列中重复值之间的条件

Question

当每个客户有多个计划时，他们都是重复的。我想将状态设置为客户：

如果每个产品都填写了'canceled_at'，则客户状态被取消，但是当不是每个产品都填写了canceled_at，而是至少有一个时，状态为'downgrade' 因为他丢了一个产品。

customer|canceled_at|status
x       |3/27/2018  |
x       |           |
y       |2/2/2018   |
y       |2/2/2018   |
z       |1/1/2018   |
a       |           |

我已经有取消状态，现在我只需要降级

df['status']=(df.groupby('customer')['canceled_at'].
  transform(lambda x: x.notna().all()).map({True:'canceled'})).fillna(df.status)

customer|canceled_at|status
x       |3/27/2018  |downgrade
x       |           |downgrade
y       |2/2/2018   |canceled
y       |2/2/2018   |canceled
z       |1/1/2018   |canceled
a       |           |

Answer 1

这里可以比较没有缺失值的列，并按 Series customer 和 GroupBy.transform and GroupBy.all 进行分组， GroupBy.any for test all values Trues (all non missing) or at least one value not missing (any non missing) and pass it to numpy.select:

g = df['canceled_at'].notna().groupby(df['customer'])
m1 = g.transform('all')
m2 = g.transform('any')

df['status'] = np.select([m1, m2],['canceled','downgrade'], np.nan)
print (df)
  customer canceled_at     status
0        x   3/27/2018  downgrade
1        x         NaN  downgrade
2        y    2/2/2018   canceled
3        y    2/2/2018   canceled
4        z    1/1/2018   canceled
5        a         NaN        nan

或：

df['status'] = np.select([m1, m2],['canceled','downgrade'], '')
print (df)
  customer canceled_at     status
0        x   3/27/2018  downgrade
1        x         NaN  downgrade
2        y    2/2/2018   canceled
3        y    2/2/2018   canceled
4        z    1/1/2018   canceled
5        a         NaN

如果只有 NaNs 组需要转换为 downgrade:

mask = df['canceled_at'].notna().groupby(df['customer']).transform('all')
df['status'] = np.where(mask,'canceled','downgrade')
print (df)
  customer canceled_at     status
0        x   3/27/2018  downgrade
1        x         NaN  downgrade
2        y    2/2/2018   canceled
3        y    2/2/2018   canceled
4        z    1/1/2018   canceled
5        a         NaN  downgrade

Answer 2

这里有一个方法：

import pandas as pd

def select_status(canceled):
    c = canceled.count()
    if c == 0:
        status = ''
    elif c == len(canceled):
        status = 'canceled'
    else:
        status = 'downgrade'
    return pd.Series(status, index=canceled.index)

df = pd.DataFrame({'customer': ['x', 'x', 'y', 'y', 'z', 'a'],
                   'canceled_at': ['3/27/2018', None, '2/2/2018', '2/2/2018', '1/1/2018', None]})
df['status'] = df.groupby('customer')['canceled_at'].apply(select_status)
print(df)

输出：

  customer canceled_at     status
0        x   3/27/2018  downgrade
1        x        None  downgrade
2        y    2/2/2018   canceled
3        y    2/2/2018   canceled
4        z    1/1/2018   canceled
5        a        None

列中重复值之间的条件

Condition between duplicated values in a column

python

pandas

pandas-groupby

pandas-loc