pandas.DatetimeIndex频率为None，不能为s

Question

我从“日期”列创建了DatetimeIndex：

sales.index = pd.DatetimeIndex(sales["date"])

现在索引如下：

DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-04', '2003-01-06',
                   '2003-01-07', '2003-01-08', '2003-01-09', '2003-01-10',
                   '2003-01-11', '2003-01-13',
                   ...
                   '2016-07-22', '2016-07-23', '2016-07-24', '2016-07-25',
                   '2016-07-26', '2016-07-27', '2016-07-28', '2016-07-29',
                   '2016-07-30', '2016-07-31'],
                  dtype='datetime64[ns]', name='date', length=4393, freq=None)

如您所见，freq属性为None。我怀疑路上的错误是由丢失的freq引起的。但是，如果我试图显式设置频率：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-148-30857144de81> in <module>()
      1 #### DEBUG
----> 2 sales_train = disentangle(df_train)
      3 sales_holdout = disentangle(df_holdout)
      4 result = sarima_fit_predict(sales_train.loc[5002, 9990]["amount_sold"], sales_holdout.loc[5002, 9990]["amount_sold"])

<ipython-input-147-08b4c4ecdea3> in disentangle(df_train)
      2     # transform sales table to disentangle sales time series
      3     sales = df_train[["date", "store_id", "article_id", "amount_sold"]]
----> 4     sales.index = pd.DatetimeIndex(sales["date"], freq="d")
      5     sales = sales.pivot_table(index=["store_id", "article_id", "date"])
      6     return sales

/usr/local/lib/python3.6/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
     89                 else:
     90                     kwargs[new_arg_name] = new_arg_value
---> 91             return func(*args, **kwargs)
     92         return wrapper
     93     return _deprecate_kwarg

/usr/local/lib/python3.6/site-packages/pandas/core/indexes/datetimes.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
    399                                          'dates does not conform to passed '
    400                                          'frequency {1}'
--> 401                                          .format(inferred, freq.freqstr))
    402 
    403         if freq_infer:

ValueError: Inferred frequency None from passed dates does not conform to passed frequency D

很显然，已经推断出了一个频率，但它既没有存储在DatetimeIndex的freq属性中，也没有存储在inferred_freq属性中——两者都不是。有人能把混乱弄清楚吗？

Answer 1

我不确定早期版本的python是否有这个功能，但是3.6有这个简单的解决方案：

# 'b' stands for business days
# 'w' for weekly, 'd' for daily, and you get the idea...
df.index.freq = 'b' 

Answer 2

你有两个选择：

• pd.infer_freq
• pd.tseries.frequencies.to_offset

I suspect that errors down the road are caused by the missing freq.

你完全正确。以下是我经常使用的：

def add_freq(idx, freq=None):
    """Add a frequency attribute to idx, through inference or directly.

    Returns a copy.  If `freq` is None, it is inferred.
    """

    idx = idx.copy()
    if freq is None:
        if idx.freq is None:
            freq = pd.infer_freq(idx)
        else:
            return idx
    idx.freq = pd.tseries.frequencies.to_offset(freq)
    if idx.freq is None:
        raise AttributeError('no discernible frequency found to `idx`.  Specify'
                             ' a frequency string with `freq`.')
    return idx

例如：

idx=pd.to_datetime(['2003-01-02', '2003-01-03', '2003-01-06'])  # freq=None

print(add_freq(idx))  # inferred
DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-06'], dtype='datetime64[ns]', freq='B')

print(add_freq(idx, freq='D'))  # explicit
DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-06'], dtype='datetime64[ns]', freq='D')

使用asfreq实际上会重新索引（填充）丢失的日期，因此如果这不是您要查找的，请小心。

The primary function for changing frequencies is the asfreq function. For a DatetimeIndex, this is basically just a thin, but convenient wrapper around reindex which generates a date_range and calls reindex.

Answer 3

它似乎与失踪日期有关，如3kt所述。正如EdChum所建议的那样，您可以使用asfreq('D')来“修复”，但这会给您一个缺少数据值的连续索引。对于我制作的一些示例数据，它工作得很好：

df=pd.DataFrame({ 'x':[1,2,4] }, 
   index=pd.to_datetime(['2003-01-02', '2003-01-03', '2003-01-06']) )

df
Out[756]: 
            x
2003-01-02  1
2003-01-03  2
2003-01-06  4

df.index
Out[757]: DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-06'], 
          dtype='datetime64[ns]', freq=None)

注意freq=None。如果应用asfreq('D')，则会更改为freq='D'：

df.asfreq('D')
Out[758]: 
              x
2003-01-02  1.0
2003-01-03  2.0
2003-01-04  NaN
2003-01-05  NaN
2003-01-06  4.0

df.asfreq('d').index
Out[759]: 
DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-04', '2003-01-05',
               '2003-01-06'],
              dtype='datetime64[ns]', freq='D')

更一般地说，并且取决于您到底要做什么，您可能需要检查以下其他选项，例如重新索引和重新采样：Add missing dates to pandas dataframe