python之Pandas 检查时间序列的连续性

我有一个带有每月索引的 DataFrame。我想检查时间索引是否在每月频率上连续，如果可能的话，检查它变得不连续的地方，例如在其索引中相邻的两个月之间有某些“间隔月”。

例子:如下时间序列数据

1964-07-31    100.00 
1964-08-31     98.81 
1964-09-30    101.21 
1964-11-30    101.42 
1964-12-31    101.45 
1965-03-31     91.49 
1965-04-30     90.33 
1965-05-31     85.23 
1965-06-30     86.10 
1965-08-31     84.26 

错过 1964/10、1965/[1,2,7]。

请您参考如下方法:

使用asfreq按月添加缺失的日期时间，将其过滤到新的 Series 并在必要时按年分组并创建月份列表:

s = s.asfreq('m') 
s1 = pd.Series(s[s.isnull()].index) 
print (s1) 
0   1964-10-31 
1   1965-01-31 
2   1965-02-28 
3   1965-07-31 
Name: 0, dtype: datetime64[ns] 
 
out = s1.dt.month.groupby(s1.dt.year).apply(list) 
print (out) 
0 
1964         [10] 
1965    [1, 2, 7] 
Name: 0, dtype: object 

设置:

s = pd.Series({pd.Timestamp('1964-07-31 00:00:00'): 100.0,  
               pd.Timestamp('1964-08-31 00:00:00'): 98.81,  
               pd.Timestamp('1964-09-30 00:00:00'): 101.21,  
               pd.Timestamp('1964-11-30 00:00:00'): 101.42,  
               pd.Timestamp('1964-12-31 00:00:00'): 101.45, 
               pd.Timestamp('1965-03-31 00:00:00'): 91.49,  
               pd.Timestamp('1965-04-30 00:00:00'): 90.33,  
               pd.Timestamp('1965-05-31 00:00:00'): 85.23,  
               pd.Timestamp('1965-06-30 00:00:00'): 86.1,  
               pd.Timestamp('1965-08-31 00:00:00'): 84.26}) 
 
print (s) 
1964-07-31    100.00 
1964-08-31     98.81 
1964-09-30    101.21 
1964-11-30    101.42 
1964-12-31    101.45 
1965-03-31     91.49 
1965-04-30     90.33 
1965-05-31     85.23 
1965-06-30     86.10 
1965-08-31     84.26 
dtype: float64 

编辑:

如果日期时间不总是月份的最后一天:

s = pd.Series({pd.Timestamp('1964-07-31 00:00:00'): 100.0,  
               pd.Timestamp('1964-08-31 00:00:00'): 98.81,  
               pd.Timestamp('1964-09-01 00:00:00'): 101.21,  
               pd.Timestamp('1964-11-02 00:00:00'): 101.42,  
               pd.Timestamp('1964-12-05 00:00:00'): 101.45, 
               pd.Timestamp('1965-03-31 00:00:00'): 91.49,  
               pd.Timestamp('1965-04-30 00:00:00'): 90.33,  
               pd.Timestamp('1965-05-31 00:00:00'): 85.23,  
               pd.Timestamp('1965-06-30 00:00:00'): 86.1,  
               pd.Timestamp('1965-08-31 00:00:00'): 84.26}) 
print (s) 
1964-07-31    100.00 
1964-08-31     98.81 
1964-09-01    101.21 
1964-11-02    101.42 
1964-12-05    101.45 
1965-03-31     91.49 
1965-04-30     90.33 
1965-05-31     85.23 
1965-06-30     86.10 
1965-08-31     84.26 
dtype: float64 
 
#convert all months to first day 
s.index = s.index.to_period('m').to_timestamp() 
#MS is start month frequency 
s = s.asfreq('MS') 
s1 = pd.Series(s[s.isnull()].index) 
print (s1) 
0   1964-10-01 
1   1965-01-01 
2   1965-02-01 
3   1965-07-01 
dtype: datetime64[ns]