Question

我正在尝试读取一个csv文件，其中包含一个示例：

datetime,check,lat,lon,co_alpha,atn,status,bc
2012-10-27 15:00:59,2,0,0,2.427,,,
2012-10-27 15:01:00,2,0,0,2.407,,,
2012-10-27 15:02:49,2,0,0,2.207,-17.358,0,-16162
2012-10-27 15:02:50,2,0,0,2.207,-17.354,0,8192
2012-10-27 15:02:51,1,0,0,2.207,-17.358,0,-8152
2012-10-27 15:02:52,1,0,0,2.207,-17.358,0,648
2012-10-27 15:06:03,0,51.195076,4.444407,2.349,-17.289,0,4909
2012-10-27 15:06:04,0,51.195182,4.44427,2.344,-17.289,0,587
2012-12-05 09:21:34,,,,,42.960,1,16430
2012-12-05 09:21:35,,,,,42.962,1,3597

我遇到的问题是，在只有整数的列中，0被转换为NaN（例如列'check'和'status'，这些是只有整数的列，但是这些列被读作浮点数，因为它有真实的缺失值）。但我只想将空值转换为NaN，而不是零。

这就是我得到的：

>>> pd.read_clipboard(sep=',', parse_dates=True, index_col=0)
                     check        lat       lon  co_alpha     atn  status     bc
datetime                                                                        
2012-10-27 15:00:59      2   0.000000  0.000000     2.427     NaN     NaN    NaN
2012-10-27 15:01:00      2   0.000000  0.000000     2.407     NaN     NaN    NaN
2012-10-27 15:02:49      2   0.000000  0.000000     2.207 -17.358     NaN -16162
2012-10-27 15:02:50      2   0.000000  0.000000     2.207 -17.354     NaN   8192
2012-10-27 15:02:51      1   0.000000  0.000000     2.207 -17.358     NaN  -8152
2012-10-27 15:02:52      1   0.000000  0.000000     2.207 -17.358     NaN    648
2012-10-27 15:06:03    NaN  51.195076  4.444407     2.349 -17.289     NaN   4909
2012-10-27 15:06:04    NaN  51.195182  4.444270     2.344 -17.289     NaN    587
2012-12-05 09:21:34    NaN        NaN       NaN       NaN  42.960       1  16430
2012-12-05 09:21:35    NaN        NaN       NaN       NaN  42.962       1   3597

因此，在“检查”和“状态”列中，有许多NaN。在'lat'和'lon'列中，0不会转换为NaN。

使用na_values=''和keep_default_na=False无济于事。有没有办法指定不将int 0转换为NaN？或者这是一个错误？
我可以使用dtype关键字将特定列的dtype指定为int。这将0保持为0，但问题是这些列还包含真正的NaN（空值）。因此，在这种情况下，这些值也会转换为0，就像在int列中一样，您不能拥有NaN。出于这个原因，我必须将所有列保持为浮点数。

编辑：升级到pandas 0.10.1之后，即使没有指定keep_default_na和na_values，它也可按预期工作：

>>> pd.read_clipboard(sep=',', parse_dates=True, index_col=0)
                     check        lat       lon  co_alpha     atn  status     bc
datetime                                                                        
2012-10-27 15:00:59      2   0.000000  0.000000     2.427     NaN     NaN    NaN
2012-10-27 15:01:00      2   0.000000  0.000000     2.407     NaN     NaN    NaN
2012-10-27 15:02:49      2   0.000000  0.000000     2.207 -17.358       0 -16162
2012-10-27 15:02:50      2   0.000000  0.000000     2.207 -17.354       0   8192
2012-10-27 15:02:51      1   0.000000  0.000000     2.207 -17.358       0  -8152
2012-10-27 15:02:52      1   0.000000  0.000000     2.207 -17.358       0    648
2012-10-27 15:06:03      0  51.195076  4.444407     2.349 -17.289       0   4909
2012-10-27 15:06:04      0  51.195182  4.444270     2.344 -17.289       0    587
2012-12-05 09:21:34    NaN        NaN       NaN       NaN  42.960       1  16430
2012-12-05 09:21:35    NaN        NaN       NaN       NaN  42.962       1   3597

Answer 1

您必须先将keep_default_na设置为False：

df = pd.read_clipboard(sep=',', index_col=0, keep_default_na=False, na_values='')

In [2]: df
Out[2]: 
                     check        lat       lon  co_alpha     atn  status     bc
datetime                                                                        
2012-10-27 15:00:59      2   0.000000  0.000000     2.427     NaN     NaN    NaN
2012-10-27 15:01:00      2   0.000000  0.000000     2.407     NaN     NaN    NaN
2012-10-27 15:02:49      2   0.000000  0.000000     2.207 -17.358       0 -16162
2012-10-27 15:02:50      2   0.000000  0.000000     2.207 -17.354       0   8192
2012-10-27 15:02:51      1   0.000000  0.000000     2.207 -17.358       0  -8152
2012-10-27 15:02:52      1   0.000000  0.000000     2.207 -17.358       0    648
2012-10-27 15:06:03      0  51.195076  4.444407     2.349 -17.289       0   4909
2012-10-27 15:06:04      0  51.195182  4.444270     2.344 -17.289       0    587
2012-12-05 09:21:34    NaN        NaN       NaN       NaN  42.960       1  16430
2012-12-05 09:21:35    NaN        NaN       NaN       NaN  42.962       1   3597

来自read_tables的文字字符串：

keep_default_na：bool，默认为True   如果指定na_values且keep_default_na为False，则为默认NaN   值被覆盖，否则它们会被附加到

na_values：类似列表或字典，默认None
  要识别为NA / NaN的其他字符串。如果dict通过，具体       每列NA值

Pandas read_csv（）：将0保持为0（不将其转换为NaN）

1 个答案: