Python 有没有办法在Pandas 中使用 usecols 读取必需和可选列

我仍然不确定这specific question在pandas API领域内是否有答案，但我已经找到了解决问题的方法.我在 comments 中提到，我正在try 在不同的数据集中运行for循环读取，运行该条件usecols.

正如@Michael S.所建议的那样，try和except是解决这个问题的方法.

首先，遍历包含required列的数据集，然后将数据集名称附加到列表中.

# list of required columns
requiredList = ['required_col1', 'required_col2']
finalDatasets = []
for i, dataName in enumerate(database)):
    try:
        data = pd.read_csv(dataName, usecols=requiredList)
    except:
        continue
    else:
        finalDatasets.append(dataName)

由于这个新列表已经go 掉了do not have the required columns中的数据集，您现在可以运行lambda函数来解析可选列:

columnsList = ['required_col1', 'required_col2', 'optional_col1', 'optional_col2']
for i, dataName in enumerate(finalDatasets):
    # read in data
    try:
        data = pd.read_csv(dataName, usecols=lambda x: x in columnsList)
    except:
        continue
    else:
        {insert analysis}

This way, you are able to parse through the datasets that have the required columns, but you are unsure if they have optional columns (this is where lambda comes in handy).