使用Datetime索引来分析数据框数据(Using Datetime indexing to analyse dataframe data)

 我正在尝试编写一个程序来计算指定日期和时间内的平均压力，温度和湿度......但不确定为什么我会得到'nan'值...？ 这是我的代码：任何想法？ 
 
import pandas as pd
import numpy as np

df = pd.DataFrame.from_csv('C:\Users\Joey\Desktop\Python\CSV\TM4CVC.csv',index_col = None)

df2 = pd.DataFrame({'temp':df['Ch1_Value'],
'press':df['Ch2_Value'],
    'humid':df['Ch3_Value'], 'Date' : df['Date'], 'Time' : df['Time']})

df2['DateTime'] = pd.to_datetime(df2.apply(lambda x: x['Date']+ ' '+ x['Time'], 1))

df2.index = pd.to_datetime(df2.pop('DateTime'))

df3 = df2.drop(['Date', 'Time'], 1)

#------------------------------------------------------------------------------

def TempPressHumid(datetime_i, datetime_e):

    index = df3[datetime_i:datetime_e]

    out = {'temp_avg':np.mean(index['temp']),
    'temp_std':np.std(index['temp']),
    'press_avg':np.mean(index['press']),
    'press_std':np.std(index['press']),
    'humid_avg':np.mean(index['humid']),
    'humid_std':np.std(index['humid'])}
    print out


TempPressHumid(datetime_i = '2012-06-25 08:27:19', datetime_e = '2012-01-25 10:59:33')
 
 我的输出是： 
 
{'humid_std': nan, 'press_std': nan, 'humid_avg': nan, 'temp_avg': nan, 'temp_std': nan, 'press_avg': nan}
 
 print df3给了我： 
 
                      humid  press  temp
DateTime                                
2012-06-25 08:21:19  1004.0   21.2  26.0
2012-06-25 08:22:19  1004.0   21.2  26.0
2012-06-25 08:23:19  1004.1   21.3  26.0
-----------------------------------------
 
 等等... 

I'm trying to write a program that will compute the average press, temp and humidity within a specified date and time.... but not sure why im getting 'nan' values... ? here is my code: any ideas?
 
import pandas as pd
import numpy as np

df = pd.DataFrame.from_csv('C:\Users\Joey\Desktop\Python\CSV\TM4CVC.csv',index_col = None)

df2 = pd.DataFrame({'temp':df['Ch1_Value'],
'press':df['Ch2_Value'],
    'humid':df['Ch3_Value'], 'Date' : df['Date'], 'Time' : df['Time']})

df2['DateTime'] = pd.to_datetime(df2.apply(lambda x: x['Date']+ ' '+ x['Time'], 1))

df2.index = pd.to_datetime(df2.pop('DateTime'))

df3 = df2.drop(['Date', 'Time'], 1)

#------------------------------------------------------------------------------

def TempPressHumid(datetime_i, datetime_e):

    index = df3[datetime_i:datetime_e]

    out = {'temp_avg':np.mean(index['temp']),
    'temp_std':np.std(index['temp']),
    'press_avg':np.mean(index['press']),
    'press_std':np.std(index['press']),
    'humid_avg':np.mean(index['humid']),
    'humid_std':np.std(index['humid'])}
    print out


TempPressHumid(datetime_i = '2012-06-25 08:27:19', datetime_e = '2012-01-25 10:59:33')
 
My output is: 
 
{'humid_std': nan, 'press_std': nan, 'humid_avg': nan, 'temp_avg': nan, 'temp_std': nan, 'press_avg': nan}
 
print df3 gives me: 
 
                      humid  press  temp
DateTime                                
2012-06-25 08:21:19  1004.0   21.2  26.0
2012-06-25 08:22:19  1004.0   21.2  26.0
2012-06-25 08:23:19  1004.1   21.3  26.0
-----------------------------------------
 
etc...

原文：https://stackoverflow.com/questions/28109673