pandas read_csv无法将ISO8601识别为datetime dtype [英] Pandas read_csv not recognizing ISO8601 as datetime dtype

问题描述

当前，我正在使用熊猫将第一列作为索引将csv文件读入DataFrame.第一列为ISO 8601格式，因此根据 read_csv ，则应将其识别为日期时间:

Currently I am using pandas to read a csv file into a DataFrame, using the first column as the index. The first column is in ISO 8601 format, so according to the documentation for read_csv, it should be recognized as a datetime:

In [1]: import pandas as pd

In [2]: df = pd.read_csv('data.csv', index_col=0)

In [3]: print df.head()
                        U     V     Z    Ubar    Udir
2014-11-01 00:00:00  0.73 -0.81  0.46  1.0904  317.97
2014-11-01 01:00:00  1.26 -1.50  0.32  1.9590  319.97
2014-11-01 02:00:00  1.50 -1.80  0.13  2.3431  320.19
2014-11-01 03:00:00  1.39 -1.65  0.03  2.1575  319.89
2014-11-01 04:00:00  0.94 -1.08 -0.03  1.4318  318.96

但是，当查询索引dtype时，它返回'object':

However, when querying the index dtype, it returns 'object':

In [4]: print df.index.dtype
object

然后我必须手动将其转换为datetime dtype:

I then have to manually convert it to datetime dtype:

In [5]: df.index = pd.to_datetime(df.index)

In [6]: print df.index.dtype
datetime64[ns]

调用read_csv()时是否有任何方法可以将索引自动设置为datetime dtype?

Is there any way to automatically have the index set to datetime dtype when calling read_csv()?

推荐答案

read_csv documentation describes parse_dates parameter:

parse_dates:布尔值或整数列表或名称列表或列表列表或字典，默认为False
-布尔值.如果为True->尝试解析索引.
-整数或名称列表.例如如果[1，2，3]->尝试将第1、2、3列解析为单独的日期列.
-列表清单.例如如果[[1，3]]->合并列1和3并解析为 一个日期列.
-dict，例如{'foo':[1，3]}->将第1、3列解析为日期，并调用结果"foo"
注意:存在iso8601格式日期的快速路径.

parse_dates : boolean or list of ints or names or list of lists or dict, default False
- boolean. If True -> try parsing the index.
- list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
- list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
- dict, e.g. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’
Note: A fast-path exists for iso8601-formatted dates.

由于要解析索引，因此可以使用:

Since you want to parse index you can use:

 import pandas as pd
 df = pd.read_csv('data.csv', index_col=0, parse_dates=True)

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