将元组和列表组合成 Dataframe

Question

我有以下代码在我的返回值中给了我一个常量+一个元组列表：

for name in ours_list:
    print(name, process.extract(name, snow_list, limit=5))

回报：

Remote Access Migration [('Remote Access Migration', 100), ('Migrate Application Access', 86), ('Migration of Z75', 86), ('Network Access', 86), ('Remote Control Access Hardening', 86)]

但我正在寻找 DataFrame：

Name  |  Pick 1 | Pick 2 ....
Remote Access Migration  |  Remote Access Migration (100) | Migrate Application Access (86) ....

关于如何调整我的代码的任何想法？ 我想这是一个加入列表，然后创建一个列表列表并将其组合成一个大的 dataframe？

Answer 1

根据您的数据结构，使用列表压缩并调用list[tuple[key,value]] -> 到dict以干净地构建 pandas dataframe

import pandas as pd
def process_extract(name: str, limit: int):
    data = {
        "Remote Access Migration": [
            ("Remote Access Migration", 100),
            ("Migrate Application Access", 86),
            ("Migration of Z75", 86),
            ("Network Access", 86),
            ("Remote Control Access Hardening", 86),
        ],
        "Some Other Junk": [
            ("Blah1", 10),
            ("Blah2", 10),
            ("Blah3", 10),
            ("Blah4", 10),
            ("Blah5", 10),
            ("Blah6", 10),
            ("Blah7", 10),
            ("Blah8", 10),
        ],
    }
    return data[name][:limit]


ours_list = ["Remote Access Migration", "Some Other Junk"]


pd.DataFrame([dict(process_extract(name, limit=5)) for name in ours_list], index=ours_list).T

                                 Remote Access Migration  Some Other Junk
Remote Access Migration                            100.0              NaN
Migrate Application Access                          86.0              NaN
Migration of Z75                                    86.0              NaN
Network Access                                      86.0              NaN
Remote Control Access Hardening                     86.0              NaN
Blah1                                                NaN             10.0
Blah2                                                NaN             10.0
Blah3                                                NaN             10.0
Blah4                                                NaN             10.0
Blah5                                                NaN             10.0