問題描述

我有一個(gè)包含一些日期時(shí)間(作為字符串)和一些空值作為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']})

我正在嘗試將這些轉(zhuǎn)換為日期時(shí)間:

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'))

但我得到了錯(cuò)誤:

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

所以我試著把這些變成實(shí)際的空值:

So I try to turn these into actual nulls:

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

然后重復(fù):

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

然后我得到錯(cuò)誤:

必須是字符串，不能是浮點(diǎn)數(shù)

must be string, not float

解決這個(gè)問題的最快方法是什么?

What is the quickest way to solve this problem?

推薦答案

只要使用to_datetime 并設(shè)置 errors='coerce' 來處理 duff 數(shù)據(jù):

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

調(diào)用 strptime 的問題是如果字符串或 dtype 不正確會(huì)引發(fā)錯(cuò)誤.

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

如果你這樣做了，那么它會(huì)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]

但是使用內(nèi)置的 to_datetime 而不是調(diào)用 apply 會(huì)更快，這實(shí)際上只是循環(huán)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.

時(shí)間

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

我們?cè)谶@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.

這篇關(guān)于如何使用空值將字符串轉(zhuǎn)換為日期時(shí)間 - python，pandas?的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！