将 WHERE 子句放在 Pandas 上合并

Question

我有两个 pandas 数据框，我正试图将它们合并到三个不同的键上......有点。每个数据框都有一个性别列和一个 country_destination 列，我想对其进行外部连接。一个数据框有一个 age_bucket 列，它是一个表示年龄范围的字符串，例如45-49、50-54、55-59 我已使用 pandas apply 方法将其转换为另一列中的列表。我的问题是，当您在多个键上的两个数据框之间进行联接时，您是否也可以在某处执行 where 语句以便能够联接不共享完全相同数据类型的列？例如，我可以说 "Join these tables on gender, and country_destination columns where the age of a user is IN the list value of age_gender's age_list column"

age_gender = pd.read_csv('data/age_gender_bkts.csv')
users = pd.read_csv('data/train_users_2.csv')

def getAgeList(row):
    clean_age = row['age_bucket'].replace('+', '')
    min_max = clean_age.split('-')

    if len(min_max) > 1:
        min_max = list(range(int(min_max[0]), int(min_max[1]) + 1))
    return min_max

age_gender['age_list'] = age_gender.apply(lambda x: getAgeList(x), axis=1)

combined_df = pd.merge(users, age_gender, on=['country_destination', 'gender'])

user.columns

Index(['id', 'date_account_created', 'timestamp_first_active',
       'date_first_booking', 'gender', 'age', 'signup_method', 'signup_flow',
       'language', 'affiliate_channel', 'affiliate_provider',
       'first_affiliate_tracked', 'signup_app', 'first_device_type',
       'first_browser', 'country_destination', 'lat_destination',
       'lng_destination', 'distance_km', 'destination_km2',
       'destination_language ', 'language_levenshtein_distance'],
      dtype='object')

age_gender.columns

Index(['age_bucket', 'country_destination', 'gender',
       'population_in_thousands', 'year', 'age_list'],
      dtype='object')

DataFrame 示例

Answer 1

我认为您需要按 age_list 列中的值扩展行，然后 merge:

#get lengths of each list
l = age_gender['age_list'].str.len()
#get all columns without age_list
cols = age_gender.columns.difference(['age_list'])
#repeat values by lengths to new DataFrame
df = pd.DataFrame({col: np.repeat(age_gender[col].values, l) for col in cols})
#flattening lists, necessary convert to int, because merge not match
df['age'] = np.concatenate(age_gender['age_list'].values).astype(int)

#inner merge is default, so how='inner' is omit
df1 = pd.merge(df, users, on=['age', 'country_destination'])

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