在Python中使用 pandas 分析YYYYMMDD和HH在单独的列中的日期 [英] Parse dates when YYYYMMDD and HH are in separate columns using pandas in Python

问题描述

我有一个与csv文件和解析日期时间有关的简单问题.

I have a simple question related with csv files and parsing datetime.

我有一个csv文件，如下所示:

I have a csv file that look like this:

YYYYMMDD, HH,    X
20110101,  1,   10
20110101,  2,   20
20110101,  3,   30

我想使用pandas(read_csv)读取它，并将其放入由datetime索引的数据帧中.到目前为止，我已经尝试实现以下内容:

I would like to read it using pandas (read_csv) and have it in a dataframe indexed by the datetime. So far I've tried to implement the following:

import pandas as pnd
pnd.read_csv("..\\file.csv",  parse_dates = True, index_col = [0,1])

我得到的结果是:

                         X
YYYYMMDD    HH            
2011-01-01 2012-07-01   10
           2012-07-02   20
           2012-07-03   30

如您所见，将HH转换为其他日期时的parse_dates.

As you see the parse_dates in converting the HH into a different date.

是否存在一种简单有效的方法来将"YYYYMMDD"列与"HH"列正确组合以产生类似的结果? :

Is there a simple and efficient way to combine properly the column "YYYYMMDD" with the column "HH" in order to have something like this? :

                      X
Datetime              
2011-01-01 01:00:00  10
2011-01-01 02:00:00  20
2011-01-01 03:00:00  30

预先感谢您的帮助.

推荐答案

如果将列表传递给index_col，则意味着您要在列表中的列之外创建层次结构索引.

If you pass a list to index_col, it means you want to create a hierarchical index out of the columns in the list.

此外，可以将parse_dates关键字设置为True或列表/字典.如果为True，则它将尝试将各个列解析为日期，否则将合并列以解析单个日期列.

In addition, the parse_dates keyword can be set to either True or a list/dict. If True, then it tries to parse individual columns as dates, otherwise it combines columns to parse a single date column.

总而言之，您要做的是:

In summary, what you want to do is:

from datetime import datetime
import pandas as pd
parse = lambda x: datetime.strptime(x, '%Y%m%d %H')
pd.read_csv("..\\file.csv",  parse_dates = [['YYYYMMDD', 'HH']], 
            index_col = 0, 
            date_parser=parse)

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