5 Best Ways to Remove Rows with Numbers in Python

💡 Problem Formulation: When handling data in Python, sometimes it’s necessary to remove rows that contain numbers from a dataset. Suppose you have a dataset where each row represents textual data, but some rows accidentally contain numerical values. The goal is to filter out these rows to maintain consistent data quality. For example, given a list of strings, you would like to retain only those without numerical characters.

Method 1: Using List Comprehensions with isalpha()

List comprehensions offer a concise way to create lists. Combined with the string method isalpha(), which checks if all characters in the string are alphabetic, we can quickly filter out any rows containing numbers.

Here’s an example:

data = ["apple", "banana3", "cherry", "date1", "elderberry"]
filtered_data = [row for row in data if row.isalpha()]

print(filtered_data)

Output:

['apple', 'cherry', 'elderberry']

The list comprehension iterates over each item in the original list, checking if they contain only alphabetic characters. Numeric rows are filtered out, resulting in a new list with rows that are purely textual.

Method 2: Using Regular Expressions with re.sub()

Regular expressions are powerful for pattern matching. In this method, we use Python’s re module with the function re.sub() to replace any digits in the string rows with an empty string, thus removing them.

Here’s an example:

import re

data = ["apple", "banana3", "42", "date1", "elderberry"]
filtered_data = [row for row in data if not re.search(r'\d', row)]

print(filtered_data)

Output:

['apple', 'elderberry']

The regular expression r'\d' matches any digit in each row. The list comprehension then filters out any row where a digit is found.

Method 3: Using pandas DataFrame

For larger datasets, pandas provides efficient data manipulation capabilities. One can use the DataFrame’s apply() method along with a lambda function to remove rows containing numbers.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
    'fruits': ["apple", "banana3", "42", "date1", "elderberry"]
})
filtered_df = df[~df['fruits'].str.contains(r'\d')]

print(filtered_df)

Output:

       fruits
0      apple
4  elderberry

The str.contains() method checks for the presence of digits in each row of the ‘fruits’ column. The ‘~’ operator inverts the boolean mask, filtering out rows with numbers.

Method 4: Using filter() Function and Lambda

The built-in filter() function allows for elegant filtering of iterable sequences. Combined with a lambda function that utilizes isalpha(), it can efficiently exclude rows with numbers.

Here’s an example:

data = ["apple", "banana3", "cherry", "date1", "elderberry"]
filtered_data = list(filter(lambda row: row.isalpha(), data))

print(filtered_data)

Output:

['apple', 'cherry', 'elderberry']

The filter() function applies the lambda function to each element in the data list. Only elements passing the lambda criteria (having only alphabetic chars) are kept in the `filtered_data` list.

Bonus One-Liner Method 5: Using List Comprehensions with isdigit() Negation

This one-liner method employs a list comprehension that negates the isdigit() method. It’s a quick way to exclude any row where any character is a digit.

Here’s an example:

data = ["apple1", "banana", "cherry3", "4date", "elderberry"]
filtered_data = [row for row in data if not any(char.isdigit() for char in row)]

print(filtered_data)

Output:

['banana', 'elderberry']

This code snippet uses a list comprehension combined with the any() function and isdigit() to create a list that doesn’t include rows with any numerical digits.

Summary/Discussion

• Method 1: List comprehensions with isalpha(). Strengths: Simple and concise. Weaknesses: Might not work for strings with whitespace or punctuation.
• Method 2: Regular expressions with re.sub(). Strengths: Highly customizable for complex patterns. Weaknesses: Can be slower for larger datasets and somewhat less readable.
• Method 3: Using pandas DataFrame. Strengths: Ideal for structured data and large datasets. Weaknesses: Additional library dependency and overhead for small datasets.
• Method 4: Using filter() Function and Lambda. Strengths: Very readable and functional programming approach. Weaknesses: Can be less intuitive for users not familiar with lambda functions.
• Bonus Method 5: List comprehension with isdigit() negation. Strengths: Quick one-liner, very elegant. Weaknesses: Requires understanding of the any() function and generator expressions.