Question

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按压，温度和湿度....但不确定为什么我会得到＆＃39; nan＆＃39;价值......？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...

Answer 1

您可以尝试这样的事情：

a = pd.Series(np.random.random_sample(1000))
b = pd.Series(np.random.random_sample(1000))
c = pd.Series(np.random.random_sample(1000))

df = pd.DataFrame({"temp": a, "press": b, "humid": c})

i = pd.date_range('20120625', periods=1000, freq="h")

df.index = pd.to_datetime(i)

此时数据框df看起来像

                       humid     press     temp
2012-06-25 00:00:00  0.910517  0.588777    ...
2012-06-25 01:00:00  0.742219  0.501180
2012-06-25 02:00:00  0.810515  0.172370
2012-06-25 03:00:00  0.215735  0.046797
2012-06-25 04:00:00  0.094144  0.822310
2012-06-25 05:00:00  0.662934  0.629981
2012-06-25 06:00:00  0.876086  0.586799
...

现在让我们计算所需日期范围的平均值和标准差

def TempPressHumid(start, end, df):
    values = {'temp_mean':np.mean(df['temp'][start:end]),
              'temp_std':np.std(df['temp'][start:end]),
              'press_mean':np.mean(df['press'][start:end]),
              'press_std':np.std(df['press'][start:end]),
              'humid_mean':np.mean(df['humid'][start:end]),
              'humid_std':np.std(df['humid'][start:end]),
              }
    print(values)
    return

因此，如果您致电TempPressHumid('2012-06-25 08:00:00', '2012-07-25 10:00:00', df)，您应该会看到所需值的词典。

使用Datetime索引来分析数据框数据

1 个答案: