在 dataframe 的某個索引處用另一行替換一行並更改單元格值

Question

我有一個樣本 csv 像這樣：

                 keys                       key_regex    datatype detailed_datatype precedence  val_regex     val_regex_2  val_regex_3  max_words  alpha_char_check
0      billingAddress      original_billing_key_regex  alphabetic           address    primary        NaN             NaN          NaN        NaN               NaN
1     deliveryAddress     original_delivery_key_regex  alphabetic           address    primary        NaN             NaN          NaN        NaN               NaN
2         notifyParty     original_notify_party_regex  alphabetic        alphabetic    primary        NaN             NaN          NaN        NaN               NaN
3       originAddress   original_seller_address_regex  alphabetic           address    primary        NaN             NaN          NaN        NaN               NaN
4   billingAddressAlt   alternative_billing_key_regex  alphabetic           address   tertiary        NaN             NaN          NaN        NaN               NaN
5  deliveryAddressAlt  alternative_delivery_key_regex  alphabetic           address   tertiary        NaN             NaN          NaN        5.0               1.0
6    originAddressAlt    alternative_seller_key_regex  alphabetic           address   tertiary        NaN  sample_val_re1          NaN        NaN               0.0

我正在嘗試將keys列的值作為tertiary_row_replacement_dict中的鍵的行替換為keys列值作為相應值的行，然后將precendence列值從'tertiary'重命名為'primary' -同時保持索引 position 與以前相同。

預期的 output 是這樣的：

              keys                       key_regex    datatype detailed_datatype precedence  val_regex     val_regex_2  val_regex_3  max_words  alpha_char_check
0   billingAddress   alternative_billing_key_regex  alphabetic           address    primary        NaN             NaN          NaN        NaN               NaN
1  deliveryAddress  alternative_delivery_key_regex  alphabetic           address    primary        NaN             NaN          NaN        5.0               1.0
2      notifyParty     original_notify_party_regex  alphabetic        alphabetic    primary        NaN             NaN          NaN        NaN               NaN
3    originAddress    alternative_seller_key_regex  alphabetic           address    primary        NaN  sample_val_re1          NaN        NaN               0.0

有 3 個原始 csv - 它們每個都很大，有很多類似的情況，即具有主要優先級的鍵和具有第三優先級的替代鍵。 我有這樣的鍵變字典：

tertiary_row_replacement_dict = {
    "originAddress": "originAddressAlt",
    "deliveryAddress": "deliveryAddressAlt",
    # "totalAmount": "totalAmountAlt",
    "billingAddress": "billingAddressAlt"
    ....
}

假設這個字典的鍵和對應的值總是出現在 csv 中，我有這個代碼：

for k, new_k in row_replacement_dict.items():
    t2 = df.loc[df['keys']==new_k].index[0]
    df.loc[df.loc[df['keys']==k].index[0]] = [i if i!='tertiary' else 'primary' for i in df.loc[t2]]
    df = df.replace([new_k, 'tertiary'], [k, 'primary']).drop([t2])

它完成了我想做的事情。 僅在測試 csv 上執行此操作大約需要 0.034 秒，並且可能不是處理僅替換行和替換單元格值的最佳或優化方法。 是否有任何更快的替代方法具有先決條件知識要替換哪些行（即，使用該字典不是強制性的，我們可以將其用作列表列表的元組列表以進行速度權衡）。

Answer 1

您可以使用replace將三級鍵替換為主鍵和groupby().first()來填寫信息：

inverse_dict = {v:k for k,v in tertiary_row_replacement_dict.items()}
(df.groupby(df['keys'].replace(inverse_dict))
   .first()
   .reset_index(drop=True)
)

Output：

    keys             key_regex                      datatype    detailed_datatype    precedence      val_regex  val_regex_2       val_regex_3    max_words    alpha_char_check
--  ---------------  -----------------------------  ----------  -------------------  ------------  -----------  --------------  -------------  -----------  ------------------
 0  billingAddress   original_billing_key_regex     alphabetic  address              primary               nan  nan                       nan          nan                 nan
 1  deliveryAddress  original_delivery_key_regex    alphabetic  address              primary               nan  nan                       nan            5                   1
 2  notifyParty      original_notify_party_regex    alphabetic  alphabetic           primary               nan  nan                       nan          nan                 nan
 3  originAddress    original_seller_address_regex  alphabetic  address              primary               nan  sample_val_re1            nan          nan                   0