python pandas：尝试读取txt文件，但显示为NaN

Question

I have a txt file and I want to read it with pandas,

and I wrote:

#!/usr/bin/python

import pandas as pd
import numpy as np
TC=pd.read_csv('D885_Ch10_ZC.csv',error_bad_lines=False,encoding='gbk')
df=pd.DataFrame(TC,columns=['t[s]','digits[]','Ch10_zc[V]'])
print(df)

and I found the data was replace by NaN which I don't know why.

What goes wrong?

---

thx @jezrael 's answer. It works after I deleted all the useless information on the top. Is there anyway to do this without editing the original file?

Answer 1

I think you need sep parameter, because default is sep=',' .

if tab :

names=['t[s]','digits[]','Ch10_zc[V]']
df=pd.read_csv('D885_Ch10_ZC.csv', 
               sep='\t', 
               error_bad_lines=False,
               encoding='gbk', 
               names=names,
               skiprows=1 )

if whitespaces:

names=['t[s]','digits[]','Ch10_zc[V]']
df=pd.read_csv('D885_Ch10_ZC.csv', 
               sep='\s+',
               encoding='gbk', 
               error_bad_lines=False,
               names=names,
               skiprows=1)

---

names=['t[s]','digits[]','Ch10_zc[V]']
df=pd.read_csv('D885_Ch10_ZC.csv', 
               delim_whitespace=True,
               encoding='gbk',
               error_bad_lines=False,
               names=names,
               skiprows=1)

and if 2 or more whitespaces:

names=['t[s]','digits[]','Ch10_zc[V]']
df=pd.read_csv('D885_Ch10_ZC.csv', 
               sep=r'\s{2,}', 
               engine='python', 
               encoding='gbk', 
               names=names,
               skiprows=1 )

EDIT:

Need change skiprows to 10 :

names=['t[s]','digits[]','Ch10_zc[V]']
df=pd.read_csv(StringIO(temp), 
               delim_whitespace=True,
               encoding='gbk', 
               names=names,
               skiprows=10)