pandas 如何将列设置为日期索引？
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how set column as date index?
提问by bikuser
My data sets looks like:
我的数据集看起来像：
Date Value
1/1/1988 0.62
1/2/1988 0.64
1/3/1988 0.65
1/4/1988 0.66
1/5/1988 0.67
1/6/1988 0.66
1/7/1988 0.64
1/8/1988 0.66
1/9/1988 0.65
1/10/1988 0.65
1/11/1988 0.64
1/12/1988 0.66
1/13/1988 0.67
1/14/1988 0.66
1/15/1988 0.65
1/16/1988 0.64
1/17/1988 0.62
1/18/1988 0.64
1/19/1988 0.62
1/20/1988 0.62
1/21/1988 0.64
1/22/1988 0.62
1/23/1988 0.60
I used this code to read this data
我用这段代码来读取这些数据
df.set_index(df['Date'], drop=False, append=False, inplace=False, verify_integrity=False).drop('Date', 1)
but the problem is index is not in date format. So the question is how to set this column as date index?
但问题是索引不是日期格式。所以问题是如何将此列设置为日期索引？
回答by EdChum
Your question lacked a proper explanation, but you can do the following:
您的问题缺乏正确的解释，但您可以执行以下操作：
In [75]:
# convert to datetime
df['Date'] = pd.to_datetime(df['Date'])
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23 entries, 0 to 22
Data columns (total 2 columns):
Date 23 non-null datetime64[ns]
Value 23 non-null float64
dtypes: datetime64[ns](1), float64(1)
memory usage: 448.0 bytes
In [76]:
# set the index
df.set_index('Date', inplace=True)
df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 23 entries, 1988-01-01 to 1988-01-23
Data columns (total 1 columns):
Value 23 non-null float64
dtypes: float64(1)
memory usage: 368.0 bytes
So here to_datetimewill convert date strings to datetimedtype, set_indexwith param inplace=Trueis all you need,
所以这里to_datetime将日期字符串转换为datetimedtype，你只需要set_indexparam inplace=True，
