, 8-11, and 3-12 data points respectively.

Input

-0.0007    -0.0236    0.00579
 0.00151   -0.0135   -0.0195
-0.0163    -0.00185   0.00722
 0.0207     0.00998  -0.0387
-0.0246    -0.0274   -0.0108
 0.0123    -0.0256    0.0137
-0.00963    0.0023    0.0305
-0.0147     0.0255   -0.00806
 0.000488  -0.0187    5.29e-05
-0.0167     0.0105   -0.0204
 0.00653    0.0176   -0.00643
 0.0154    -0.0136    0.00415
-0.0147    -0.00339   0.0175

Expected output

-0.0007    -0.0236    0.00579
 0.0015    -0.0135   -0.0195
-0.0163    -0.00185   0.00722
 0.0207     0.00998   0.0000
-0.0246    -0.0274    0.0000
 0.0000    -0.0256    0.0000
 0.0000     0.0023    0.0000
 0.0000     0.0255    0.0000
 0.0000     0.0000    0.0000
 0.0000     0.0000    0.0000
 0.0000     0.0000    0.0000
 0.0154     0.0000    0.0000
-0.0147    -0.00339   0.0000

script:

import numpy as np
import pandas as pd
data = pd.DataFrame(np.loadtxt("input"),columns=list('ABC'))
data['A'] = data['A'].replace([5:10],'0.0')
data['B'] = data['B'].replace([8:11],'0.0')
data['C'] = data['C'].replace([3:12],'0.0')

error:

SyntaxError: invalid syntax

Please suggest how I can do it in easier way.Thanks.

CodePudding user response：

You're almost there, just use df.loc will do the trick.

df["A"].loc[5:10] = 0
df["B"].loc[8:11] = 0
df["C"].loc[3:12] = 0

On the other hand, if you want to keep the mentioned value while putting other rows to zero, you can do this.

df["A"] = df["A"].iloc[5:10]
df["B"] = df["B"].iloc[8:11]
df["C"] = df["C"].iloc[3:12]

df.fillna(0,inplace=True)

df
Out[159]: 
           A         B         C
0   0.000000  0.000000  0.000000
1   0.000000  0.000000  0.000000
2   0.000000  0.000000  0.000000
3   0.000000  0.000000  0.034555
4   0.000000  0.000000  0.299927
5   0.132431  0.000000  0.869628
6   0.950144  0.000000  0.103173
7   0.274763  0.000000  0.636844
8   0.692817  0.287867  0.009558
9   0.992086  0.963698  0.741168
10  0.000000  0.818806  0.723860
11  0.000000  0.000000  0.831885