我有以下內容df，其中的每個單元格（索引單元格除外）都是字串型別：
Time Currency Volatility expected Event
0 02:00 GBP Low Volatility Expected Construction Output (MoM) (Jan)
1 02:00 GBP Low Volatility Expected U.K. Construction Output (YoY) (Jan)
2 02:00 GBP High Volatility Expected GDP (YoY)
3 02:00 GBP High Volatility Expected GDP (MoM)
4 02:00 GBP Low Volatility Expected Index of Services
5 02:00 GBP Low Volatility Expected Industrial Production (YoY) (Jan)
6 02:00 GBP Moderate Volatility Expected Industrial Production (MoM) (Jan)
7 02:00 GBP High Volatility Expected Manufacturing Production (MoM) (Jan)
8 02:00 GBP Low Volatility Expected Manufacturing Production (YoY) (Jan)
9 02:00 GBP High Volatility Expected Monthly GDP 3M/3M Change
10 02:00 GBP Moderate Volatility Expected Trade Balance (Jan)
11 02:00 GBP Moderate Volatility Expected Trade Balance Non-EU (Jan)
12 02:00 EUR Moderate Volatility Expected German CPI (MoM) (Feb)
13 02:00 EUR Low Volatility Expected German CPI (YoY) (Feb)
14 02:00 EUR Low Volatility Expected German HICP (MoM) (Feb)
15 02:00 EUR Low Volatility Expected German HICP (YoY) (Feb)
16 03:00 EUR Moderate Volatility Expected Spanish CPI (YoY) (Feb)
17 03:00 EUR Low Volatility Expected Spanish CPI (MoM) (Feb)
18 03:00 EUR Moderate Volatility Expected Spanish HICP (YoY) (Feb)
19 03:00 EUR Low Volatility Expected Spanish HICP (MoM) (Feb)
20 03:00 CNY Low Volatility Expected Chinese Total Social Financing (Feb)
21 03:01 CNY Low Volatility Expected M2 Money Stock (YoY) (Feb)
22 03:01 CNY Moderate Volatility Expected New Loans (Feb)
23 03:01 CNY Low Volatility Expected Outstanding Loan Growth (YoY) (Feb)
24 04:30 GBP Low Volatility Expected Inflation Expectations
25 05:00 EUR High Volatility Expected EU Leaders Summit
26 05:10 EUR Low Volatility Expected Italian 15-Year BTP Auction
27 05:10 EUR Low Volatility Expected Italian 3-Year BTP Auction
28 05:10 EUR Low Volatility Expected Italian 7-Year BTP Auction
29 06:00 EUR Low Volatility Expected Spanish Consumer Confidence
30 06:30 INR Low Volatility Expected Bank Loan Growth
31 06:30 INR Low Volatility Expected Deposit Growth
32 06:30 INR Low Volatility Expected FX Reserves, USD
33 07:00 INR Low Volatility Expected Cumulative Industrial Production (Jan)
34 07:00 INR Low Volatility Expected Industrial Production (YoY) (Jan)
35 07:00 INR Low Volatility Expected Manufacturing Output (MoM) (Jan)
36 07:00 BRL Moderate Volatility Expected CPI (YoY) (Feb)
37 07:00 BRL Moderate Volatility Expected CPI (MoM) (Feb)
38 08:06 BRL Moderate Volatility Expected Brazilian IPCA Inflation Index SA (MoM) (Feb)
39 08:30 CAD Low Volatility Expected Capacity Utilization Rate (Q4)
40 08:30 CAD High Volatility Expected Employment Change (Feb)
41 08:30 CAD Low Volatility Expected Full Employment Change (Feb)
42 08:30 CAD Low Volatility Expected Part Time Employment Change (Feb)
43 08:30 CAD Low Volatility Expected Participation Rate (Feb)
44 08:30 CAD Moderate Volatility Expected Unemployment Rate (Feb)
45 10:00 USD Low Volatility Expected Michigan 5-Year Inflation Expectations (Mar)
從那df以后，我只對與此時間間隔（24 小時）匹配的那些行（如果有的話）感興趣：
- 04:00 開始
- 08:59結束
由于列中的每個單元格都Time包含字串值，我創建了以下函式來將這些值中的任何一個轉換為datetime.time物件：
import datetime
def convert_string_to_time(str):
if len(str) < 5 and len(str) > 3:
return datetime.time(hour=int(str[0]), minute=int(str[2:4]))
elif len(str) == 5:
return datetime.time(hour=int(str[0:2]), minute=int(str[3:5]))
else:
return 'not a valid string time'
使用示例：
time1 = '04:35'
timestamp1 = convert_string_to_time(time1)
print(type(timestamp1))
print(timestamp1)
輸出：
<class 'datetime.time'>
04:35:00
但是現在我被困在需要使用上面的函式來創建以下輸出并將其保存在的部分sub_df：
Time Currency Volatility expected Event
24 04:30 GBP Low Volatility Expected Inflation Expectations
25 05:00 EUR High Volatility Expected EU Leaders Summit
26 05:10 EUR Low Volatility Expected Italian 15-Year BTP Auction
27 05:10 EUR Low Volatility Expected Italian 3-Year BTP Auction
28 05:10 EUR Low Volatility Expected Italian 7-Year BTP Auction
29 06:00 EUR Low Volatility Expected Spanish Consumer Confidence
30 06:30 INR Low Volatility Expected Bank Loan Growth
31 06:30 INR Low Volatility Expected Deposit Growth
32 06:30 INR Low Volatility Expected FX Reserves, USD
33 07:00 INR Low Volatility Expected Cumulative Industrial Production (Jan)
34 07:00 INR Low Volatility Expected Industrial Production (YoY) (Jan)
35 07:00 INR Low Volatility Expected Manufacturing Output (MoM) (Jan)
36 07:00 BRL Moderate Volatility Expected CPI (YoY) (Feb)
37 07:00 BRL Moderate Volatility Expected CPI (MoM) (Feb)
38 08:06 BRL Moderate Volatility Expected Brazilian IPCA Inflation Index SA (MoM)(Feb)
39 08:30 CAD Low Volatility Expected Capacity Utilization Rate (Q4)
40 08:30 CAD High Volatility Expected Employment Change (Feb)
41 08:30 CAD Low Volatility Expected Full Employment Change (Feb)
42 08:30 CAD Low Volatility Expected Part Time Employment Change (Feb)
43 08:30 CAD Low Volatility Expected Participation Rate (Feb)
44 08:30 CAD Moderate Volatility Expected Unemployment Rate (Feb)
我不知道如何在Time列上垂直迭代以應用該convert_string_to_time(str)函式以僅獲取與所需時間間隔匹配的那些行并將它們存盤在一個名為的新 df 中sub_df，我可以在這里獲得一些幫助嗎？
uj5u.com熱心網友回復：
它實際上比你想象的要簡單。只需使用pd.to_datetime將時間轉換為datetime物件，然后使用pd.Series.between
mask = pd.to_datetime(df['Time']).between('4:30', '8:59')
filtered = df[mask]
輸出：
>>> filtered
Time Currency Volatility expected Event
24 04:30 GBP Low Volatility Expected Inflation Expectations
25 05:00 EUR High Volatility Expected EU Leaders Summit
26 05:10 EUR Low Volatility Expected Italian 15-Year BTP Auction
27 05:10 EUR Low Volatility Expected Italian 3-Year BTP Auction
28 05:10 EUR Low Volatility Expected Italian 7-Year BTP Auction
29 06:00 EUR Low Volatility Expected Spanish Consumer Confidence
30 06:30 INR Low Volatility Expected Bank Loan Growth
31 06:30 INR Low Volatility Expected Deposit Growth
32 06:30 INR Low Volatility Expected FX Reserves, USD
33 07:00 INR Low Volatility Expected Cumulative Industrial Production (Jan)
34 07:00 INR Low Volatility Expected Industrial Production (YoY) (Jan)
35 07:00 INR Low Volatility Expected Manufacturing Output (MoM) (Jan)
36 07:00 BRL Moderate Volatility Expected CPI (YoY) (Feb)
37 07:00 BRL Moderate Volatility Expected CPI (MoM) (Feb)
38 08:06 BRL Moderate Volatility Expected Brazilian IPCA Inflation Index SA (MoM) (Feb)
39 08:30 CAD Low Volatility Expected Capacity Utilization Rate (Q4)
40 08:30 CAD High Volatility Expected Employment Change (Feb)
41 08:30 CAD Low Volatility Expected Full Employment Change (Feb)
42 08:30 CAD Low Volatility Expected Part Time Employment Change (Feb)
43 08:30 CAD Low Volatility Expected Participation Rate (Feb)
44 08:30 CAD Moderate Volatility Expected Unemployment Rate (Feb)
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標籤：python-3.x 熊猫 数据框 约会时间 筛选