将STRING MONTH中的python pandas中的列转换为INT [英] convert a column in a python pandas from STRING MONTH into INT

问题描述

在Python 2.7.11&中熊猫0.18.1:

In Python 2.7.11 & Pandas 0.18.1:

如果我们有以下csv文件:

If we have the following csv file:

YEAR,MONTH,ID
2011,JAN,1
2011,FEB,1
2011,MAR,1

有没有办法将其作为Pandas数据框读取并将MONTH列转换为这样的字符串?

Is there any way to read it as a Pandas data frame and convert the MONTH column into strings like this?

YEAR,MONTH,ID
2011,1,1
2011,2,1
2011,3,1

某些熊猫函数，例如"dt.strftime('％b')"似乎不起作用.有人可以开导吗?

Some pandas functions such as "dt.strftime('%b')" doesn't seem to work. Could someone enlighten?

推荐答案

我想最简单也是最快的方法之一就是创建一个映射字典和映射，如下所示:

I guess the easiest and one of the fastest method would be to create a mapping dict and map like as follows:

In [2]: df
Out[2]:
   YEAR MONTH  ID
0  2011   JAN   1
1  2011   FEB   1
2  2011   MAR   1

In [3]: d = {'JAN':1, 'FEB':2, 'MAR':3, 'APR':4, }

In [4]: df.MONTH = df.MONTH.map(d)

In [5]: df
Out[5]:
   YEAR  MONTH  ID
0  2011      1   1
1  2011      2   1
2  2011      3   1

如果并非所有MONTH值都大写，则可能要使用df.MONTH = df.MONTH.str.upper().map(d)

you may want to use df.MONTH = df.MONTH.str.upper().map(d) if not all MONTH values are in upper case

另一种更慢但更可靠的方法:

another more slower but more robust method:

In [11]: pd.to_datetime(df.MONTH, format='%b').dt.month
Out[11]:
0    1
1    2
2    3
Name: MONTH, dtype: int64

更新:我们可以自动创建映射(

UPDATE: we can create a mapping automatically (thanks to @Quetzalcoatl)

import calendar

d = dict((v,k) for k,v in enumerate(calendar.month_abbr))

或(仅使用熊猫):

d = dict(zip(range(1,13), pd.date_range('2000-01-01', freq='M', periods=12).strftime('%b')))

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