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. Python3 1==

# importing pandas module 
import pandas as pd 
  
# making data frame 
df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv") 

df.head(10)

Calling Series constructor on Number column and then selecting first 10 rows. Python3 1==

# importing pandas module 
import pandas as pd 
  
# making data frame 
df = pd.read_csv("nba.csv") 


# get first ten 'numbers'
ser = pd.Series(df['Number']).head(10)
ser

Output: Using pd.to_numeric() method. Observe that by using downcast='signed', all the values will be casted to integer. Python3 1==

pd.to_numeric(ser, downcast ='signed')

Output:   Code #2: Using errors='ignore'. It will ignore all non-numeric values. Python3 1==

# importing pandas module 
import pandas as pd 
  

# get first ten 'numbers'
ser = pd.Series(['Geeks', 11, 22.7, 33])

pd.to_numeric(ser, errors ='ignore')

Output:   Code #3: Using errors='coerce'. It will replace all non-numeric values with NaN. Python3 1==

# importing pandas module 
import pandas as pd 
  

# get first ten 'numbers'
ser = pd.Series(['Geeks', 11, 22.7, 33])

pd.to_numeric(ser, errors ='coerce')

Output: