Question

我有多个数据框，我想过滤每个数据框，以便每个df仅保留由单词“ Overall”组成的列。我有以下for循环，但它的效果不如手动完成[aka y15 = y15.filter（like ='Overall'）]。

pit_dfs = [y15,y16,y17]

for i in pit_dfs:
    i = i.filter(like='Overall')

可复制的示例：

y15 = pd.DataFrame({'Col1-Overall': ['a','b','c','d'],
              'Col2': ['a','b','c','d'],
              'Col3': ['a','b','c','d'],
              'Col4': ['a','b','c','d']})

y16 = pd.DataFrame({'Col1-Overall': ['a','b','c','d'],
              'Col2': ['a','b','c','d'],
              'Col3': ['a','b','c','d'],
              'Col4': ['a','b','c','d']})

y17 = pd.DataFrame({'Col1-Overall': ['a','b','c','d'],
              'Col2': ['a','b','c','d'],
              'Col3': ['a','b','c','d'],
              'Col4': ['a','b','c','d']})

预期输出：

y15
+--------------+
| Col1-Overall |
+--------------+
| a            |
+--------------+
| b            |
+--------------+
| c            |
+--------------+
| d            |
+--------------+

y16
+--------------+
| Col1-Overall |
+--------------+
| a            |
+--------------+
| b            |
+--------------+
| c            |
+--------------+
| d            |
+--------------+

y17
+--------------+
| Col1-Overall |
+--------------+
| a            |
+--------------+
| b            |
+--------------+
| c            |
+--------------+
| d            |
+--------------+

我知道这是一个简单的例子，但是过去一个小时一直在研究Stack，但是找不到类似的例子。我想念什么？谢谢！

Answer 1

有关Python for循环，请参见this answer和this example。循环中的变量是 not 指针，因此您无需更改实际的数据帧。

您可以（我尚未测试过）：

pit_dfs = [y15,y16,y17,y18,y19]

for idx in range(len(pit_dfs)):
    pit_dfs[idx] = pit_dfs[idx].filter(like='Overall')

Answer 2

这是另一种选择：

pit_dfs = [y15,y16,y17,y18,y19]

def filter_cols_like(df, like):
    cols_not_like = [col for col in df.columns if like not in col]
    df.drop(columns=cols_not_like,inplace=True)

for i in pit_dfs:
    filter_cols_like(i,like='Overall')

使用过滤器功能和循环过滤多个数据帧

2 个答案: