我有不同類型的DateTime索引(可能是每週,每月,每年的數據)的數據幀。我想要生成其他列的滯後值的列。我從電子表格中導入這些文件,我不在python中生成日期時間索引。延遲日期時間索引列的pythonic方式
我正在努力尋找這樣做的'pythonic'方式。我想如果我使用Pandas的日期時間功能,在奇怪或異常數據的情況下,滯後可能更加穩健。
我做了一個玩具的例子,似乎工作,但它失敗了我的現實世界的例子。
玩具例子正常工作(使有前一個月的「富」值的新列)
rng = pd.date_range('2012-01-01', '2013-1-01', freq="M")
toy2 = pd.DataFrame(pd.Series(np.random.randint(0, 50, len(rng)), index=rng, name="foo"))
foo
2012-01-31 4
2012-02-29 2
2012-03-31 27
2012-04-30 7
2012-05-31 44
2012-06-30 22
2012-07-31 16
2012-08-31 18
2012-09-30 35
2012-10-31 35
2012-11-30 16
2012-12-31 32
toy2['lag_foo']= toy2['foo'].shift(1,'m')
foo lag_foo
2012-01-31 4 NaN
2012-02-29 2 4.0
2012-03-31 27 2.0
2012-04-30 7 27.0
2012-05-31 44 7.0
2012-06-30 22 44.0
2012-07-31 16 22.0
2012-08-31 18 16.0
2012-09-30 35 18.0
2012-10-31 35 35.0
2012-11-30 16 35.0
2012-12-31 32 16.0
但是,當我在我的現實生活中的例子運行它,它失敗:
ValueError: cannot reindex from a duplicate axis
print type(toy)
print toy.columns
print toy['IPE m2'][0:5]
<class 'pandas.core.frame.DataFrame'>
Index([u'IPE m2'], dtype='object')
Date
2016-04-30 43.29
2016-03-31 40.44
2016-02-29 34.17
2016-01-31 32.47
2015-12-31 39.35
Name: IPE m2, dtype: float64
的異常跟蹤:
ValueError Traceback (most recent call last)
<ipython-input-170-9cb57a2ed681> in <module>()
----> 1 toy['prev_1m']= toy['IPE m2'].shift(1,'m')
C:\Users\mds\Anaconda2\lib\site-packages\pandas\core\frame.pyc in __setitem__(self, key, value)
2355 else:
2356 # set column
-> 2357 self._set_item(key, value)
2358
2359 def _setitem_slice(self, key, value):
C:\Users\mds\Anaconda2\lib\site-packages\pandas\core\frame.pyc in _set_item(self, key, value)
2421
2422 self._ensure_valid_index(value)
-> 2423 value = self._sanitize_column(key, value)
2424 NDFrame._set_item(self, key, value)
2425
C:\Users\mds\Anaconda2\lib\site-packages\pandas\core\frame.pyc in _sanitize_column(self, key, value)
2555
2556 if isinstance(value, Series):
-> 2557 value = reindexer(value)
2558
2559 elif isinstance(value, DataFrame):
C:\Users\mds\Anaconda2\lib\site-packages\pandas\core\frame.pyc in reindexer(value)
2547 # duplicate axis
2548 if not value.index.is_unique:
-> 2549 raise e
2550
2551 # other
ValueError: cannot reindex from a duplicate axis
似乎我錯過了熊貓日期時間指數的一些微妙之處,我認爲。另外我甚至不確定這是做這件事的理想方式。我懷疑的唯一的事情是,非工作toy.index具有無作爲頻率,而工作toy2例子,有其頻率設置爲「M」
toy.index
DatetimeIndex(['2016-04-30', '2016-03-31', '2016-02-29', '2016-01-31',
'2015-12-31', '2015-11-30', '2015-10-31', '2015-09-30',
'2015-08-31', '2015-07-31',
...
'NaT', 'NaT', 'NaT', 'NaT',
'NaT', 'NaT', 'NaT', 'NaT',
'NaT', 'NaT'],
dtype='datetime64[ns]', name=u'Date', length=142, freq=None)
toy2.index
DatetimeIndex(['2012-01-31', '2012-02-29', '2012-03-31', '2012-04-30',
'2012-05-31', '2012-06-30', '2012-07-31', '2012-08-31',
'2012-09-30', '2012-10-31', '2012-11-30', '2012-12-31'],
dtype='datetime64[ns]', freq='M')
In [ ]:
======== ===================
我扔掉了NAT的
toy = toy.dropna()
toy['prev_1m']= toy['IPE m2'].shift(1,'m')
和我得到我想要的結果。不過,我也得到一個警告:
C:\Users\mds\Anaconda2\lib\site-packages\ipykernel\__main__.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
if __name__ == '__main__':
====
分配的這種方式抑制了警告:
toy.loc[:,'prev_1m2']= toy['IPE m2'].shift(1,'m')
我用dropna()拋出NaTs,它的工作原理,但它給出了一些警告。添加了原始問題的附錄。 – user3556757
你確定你需要'dropna'嗎?如果需要在索引中用'NaN'刪除記錄,使用'toy = toy [ pd.notnull(toy.index)]'。 – jezrael