Comment changer un groupby DF en une série datetime mensuelle en Python Python

Question

monthly_dividend 
1994 10 NaN 
     11 NaN 
     12 NaN 
     12 NaN 
... 
2012 4   NaN 
     5   NaN 
     6   NaN 
     7  1.746622 
     8  1.607685 
     9  1.613936 
     10 1.620187 
     11 1.626125 
     12 1.632375 
2013 1  1.667792 
     2  1.702897 
     3  1.738314 
     4  1.773731 
     5  1.808835 
     6  1.844252 
Length: 225 

J'ai un code qui ressemble à celui ci-dessus. Ceci est un groupedby DataFrame, mais je voudrais le transformer en un TimeSeries régulier à nouveau. Le asfreq ('M') ne fonctionne plus sur le groupedby, donc je ne suis pas sûr s'il y a un moyen facile de le convertir.

dividends 
1994-10-31 0.0750 
1994-11-30 0.0750 
1994-12-31 0.0750 
1995-12-31 0.3450 
... 
2012-03-31 0.145812 
2012-04-30 0.145812 
2012-05-31 0.145812 
2012-06-30 0.146125 
2012-07-31 0.146125 
2012-08-31 0.151125 
2012-09-30 0.151438 
2012-10-31 0.151438 
2012-11-30 0.151438 
2012-12-31 0.151750 
2013-01-31 0.180917 
2013-02-28 0.180917 
2013-03-31 0.181229 
2013-04-30 0.181229 
2013-05-31 0.181229 
Freq: M, Length: 224 

Answer 1

Créer vos données haut

In [172]: df = DataFrame(randn(200,1),columns=['A'],index=pd.date_range('2000',periods=200,freq='M')) 

In [173]: df['month'] = df.index.month 

In [174]: df['year'] = df.index.year 

In [175]: df = df.reset_index(drop=True).set_index(['year','month']) 

In [176]: df 
Out[176]: 
<class 'pandas.core.frame.DataFrame'> 
MultiIndex: 200 entries, (2000, 7) to (2017, 2) 
Data columns (total 1 columns): 
A 200 non-null values 
dtypes: float64(1) 

In [177]: df.head() 
Out[177]: 
        A 
year month   
2000 7  0.084256 
    8  2.507213 
    9  -0.642151 
    10  1.972307 
    11  0.926586 

Cela va créer un PeriodIndex de fréq mensuelle. Notez que itérer sur l'indice donne les tuples (comme entiers)

In [179]: pd.PeriodIndex([ pd.Period(year=year,month=month,freq='M') for year, month in df.index ]) 
Out[179]: 
<class 'pandas.tseries.period.PeriodIndex'> 
freq: M 
[2000-07, ..., 2017-02] 
length: 200 

converstion direct à un DateTimeIndex

In [180]: new_index = pd.PeriodIndex([ pd.Period(year=year,month=month,freq='M') for year, month in df.index ]).to_timestamp() 
Out[180]: 
<class 'pandas.tseries.index.DatetimeIndex'> 
[2000-07-01 00:00:00, ..., 2017-02-01 00:00:00] 
Length: 200, Freq: MS, Timezone: None 

À ce stade, vous pouvez le faire

In [182]: df.index = new_index 

In [183]: df 
Out[183]: 
<class 'pandas.core.frame.DataFrame'> 
DatetimeIndex: 200 entries, 2000-07-01 00:00:00 to 2017-02-01 00:00:00 
Freq: MS 
Data columns (total 1 columns): 
A 200 non-null values 
dtypes: float64(1) 

In [184]: df.head() 
Out[184]: 
        A 
2000-07-01 0.084256 
2000-08-01 2.507213 
2000-09-01 -0.642151 
2000-10-01 1.972307 
2000-11-01 0.926586 

to_timestamp retourne normalement le premier jour du mois pour retourner la fin, passer how='e'

In [1]: pr = pd.period_range('200001',periods=20,freq='M') 

In [2]: pr 
Out[2]: 
<class 'pandas.tseries.period.PeriodIndex'> 
freq: M 
[2000-01, ..., 2001-08] 
length: 20 

In [3]: pr.to_timestamp() 
Out[3]: 
<class 'pandas.tseries.index.DatetimeIndex'> 
[2000-01-01 00:00:00, ..., 2001-08-01 00:00:00] 
Length: 20, Freq: MS, Timezone: None 

In [4]: pr.to_timestamp(how='e') 
Out[4]: 
<class 'pandas.tseries.index.DatetimeIndex'> 
[2000-01-31 00:00:00, ..., 2001-08-31 00:00:00] 
Length: 20, Freq: M, Timezone: None