pandas.DatetimeIndex频率为None,无法设置

Question

我从"日期"列创建了一个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失踪引起的freq.但是,如果我尝试明确设置频率:

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

所以显然已经推断出一个频率,但它既不存储在DatetimeIndex 的freqnor inferred_freq属性中也不存储- 都是None.有人可以清除混乱吗？

Answer 1

你有两个选择:

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

我怀疑道路上的错误是由于缺少的频率造成的.

你是绝对正确的.这是我经常使用的:

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

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将实际重新索引(填充)缺少日期,所以如果那不是您正在寻找的,请小心.

改变频率的主要功能是asfreq功能.对于a DatetimeIndex,这基本上只是一个很薄但很方便的包装器,reindex可以生成一个date_range和调用reindex.

Answer 2

我不确定早期版本的 python 是否有这个，但 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 3

3kt音符似乎与缺失的日期有关。您可能可以asfreq('D')按照EdChum的建议进行“修复”，但这会为您提供缺少数据值的连续索引。对于我组成的一些样本数据，它工作正常：

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。如果您申请asfreq('D')，则更改为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')

更一般而言，根据您要尝试执行的操作，您可能需要检查以下内容以获取其他选项，例如重新索引和重新采样： 将缺失的日期添加到熊猫数据框