将文本文件转换为pandas datafram

Question

我有.TX0文件（某种csv txt文件），并通过python.readlines（）、open（filename，'w'）等方法将其转换为.txt文件。我有这个新保存的txt文件，但当我试图将其转换为数据帧时，它只给我一列。txt文件如下：

Empty DataFrame
Columns: [ '"Software Version:", 6.3.2.0646, Date:, 19/08/2015 09:26:04\n',  '"Reprocess Number:", vma2:  261519, Unnamed: 7, \n',  '"Sample Name:",  , Data Acquisition Time:, 18/08/2015 17:23:23\n',  '"Instrument Name:", natural gas (PE ASXL-TCD/FID), Channel:, B\n',  '"Rack/Vial:", 0, 0.1, Operator:, joey.walker\n',  '"Sample Amount:", 1.000000, Dilution Factor:, 1.000000\n',  '"Cycle:", 1, Result File :, \\\\vma2\\TotalChrom\11170_he_tcd001.rst \n',  '"Sequence File :", \\\\vma\C1_C2_binary.seq \n',  '"===================================================================================================================================="\n',  '""\n',  '""\n'.1,  '"condensate analysis (HP4890 Optic - FID)"\n',  '"Peak", Component, Time, Area, Height, BL\n',  '"#", Name, [min], [uV*sec], [uV], \n'.1,  '------, ------, ------.1, ------.2, ------.3, ------\n',  '1, Unnamed: 55, 0.810, 706.42, 304.38, *BB\n',  '2, CH4, 0.900, 1113518.24, 495918.41, *BB\n'.1,  '3, C2H6, 1.373, 901670.23, 295381.12, *BB\n'.2,  '"", Unnamed: 73, Unnamed: 74, ------.4, ------.5, \n'.2,  '"".1, Unnamed: 79, Unnamed: 80, 2015894.89, 791603.91, \n'.3,  '"Missing Component Report"\n',  '"Component", Expected Retention (Calibration File)\n',  '------.1, ------\n'.1,  '"All components were found"\n',  '"Report stored in ASCII file :", C:\\Shared Folders\\TotalChrom\\11170_he_tcd001.TX0 \n']]
Index: []

为了便于阅读：

Empty DataFrame

Columns: [ '"Software Version:", 6.3.2.0646, Date:, 19/08/2015 09:26:04\n', '"Reprocess Number:", vma2: 261519, Unnamed: 7, \n', '"Sample Name:", , Data Acquisition Time:, 18/08/2015 17:23:23\n', '"Instrument Name:", natural gas (PE ASXL-TCD/FID), Channel:, B\n', '"Rack/Vial:", 0, 0.1, Operator:, joey.walker\n', '"Sample Amount:", 1.000000, Dilution Factor:, 1.000000\n', '"Cycle:", 1, Result File :, \\vma2\TotalChrom\data\Joey\Binary_Mixtures\Std1\11170_he_tcd001.rst \n', '"Sequence File :", \\vma2\TotalChrom\sequences\Joey\C1_C2_binary.seq \n', '"===================================================================================================================================="\n', '""\n', '""\n'.1, '"condensate analysis (HP4890 Optic - FID)"\n', '"Peak", Component, Time, Area, Height, BL\n', '"#", Name, [min], [uV*sec], [uV], \n'.1, '------, ------, ------.1, ------.2, ------.3, ------\n', '1, Unnamed: 55, 0.810, 706.42, 304.38, *BB\n', '2, CH4, 0.900, 1113518.24, 495918.41, *BB\n'.1, '3, C2H6, 1.373, 901670.23, 295381.12, *BB\n'.2, '"", Unnamed: 73, Unnamed: 74, ------.4, ------.5, \n'.2, '"".1, Unnamed: 79, Unnamed: 80, 2015894.89, 791603.91, \n'.3, '"Missing Component Report"\n', '"Component", Expected Retention (Calibration File)\n', '------.1, ------\n'.1, '"All components were found"\n', '"Report stored in ASCII file :", C:\Shared Folders\TotalChrom\data\Joey\Binary_Mixtures\Std1\11170_he_tcd001.TX0 \n']] Index: []

如您所见，这是逗号分隔的。有什么方法可以把这个文本转换成逗号分隔的数据帧吗？在

谢谢。在

J

Answer 1

您可以尝试使用下面的函数，它将帮助您从本地csv文件加载所有数据

ps.read_csv()

更多详细信息请参见pandas.read_csv tutorial

Answer 2

您可以尝试下面的代码将文本文件转换为数据帧。在

data = pd.read_csv('file.txt', sep=',')

希望它不言自明。在

Answer 3

我对这个问题给出了一个大致的答案：

import re
import pandas as pd

#first u have to open  the file and seperate every line like below:

df = open('file.txt', "r")
lines = df.readlines()
df.close()

# remove /n at the end of each line
for index, line in enumerate(lines):
      lines[index] = line.strip()



#creating a dataframe(consider u want to convert your data to 2 columns)

df_result = pd.DataFrame(columns=('first_col', 'second_col'))
i = 0  
first_col = "" 
second_col = ""  
for line in lines:
    #you can use "if" and "replace" in case you had some conditions to manipulate the txt data
    if 'X' in line:
        first_col = line.replace('X', "")
    else:
        #you have to kind of define what are the values in columns,for example second column includes:
        second_col = re.sub(r' \(.*', "", line)
        #this is how you create next line data
        df_result.loc[i] = [first_col, second_col]
        i =i+1