Python-Pandas-Dataframe-datetime转换不包含空值单元格

Question

感谢您花时间查看我的问题。

我尝试使用下面的函数转换熊猫数据框中的两个日期列。我使用这个函数，因为“Closed Date”有4221行，所以它不应该在null单元格上崩溃。

最终，更改结果为原始行号的数据框。所以，我不想放松在关闭日期有空值的行。

数据帧概述：

<class 'pandas.core.frame.DataFrame'> 
Int64Index: 4272 entries, 0 to 4271 
Data columns (total 4 columns): 
Created Date 4272 non-null object 
Closed Date  4221 non-null object 
Agency   4272 non-null object 
Borough   4272 non-null object 
dtypes: object(4) 

设计功能：

col='Closed Date' 
df[(df[col].notnull())] = df[(df[col].notnull())].apply(lambda x:datetime.datetime.strptime(x,'%m/%d/%Y %I:%M:%S %p')) 

产生错误：

TypeError         Traceback (most recent call last) 
<ipython-input-155-49014bb3ecb3> in <module>() 
     9 
    10 col='Closed Date' 
---> 11 df[(df[col].notnull())] = df[(df[col].notnull())].apply(lambda  x:datetime.datetime.strptime(x,'%m/%d/%Y %I:%M:%S %p')) 
    12 print(type(df[(df[col].notnull())])) 

/anaconda/lib/python3.6/site-packages/pandas/core/frame.py in  apply(self, func, axis, broadcast, raw, reduce, args, **kwds) 
    4358       f, axis, 
    4359       reduce=reduce, 
-> 4360       ignore_failures=ignore_failures) 
    4361    else: 
    4362     return self._apply_broadcast(f, axis) 

/anaconda/lib/python3.6/site-packages/pandas/core/frame.py in  _apply_standard(self, func, axis, ignore_failures, reduce) 
    4454    try: 
    4455     for i, v in enumerate(series_gen): 
-> 4456      results[i] = func(v) 
    4457      keys.append(v.name) 
    4458    except Exception as e: 

<ipython-input-155-49014bb3ecb3> in <lambda>(x) 
     9 
    10 col='Closed Date' 
---> 11 df[(df[col].notnull())] = df[(df[col].notnull())].apply(lambda  x:datetime.datetime.strptime(x,'%m/%d/%Y %I:%M:%S %p')) 
    12 print(type(df[(df[col].notnull())])) 

TypeError: ('strptime() argument 1 must be str, not Series', 'occurred  at index Created Date') 

Answer 1

我想你只需要to_datetime - 它转换NaN到NaT，使所有的值是列在日期时间：

col='Closed Date' 
df[col] = pd.to_datetime(df[col], format='%m/%d/%Y %I:%M:%S %p') 

样品：

df = pd.DataFrame({'Closed Date':['05/01/2016 05:10:10 AM', 
            '05/01/2016 05:10:10 AM', 
            np.nan]}) 

col='Closed Date' 
df[col] = pd.to_datetime(df[col], format='%m/%d/%Y %I:%M:%S %p') 
print (df) 
      Closed Date 
0 2016-05-01 05:10:10 
1 2016-05-01 05:10:10 
2     NaT 

print (df.dtypes) 
Closed Date datetime64[ns] 
dtype: object