在Python中从目录中的多个CSV文件中提取特定列

Question

我的目录中有大约 200 个 CSV 文件，其中包含不同的列，但有些文件包含我想要提取的数据。我想要拉的一列称为“程序”（行的顺序不同，但名称相同），另一列包含“建议”（并非所有措辞都相同，但它们都会包含该措辞）。最终，我想为每个 CSV 提取这些列下的所有行，并将它们附加到仅包含这两列的数据框中。我曾尝试先使用一个 CSV 执行此操作，但无法使其工作。这是我尝试过的：import pandas as pdfrom io import StringIOdf =  pd.read_csv("test.csv")dfout = pd.DataFrame(columns=['Programme', 'Recommends'])for file in [df]:    dfn = pd.read_csv(file)    matching = [s for s in dfn.columns if "would recommend" in s]    if matching:        dfn = dfn.rename(columns={matching[0]:'Recommends'})        dfout = pd.concat([dfout, dfn], join="inner")print(dfout)我收到以下错误消息，所以我认为这是格式问题（它不喜欢 pandas df？）： ValueError(msg.format(_type=type(filepath_or_buffer))) ValueError: 无效的文件路径或缓冲区对象类型： <类'pandas.core.frame.DataFrame'>当我尝试这个时：csv1 = StringIO("""Programme,"Overall, I am satisfied with the quality of the programme",I would recommend the company to a friend or colleague,Please comment on any positive aspects of your experience of this programmeNursing,4,4,IMAGENursing,1,3,very goodNursing,4,5,I enjoyed studying tis programme""")csv2 = StringIO("""Programme,I would recommend the company to a friend,The programme was well organised and running smoothly,It is clear how students' feedback on the programme has been acted onIT,4,2,4IT,5,5,5IT,5,4,5""")dfout = pd.DataFrame(columns=['Programme', 'Recommends'])for file in [csv1,csv2]:    dfn = pd.read_csv(file)    matching = [s for s in dfn.columns if "would recommend" in s]    if matching:        dfn = dfn.rename(columns={matching[0]:'Recommends'})        dfout = pd.concat([dfout, dfn], join="inner")print(dfout)这工作正常，但我需要读取 CSV 文件。有任何想法吗？上面示例的预期输出： 

Answer 1

以下作品：

import pandas as pd

import glob

dfOut = []

for myfile in glob.glob("*.csv"):

    tmp = pd.read_csv(myfile, encoding='latin-1')

    

    matching = [s for s in tmp.columns if "would recommend" in s]

    if len(matching) > 0:

        tmp.rename(columns={matching[0]: 'Recommend'}, inplace=True)

        tmp = tmp[['Subunit', 'Recommend']]

        dfOut.append(tmp)

        

df = pd.concat(dfOut)