python pandas从日期时间提取年份:df ['year'] = df ['date'].year不起作用 [英] python pandas extract year from datetime: df['year'] = df['date'].year is not working
问题描述
我通过 read_csv
导入了一个数据框,但是由于某种原因,无法从 df ['date']
系列中提取年份或月份,尝试给出 AttributeError:系列"对象没有属性年份"
:
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:当我在熊猫版本0.14.1上尝试使用 df ['date'].dt
解决方案时,出现"AttributeError:'Series'对象没有属性'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
很抱歉,这个问题似乎是重复的-我希望答案会让我觉得自己像骨头一样……但是我对使用类似的类似问题的答案并没有任何运气.
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.
关注度:在Anaconda环境中,我似乎无法将熊猫0.14.1更新为较新版本,下面的每种尝试都会生成无效的语法错误.我正在使用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?
推荐答案
如果您运行的是最新版本的熊猫,则可以使用datetime属性
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
您似乎正在运行较早版本的熊猫,在这种情况下,以下方法将起作用:
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
关于为什么没有将其解析为 read_csv
中的日期时间,您需要传递列的顺序位置( [0]
),因为当True
会尝试解析列 [1,2,3]
,请参见
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
,则无需在日期上调用 to_datetime
"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.
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