Python/Pandas基于DateTime值创建分类列

Question

我有一个带有列['date']的pandas dataframe（数据），其值是“ yyyy-mm-dd hh：mm：ss”，我将我转换为pandas dateTime格式。

data ['date'] = pd.to_datetime（data ['date']）

如何使用类别'早期'，'mid--创建一个新的分类列['time']一天，“迟到”，根据每天的时间？

我尝试了以下错误：

def time(x):
    if '03:00:00' < x <= '11:00:00':
        return 'Early'
    elif '11:00:00' < x <= '17:00:00':
        return 'Mid-day'
    return 'Late'

data['Time'] = data['Date'].dt.time.apply(time)

但是我得到了这个错误：“ TypeError：'＆lt;'在“ str”和“ dateTime”实例之间不支持“时间”。“

感谢您的帮助！

Answer 1

您可以使用 pandas.cut.cut.cut 。但是，这有点棘手，因为您需要先将日期时间转换为TimeDelta：

df = pd.DataFrame({'date': ['2022-04-27 01:00:00', '2022-04-27 04:00:00',
                            '2022-04-27 12:00:00', '2022-04-27 17:00:00']})

# define bins and labels
bins = ['00:00:00', '03:00:00', '11:00:00', '17:00:00', '23:59:59']
labels = ['Late', 'Early', 'Mid-Day', 'Late']

# convert to timedelta
s = pd.to_timedelta(pd.to_datetime(df['date']).dt.time.astype(str))
df['time'] = pd.cut(s, bins=pd.to_timedelta(bins), labels=labels, ordered=False)

输出：

                  date     time
0  2022-04-27 01:00:00     Late
1  2022-04-27 04:00:00    Early
2  2022-04-27 12:00:00  Mid-Day
3  2022-04-27 17:00:00  Mid-Day

Answer 2

如何将所有条件包装在to_dateTime（）中？

def time(x):
    x=pd.to_datetime(x,format='%H:%M:%S')
    if pd.to_datetime('03:00:00') < x <= pd.to_datetime('11:00:00'):
        return 'Early'
    elif pd.to_datetime('11:00:00') < x <= pd.to_datetime('17:00:00'):
        return 'Mid-day'
    return 'Late'

df['Time'] = df['Date'].dt.time.apply(time)

Answer 3

您可以将np.select与dateTimeIndex.indexer_between_time功能一起创建标签。 indexer_between_time返回所提供端点之间的时间的数组索引，因此您需要从 in 使用dataframe的阵列中核对 in 检查。

import pandas as pd
import numpy as np

df = pd.DataFrame({'Date': pd.date_range('2010-01-01', freq='3H', periods=11)},
                  index=list('ABCDEFGHIJK'))

---

idx = pd.DatetimeIndex(df['Date'])
ilocs = np.arange(len(df))

conds = [np.in1d(ilocs, idx.indexer_between_time('03:00:00', '11:00:00', include_start=False, include_end=True)),
         np.in1d(ilocs, idx.indexer_between_time('11:00:00', '17:00:00', include_start=False, include_end=True))]

choices = ['early', 'mid-day']

df['time_of_day'] = np.select(conds, choices, default='late')

---

                 Date time_of_day
A 2010-01-01 00:00:00        late
B 2010-01-01 03:00:00        late
C 2010-01-01 06:00:00       early
D 2010-01-01 09:00:00       early
E 2010-01-01 12:00:00     mid-day
F 2010-01-01 15:00:00     mid-day
G 2010-01-01 18:00:00        late
H 2010-01-01 21:00:00        late
I 2010-01-02 00:00:00        late
J 2010-01-02 03:00:00        late
K 2010-01-02 06:00:00       early