重采样 pandas 数据框正在删除列 [英] Resampling pandas dataframe is deleting column

问题描述

                    Val         ts  year  doy     interpolat  region_id
2000-02-18          NaN  950832000  2000   49           NaN      19987
2000-03-05          NaN  952214400  2000   65           NaN      19987
2000-03-21          NaN  953596800  2000   81           NaN      19987
2000-04-06  0.402539365  954979200  2000   97           NaN      19987
2000-04-22   0.54021746  956361600  2000  113           NaN      19987

以上数据框具有日期时间索引.我这样重新采样:

The above dataframe has a datetime index. I resample it like so:

df = df.resample('D')

但是，这种重新采样会导致以下数据帧:

However, this resampling results in this dataframe:

                    ts  year  doy    interpolat  region_id
2000-01-01  1199180160  2008    1             1      19990
2000-01-02         NaN   NaN  NaN           NaN        NaN
2000-01-03         NaN   NaN  NaN           NaN        NaN
2000-01-04         NaN   NaN  NaN           NaN        NaN
2000-01-05         NaN   NaN  NaN           NaN        NaN

"Val"列为什么消失了?其他所有列似乎也弄乱了.请参阅对熊猫数据框中的缺失行进行线性插值，以了解在何处数据帧来自.

Why did the 'Val' column disappear? and all the other columns seem messed up too. See Linearly interpolate missing rows in pandas dataframe for an explanation of where the dataframe is coming from.

-编辑 基于@unutbu的问题:

--EDIT Based on @unutbu's questions:

df.reset_index().to_dict('list')

{'index': [Timestamp('2000-02-18 00:00:00'), Timestamp('2000-03-05 00:00:00'), Timestamp('2000-03-21 00:00:00'), ... '0.670709965', '0.631584375', '0.562112815', '0.50740686', '0.4447712', '0.47880806', nan, nan]}

-上面数据框的csv文件全文在这里:

-- The csv file for the above data frame in its entirety is here:

https://www.dropbox.com/s/dp76hk6yfs6c1og /test.csv?dl=0

推荐答案

出于某些原因，Val列可能没有数字dtype，并且在resample.

The Val columns will probably not have a numerical dtype for some reason, and all non-numerical (eg object dtype) columns are removed in resample.

要检查，只需查看df.info().
要将其转换为数字列，可以使用astype(float)或convert_objects(从v0.17开始的pd.to_numeric).

To check, just look at df.info().
To convert it to a numerical columns, you can use astype(float) or the convert_objects (pd.to_numeric starting from v0.17).

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