使用 pandas 将.dat 文件转换为 csv 格式时出现问题，需要将 1 列拆分为多列

Question

I am facing an issue in converting a.dat extension file into csv file using pandas. I have written the following basic code into google colab:

from google.colab import files
uploaded = files.upload()
KY1801.32m.dat(n/a) - 335449 bytes, last modified: 4/27/2021 - 100% done
Saving KY1801.32m.dat to KY1801.32m (2).dat
dat_file = "KY1801.32m.dat"
with open(dat_file,'r') as file:
  text = file.read()
  print(text)
import pandas as pd
import numpy as np
with open(dat_file,'r') as file:
  df = pd.DataFrame(file)
  print(df.head())
df["sno","year","month","day","hours","minutes","sec","x","y","z","w"]= df[0].str.split(" " , expand 
= True)
df

My issue remains that after using the last line of my code, I am getting an error something like the following:

--------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in _set_item(self, key, value) 3575 try: -> 3576
loc = self._info_axis.get_loc(key) 3577 except KeyError:

8 frames KeyError: ('sno', 'year', 'month', 'day', 'hours', 'minutes', 'sec', 'x', 'y', 'z', 'w')

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/pandas/core/internals/blocks.py in init (self, values, placement, ndim) 129 if self._validate_ndim and self.ndim and len(self.mgr_locs).= len(self:values). 130 raise ValueError( --> 131 f"Wrong number of items passed {len(self,values)}. " 132 f"placement implies {len(self.mgr_locs)}" 133 )

ValueError: Wrong number of items passed 12, placement implies 1

What I want to do is to split my single column 0 in dat file into 12 constituent columns namely: sno, year, month, day, hours, minutes, seconds,x,y,z,w columns based on " " separation. How should I do that? What are the changes needed in my code?

My.dat file can be found here: https://drive.google.com/file/d/1SXEB0Dj2PDUYU31RlLYBPG4858Re8M-J/view?usp=sharing

Answer 1

.dat is creating confusion here, in this case, it's a file with space delimited. so just put the separator = " "

import pandas as pd
df=pd.read_csv("KY1801.32m.dat",sep= " ")