我已经导入了一个excel文件，并且在pandas迭代中遇到了麻烦。
导入后数据如下所示。
Acct Time Typ Name
01/02/2018 NaN NaN NaN
52 07:58:34 1 John
53 07:58:35 1 Jack
54 07:58:35 1 Ron
55 07:58:35 1 Lux
01/03/2018 NaN NaN NaN
79 12:39:25 1 Mike
01/06/2018 NaN NaN NaN
371 12:15:47 1 Eric
422 17:07:33 1 Shawn
01/07/2018 NaN NaN NaN
492 12:43:54 1 George
我要遍历第一列（"Acct"）的字符串，检查其是否为日期或数字。
我在新的数据框中创建了另一列，并尝试使用date字段填充，但是很少出现Series错误。
期望这样的输出：
Date_New Acct Time Typ Name
01/02/2018 52 07:58:34 1 John
01/02/2018 53 07:58:35 1 Jack
01/02/2018 54 07:58:35 1 Ron
01/02/2018 55 07:58:35 1 Lux
01/03/2018 79 12:39:25 1 Mike
01/06/2018 371 12:15:47 1 Eric
01/06/2018 422 17:07:33 1 Shawn
01/07/2018 492 12:43:54 1 George
如果我是pandas的新手，如果有人可以向我发送一些指导，我将不胜感激。
答案 0 :(得分：2)
我建议不要迭代，而应使用pandas函数。要查找正确的日期，可以使用pd.to_datetime将Acct列转换为正确的日期，并使用参数errors = 'coerce'。非日期将变为空（NaT）。然后，使用ffill用正确的日期向前填充该列，并删除其中Time，Typ和Name列均为NaN的列通过索引。最后，您可以对列进行重新排序：
# Find proper dates, create new column:
df['Date_New'] = pd.to_datetime(df['Acct'], errors='coerce')
# Fill non-valid dates:
df['Date_New'].ffill(inplace=True)
# Get rid of `NaN` rows:
df = df[~df[['Time','Typ', 'Name']].isnull().all(1)]
# Reorder Columns
df = df[['Date_New', 'Acct', 'Time', 'Typ', 'Name']]
>>> df
Date_New Acct Time Typ Name
1 2018-01-02 52 07:58:34 1.0 John
2 2018-01-02 53 07:58:35 1.0 Jack
3 2018-01-02 54 07:58:35 1.0 Ron
4 2018-01-02 55 07:58:35 1.0 Lux
6 2018-01-03 79 12:39:25 1.0 Mike
8 2018-01-06 371 12:15:47 1.0 Eric
9 2018-01-06 422 17:07:33 1.0 Shawn
11 2018-01-07 492 12:43:54 1.0 George
进一步的解释：
为便于理解，以下是每个步骤之后的结果：
>>> df['Date_New'] = pd.to_datetime(df['Acct'], errors='coerce')
>>> df
Acct Time Typ Name Date_New
0 01/02/2018 NaN NaN NaN 2018-01-02
1 52 07:58:34 1.0 John NaT
2 53 07:58:35 1.0 Jack NaT
3 54 07:58:35 1.0 Ron NaT
4 55 07:58:35 1.0 Lux NaT
5 01/03/2018 NaN NaN NaN 2018-01-03
6 79 12:39:25 1.0 Mike NaT
7 01/06/2018 NaN NaN NaN 2018-01-06
8 371 12:15:47 1.0 Eric NaT
9 422 17:07:33 1.0 Shawn NaT
10 01/07/2018 NaN NaN NaN 2018-01-07
11 492 12:43:54 1.0 George NaT
>>> df['Date_New'].ffill(inplace=True)
>>> df
Acct Time Typ Name Date_New
0 01/02/2018 NaN NaN NaN 2018-01-02
1 52 07:58:34 1.0 John 2018-01-02
2 53 07:58:35 1.0 Jack 2018-01-02
3 54 07:58:35 1.0 Ron 2018-01-02
4 55 07:58:35 1.0 Lux 2018-01-02
5 01/03/2018 NaN NaN NaN 2018-01-03
6 79 12:39:25 1.0 Mike 2018-01-03
7 01/06/2018 NaN NaN NaN 2018-01-06
8 371 12:15:47 1.0 Eric 2018-01-06
9 422 17:07:33 1.0 Shawn 2018-01-06
10 01/07/2018 NaN NaN NaN 2018-01-07
11 492 12:43:54 1.0 George 2018-01-07
>>> df = df[~df[['Time','Typ', 'Name']].isnull().all(1)]
>>> df
Acct Time Typ Name Date_New
1 52 07:58:34 1.0 John 2018-01-02
2 53 07:58:35 1.0 Jack 2018-01-02
3 54 07:58:35 1.0 Ron 2018-01-02
4 55 07:58:35 1.0 Lux 2018-01-02
6 79 12:39:25 1.0 Mike 2018-01-03
8 371 12:15:47 1.0 Eric 2018-01-06
9 422 17:07:33 1.0 Shawn 2018-01-06
11 492 12:43:54 1.0 George 2018-01-07