我想为您提供帮助。
在我的工作中，我有两个DataFrame。第一个名为df_card_features，具有卡功能，并且card_id列具有每个卡的唯一ID。第二个称为df_cart_historic，具有来自第一个数据帧的卡数据；在第二个数据帧中，card_id列没有唯一值，但与第一个数据帧的card_id列相同。
作为解决方案，我考虑过创建一个字典，然后将其包含在数据框中，但就性能而言，此提议对我而言似乎是非常昂贵的，因为历史记录的csv文件大约有5 GB。
# card features:
card_id = ['card_a', 'card_b', 'card_c', 'card_d', 'card_e']
date_activation = ['2019-02-01', '2019-05-02', '2018-01-20', '2015-07-23', '2013-07-23']
feature_1_1 = [0, 1, 1, 1, 0]
feature_1_2 = [1, 0, 0, 0, 1]
df_card_features = pd.DataFrame()
df_card_features['card_id'] = card_id
df_card_features['date_activation'] = date_activation
df_card_features['feature_1_1'] = feature_1_1
df_card_features['feature_1_2'] = feature_1_2;
df_card_features.head()
# card historic
card_id = ['card_a', 'card_b', 'card_c', 'card_d', 'card_e', 'card_a', 'card_b', 'card_c', 'card_d', 'card_e', 'card_a', 'card_b', 'card_c', 'card_d', 'card_e']
denied_purchase = ['N', 'Y', 'N', 'Y', 'N', 'N', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'N', 'Y']
purchase_date = ['2019-02-01', '2019-02-01', '2019-02-01', '2019-02-01', '2019-02-01', '2019-02-10', '2019-02-11', '2019-02-21', '2019-03-01', '2019-03-01', '2019-03-01', '2019-03-31', '2018-04-01', '2016-02-01', '2013-12-01']
installments = [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 8, 4, 0 ]
month_lag = [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5]
df_cart_historic = pd.DataFrame()
df_cart_historic['card_id'] = card_id
df_cart_historic['denied_purchase'] = denied_purchase
df_cart_historic['purchase_date'] = purchase_date
df_cart_historic['installments'] = installments
df_cart_historic['month_lag'] = month_lag
我需要在df_card_features数据框中创建以下列：
答案 0 :(得分：2)
您需要在groupby的{{1}}列上使用'card_id'，以便仅使用df_cart_historic具有相同值的行来构建新列。
通过调用'card_id'，您可以使用自定义函数groupby('card_id').apply(func)来完成工作。
这是一个工作示例：
func请注意，带有日期的列使用import pandas as pd
# card features:
card_id = ['card_a', 'card_b', 'card_c', 'card_d', 'card_e']
date_activation = ['2019-02-01', '2019-05-02', '2018-01-20', '2015-07-23', '2013-07-23']
feature_1_1 = [0, 1, 1, 1, 0]
feature_1_2 = [1, 0, 0, 0, 1]
df_card_features = pd.DataFrame()
df_card_features['card_id'] = card_id
df_card_features['date_activation'] = pd.to_datetime(date_activation) #converting to datetime
df_card_features['feature_1_1'] = feature_1_1
df_card_features['feature_1_2'] = feature_1_2;
df_card_features.head()
# card historic
card_id = ['card_a', 'card_b', 'card_c', 'card_d', 'card_e', 'card_a', 'card_b', 'card_c', 'card_d', 'card_e', 'card_a', 'card_b', 'card_c', 'card_d', 'card_e']
denied_purchase = ['N', 'Y', 'N', 'Y', 'N', 'N', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'N', 'Y']
purchase_date = ['2019-02-01', '2019-02-01', '2019-02-01', '2019-02-01', '2019-02-01', '2019-02-10', '2019-02-11', '2019-02-21', '2019-03-01', '2019-03-01', '2019-03-01', '2019-03-31', '2018-04-01', '2016-02-01', '2013-12-01']
installments = [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 8, 4, 0 ]
month_lag = [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5]
df_cart_historic = pd.DataFrame()
df_cart_historic['card_id'] = card_id
df_cart_historic['denied_purchase'] = denied_purchase
df_cart_historic['purchase_date'] = pd.to_datetime(purchase_date) #converting to datetime
df_cart_historic['installments'] = installments
df_cart_historic['month_lag'] = month_lag
df_card_features.set_index('card_id', inplace=True) #using card_id column as index
def getnewcols(x):
res = pd.DataFrame()
res['denied_purchase?'] = pd.Series(['Y' if 'Y' in x['denied_purchase'].unique() else 'N'])
res['oldest_Date'] = x['purchase_date'].min()
res['max_installments'] = x['installments'].max()
res['max_month_lag'] = x['month_lag'].max()
return res
newcols = df_cart_historic.groupby('card_id').apply(getnewcols)
newcols = newcols.reset_index().drop('level_1', axis=1).set_index('card_id')
df_card_features_final = pd.concat([df_card_features, newcols], axis=1)
进行了解析，以便拥有pandas.to_datetime对象而不是简单的字符串（使用日期非常有用）。
datetime是保存新列的数据框，newcols是包含所有列的最终数据框：
df_card_features_final