Filtering Pandas DataFrames: Finding Rows That Don't Contain Specific Values

Understanding the Task:

• You have a DataFrame containing text data in one or more columns.
• You want to filter the DataFrame to keep only rows where the text in a specific column does not include a particular value (substring).

Methods for "Does-Not-Contain" Search:

Pandas doesn't have a built-in "does not contain" operator, but you can achieve this using negation with the isin() method:

1. ~ (Bitwise NOT) with isin():

   • This is the most common approach.
   • Use ~ (tilde) before df[column_name].isin(list_of_values) to invert the selection.
   • Example:

   import pandas as pd
   
   data = {'column_name': ['apple', 'banana', 'cherry', 'grapefruit']}
   df = pd.DataFrame(data)
   
   # Find rows where 'column_name' does not contain 'apple'
   filtered_df = df[~df['column_name'].isin(['apple'])]
   print(filtered_df)
   

   This will output:

     column_name
   1       banana
   2       cherry
   

2. str.contains() with Negation:

   • Use df[column_name].str.contains(pattern) == False to directly filter for non-matches.
   • This might be less efficient for large DataFrames due to string operations.

   filtered_df = df[df['column_name'].str.contains('apple') == False]
   print(filtered_df)
   

   (This will produce the same output as the previous example.)

Choosing the Right Method:

• If performance is a concern, especially for very large DataFrames, ~ with isin() is often preferred. However, both methods achieve the desired result.

Additional Considerations:

• Case Sensitivity: By default, both methods are case-sensitive. If you need case-insensitive matching, use str.lower() or str.upper() on the column and pattern before comparison.
• Regular Expressions: For more complex matching patterns, you can use regular expressions with str.contains().

I hope this explanation clarifies how to search for "does-not-contain" data in pandas DataFrames!

Example 1: Case-Insensitive Matching (~ with isin()):

import pandas as pd

data = {'column_name': ['Apple', 'Banana', 'CHERRY', 'Grapefruit']}
df = pd.DataFrame(data)

# Find rows where 'column_name' does not contain 'apple' (case-insensitive)
pattern = 'apple'  # Case-insensitive pattern
filtered_df = df[~df['column_name'].str.lower().isin([pattern.lower()])].copy()  # Make a copy to avoid SettingWithCopy warning
print(filtered_df)

This code ensures case-insensitive matching by converting both the column values and the pattern to lowercase before comparison using str.lower(). The .copy() method is used to avoid a potential SettingWithCopy warning.

Example 2: Regular Expression with str.contains():

import pandas as pd

data = {'column_name': ['apple pie', 'banana shake', 'cherry cobbler', 'grapefruit juice']}
df = pd.DataFrame(data)

# Find rows where 'column_name' does not contain a word starting with 'app'
pattern = r'^app\b'  # Regular expression for word starting with 'app'
filtered_df = df[~df['column_name'].str.contains(pattern)]
print(filtered_df)

This example demonstrates using a regular expression (r'^app\b') with str.contains() to find rows where the column value doesn't start with the word "app" (whole word match).

These examples illustrate different approaches to handle "does-not-contain" searches in pandas, considering case sensitivity and regular expressions when needed.

Boolean Indexing with np.where:

This method uses NumPy's np.where function to create a boolean mask and then filter the DataFrame:

import pandas as pd
import numpy as np

data = {'column_name': ['apple', 'banana', 'cherry', 'grapefruit']}
df = pd.DataFrame(data)

pattern = 'apple'

# Create a mask for rows where 'column_name' does not contain 'apple'
mask = np.where(~df['column_name'].isin([pattern]))[0]

# Filter the DataFrame using the mask
filtered_df = df.iloc[mask]
print(filtered_df)

This approach can be less efficient than ~ with isin() for larger DataFrames due to the use of NumPy functions.

Custom Function with apply:

For more complex filtering criteria, you can define a custom function and apply it to the column using apply():

import pandas as pd

def does_not_contain(text, pattern):
  return not pattern in text

data = {'column_name': ['apple pie', 'banana shake', 'cherry cobbler', 'grapefruit juice']}
df = pd.DataFrame(data)

pattern = 'apple'

# Apply the custom function to filter rows
filtered_df = df[df['column_name'].apply(does_not_contain, args=(pattern,))]
print(filtered_df)

This method offers flexibility but might be slower for large DataFrames compared to ~ with isin().

Remember to choose the method that best suits your specific needs based on factors like performance, complexity, and readability.