大 pandas :to_numeric用于多列 [英] pandas: to_numeric for multiple columns

问题描述

我正在使用以下 df :

c.sort_values('2005', ascending=False).head(3)
      GeoName ComponentName     IndustryId IndustryClassification Description                                2004 2005  2006  2007  2008  2009 2010 2011 2012 2013 2014
37926 Alabama Real GDP by state 9          213                    Support activities for mining              99   98    117   117   115   87   96   95   103  102  (NA)
37951 Alabama Real GDP by state 34         42                     Wholesale trade                            9898 10613 10952 11034 11075 9722 9765 9703 9600 9884 10199
37932 Alabama Real GDP by state 15         327                    Nonmetallic mineral products manufacturing 980  968   940   1084  861   724  714  701  589  641  (NA)

我想在所有年份中强制使用数字:

I want to force numeric on all of the years:

c['2014'] = pd.to_numeric(c['2014'], errors='coerce')

是否有一种简便的方法来执行此操作，或者我必须将它们全部打出来?

is there an easy way to do this or do I have to type them all out?

推荐答案

更新:之后，您无需转换值，就可以即时读取CSV时:

UPDATE: you don't need to convert your values afterwards, you can do it on-the-fly when reading your CSV:

In [165]: df=pd.read_csv(url, index_col=0, na_values=['(NA)']).fillna(0)

In [166]: df.dtypes
Out[166]:
GeoName                    object
ComponentName              object
IndustryId                  int64
IndustryClassification     object
Description                object
2004                        int64
2005                        int64
2006                        int64
2007                        int64
2008                        int64
2009                        int64
2010                        int64
2011                        int64
2012                        int64
2013                        int64
2014                      float64
dtype: object

如果您需要将多列转换为数字dtype，请使用以下技术:

If you need to convert multiple columns to numeric dtypes - use the following technique:

样本源DF:

In [271]: df
Out[271]:
     id    a  b  c  d  e    f
0  id_3  AAA  6  3  5  8    1
1  id_9    3  7  5  7  3  BBB
2  id_7    4  2  3  5  4    2
3  id_0    7  3  5  7  9    4
4  id_0    2  4  6  4  0    2

In [272]: df.dtypes
Out[272]:
id    object
a     object
b      int64
c      int64
d      int64
e      int64
f     object
dtype: object

将选定的列转换为数字dtype:

Converting selected columns to numeric dtypes:

In [273]: cols = df.columns.drop('id')

In [274]: df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')

In [275]: df
Out[275]:
     id    a  b  c  d  e    f
0  id_3  NaN  6  3  5  8  1.0
1  id_9  3.0  7  5  7  3  NaN
2  id_7  4.0  2  3  5  4  2.0
3  id_0  7.0  3  5  7  9  4.0
4  id_0  2.0  4  6  4  0  2.0

In [276]: df.dtypes
Out[276]:
id     object
a     float64
b       int64
c       int64
d       int64
e       int64
f     float64
dtype: object

如果要选择全部 string(object)列，请使用以下简单技巧:

PS if you want to select all string (object) columns use the following simple trick:

cols = df.columns[df.dtypes.eq('object')]

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