Consider the following situation:
In [2]: a = pd.Series([1,2,3,4,'.'])
In [3]: a
Out[3]:
0 1
1 2
2 3
3 4
4 .
dtype: object
In [8]: a.astype('float64', raise_on_error = False)
Out[8]:
0 1
1 2
2 3
3 4
4 .
dtype: object
I would have expected an option that allows conversion while turning erroneous values (such as that .) to NaNs. Is there a way to achieve this?
pd.to_numeric with errors='coerce'# Setup
s = pd.Series(['1', '2', '3', '4', '.'])
s
0 1
1 2
2 3
3 4
4 .
dtype: object
pd.to_numeric(s, errors='coerce')
0 1.0
1 2.0
2 3.0
3 4.0
4 NaN
dtype: float64
If you need the NaNs filled in, use Series.fillna.
pd.to_numeric(s, errors='coerce').fillna(0, downcast='infer')
0 1
1 2
2 3
3 4
4 0
dtype: float64
Note, downcast='infer' will attempt to downcast floats to integers where possible. Remove the argument if you don't want that.
From v0.24+, pandas introduces a Nullable Integer type, which allows integers to coexist with NaNs. If you have integers in your column, you can use
pd.__version__ # '0.24.1' pd.to_numeric(s, errors='coerce').astype('Int32') 0 1 1 2 2 3 3 4 4 NaN dtype: Int32There are other options to choose from as well, read the docs for more.
DataFramesIf you need to extend this to DataFrames, you will need to apply it to each row. You can do this using DataFrame.apply.
# Setup.
np.random.seed(0)
df = pd.DataFrame({
'A' : np.random.choice(10, 5),
'C' : np.random.choice(10, 5),
'B' : ['1', '###', '...', 50, '234'],
'D' : ['23', '1', '...', '268', '$$']}
)[list('ABCD')]
df
A B C D
0 5 1 9 23
1 0 ### 3 1
2 3 ... 5 ...
3 3 50 2 268
4 7 234 4 $$
df.dtypes
A int64
B object
C int64
D object
dtype: object
df2 = df.apply(pd.to_numeric, errors='coerce')
df2
A B C D
0 5 1.0 9 23.0
1 0 NaN 3 1.0
2 3 NaN 5 NaN
3 3 50.0 2 268.0
4 7 234.0 4 NaN
df2.dtypes
A int64
B float64
C int64
D float64
dtype: object
You can also do this with DataFrame.transform; although my tests indicate this is marginally slower:
df.transform(pd.to_numeric, errors='coerce')
A B C D
0 5 1.0 9 23.0
1 0 NaN 3 1.0
2 3 NaN 5 NaN
3 3 50.0 2 268.0
4 7 234.0 4 NaN
If you have many columns (numeric; non-numeric), you can make this a little more performant by applying pd.to_numeric on the non-numeric columns only.
df.dtypes.eq(object)
A False
B True
C False
D True
dtype: bool
cols = df.columns[df.dtypes.eq(object)]
# Actually, `cols` can be any list of columns you need to convert.
cols
# Index(['B', 'D'], dtype='object')
df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
# Alternatively,
# for c in cols:
# df[c] = pd.to_numeric(df[c], errors='coerce')
df
A B C D
0 5 1.0 9 23.0
1 0 NaN 3 1.0
2 3 NaN 5 NaN
3 3 50.0 2 268.0
4 7 234.0 4 NaN
Applying pd.to_numeric along the columns (i.e., axis=0, the default) should be slightly faster for long DataFrames.
In [30]: pd.Series([1,2,3,4,'.']).convert_objects(convert_numeric=True)
Out[30]:
0 1
1 2
2 3
3 4
4 NaN
dtype: float64
Source: Stackoverflow.com