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This tutorial explains how to pandas.to_numeric()convert string values ​​of a Pandas DataFrame into numeric type using the method.

import pandas as pd

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708, 103, 343, 565],
        "Name": ["Watch", "Camera", "Phone", "Shoes", "Laptop", "Bed"],
        "Cost": ["300", "400", "350", "100", "1000", "400"],
    }
)

print(items_df)

Output:

    Id    Name  Cost
0  302   Watch   300
1  504  Camera   400
2  708   Phone   350
3  103   Shoes   100
4  343  Laptop  1000
5  565     Bed   400

We will use the above example to demonstrate how to convert the values ​​of a DataFrame to numeric type.

pandas.to_numeric()method

grammar

pandas.to_numeric(arg, errors="raise", downcast=None)

It argconverts the parameter passed as to a numeric type. By default, argit will be converted to int64or float64. We can set downcastthe value of the parameter to argconvert to other data types.

Use pandas.to_numeric()the method to convert string values ​​of Pandas DataFrame to numeric type

import pandas as pd

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708, 103, 343, 565],
        "Name": ["Watch", "Camera", "Phone", "Shoes", "Laptop", "Bed"],
        "Cost": ["300", "400", "350", "100", "1000", "400"],
    }
)

print("The items DataFrame is:")
print(items_df, "\n")

print("Datatype of Cost column before type conversion:")
print(items_df["Cost"].dtypes, "\n")

items_df["Cost"] = pd.to_numeric(items_df["Cost"])
print("Datatype of Cost column after type conversion:")
print(items_df["Cost"].dtypes)

Output:

The items DataFrame is:
    Id    Name  Cost
0  302   Watch   300
1  504  Camera   400
2  708   Phone   350
3  103   Shoes   100
4  343  Laptop  1000
5  565     Bed   400 

Datatype of Cost column before type conversion:
object 

Datatype of Cost column after type conversion:
int64

It converts the data type of items_dfthe column in from to .Costobjectint64

Convert string values ​​in Pandas DataFrame to numeric type with other characters

If we want to convert a column into numeric type which has some character values ​​in it, we get an error saying ValueError: Unable to parse string. In this case, we can remove all the non-numeric characters and then do the type conversion.

import pandas as pd

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708, 103, 343, 565],
        "Name": ["Watch", "Camera", "Phone", "Shoes", "Laptop", "Bed"],
        "Cost": ["$300", "$400", "$350", "$100", "$1000", "$400"],
    }
)

print("The items DataFrame is:")
print(items_df, "\n")

print("Datatype of Cost column before type conversion:")
print(items_df["Cost"].dtypes, "\n")

items_df["Cost"] = pd.to_numeric(items_df["Cost"].str.replace("$", ""))
print("Datatype of Cost column after type conversion:")
print(items_df["Cost"].dtypes, "\n")

print("DataFrame after Type Conversion:")
print(items_df)

Output:

The items DataFrame is:
    Id    Name   Cost
0  302   Watch   $300
1  504  Camera   $400
2  708   Phone   $350
3  103   Shoes   $100
4  343  Laptop  $1000
5  565     Bed   $400 

Datatype of Cost column before type conversion:
object 

Datatype of Cost column after type conversion:
int64 

DataFrame after Type Conversion:
    Id    Name  Cost
0  302   Watch   300
1  504  Camera   400
2  708   Phone   350
3  103   Shoes   100
4  343  Laptop  1000
5  565     Bed   400

It removes the characters Costthat are attached to the values ​​of the column $and then pandas.to_numeric()converts the values ​​to numeric type using the method.