Python中如何筛选匹配另一数据框IDBANK列的行？

已知DataFrame结构 #

df_idbank的列类型： #

>>> df_idbank.dtypes

DATASET                   object
IDBANK                    object
KEY                       object
FREQ                      object
INDICATEUR                object
CORRECTION_label_en       object

df_INDICATORS的结构： #

>>> df_INDICATORS
    Label                                               IDBANK
0   Summary indicator of overall economic situatio...   1586891
1   Business climate summary indicator - SA series      1586890
2   Trend of expected activity - Overall - SA series    1586916
3   Trend of expected activity - Building structur...   1586885
4   Trend of expected activity - Finishings - SA s...   1586886

单ID筛选示例： #

已知筛选IDBANK为"001586891"的行代码：

df_idbank = df_idbank.loc[(df_idbank.FREQ == "M") & (df_idbank.CORRECTION_label_en == "Seasonal adjusted") & (df_idbank.IDBANK =="001586891") ]

批量匹配df_INDICATORS中IDBANK的筛选方法 #

注意两个DataFrame的IDBANK格式差异：df_idbank中是带前导零的字符串（如"001586891"），df_INDICATORS中是整数（如1586891），需先统一格式再批量匹配。

具体代码： #

1. 先将df_INDICATORS的IDBANK转换为和df_idbank一致的7位带前导零字符串：

target_ids = df_INDICATORS['IDBANK'].apply(lambda x: f"{x:07d}")

2. 结合原有筛选条件，用isin()实现批量匹配：

df_idbank_filtered = df_idbank.loc[
    (df_idbank.FREQ == "M") & 
    (df_idbank.CORRECTION_label_en == "Seasonal adjusted") & 
    (df_idbank.IDBANK.isin(target_ids))
]

格式兼容补充： #

如果不确定格式是否统一，可强制将两边IDBANK都转为标准字符串格式，避免类型不匹配：

# 统一转换为7位带前导零的字符串
df_idbank['IDBANK'] = df_idbank['IDBANK'].astype(str).str.zfill(7)
target_ids = df_INDICATORS['IDBANK'].astype(str).str.zfill(7)

内容的提问来源于stack exchange，提问作者prashanth manohar