python - pandas: to_numeric for multiple columns

Question

I'm working with the following 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)

I want to force numeric on all of the years:

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

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

Answer 1

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

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

Sample source 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

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

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

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