遍历多个 Pandas 列表类型系列并找到匹配项

Question

我有一个 Pandas DF，其中包含三个类似列表的系列，我需要对其进行迭代并与外部列表进行比较，然后为找到这些外部列表的完全匹配的行创建一个 True/NaN 系列

休闲密码：

data = {
    "num_elements": [1,3,3,4], 
    "elements_bool_identifiers": [["Y"],["N", "Y"],["N"],["N"]], 
    "elements_identifiers": [["FOO"],["FOO", "BAR"],["FOOBAR"],["FOO", "BAZ"]], 
    "identifiers_selections": [["A"],["A", "B", "B"],["A", "B", "B"],["A", "B", "A"]], 
}

df = pd.DataFrame(data)

valid_elements_bool_identifiers = "N"

valid_elements_identifiers = ["FOOBAR"] # (might be expanded in the future)

valid_identifiers_selections = ["A", "B", "B"]

列表系列同时是一个转换集 (.apply(set).apply(list))

Answer 1

Use tuples instead of lists :

import pandas as pd
data = {
    "num_elements": [1,3,3,4], 
    "elements_bool_identifiers": [["Y"],["N", "Y"],["N"],["N"]], 
    "elements_identifiers": [["FOO"],["FOO", "BAR"],["FOOBAR"],["FOO", "BAZ"]], 
    "identifiers_selections": [["A"],["A", "B", "B"],["A", "B", "B"],["A", "B", "A"]], 
}

df = pd.DataFrame(data)

valid_elements_bool_identifiers = ("N",)  # use tuple

valid_elements_identifiers = ("FOOBAR",)  # use tuple

valid_identifiers_selections = ("A", "B", "B")  # use tuple


df["match"] = ((df["elements_bool_identifiers"].map(tuple) == valid_elements_bool_identifiers) &
               (df["elements_identifiers"].map(tuple) == valid_elements_identifiers) &
               (df["identifiers_selections"].map(tuple) == valid_identifiers_selections))
df

否则使用列表会出错：

('长度必须匹配才能比较', (4,), (3,))