berechnen gegeben bedeuten auf Datenrahmen eine Frequenz Pandas mit

Question

Ich habe einen Datenrahmen, wo Werte alle 30 Minuten gemessen werden, wie unten dargestellt:

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 

So, jeden Tag gibt es insgesamt 48 Werte. Die Faust Spalte ist der Zeitindex unter Verwendung erstellt:

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

Was würde ich tun möchte, ist der Mittelwert für jede Tageszeit und Wochentag zu erhalten. Etwas wie folgt aus:

Meine ursprüngliche Idee war, zu einer Gruppe von Wochentag und Frequenz (30 min.) Wie folgt:

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 

Mit Blick auf diese, können Sie es gerade Gruppe von Wochentag sehen. Das ist also nicht die richtige Art zu gruppieren, um den Mittelwert so zu berechnen, wie ich es wollte.

Answer 1

würde ich df.index.weekday und df.index.time

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