Python | pandas.to_numeric method

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

pandas.to_numeric() is one of the general functions in Pandas which is used to convert argument to a numeric type.

Syntax: pandas.to_numeric(arg, errors=’raise’, downcast=None)

Parameters:
arg : list, tuple, 1-d array, or Series
errors : {‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’
-> If ‘raise’, then invalid parsing will raise an exception
-> If ‘coerce’, then invalid parsing will be set as NaN
-> If ‘ignore’, then invalid parsing will return the input
downcast : [default None] If not None, and if the data has been successfully cast to a numerical dtype downcast that resulting data to the smallest numerical dtype possible according to the following rules:
-> ‘integer’ or ‘signed’: smallest signed int dtype (min.: np.int8)
-> ‘unsigned’: smallest unsigned int dtype (min.: np.uint8)
-> ‘float’: smallest float dtype (min.: np.float32)

Returns: numeric if parsing succeeded. Note that return type depends on input. Series if Series, otherwise ndarray.

Code #1:

Observe this dataset first. We’ll use ‘Numbers’ column of this data in order to make Series and then do the operation.

Calling Series constructor on Number column and then selecting first 10 rows.

Output:

Using pd.to_numeric() method. Observe that by using downcast=’signed’, all the values will be casted to integer.

Output:

Code #2: Using errors=’ignore’. It will ignore all non-numeric values.

Output:

 
Code #3: Using errors=’coerce’. It will replace all non-numeric values with NaN.

Output: