Pandas.read_csv錯誤符號化數據

Question

我有Pandas.read_csv

麻煩

我想讀這個文本文件（見下文）當我將這些數據複製到Excel>文本列>由「空間」分隔它給了我正在尋找的確切輸出。

我嘗試了一堆不同的方法，我認爲regEx佔多個空間會做的伎倆，但我沒能使它工作。

我試試這個代碼：

petrelTxt = pd.read_csv(petrelfile, sep = ' ', header = None) 

，它給我的錯誤

CParserError: Error tokenizing data. C error: Expected 6 fields in line 2, saw 17 

當我嘗試改變 「SEP = '\ S +'」 這使得它越往下的文件，但仍然不起作用。

petrelTxt = pd.read_csv(petrelfile, sep = '\s+', header = None) 


CParserError: Error tokenizing data. C error: Expected 5 fields in line 3, saw 6 

這是原來的txt文件：

# WELL TRACE FROM PETREL 
# WELL NAME:    ZZ-0113 
# WELL HEAD X-COORDINATE: 9999999.00000000 (m) 
# WELL HEAD Y-COORDINATE: 9999999.00000000 (m) 
# WELL KB:    159.00000000 (ft) 
# WELL TYPE:    OIL 
# MD AND TVD ARE REFERENCED (=0) AT KB AND INCREASE DOWNWARDS 
# ANGLES ARE GIVEN IN DEGREES 
# XYZ TRACE IS GIVEN IN COORDINATE SYSTEM WGS_1924_UTM_Zone_42N 
# AZIMUTH REFERENCE TRUE NORTH 
# DX DY ARE GIVEN IN GRID NORTH IN m-UNITS 
# DEPTH (Z, TVD) GIVEN IN ft-UNITS 
#====================================================================================================================================== 
     MD    X    Y    Z   TVD   DX   DY   AZIM   INCL   DLS 
#====================================================================================================================================== 
0.0000000000 999999.00000 9999999.0000 159.00000000 0.0000000000 0.0000005192 -0.000000000 1.3487006929 0.0000000000 0.0000000000 
132.00000000 999999.08032 9999999.9116 27.000774702 131.99922530 0.0803153923 -0.088388779 139.08870069 0.3400000000 0.2575757504 
221.00000000 999999.19115 9999999.8017 -61.99775149 220.99775149 0.1911487882 -0.198290891 132.93870069 0.3200000000 0.0456726104 

Answer 1

嘗試comment="#"

使用io模塊以模擬文件

data = '''# WELL TRACE FROM PETREL 
# WELL NAME:    ZZ-0113 
# WELL HEAD X-COORDINATE: 9999999.00000000 (m) 
# WELL HEAD Y-COORDINATE: 9999999.00000000 (m) 
# WELL KB:    159.00000000 (ft) 
# WELL TYPE:    OIL 
# MD AND TVD ARE REFERENCED (=0) AT KB AND INCREASE DOWNWARDS 
# ANGLES ARE GIVEN IN DEGREES 
# XYZ TRACE IS GIVEN IN COORDINATE SYSTEM WGS_1924_UTM_Zone_42N 
# AZIMUTH REFERENCE TRUE NORTH 
# DX DY ARE GIVEN IN GRID NORTH IN m-UNITS 
# DEPTH (Z, TVD) GIVEN IN ft-UNITS 
#====================================================================================================================================== 
     MD    X    Y    Z   TVD   DX   DY   AZIM   INCL   DLS 
#====================================================================================================================================== 
0.0000000000 999999.00000 9999999.0000 159.00000000 0.0000000000 0.0000005192 -0.000000000 1.3487006929 0.0000000000 0.0000000000 
132.00000000 999999.08032 9999999.9116 27.000774702 131.99922530 0.0803153923 -0.088388779 139.08870069 0.3400000000 0.2575757504 
221.00000000 999999.19115 9999999.8017 -61.99775149 220.99775149 0.1911487882 -0.198290891 132.93870069 0.3200000000 0.0456726104''' 

import pandas as pd 
import io 

f = io.StringIO(data) 

df = pd.read_csv(f, comment="#", sep='\s+') 

print(df.columns) 
print(df.head()) 

結果實施例：

Index(['MD', 'X', 'Y', 'Z', 'TVD', 'DX', 'DY', 'AZIM', 'INCL', 'DLS'], dtype='object') 

     MD    X    Y   Z   TVD   DX \ 
0 0.0 999999.00000 9.999999e+06 159.000000 0.000000 5.192000e-07 
1 132.0 999999.08032 1.000000e+07 27.000775 131.999225 8.031539e-02 
2 221.0 999999.19115 1.000000e+07 -61.997751 220.997751 1.911488e-01 

     DY  AZIM INCL  DLS 
0 -0.000000 1.348701 0.00 0.000000 
1 -0.088389 139.088701 0.34 0.257576 
2 -0.198291 132.938701 0.32 0.045673