如何使用空值将字符串转换为日期时间 - python，pandas?

2022-01-13 00:00:00 python pandas datetime string type-conversion

问题描述

我有一个包含一些日期时间(作为字符串)和一些空值作为nan"的系列:

I have a series with some datetimes (as strings) and some nulls as 'nan':

import pandas as pd, numpy as np, datetime as dt
df = pd.DataFrame({'Date':['2014-10-20 10:44:31', '2014-10-23 09:33:46', 'nan', '2014-10-01 09:38:45']})

我正在尝试将这些转换为日期时间:

I'm trying to convert these to datetime:

df['Date'] = df['Date'].apply(lambda x: dt.datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))

但我得到了错误:

time data 'nan' does not match format '%Y-%m-%d %H:%M:%S'

所以我试着把这些变成实际的空值:

So I try to turn these into actual nulls:

df.ix[df['Date'] == 'nan', 'Date'] = np.NaN

然后重复:

df['Date'] = df['Date'].apply(lambda x: dt.datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))

然后我得到错误:

必须是字符串，不能是浮点数

must be string, not float

解决这个问题的最快方法是什么?

What is the quickest way to solve this problem?

解决方案

只要使用to_datetime 并设置 errors='coerce' 来处理 duff 数据:

Just use to_datetime and set errors='coerce' to handle duff data:

In [321]:

df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
df
Out[321]:
                 Date
0 2014-10-20 10:44:31
1 2014-10-23 09:33:46
2                 NaT
3 2014-10-01 09:38:45

In [322]:

df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4 entries, 0 to 3
Data columns (total 1 columns):
Date    3 non-null datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 64.0 bytes

调用 strptime 的问题是如果字符串或 dtype 不正确会引发错误.

the problem with calling strptime is that it will raise an error if the string, or dtype is incorrect.

如果你这样做了，那么它会起作用:

If you did this then it would work:

In [324]:

def func(x):
    try:
        return dt.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
    except:
        return pd.NaT

df['Date'].apply(func)
Out[324]:
0   2014-10-20 10:44:31
1   2014-10-23 09:33:46
2                   NaT
3   2014-10-01 09:38:45
Name: Date, dtype: datetime64[ns]

但是使用内置的 to_datetime 而不是调用 apply 会更快，这实际上只是循环您的系列.

but it will be faster to use the inbuilt to_datetime rather than call apply which essentially just loops over your series.

时间

In [326]:

%timeit pd.to_datetime(df['Date'], errors='coerce')
%timeit df['Date'].apply(func)
10000 loops, best of 3: 65.8 µs per loop
10000 loops, best of 3: 186 µs per loop

我们在这里看到使用 to_datetime 的速度提高了 3 倍.

We see here that using to_datetime is 3X faster.

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