pandas的to_datetime时间转换使用及学习心得

0 2007-03-02
1 2007-03-22
2 2007-04-06
3 2007-04-14
4 2007-04-15
Name: date_parsed, dtype: datetime64[ns]

data = pd.read_csv('path') #这里我们得到data数据
data['date'].heade() #查看一下日期列的样子

0 01/02/1965
1 01/04/1965
2 01/05/1965
3 01/08/1965
4 01/09/1965
Name: Date, dtype: object

data['date_parsed'] = pd.to_datetime(data['date'],format="%m/%d/%y")

dara['date_parsed'].head()  #查看一下结果

0  1965-01-02
1  1965-01-04
2  1965-01-05
3  1965-01-08
4  1965-01-09
 
Name: data_parsed, dtype: datetime64[ns]

/opt/conda/lib/python3.6/site-packages/pandas/core/tools/datetimes.py in _convert_listlike(arg, box, format, name, tz)
  271           try:
  272             result = array_strptime(arg, format, exact=exact,
--> 273                         errors=errors)
  274           except tslib.OutOfBoundsDatetime:
  275             if errors == 'raise':
 
pandas/_libs/tslibs/strptime.pyx in pandas._libs.tslibs.strptime.array_strptime()
 
ValueError: time data '1975-02-23T02:58:41.000Z' does not match format '%m/%d/%Y' (match)

data['over_long'] = data['Date'].apply(len)  #添加一列记录没行时间的长度
data.loc[data['over_long'] > 10]  #输出大于正常数据的行  这里会发现缺失有那么几行在作怪！！！

normal_dates = data.loc[data['over_long'] < 11]  #筛选出正常数据
normal_dates = normal_dates.copy()    #拷贝
normal_dates['data_parsed'] = pd.to_datetime(normal_dates['Date'],format='%m/%d/%Y')  #再次转换时间，发现没有报错了 哈哈
normal_dates['data_parsed'].head(10)  #输出查看没问题的

data['date_parsed'] = pd.to_datetime(data['Date'], format = "%m/%d/%Y", errors = 'coerce')

data['date_parsed'] = pd.to_datetime(data['Date'],infer_datetime_format=True)