python pandas 从日期时间中提取年份:df['year'] = df['date'].year 不起作用

问题描述：

我通过 read_csv 导入了一个数据框，但由于某种原因无法从系列 df['date'] 中提取年份或月份，尝试这给出 AttributeError: 'Series' 对象没有属性 'year':

I import a dataframe via read_csv, but for some reason can't extract the year or month from the series df['date'], trying that gives AttributeError: 'Series' object has no attribute 'year':

date    Count
6/30/2010   525
7/30/2010   136
8/31/2010   125
9/30/2010   84
10/29/2010  4469

df = pd.read_csv('sample_data.csv', parse_dates=True)

df['date'] = pd.to_datetime(df['date'])

df['year'] = df['date'].year
df['month'] = df['date'].month

更新:当我在我的 Pandas 0.14.1 版本上尝试使用 df['date'].dt 的解决方案时，我得到AttributeError: 'Series' object has no attribute 'dt'":>

UPDATE: and when I try solutions with df['date'].dt on my pandas version 0.14.1, I get "AttributeError: 'Series' object has no attribute 'dt' ":

df = pd.read_csv('sample_data.csv',parse_dates=True)

df['date'] = pd.to_datetime(df['date'])

df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month

对于这个看似重复的问题，我很抱歉 - 我希望答案会让我觉得自己像个傻瓜...但我在使用 SO 上类似问题的答案时没有任何运气.

Sorry for this question that seems repetitive - I expect the answer will make me feel like a bonehead... but I have not had any luck using answers to the similar questions on SO.

跟进:我似乎无法在我的 Anaconda 环境中将我的 Pandas 0.14.1 更新到更新的版本，下面的每次尝试都会产生一个无效的语法错误.我使用的是 Python 3.4.1 64 位.

FOLLOWUP: I can't seem to update my pandas 0.14.1 to a newer release in my Anaconda environment, each of the attempts below generates an invalid syntax error. I'm using Python 3.4.1 64bit.

conda update pandas

conda install pandas==0.15.2

conda install -f pandas

有什么想法吗?

答

如果您运行的是最新版本的 Pandas，那么您可以使用 datetime 属性 dt 访问日期时间组件:

If you're running a recent-ish version of pandas then you can use the datetime attribute dt to access the datetime components:

In [6]:

df['date'] = pd.to_datetime(df['date'])
df['year'], df['month'] = df['date'].dt.year, df['date'].dt.month
df
Out[6]:
        date  Count  year  month
0 2010-06-30    525  2010      6
1 2010-07-30    136  2010      7
2 2010-08-31    125  2010      8
3 2010-09-30     84  2010      9
4 2010-10-29   4469  2010     10

编辑

看起来您运行的是旧版本的 Pandas，在这种情况下，以下方法可行:

It looks like you're running an older version of pandas in which case the following would work:

In [18]:

df['date'] = pd.to_datetime(df['date'])
df['year'], df['month'] = df['date'].apply(lambda x: x.year), df['date'].apply(lambda x: x.month)
df
Out[18]:
        date  Count  year  month
0 2010-06-30    525  2010      6
1 2010-07-30    136  2010      7
2 2010-08-31    125  2010      8
3 2010-09-30     84  2010      9
4 2010-10-29   4469  2010     10

关于为什么它没有将其解析为 read_csv 中的日期时间，您需要传递列的序数位置 ([0]) 因为当 True 它尝试解析列 [1,2,3] 参见文档

Regarding why it didn't parse this into a datetime in read_csv you need to pass the ordinal position of your column ([0]) because when True it tries to parse columns [1,2,3] see the docs

In [20]:

t="""date   Count
6/30/2010   525
7/30/2010   136
8/31/2010   125
9/30/2010   84
10/29/2010  4469"""
df = pd.read_csv(io.StringIO(t), sep='s+', parse_dates=[0])
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Data columns (total 2 columns):
date     5 non-null datetime64[ns]
Count    5 non-null int64
dtypes: datetime64[ns](1), int64(1)
memory usage: 120.0 bytes

因此，如果您将参数 parse_dates=[0] 传递给 read_csv，则不需要在日期"上调用 to_datetime' 加载后的列.

So if you pass param parse_dates=[0] to read_csv there shouldn't be any need to call to_datetime on the 'date' column after loading.

python pandas 从日期时间中提取年份:df['year'] = df['date'].year 不起作用

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