Python 如果条件为真，则Groupby.mean()

我得到了以下氨纶:

   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与默认_shipping_cost结合起来，并计算合并shipping_costs的平均值，但前提是至少有一个给出的shipping_costs.

解释:

• user_1给出了shipping_cost(至少一次)，以便我们可以计算平均值
• user_2没有shipping_cost，所以我想和Nan一起go

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yields :

   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