計算上數據幀意味着使用熊貓

Question

我有其中值被測量，每次30分鐘，如以下所示的數據幀給定的頻率：

2015-01-01 00:00:00 94.50 
2015-01-01 00:30:00 78.75 
2015-01-01 01:00:00 85.87 
2015-01-01 01:30:00 85.88 
2015-01-01 02:00:00 84.75 
2015-01-01 02:30:00 87.50 

所以，每天有48個值。拳頭列是通過使用創建的時間指數：

date= pd.date_range('1/1/2015', periods=len(series),freq='30min') 
series=series.values.reshape(-1,1) 
df=pd.DataFrame(series, index=date) 

我想這樣做是爲了獲得平均一天和工作日的每個時間。事情是這樣的：

我最初的想法是由一羣平日和頻率（30分鐘）如下：

df= df.groupby([ df.index.weekday,df.index.freq]) 
print(df.describe()) 


     count mean std  min  25%  50%  75% 
0 2015-01-05 00:30:00 1.0 93.75 NaN 93.75 93.75 93.75 93.75 
    2015-01-05 01:00:00 1.0 110.25 NaN 110.25 110.25 110.25 110.25 
    2015-01-05 01:30:00 1.0 110.88 NaN 110.88 110.88 110.88 110.88 
    2015-01-05 02:00:00 1.0 90.12 NaN 90.12 90.12 90.12 90.12 
    2015-01-05 02:30:00 1.0 91.50 NaN 91.50 91.50 91.50 91.50 
    2015-01-05 03:00:00 1.0 94.13 NaN 94.13 94.13 94.13 94.13 
    2015-01-05 03:30:00 1.0 90.62 NaN 90.62 90.62 90.62 90.62 
    2015-01-05 04:00:00 1.0 91.88 NaN 91.88 91.88 91.88 91.88 
    2015-01-05 04:30:00 1.0 92.50 NaN 92.50 92.50 92.50 92.50 
    2015-01-05 05:00:00 1.0 98.12 NaN 98.12 98.12 98.12 98.12 
    2015-01-05 05:30:00 1.0 105.75 NaN 105.75 105.75 105.75 105.75 
    2015-01-05 06:00:00 1.0 100.50 NaN 100.50 100.50 100.50 100.50 
    2015-01-05 06:30:00 1.0 82.25 NaN 82.25 82.25 82.25 82.25 
    2015-01-05 07:00:00 1.0 81.75 NaN 81.75 81.75 81.75 81.75 
    2015-01-05 07:30:00 1.0 90.50 NaN 90.50 90.50 90.50 90.50 
    2015-01-05 08:00:00 1.0 89.50 NaN 89.50 89.50 89.50 89.50 
    2015-01-05 08:30:00 1.0 89.63 NaN 89.63 89.63 89.63 89.63 
    2015-01-05 09:00:00 1.0 84.62 NaN 84.62 84.62 84.62 84.62 
    2015-01-05 09:30:00 1.0 86.63 NaN 86.63 86.63 86.63 86.63 
    2015-01-05 10:00:00 1.0 96.12 NaN 96.12 96.12 96.12 96.12 
    2015-01-05 10:30:00 1.0 104.13 NaN 104.13 104.13 104.13 104.13 
    2015-01-05 11:00:00 1.0 101.12 NaN 101.12 101.12 101.12 101.12 
    2015-01-05 11:30:00 1.0 85.88 NaN 85.88 85.88 85.88 85.88 
    2015-01-05 12:00:00 1.0 77.12 NaN 77.12 77.12 77.12 77.12 
    2015-01-05 12:30:00 1.0 78.88 NaN 78.88 78.88 78.88 78.88 
    2015-01-05 13:00:00 1.0 76.62 NaN 76.62 76.62 76.62 76.62 
    2015-01-05 13:30:00 1.0 78.63 NaN 78.63 78.63 78.63 78.63 
    2015-01-05 14:00:00 1.0 85.37 NaN 85.37 85.37 85.37 85.37 
    2015-01-05 14:30:00 1.0 103.63 NaN 103.63 103.63 103.63 103.63 
    2015-01-05 15:00:00 1.0 112.87 NaN 112.87 112.87 112.87 112.87 
...      ...  ... ..  ...  ...  ...  ... 
6 2016-10-02 09:30:00 1.0 84.75 NaN 84.75 84.75 84.75 84.75 
    2016-10-02 10:00:00 1.0 60.49 NaN 60.49 60.49 60.49 60.49 
    2016-10-02 10:30:00 1.0 76.25 NaN 76.25 76.25 76.25 76.25 
    2016-10-02 11:00:00 1.0 68.13 NaN 68.13 68.13 68.13 68.13 
    2016-10-02 11:30:00 1.0 54.15 NaN 54.15 54.15 54.15 54.15 
    2016-10-02 12:00:00 1.0 79.91 NaN 79.91 79.91 79.91 79.91 
    2016-10-02 12:30:00 1.0 72.79 NaN 72.79 72.79 72.79 72.79 
    2016-10-02 13:00:00 1.0 77.49 NaN 77.49 77.49 77.49 77.49 
    2016-10-02 13:30:00 1.0 77.65 NaN 77.65 77.65 77.65 77.65 
    2016-10-02 14:00:00 1.0 70.44 NaN 70.44 70.44 70.44 70.44 
    2016-10-02 14:30:00 1.0 82.47 NaN 82.47 82.47 82.47 82.47 
    2016-10-02 15:00:00 1.0 41.53 NaN 41.53 41.53 41.53 41.53 
    2016-10-02 15:30:00 1.0 66.65 NaN 66.65 66.65 66.65 66.65 
    2016-10-02 16:00:00 1.0 55.23 NaN 55.23 55.23 55.23 55.23 
    2016-10-02 16:30:00 1.0 59.45 NaN 59.45 59.45 59.45 59.45 
    2016-10-02 17:00:00 1.0 79.92 NaN 79.92 79.92 79.92 79.92 
    2016-10-02 17:30:00 1.0 58.48 NaN 58.48 58.48 58.48 58.48 
    2016-10-02 18:00:00 1.0 92.56 NaN 92.56 92.56 92.56 92.56 
    2016-10-02 18:30:00 1.0 86.92 NaN 86.92 86.92 86.92 86.92 
    2016-10-02 19:00:00 1.0 88.61 NaN 88.61 88.61 88.61 88.61 
    2016-10-02 19:30:00 1.0 99.21 NaN 99.21 99.21 99.21 99.21 
    2016-10-02 20:00:00 1.0 81.02 NaN 81.02 81.02 81.02 81.02 
    2016-10-02 20:30:00 1.0 84.83 NaN 84.83 84.83 84.83 84.83 
    2016-10-02 21:00:00 1.0 59.29 NaN 59.29 59.29 59.29 59.29 
    2016-10-02 21:30:00 1.0 95.99 NaN 95.99 95.99 95.99 95.99 
    2016-10-02 22:00:00 1.0 76.95 NaN 76.95 76.95 76.95 76.95 
    2016-10-02 22:30:00 1.0 112.49 NaN 112.49 112.49 112.49 112.49 
    2016-10-02 23:00:00 1.0 88.85 NaN 88.85 88.85 88.85 88.85 
    2016-10-02 23:30:00 1.0 122.40 NaN 122.40 122.40 122.40 122.40 
    2016-10-03 00:00:00 1.0 82.84 NaN 82.84 82.84 82.84 82.84 

通過看這個，你可以看到平日只是組。所以這不是組合的正確方法，以便按照我的意願計算平均值。

Answer 1

我會使用df.index.weekday和df.index.time

df.groupby([ df.index.weekday,df.index.time]).mean()