[英]How to split a date column into separate day , month ,year column in pandas
I have a dataset df
:我有一个数据集df
:
Dewptm Fog Humidity Pressurem Tempm Wspdm Rainfall
datetime_utc
1996-11-01 11.666667 0.0 52.916667 -2659.666667 22.333333 2.466667 0
1996-11-02 10.458333 0.0 48.625000 1009.833333 22.916667 8.028571 0
1996-11-03 12.041667 0.0 55.958333 1010.500000 21.791667 4.804545 0
1996-11-04 10.222222 0.0 48.055556 1011.333333 22.722222 1.964706 0
...
Here is df.columns
:这是df.columns
:
Index(['Dewptm', 'Fog', 'Humidity', 'Pressurem', 'Rain', 'Tempm', 'Wspdm',
'Rainfall'],
dtype='object')
How could I split datetime_utc
column into the year, month and day column?如何将datetime_utc
列拆分为年、月和日列?
I tried:我试过:
df["day"] = df['datetime_utc'].map(lambda x: x.day)
df["month"] = df['datetime_utc'].map(lambda x: x.month)
df["year"] = df['datetime_utc'].map(lambda x: x.year)
Error:错误:
KeyError: 'datetime_utc' KeyError: 'datetime_utc'
Also还
pd.concat([df.drop('datetime_utc', axis = 1),
(df.datetime_utc.str.split("-).str[:3].apply(pd.Series)
.rename(columns={0:'year', 1:'month', 2:'day'}))], axis = 1)
I am getting error:我收到错误:
KeyError: "['datetime_utc'] not found in axis" The problem I am facing is the column
datetime_utc
is the default index column in my dataset, Please suggest me an approach. KeyError: "['datetime_utc'] not found in axis" 我面临的问题是列datetime_utc
是我数据集中的默认索引列,请建议我一种方法。
The problem is that datetime_utc
is in your index instead a column, so you have to access your index to be able to make your new columns:问题是datetime_utc
在您的索引中而不是列中,因此您必须访问索引才能创建新列:
df['day'] = df.index.day
df['month'] = df.index.month
df['year'] = df.index.year
print(df)
Dewptm Fog Humidity Pressurem Tempm Wspdm \
datetime_utc
1996-11-01 11.666667 0.0 52.916667 -2659.666667 22.333333 2.466667
1996-11-02 10.458333 0.0 48.625000 1009.833333 22.916667 8.028571
1996-11-03 12.041667 0.0 55.958333 1010.500000 21.791667 4.804545
1996-11-04 10.222222 0.0 48.055556 1011.333333 22.722222 1.964706
Rainfall day month year
datetime_utc
1996-11-01 0 1 11 1996
1996-11-02 0 2 11 1996
1996-11-03 0 3 11 1996
1996-11-04 0 4 11 1996
If you want datetime_utc
as a column you have to reset your index and then you can access the datetime methods with dt.month
, dt.year
and dt.day
like following:如果您希望datetime_utc
作为列,您必须重置索引,然后您可以使用dt.month
、 dt.year
和dt.day
访问 datetime 方法,如下所示:
# Reset our index so datetime_utc becomes a column
df.reset_index(inplace=True)
# Create new columns
df['day'] = df['datetime_utc'].dt.day
df['month'] = df['datetime_utc'].dt.month
df['year'] = df['datetime_utc'].dt.year
print(df)
datetime_utc Dewptm Fog Humidity Pressurem Tempm Wspdm \
0 1996-11-01 11.666667 0.0 52.916667 -2659.666667 22.333333 2.466667
1 1996-11-02 10.458333 0.0 48.625000 1009.833333 22.916667 8.028571
2 1996-11-03 12.041667 0.0 55.958333 1010.500000 21.791667 4.804545
3 1996-11-04 10.222222 0.0 48.055556 1011.333333 22.722222 1.964706
Rainfall day month year
0 0 1 11 1996
1 0 2 11 1996
2 0 3 11 1996
3 0 4 11 1996
Note if your index is not in datetime
type yet, use the following before you try to extract year, month and day:请注意,如果您的索引还不是datetime
时间类型,请在尝试提取年、月和日之前使用以下内容:
df.index = pd.to_datetime(df.index)
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