Question

我正在尝试将一系列时间戳分为几组：

定义变量：

Very old = Date < '20190101'
Current = Today's date as %Y-%m (Year-Month)

条件

1. timestamp < very old
2. Very old < timestamp < current
3. timestamp = current
4. timestamp > current

与原始DataFrame分离的系列：

timestamp_dict = \
{0: Timestamp('2019-05-01 00:00:00'),
 1: Timestamp('2019-05-01 00:00:00'),
 2: Timestamp('2018-12-01 00:00:00'),
 3: Timestamp('2019-05-01 00:00:00'),
 4: Timestamp('2019-05-01 00:00:00'),
 5: Timestamp('2019-05-01 00:00:00'),
 6: Timestamp('2019-04-01 00:00:00'),
 7: Timestamp('2019-08-01 00:00:00')}

日期时间存储为datetime64 [ns]。我感觉将当前时间戳转换为str是错误的，但是，我不确定如何将当前时间戳提取为格式%Y-%m。

我对访问当前日期（例如月份，年份整数）然后进行级联有一个想法，但是随后我可能会遇到零填充问题：

_month = dt.datetime.today().month
_year = dt.datetime.today().year

# Would run into zero padding for months 1-9:
current = str(_year) + str(_month)

在这里，我尝试使用np.select并指定所需条件来生成新的DataFrame列。

import datetime as dt

current = dt.datetime.today().strftime('%Y-%m')
veryold = '20190101'

conditions = [
    df.Delivery < veryold,
    (df.Delivery >= veryold | (df.Delivery < current),
    df.Delivery == current,
    df.Delivery > current
]

outcome = [
    'Very old',
    'Old',
    'Current',
    'Future'
]

df['New'] = np.select(conditions, outcome)

df.New

我的预期输出是在我的DataFrame中增加一列标记结果。

Answer 1

想法是按Series.dt.to_period创建月份，以供YYYY-MM进行比较：

current = pd.Timestamp(pd.datetime.today()).to_period('M')
veryold = pd.Timestamp('20190101')

conditions = [
    df.Delivery < veryold,
    (df.Delivery >= veryold) | (df.Delivery.dt.to_period('M') < current),
    df.Delivery.dt.to_period('M') == current,
    df.Delivery.dt.to_period('M') > current]

outcome = [
    'Very old',
    'Old',
    'Current',
    'Future'
]

df = pd.Series(pd.Timestamp_dict).to_frame('Delivery')
df['New'] = np.select(conditions, outcome)
print(df)
    Delivery       New
0 2019-05-01       Old
1 2019-05-01       Old
2 2018-12-01  Very old
3 2019-05-01       Old
4 2019-05-01       Old
5 2019-05-01       Old
6 2019-04-01       Old
7 2019-08-01       Old

有条件的日期过滤器

1 个答案: