read file using read_table /read_csv command

Question

i have text file which contains following information

# Column1 Column2
10  12
15  7
20  9
30  11
40  20
60  25

i have tried to read this file like this

import numpy as np
import pandas as pd
file =pd.read_table("results.txt",sep="\t")
print(file.head())

result is this :

   # Column1 Column2
10                 12
15                  7
20                  9
30                 11
40                 20

of course i can use skiprows to skip first rows like this

import numpy as np
import pandas as pd
file =pd.read_table("results.txt",sep="\t",skiprows=1,header=None)
print(file.head())

result is :

   0   1
0  10  12
1  15   7
2  20   9
3  30  11
4  40  20

but i want to keep original columns, how to do it? How to separate columns well?

Answer 1

This should do it.

Read in your data using read_csv use the Sep=" " so as to use spaces.

Input is messy so a little cleanup. Drop the unwanted column1 which will be full of NAs. Then rename the column holding the data you want to call column1.

import pandas as pd
data = pd.read_csv(r"results.txt", sep=" ")

data.drop('Column1', inplace=True, axis=1)
data.rename(columns={'#': 'Column1'}, inplace=True)
print(data)

output

   Column1  Column2
0       10       12
1       15        7
2       20        9
3       30       11
4       40       20
5       60       25