如何使用 pandas 将温度和湿度数据存储在 excel 文件中，并在一段时间后更新该数据，然后将更新的数据存储在每行中？

好的，这里有一个解决方案，您可以将其用作实现目标的起点。
如果有什么不清楚的地方，请告诉我。

1. 下次，请尝试发布您自己的解决方案（即使它不起作用），并附上您的想法和更多信息。
2. 避免使用不准确的 time.sleep（），最重要的是，它会阻止程序的执行。
3. 与其检索数据并将它们永远存储在.csv中，直到发生
   外部中断（=> while True 语句），不如将最大时间间隔或最大扫描次数固定为限制。
4. 第一次只创建 .csv 文件，以后使用时只更新同一个文件会更方便。
5. 您可以将 #** 指示的行替换为 #*** 以避免警告：“FutureWarning：frame.append 方法已弃用，将在将来的版本中从 pandas 中删除。请改用 pandas.concat。

import time
import random
import os.path
import datetime
import pandas as pd 

csv_file = 'my_file.csv'

# Create temporary dataframe to store new readings 
temp_df = pd.DataFrame(columns=['Timestamp', 'Temperature(°C)', 'Humidity(%)'])

# Check if "my_file.csv" exists (it should be placed in the same directory)
if not os.path.isfile(csv_file):
    already = False
else:
    already = True

# Set the time limit in seconds
loop_duration = 20 
# Set the starting times 
start_time_extern = time.time()              #as float
start_time_inside = datetime.datetime.now()  #as datetime object

first_round = True
# Until the loop_duration expires
while (time.time() - start_time_extern) < loop_duration:
     current_time = datetime.datetime.now()
     elapsed_time = (current_time - start_time_inside).total_seconds()
     
     if first_round or elapsed_time >= 5:
        """ Now! and every 5 seconds: 
            _Get your data from sensors someway;
            _Do your additional operations on readings to create "temp" and "humi";
            _Add sensors' ouputs + timestamp to csv.
        N.B. Here, I've used random variables as placeholders (with 2f precision)
        N.B.2. Change to "if elapsed_time >= 5:" to wait 5 sec even the first time)
        """
        temperature = random.uniform(-10, 40)
        # Reduce the digits after the decimal point to 2
        temperature_ok = round(temperature * 100) / 100    

        humidity_percent = random.uniform(0, 100)
        humidity = humidity_percent / 100
        # Round digits as above
        humidity_ok = round(humidity * 100) / 100

        # Create current timestamp 
        timestamp = current_time.strftime('%Y-%m-%d %H:%M:%S')
        # Append to the temporary dataframe
        #**
        temp_df = temp_df.append({'Timestamp': timestamp, 
                                  'Temperature(°C)': temperature_ok, 
                                  'Humidity(%)': humidity_ok}, 
                                  ignore_index=True) 
        #***        
        #temp_df = pd.concat([temp_df, pd.DataFrame.from_records([{'Timestamp': timestamp,  
        #'Temperature(°C)': temperature_ok, 'Humidity(%)': humidity_ok}])],
        #ignore_index=True)  

        if already:
            """ If "my_file.csv" already exists...
            - Copy to csv appending data to the end of the file (mode='a' param)
              rather than overwriting any existing data.
            - Specify that the file does not add the header row with the names 
              of colums (header=False param).
            - State that the dataframe's index is not be included 
              (index=False parameter).
            """
            temp_df.to_csv(csv_file, mode='a', header=False, index=False)
            # Empty the df, to avoid to insert int the csv duplicate rows 
            temp_df = pd.DataFrame(columns=['Timestamp', 'Temperature(°C)', 'Humidity(%)'])
        else: 
            # Copy to csv including headers
            temp_df.to_csv(csv_file, index=False)
        
        # Set the starting time to now, to count again for 5 seconds 
        start_time_inside = current_time 

        first_round = False

CSV格式

Timestamp,Temperature(°C),Humidity(%)
2023-07-26 16:47:42,10.54,0.23
2023-07-26 16:47:47,28.19,0.71
2023-07-26 16:47:52,20.14,0.8
2023-07-26 16:47:57,-9.6,0.9
2023-07-26 16:48:10,-5.33,0.91
2023-07-26 16:48:15,21.86,0.32
2023-07-26 16:48:20,14.44,0.68
2023-07-26 16:48:25,24.9,0.59