Python Pandas 检查字符串是否仅为 "Date"或仅为 "Time"或 "Datetime"

Question

我正在使用 pandas 读取 csv

str,date,float,time,datetime
a,10/11/19,1.1,10:30:00,10/11/19 10:30
b,10/11/19,1.2,10:00:00,10/11/19 10:30
c,10/11/19,1.3,11:10:11,10/11/19 10:30

df = pd.read_csv(file)

现在我的业务需求是要区分哪一列是纯日期字段，纯时间字段，哪一列是完整的日期时间。对于特定的列，我的代码是:

try:
                    dt = pd.to_datetime(df[col])
                    dates = [obj.date() for obj in dt]
                    times = [obj.time() for obj in dt]

                    if dates and (set(times) == set([datetime.time(0, 0)])):
                        # Its a pure date field
                    elif <something>:
                       # Its a  pure time field
                    else:
                       #Its a Datetime field


except:
            # its not a datefield

我的代码的问题是当只有时间字段时，pd.to_datetime 将采用今天的默认日期，因此我无法将它与日期时间区分开来。有什么简单的解决办法吗？请帮我在上面的代码中填写“something”

Answer 1

如果想要测试时间，pandas 默认使用今天的日期，所以可能的解决方案是用 Series.dt.date 测试它们, Timestamp.date和 Series.all如果列的所有值都匹配。

还为测试日期添加了另一个解决方案 - 通过 Series.dt.floor 测试删除时间后是否有相同的值:

df = pd.DataFrame({'a':['2019-01-01 12:23:10',
                        '2019-01-02 12:23:10'],
                   'b':['2019-01-01',
                        '2019-01-02'],
                   'c':['12:23:10',
                        '15:23:10'],
                   'd':['a','b']})
print (df)
                     a           b         c  d
0  2019-01-01 12:23:10  2019-01-01  12:23:10  a
1  2019-01-02 12:23:10  2019-01-02  15:23:10  b

def check(col):
    try:
        dt = pd.to_datetime(df[col])

        if (dt.dt.floor('d') == dt).all():
            return ('Its a pure date field')
        elif (dt.dt.date == pd.Timestamp('now').date()).all():
            return ('Its a pure time field')
        else:
            return ('Its a Datetime field') 
    except:
        return ('its not a datefield')


print (check('a'))
print (check('b'))
print (check('c'))
print (check('d'))
Its a Datetime field
Its a pure date field
Its a pure time field
its not a datefield

---

另一个想法也是测试数字列是否默认返回非数字，以防止将数字转换为日期时间，但如果可能，所有日期时间都只包含今天的日期(f 列)，那么时间测试是不同的与 Series.str.contains对于匹配模式 HH:MM:SS 或 H:MM:SS:

df = pd.DataFrame({'a':['2019-01-01 12:23:10',
                        '2019-01-02'],
                   'b':['2019-01-01',
                        '2019-01-02'],
                   'c':['12:23:10',
                        '15:23:10'],
                   'd':['a','b'],
                   'e':[1,2],
                  'f':['2019-11-13 12:23:10',
                       '2019-11-13'],})
print (df)
                     a           b         c  d  e                    f
0  2019-01-01 12:23:10  2019-01-01  12:23:10  a  1  2019-11-13 12:23:10
1           2019-01-02  2019-01-02  15:23:10  b  2           2019-11-13

---

def check(col):
    if np.issubdtype(df[col].dtype, np.number):
        return ('its not a datefield')

    try:
        dt = pd.to_datetime(df[col])
        if (dt.dt.floor('d') == dt).all():
            return ('Its a pure date field')
        elif df[col].str.contains(r"^\d{1,2}:\d{2}:\d{2}$").all():
            return ('Its a pure time field')
        else:
            return ('Its a Datetime field') 
    except:
        return ('its not a datefield')


print (check('a'))
print (check('b'))
print (check('c'))
print (check('d'))
print (check('e'))
print (check('f'))
Its a Datetime field
Its a pure date field
Its a pure time field
its not a datefield
its not a datefield
Its a Datetime field