Drop non-numeric columns from a pandas dataframe

Given a pandas dataframe, we have to drop non-numeric columns. By Pranit Sharma Last updated : September 29, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Problem statement

Suppose we have a DataFrame df with multiple columns A, B, and C. Suppose, the data type of column A is an object, the data type of B is a number (int) and the data type of column C is a list.

Dropping non-numeric columns

We need to filter out that column that has a data type int. For this purpose, we will use df.get_numeric_data() method.

Let us understand with the help of an example,

Python program to drop non-numeric columns from a pandas dataframe

# Importing pandas package
import pandas as pd

# Importing methods from sklearn
from sklearn.preprocessing import MinMaxScaler

# Creating a dictionary
d = {
    'A':['Madison','California','Boston','Las Vegas'],

# Creating DataFrame
df = pd.DataFrame(d)

# Display the DataFrame
print("Original DataFrame:\n",df,"\n")

# Finding that column that has only numbers
result = df._get_numeric_data()

# Display result


The output of the above program is:

Example: Drop non-numeric columns from a pandas dataframe

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