如何使用空值将字符串转换为日期时间 -百家乐凯发k8

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?

推荐答案

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()

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.