Groupby.mean() 如果条件为真

Question

我得到了以下数据框：

   index  user  default_shipping_cost     category  shipping_cost  shipping_coalesce  estimated_shipping_cost
0      0     1                      1      clothes            NaN                1.0                      6.0
1      1     1                      1  electronics            2.0                2.0                      6.0
2      2     1                     15    furniture            NaN               15.0                      6.0
3      3     2                     15    furniture            NaN               15.0                     15.0
4      4     2                     15    furniture            NaN               15.0                     15.0

每个用户，将 Shipping_cost 与 default_shipping_cost 结合起来，并计算组合后的 Shipping_costs 的平均值，但前提是至少给出一个 Shipping_cost。

说明：

• user_1

  shipping_cost

  被给出（至少一次），这样我们就可以计算平均值
• user_2没有

  shipping_cost

  ，所以我想和Nan一起去

代码：

import pandas as pd

pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
pd.set_option('display.width', 1000)

df = pd.DataFrame(
    {
        'user': [1, 1, 1, 2, 2],
        'default_shipping_cost': [1, 1, 15, 15, 15],
        'category': ['clothes', 'electronics', 'furniture', 'furniture', 'furniture'],
        'shipping_cost': [None, 2, None, None, None]
    }
)
df.reset_index(inplace=True)
df['shipping_coalesce'] = df.shipping_cost.combine_first(df.default_shipping_cost)

dfg_user = df.groupby(['user'])
df['estimated_shipping_cost'] = dfg_user['shipping_coalesce'].transform("mean")
print(df)

预期输出：

   index  user  default_shipping_cost     category  shipping_cost  shipping_coalesce  estimated_shipping_cost
0      0     1                      1      clothes            NaN                1.0                      6.0
1      1     1                      1  electronics            2.0                2.0                      6.0
2      2     1                     15    furniture            NaN               15.0                      6.0
3      3     2                     15    furniture            NaN               15.0                      NaN
4      4     2                     15    furniture            NaN               15.0                      NaN

Answer 1

使用

transform('any')

和

where

添加额外条件:

df['estimated_shipping_cost'] = (dfg_user['shipping_coalesce'].transform('mean')
                                .where(dfg_user['shipping_cost'].transform('any'))
                                )

输出：

   index  user  default_shipping_cost     category  shipping_cost  shipping_coalesce  estimated_shipping_cost
0      0     1                      1      clothes            NaN                1.0                      6.0
1      1     1                      1  electronics            2.0                2.0                      6.0
2      2     1                     15    furniture            NaN               15.0                      6.0
3      3     2                     15    furniture            NaN               15.0                      NaN
4      4     2                     15    furniture            NaN               15.0                      NaN

中级：

dfg_user['shipping_cost'].transform('any')

0     True
1     True
2     True
3    False
4    False
Name: shipping_cost, dtype: bool