處理熊貓的時間序列數據

Question

我有一個每日時間序列數據。我試圖通過乘以一些月度因素來糾正這些時間系列值。

我的時間序列數據的樣子：

model: 

2010-01-30 0.008909 
2010-01-31 0.007562 
2010-02-01 0.
2010-02-02 0.010286 
2010-02-03 0.012244 
2010-02-04 0.011367 
2010-02-05 0.010800 
2010-02-06 0.007610 
2010-02-07 0.006534 
2010-02-08 0.004721 
       ... 
2015-12-02 0.005415 
2015-12-03 0.004358 
2015-12-04 0.006844 
2015-12-05 0.002373 

我有一個每月因素，如：

mon_slope: 

month 
January  -0.168627 
February -0.165102 
March  -0.112321 
April  -0.112232 
May   -0.080092 
June  -0.129905 
July  -0.078751 
August  -0.095756 
September -0.090188 
October  -0.109919 
November -0.155380 
December -0.137885 
Name: slope, dtype: float64 

我所做的：

jan_corr = pd.DataFrame(model[model.index.month ==1]*mon_slope.ix[0][1]) 
feb_corr = pd.DataFrame(model[model.index.month ==2]*mon_slope.ix[1][1]) 
mar_corr = pd.DataFrame(model[model.index.month ==3]*mon_slope.ix[2][1]) 

.................. 
.................. 

final = pd.concat([jan_corr,feb_corr,mar_corr]) 

喜歡它做它喜歡手動校正

但我相信這不是正確的做法。有沒有簡單的方法來做到這一點：

Answer 1

首先，創建一個映射：

mapping = dict(months.values) 
mapping 

{'April': -0.112232, 
'August': -0.095756, 
'December': -0.13788499999999998, 
'February': -0.165102, 
'January': -0.168627, 
'July': -0.078751, 
'June': -0.129905, 
'March': -0.112321, 
'May': -0.080092, 
'November': -0.15538, 
'October': -0.109919, 
'September': -0.090188} 

您可以使用Series.dt.strftime檢索MonthNames中：

df.iloc[:, 0].dt.strftime('%B') 
Out[143]: 
0  January 
1  January 
2  February 
3  February 
4  February 
5  February 
6  February 
7  February 
8  February 
9  February 
10 December 
11 December 
12 December 
13 December 
Name: 0, dtype: object 

現在，使用此與訪問乘法器df.replace或df.map：

df.iloc[:, 1] = df.iloc[:, 0].dt.strftime('%B').map(mapping) * df.iloc[:, 1] 
df 

      0   1 
0 2010-01-30 -0.001502 
1 2010-01-31 -0.001275 
2 2010-02-01 -0.002043 
3 2010-02-02 -0.001698 
4 2010-02-03 -0.002022 
5 2010-02-04 -0.001877 
6 2010-02-05 -0.001783 
7 2010-02-06 -0.001256 
8 2010-02-07 -0.001079 
9 2010-02-08 -0.000779 
10 2015-12-02 -0.000747 
11 2015-12-03 -0.000601 
12 2015-12-04 -0.000944 
13 2015-12-05 -0.000327 

---

詳情：

df 

      0   1 
0 2010-01-30 0.008909 
1 2010-01-31 0.007562 
2 2010-02-01 0.
3 2010-02-02 0.010286 
4 2010-02-03 0.012244 
5 2010-02-04 0.011367 
6 2010-02-05 0.010800 
7 2010-02-06 0.007610 
8 2010-02-07 0.006534 
9 2010-02-08 0.004721 
10 2015-12-02 0.005415 
11 2015-12-03 0.004358 
12 2015-12-04 0.006844 
13 2015-12-05 0.002373 

months 

      0   1 
0  January -0.168627 
1 February -0.165102 
2  March -0.112321 
3  April -0.112232 
4   May -0.080092 
5  June -0.129905 
6  July -0.078751 
7  August -0.095756 
8 September -0.090188 
9  October -0.109919 
10 November -0.155380 
11 December -0.137885 

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