keys1 = ['User_id'] prefixs = 'history_' + '_'.join(keys1) + '_' # 用户核销率(领券消费数/领券数) feature_user[prefixs+'received_consume_rate'] = feature_user.apply(lambda x: x[prefixs+'received_consume_cnt']/x[prefixs+'received_cnt'] if x[prefixs+'received_cnt'] != 0 else 0, axis=1) # 用户在多少不同商家领取优惠券 pivot = pd.pivot_table(data[data['Date_received'].notnull()][['User_id', 'Merchant_id']], index=keys1, values='Merchant_id', aggfunc=lambda x:len(set(x))) pivot = pd.DataFrame(pivot).rename(columns={'Merchant_id':prefixs + 'received_differ_merchant'}).reset_index() feature_user = pd.merge(feature_user, pivot, on=keys1, how='left')改写代码 功能不变
时间: 2024-01-21 08:05:03 浏览: 27
keys1 = ['User_id']
prefixs = 'history_' + '_'.join(keys1) + '_'
# 用户核销率(领券消费数/领券数)
feature_user[prefixs + 'received_consume_rate'] = feature_user[prefixs + 'received_consume_cnt'] / feature_user[prefixs + 'received_cnt'].apply(lambda x: x if x != 0 else 1)
# 用户在多少不同商家领取优惠券
pivot = data[data['Date_received'].notnull()][['User_id', 'Merchant_id']].groupby(keys1)['Merchant_id'].nunique().reset_index()
pivot = pivot.rename(columns={'Merchant_id':prefixs + 'received_differ_merchant'})
feature_user = pd.merge(feature_user, pivot, on=keys1, how='left')
相关问题
history_dict = history.history history_dict.keys()
根据提供的引用内容,`history_dict`是一个字典,其中包含了训练模型时的历史记录。`history_dict.keys()`可以用来获取这个字典的所有键值。下面是一个示例代码:
```python
history_dict = history.history
keys = history_dict.keys()
print(keys) # 输出:dict_keys(['loss', 'accuracy'])
```
这段代码将打印出`history_dict`字典的所有键值,即`['loss', 'accuracy']`。
# 用户核销率(领券消费数/领券数) feature_user[prefixs+'received_consume_rate'] = feature_user.apply(lambda x: x[prefixs+'received_consume_cnt']/x[prefixs+'received_cnt'] if x[prefixs+'received_cnt'] != 0 else 0, axis=1) # 用户在多少不同商家领取优惠券 pivot = pd.pivot_table(data[data['Date_received'].notnull()][['User_id', 'Merchant_id']], index=keys1, values='Merchant_id', aggfunc=lambda x:len(set(x))) pivot = pd.DataFrame(pivot).rename(columns={'Merchant_id':prefixs + 'received_differ_merchant'}).reset_index() feature_user = pd.merge(feature_user, pivot, on=keys1, how='left')改写代码,功能不变
# 用户核销率(领券消费数/领券数)
feature_user['received_consume_rate'] = feature_user['received_consume_cnt'] / feature_user['received_cnt'].apply(lambda x: x if x != 0 else 1)
# 用户在多少不同商家领取优惠券
pivot = data[data['Date_received'].notnull()][['User_id', 'Merchant_id']].groupby(keys1)['Merchant_id'].nunique().reset_index()
pivot = pivot.rename(columns={'Merchant_id': 'received_differ_merchant'})
feature_user = pd.merge(feature_user, pivot, on=keys1, how='left')