pandas如何把时间转成index_pandas将“字符类型的日期列”转化成“时间戳索引（DatetimeIndex）”...

假设目前已经引入了 pandas&＃xff0c;同时也拥有 pandas 的 DataFrame 类型数据。import pandas as pd

数据集如下df.head(3)date open close high low volume code

0 2006-12-18 3.905 3.886 3.943 3.867 171180.67 600001

1 2006-12-19 3.886 3.924 3.981 3.867 276799.39 600001

2 2006-12-20 3.934 3.934 3.962 3.809 265653.85 600001

查看每一列的类型df.info()

从结果的第四排可以看见 date 这一列类型是"object"&＃xff0c;即字符类型。

RangeIndex: 640 entries, 0 to 639

Data columns (total 7 columns):

date 640 non-null object

open 640 non-null float64

close 640 non-null float64

high 640 non-null float64

low 640 non-null float64

volume 640 non-null float64

code 640 non-null object

dtypes: float64(5), object(2)

memory usage: 35.1&＃43; KB

现在的目标是&＃xff1a;把 date 这一列用作索引

把 date 用作索引时&＃xff0c;类型需要是 DatetimeIndex。

方法1&＃xff1a; .to_datetime 和 .set_index

首先&＃xff0c;利用 pandas 的to_datetime 方法&＃xff0c;把 "date" 列的字符类型数据解析成 datetime 对象。

然后&＃xff0c;把 "date" 列用作索引。df[&＃39;date&＃39;] &＃61; pd.to_datetime(df[&＃39;date&＃39;])

df.set_index("date", inplace&＃61;True)

结果&＃xff1a;df.head(3)open close high low volume code

date

2006-12-18 3.905 3.886 3.943 3.867 171180.67 600001

2006-12-19 3.886 3.924 3.981 3.867 276799.39 600001

2006-12-20 3.934 3.934 3.962 3.809 265653.85 600001

查看索引是否成为 DatetimeIndex 类型&＃xff0c;可以看见确实已经成功转化类型。df.axes[DatetimeIndex([&＃39;2006-12-18&＃39;, &＃39;2006-12-19&＃39;, &＃39;2006-12-20&＃39;, &＃39;2006-12-21&＃39;,

&＃39;2006-12-22&＃39;, &＃39;2006-12-25&＃39;, &＃39;2006-12-26&＃39;, &＃39;2006-12-27&＃39;,

&＃39;2006-12-28&＃39;, &＃39;2006-12-29&＃39;,

...

&＃39;2009-12-02&＃39;, &＃39;2009-12-03&＃39;, &＃39;2009-12-04&＃39;, &＃39;2009-12-07&＃39;,

&＃39;2009-12-08&＃39;, &＃39;2009-12-09&＃39;, &＃39;2009-12-10&＃39;, &＃39;2009-12-11&＃39;,

&＃39;2009-12-14&＃39;, &＃39;2009-12-15&＃39;],

dtype&＃61;&＃39;datetime64[ns]&＃39;, name&＃61;&＃39;date&＃39;, length&＃61;640, freq&＃61;None),

Index([&＃39;open&＃39;, &＃39;close&＃39;, &＃39;high&＃39;, &＃39;low&＃39;, &＃39;volume&＃39;, &＃39;code&＃39;], dtype&＃61;&＃39;object&＃39;)]

方法2&＃xff1a; .DatetimeIndex

首先是原始数据。df2.head(3)date open close high low volume code

0 2003-08-01 4.997 4.949 5.016 4.949 20709.15 600002

1 2003-08-04 4.949 5.045 5.054 4.949 23923.35 600002

2 2003-08-05 5.054 5.093 5.131 5.006 35224.00 600002

先把 "date" 列用作索引&＃xff0c;然后使用 DatetimeIndex 将字符类型转化成 DateIndex。df2.set_index("date", inplace&＃61;True)

这个时候索引还是 object 类型&＃xff0c;就是字符串类型。df2.axes[Index([&＃39;2003-08-01&＃39;, &＃39;2003-08-04&＃39;, &＃39;2003-08-05&＃39;, &＃39;2003-08-06&＃39;, &＃39;2003-08-07&＃39;,

&＃39;2003-08-08&＃39;, &＃39;2003-08-11&＃39;, &＃39;2003-08-12&＃39;, &＃39;2003-08-13&＃39;, &＃39;2003-08-14&＃39;,

...

&＃39;2006-03-24&＃39;, &＃39;2006-03-27&＃39;, &＃39;2006-03-28&＃39;, &＃39;2006-03-29&＃39;, &＃39;2006-03-30&＃39;,

&＃39;2006-03-31&＃39;, &＃39;2006-04-03&＃39;, &＃39;2006-04-04&＃39;, &＃39;2006-04-05&＃39;, &＃39;2006-04-06&＃39;],

dtype&＃61;&＃39;object&＃39;, name&＃61;&＃39;date&＃39;, length&＃61;640),

Index([&＃39;open&＃39;, &＃39;close&＃39;, &＃39;high&＃39;, &＃39;low&＃39;, &＃39;volume&＃39;, &＃39;code&＃39;], dtype&＃61;&＃39;object&＃39;)]

将其转化成 DateIndex 类型。df2.index &＃61; pd.DatetimeIndex(df.index)

再次查看结果df2.axes

转化成功[DatetimeIndex([&＃39;2006-12-18&＃39;, &＃39;2006-12-19&＃39;, &＃39;2006-12-20&＃39;, &＃39;2006-12-21&＃39;,

&＃39;2006-12-22&＃39;, &＃39;2006-12-25&＃39;, &＃39;2006-12-26&＃39;, &＃39;2006-12-27&＃39;,

&＃39;2006-12-28&＃39;, &＃39;2006-12-29&＃39;,

...

&＃39;2009-12-02&＃39;, &＃39;2009-12-03&＃39;, &＃39;2009-12-04&＃39;, &＃39;2009-12-07&＃39;,

&＃39;2009-12-08&＃39;, &＃39;2009-12-09&＃39;, &＃39;2009-12-10&＃39;, &＃39;2009-12-11&＃39;,

&＃39;2009-12-14&＃39;, &＃39;2009-12-15&＃39;],

dtype&＃61;&＃39;datetime64[ns]&＃39;, name&＃61;&＃39;date&＃39;, length&＃61;640, freq&＃61;None),

Index([&＃39;open&＃39;, &＃39;close&＃39;, &＃39;high&＃39;, &＃39;low&＃39;, &＃39;volume&＃39;, &＃39;code&＃39;], dtype&＃61;&＃39;object&＃39;)]

结论&＃xff1a;.to_datetime仅转换格式&＃xff0c;.DatetimeIndex还能设置为索引

两者在转化格式的功能上效果一样&＃xff0c;都可以把字符串对象转换成 datetime 对象。

pd.DatetimeIndex 是把某一列进行转换&＃xff0c;同时把该列的数据设置为索引 index。

比如df2.index &＃61; pd.DatetimeIndex(df2["date"])

得到一个以 date 作为索引的结果。

.DatetimeIndex 的问题是原来的 date 列数据仍然存在&＃xff0c;形成了重复。date open close high low volume code

date

2003-08-01 2003-08-01 4.997 4.949 5.016 4.949 20709.15 600002

2003-08-04 2003-08-04 4.949 5.045 5.054 4.949 23923.35 600002

2003-08-05 2003-08-05 5.054 5.093 5.131 5.006 35224.00 600002

最终还需要把 date 这一列删掉。del df2["date"]

才能得到正常数据open close high low volume code

date

2003-08-01 4.997 4.949 5.016 4.949 20709.15 600002

2003-08-04 4.949 5.045 5.054 4.949 23923.35 600002

2003-08-05 5.054 5.093 5.131 5.006 35224.00 600002