将pandas列转换为datetime64,包括缺失值

Question

使用Pandas处理包含日期,数字,类别等的一些基于时间序列的数据.

我遇到的问题是让pandas从CSV创建的DataFrame中正确处理我的日期/时间列.我的数据中有18个日期列,它们不是连续的,原始CSV中的未知值的字符串值为"未知".有些列的ALL单元格中包含有效的日期时间,并正确地通过pandas read_csv方法猜测它们的dtype.但是有些列在特定数据样本中将所有单元格设置为"未知",并将这些列作为对象输入.

我加载CSV的代码如下:

self.datecols = ['Claim Date', 'Lock Date', 'Closed Date', 'Service Date', 'Latest_Submission', 'Statement Date 1', 'Statement Date 2', 'Statement Date 3', 'Patient Payment Date 1', 'Patient Payment Date 2', 'Patient Payment Date 3', 'Primary 1 Payment Date', 'Primary 2 Payment Date', 'Primary 3 Payment Date', 'Secondary 1 Payment Date', 'Secondary 2 Payment Date', 'Tertiary Payment Date']
self.csvbear = pd.read_csv(file_path, index_col="Claim ID", parse_dates=True, na_values=['Unknown'])
self.csvbear = pd.DataFrame.convert_objects(self.csvbear, convert_dates='coerce')
print self.csvbear.dtypes
print self.csvbear['Tertiary Payment Date'].values

打印self.csvbear.dtypes的输出

Prac                            object
Doctor Name                     object
Practice Name                   object
Specialty                       object
Speciality Code                  int64
Claim Date              datetime64[ns]
Lock Date               datetime64[ns]
Progress Note Locked            object
Aging by Claim Date              int64
Aging by Lock Date               int64
Closed Date             datetime64[ns]
Service Date            datetime64[ns]
Week Number                      int64
Month                   datetime64[ns]
Current Insurance               object
...
Secondary 2 Deductible        float64
Secondary 2 Co Insurance      float64
Secondary 2 Member Balance    float64
Secondary 2 Paid              float64
Secondary 2 Witheld           float64
Secondary 2 Ins                object
Tertiary Payment Date          object
Tertiary Payment ID           float64
Tertiary Allowed              float64
Tertiary Deductible           float64
Tertiary Co Insurance         float64
Tertiary Member Balance       float64
Tertiary Paid                 float64
Tertiary Witheld              float64
Tertiary Ins                  float64
Length: 96, dtype: object
[nan nan nan ..., nan nan nan]
Press any key to continue . . .

正如您所看到的,Tertiary Payment Date col应该是datetime64 dtype,但它只是一个对象,它的实际内容只是NaN(从read_csv函数中放入字符串'Unknown').

如何可靠地转换所有日期列以将datetime64作为dtype并将NaT用于"未知"单元格？

Answer 1

如果你有一个全纳列,它将不会被正确强制read_csv.最简单的就是这样做(如果列已经是datetime64 [ns]将直接通过).

In [3]: df = DataFrame(dict(A = Timestamp('20130101'), B = np.random.randn(5), C = np.nan))

In [4]: df
Out[4]: 
                    A         B   C
0 2013-01-01 00:00:00 -0.859994 NaN
1 2013-01-01 00:00:00 -2.562136 NaN
2 2013-01-01 00:00:00  0.410673 NaN
3 2013-01-01 00:00:00  0.480578 NaN
4 2013-01-01 00:00:00  0.464771 NaN

[5 rows x 3 columns]

In [5]: df.dtypes
Out[5]: 
A    datetime64[ns]
B           float64
C           float64
dtype: object

In [6]: df['A'] = pd.to_datetime(df['A'])

In [7]: df['C'] = pd.to_datetime(df['C'])

In [8]: df
Out[8]: 
                    A         B   C
0 2013-01-01 00:00:00 -0.859994 NaT
1 2013-01-01 00:00:00 -2.562136 NaT
2 2013-01-01 00:00:00  0.410673 NaT
3 2013-01-01 00:00:00  0.480578 NaT
4 2013-01-01 00:00:00  0.464771 NaT

[5 rows x 3 columns]

In [9]: df.dtypes
Out[9]: 
A    datetime64[ns]
B           float64
C    datetime64[ns]
dtype: object

convert_objects不会强制将列转换为datetime,除非它具有至少1个非日期的日期(这就是为什么你的例子失败).to_datetime可能会更积极,因为它"知道"你真的想转换它.