我有多个CSV文件，其中有多个表格的格式，由换行符分隔。
示例：

Technology  C_inv [MCHF/y]  C_maint [MCHF/y]
NUCLEAR 70.308020   33.374568
HYDRO_DAM_EXISTING  0.000000    195.051200
HYDRO_DAM   67.717942   1.271600
HYDRO_RIVER_EXISTING    0.000000    204.820000
IND_BOILER_OIL  2.053610    0.532362
IND_BOILER_COAL 4.179935    1.081855
IND_BOILER_WASTE    11.010126   2.849652
DEC_HP_ELEC 554.174644  320.791276
DEC_THERMAL_HP_GAS  77.077291   33.717477
DEC_BOILER_GAS  105.586089  41.161335
DEC_BOILER_OIL  33.514266   25.948450
H2_FROM_GAS 145.185290  59.178082
PYROLYSIS   132.200818  112.392123

Storage technology  C_inv [MCHF/y]  C_maint [MCHF/y]
HYDRO_STORAGE   0.000000    0.000000

Resource    C_op [MCHF/y]
ELECTRICITY 1174.452848
GASOLINE    702.000000
DIESEL  96.390000
OIL 267.787558
NG  1648.527242
WOOD    592.110000
COAL    84.504083
URANIUM 18.277626
WASTE   0.000000

我的所有CSV文件都有不同的子表名称，但数量很少，我可以手动输入它们，以便在需要时检测它们。
另一个问题是，许多标题都包含空格（如“存储技术”），Pandas将其视为两栏。
我最初尝试直接用Pandas和手动拆分来做，但是允许避免错误的参数on_bad_lines='skip'也跳过了有用的行：

Cost_bd = pd.read_csv(f"{Directory}/cost_breakdown.csv",on_bad_lines='skip',delim_whitespace=True).dropna(axis=1,how='all')
colnames=['Technnolgy', 'C_inv[MCHF/y]', 'C_maint[MCHF/y]'] 
Cost_bd.columns = colnames

我相信扫描.txt文件并将其拆分可能会更好，但我不确定如何以最佳方式进行此操作。
我还尝试使用此feed中提供的解决方案

import csv
from os.path import dirname # gets parent folder in a path
from os.path import join # concatenate paths
table_names = ["Technology", "Storage technology", "Resource"]

df = pd.read_csv(f"{Directory}/cost_breakdown.csv", header=None, names=range(3))
groups = df[0].isin(table_names).cumsum()
tables = {g.iloc[0,0]: g.iloc[1:] for k,g in df.groupby(groups)}

但它不起作用：

tables.keys()=
dict_keys(['Technology\tC_inv [MCHF/y]\tC_maint [MCHF/y]'])

编辑：基于@Rabinzel的最终解决方案：

import re
def make_df(group,dict_of_dfs):
    header, data = re.split(r'\t',group[0]), list(map(str.split, group[1:]))
    if len(header) != len(data[0]): # If missing columns list, take former
        header  = header + dict_of_dfs[list(dict_of_dfs.keys())[0]].columns.tolist()[1:]
    dict_of_dfs[header[0]] = pd.DataFrame(data, columns=header)
    return dict_of_dfs

def Read_csv_as_df(path, file_name):
    
    with open(path+file_name) as f:
            dict_of_dfs = {}
            group = []
            for line in f:
                if line!='\n':
                    group.append(line.strip())
                else:
                    print(dict_of_dfs)
                    dict_of_dfs = make_df(group,dict_of_dfs)
                    group = []
            dict_of_dfs = make_df(group,dict_of_dfs)
    return dict_of_dfs