AttributeError:'Series'物件沒有屬性'time'

使用這個資料集：

import numpy as np
import pandas as pd

# Create a reproducible, static dataframe.
# 1 minute SPY data. Skip to the bottom...
df = pd.DataFrame([
    {
        "time": "2021-10-26 9:30",
        "open": "457.2",
        "high": "457.29",
        "low": "456.78",
        "close": "456.9383",
        "volume": "594142"
    },
    {
        "time": "2021-10-26 9:31",
        "open": "456.94",
        "high": "457.07",
        "low": "456.8",
        "close": "456.995",
        "volume": "194061"
    },
    {
        "time": "2021-10-26 9:32",
        "open": "456.99",
        "high": "457.22",
        "low": "456.84",
        "close": "457.21",
        "volume": "186114"
    },
    {
        "time": "2021-10-26 9:33",
        "open": "457.22",
        "high": "457.45",
        "low": "457.2011",
        "close": "457.308",
        "volume": "294158"
    },
    {
        "time": "2021-10-26 9:34",
        "open": "457.31",
        "high": "457.4",
        "low": "457.25",
        "close": "457.32",
        "volume": "172574"
    },
    {
        "time": "2021-10-26 9:35",
        "open": "457.31",
        "high": "457.48",
        "low": "457.18",
        "close": "457.44",
        "volume": "396668"
    },
    {
        "time": "2021-10-26 9:36",
        "open": "457.48",
        "high": "457.6511",
        "low": "457.44",
        "close": "457.57",
        "volume": "186777"
    }
 ])

...我想計算一個布爾列，表示True“時間”列中的 TIME（日期不重要）是否在開始和結束時間之內，否則False。到目前為止我所擁有的是：

from datetime import datetime
start_time = datetime.strptime("09"   "31", "%H%M").time()
end_time = datetime.strptime("09"   "33", "%H%M").time()

# Convert datetime column to datetime objects
df['time'] = pd.to_datetime(df['time'])

df['isWithinTimeframe'] = np.where(df['time'].time() >= start_time and df['time'].time() < end_time, True, False)

我目前收到錯誤：

AttributeError: 'Series' object has no attribute 'time'

我閱讀了其他一些您需要.dt在df['time']and之間呼叫的解決方案.time()，所以我也嘗試過，但得到：

TypeError: 'Series' object is not callable

想法？

uj5u.com熱心網友回復：

一些必要的更正將使您獲得布爾標志。

如評論中所述，您必須使用.dt來訪問日期時間方法和屬性（類似于如何.str提供對字串方法和屬性的訪問）

而不是and使用按位與運算子&，并且為了避免由于運算子優先級而導致的錯誤，必須將>=&<比較放在括號中。

df['isWithinTimeframe'] = (df.time.dt.time >= start_time) & (df.time.dt.time < end_time)

df 現在看起來像：

uj5u.com熱心網友回復：

將列時間強制轉換為日期時間并將其設定為索引。這允許您使用 between_time 子句來選擇適合您的條件的內容。因為這會回傳一個 subdf，所以檢查 subdf 中的時間列是否在主 df 中。下面的代碼

df['isWithinTimeframe']=df['time'].isin(df.set_index(pd.to_datetime(df['time'])).between_time(start_time,end_time)['time'])


     time    open      high       low     close  volume  \
0  2021-10-26 9:30   457.2    457.29    456.78  456.9383  594142   
1  2021-10-26 9:31  456.94    457.07     456.8   456.995  194061   
2  2021-10-26 9:32  456.99    457.22    456.84    457.21  186114   
3  2021-10-26 9:33  457.22    457.45  457.2011   457.308  294158   
4  2021-10-26 9:34  457.31     457.4    457.25    457.32  172574   
5  2021-10-26 9:35  457.31    457.48    457.18    457.44  396668   
6  2021-10-26 9:36  457.48  457.6511    457.44    457.57  186777   

   isWithinTimeframe  
0              False  
1               True  
2               True  
3               True  
4              False  
5              False  
6              False  
print(df.where(df['life_day

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