pandas.DatetimeIndex 频率为 None 且无法设置

2022-01-11 00:00:00 python pandas indexing time-series

问题描述

我从日期"列创建了一个 DatetimeIndex:

I created a DatetimeIndex from a "date" column:

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 属性为无.我怀疑未来的错误是由缺少 freq 引起的.但是，如果我尝试明确设置频率:

As you see, the freq attribute is None. I suspect that errors down the road are caused by the missing freq. However, if I try to set the frequency explicitly:

---------------------------------------------------------------------------
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

显然已经推断出频率，但既没有存储在 DatetimeIndex 的 freq 属性中，也没有存储在 inferred_freq 属性中 - 两者都是 None.有人能解惑吗?

So apparently a frequency has been inferred, but is stored neither in the freq nor inferred_freq attribute of the DatetimeIndex - both are None. Can someone clear up the confusion?

解决方案

这里有几个选项:

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

我怀疑后面的错误是由缺少频率引起的.

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

你完全正确.这是我经常使用的:

You are absolutely right. Here's what I use often:

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

一个例子:

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)缺失的日期，因此如果这不是您要查找的内容，请小心.

Using asfreq will actually reindex (fill) missing dates, so be careful of that if that's not what you're looking for.

改变频率的主要函数是 asfreq 函数.对于 DatetimeIndex，这基本上只是一个简单但方便的reindex 的包装器，它生成一个 date_range 并调用 reindex.

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.

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