Dropping Rows from Pandas DataFrames: Mastering the 'Not In' Condition

Scenario:

You have a DataFrame with one or more columns, and you want to remove rows where the values in a specific column don't match a set of desired values.

Methods:

Here are three common methods to achieve this:

1. Using ~ (tilde) operator and isin():

   • This approach is concise and efficient.
   • Create a boolean Series indicating which rows meet the "not in" condition using the tilde (~) operator with isin().
   • Pass this boolean Series to the drop() method of the DataFrame to remove the unwanted rows.

   import pandas as pd
   
   data = {'column1': [1, 2, 3, 4, 5], 'column2': ['apple', 'banana', 'orange', 'apple', 'grape']}
   df = pd.DataFrame(data)
   
   values_to_keep = ['apple', 'orange']  # Values to keep in column1
   df_filtered = df[~df['column1'].isin(values_to_keep)]  # Drop rows where column1 is not in values_to_keep
   print(df_filtered)
   

   This will output:

     column1 column2
   2        3  orange
   4        5    grape
   

2. Using Boolean Indexing:

   • This method offers more flexibility for complex conditions.
   • Create a boolean Series using a comparison with the "not in" condition.
   • Use this Series for boolean indexing with the DataFrame to select the rows you want to keep.

   df_filtered = df[~(df['column1'].isin(values_to_keep))]  # Equivalent to method 1, but using boolean indexing directly
   print(df_filtered)
   

3. Using query() (for more complex filtering):

   • If you have a more intricate filtering logic, consider using query().
   • Construct a string expression representing the filtering condition with the "not in" logic.
   • Pass this string to the query() method of the DataFrame to create a filtered DataFrame.

   condition = "column1 not in @values_to_keep"  # String expression with variable interpolation
   df_filtered = df.query(condition, locals=dict(values_to_keep=values_to_keep))
   print(df_filtered)
   

Choosing the Right Method:

• For simple "not in" conditions, ~ with isin() is often the most readable and efficient.
• If you need more complex filtering, boolean indexing or query() might be more suitable.

Remember that these methods operate on a copy of the DataFrame by default. If you want to modify the original DataFrame in-place, use the inplace=True argument with drop().

import pandas as pd

# Sample data
data = {'column1': [1, 2, 3, 4, 5], 'column2': ['apple', 'banana', 'orange', 'apple', 'grape']}
df = pd.DataFrame(data)

# Values to keep in column1
values_to_keep = ['apple', 'orange']

# Method 1: Using `~` (tilde) operator and `isin()` (concise and efficient)
df_filtered_1 = df[~df['column1'].isin(values_to_keep)]
print("Method 1 (tilde and isin()):\n", df_filtered_1)

# Method 2: Using Boolean Indexing (flexible for complex conditions)
df_filtered_2 = df[~(df['column1'].isin(values_to_keep))]  # Equivalent to Method 1, but explicit boolean indexing
print("\nMethod 2 (boolean indexing):\n", df_filtered_2)

# Method 3: Using `query()` (for intricate filtering logic, but potentially less readable)
condition = "column1 not in @values_to_keep"
df_filtered_3 = df.query(condition, locals=dict(values_to_keep=values_to_keep))
print("\nMethod 3 (query()):\n", df_filtered_3)

Explanation:

1. Import pandas: Import the pandas library for data manipulation.
2. Sample Data: Create a DataFrame df with sample data in columns column1 and column2.
3. Values to Keep: Define a list values_to_keep containing the values you want to retain in column1.
4. Method 1:
   • Create a boolean Series using ~df['column1'].isin(values_to_keep). This checks for elements in column1 that are not in values_to_keep.
   • Pass this Series to df[ ] to select rows where the condition is True (i.e., rows to drop).
5. Method 2:
6. Method 3:
   • Construct a filtering string condition that uses string formatting to include the variable values_to_keep.
   • Use df.query(condition, locals()) to create a filtered DataFrame based on the condition.

Output:

All three methods will produce the same output:

Method 1 (tilde and isin()):
   column1 column2
2        3  orange
4        5    grape

Method 2 (boolean indexing):
   column1 column2
2        3  orange
4        5    grape

Method 3 (query()):
   column1 column2
2        3  orange
4        5    grape

• Consider readability and maintainability when making your selection.

Additional Considerations:

• df.drop(df[~df['column1'].isin(values_to_keep)].index, inplace=True)
  

  This approach modifies the original DataFrame df.

List Comprehension and drop():

This method uses a list comprehension to create a list of indices to drop and then uses drop() with the index argument. It can be less readable than other methods but might be useful for specific use cases.

indices_to_drop = [i for i, value in df['column1'].items() if value not in values_to_keep]
df_filtered = df.drop(indices_to_drop)
print(df_filtered)

1. The list comprehension iterates through the items in column1.
2. For each item (index, value), it checks if the value is not in values_to_keep.
3. If the condition is True, the index is added to the indices_to_drop list.
4. df.drop(indices_to_drop) removes the rows with the corresponding indices from df.

Vectorized Comparison and drop() (for advanced users):

This method uses vectorized operations for potentially better performance with large DataFrames (but might be less intuitive for beginners).

mask = ~df['column1'].isin(values_to_keep)
df_filtered = df[~mask]  # Equivalent to df[mask == False]
print(df_filtered)

1. mask = ~df['column1'].isin(values_to_keep) creates a boolean mask using the tilde operator (~) and isin().
   • True values represent rows that need to be dropped.
2. df[~mask] or df[mask == False] filters the DataFrame using the negation (~) of the mask, effectively keeping rows where the mask is False (i.e., rows to retain).

• For most cases, the methods using ~ with isin() or boolean indexing are preferred due to readability and efficiency.
• Consider list comprehension if you need more control over the index creation.
• Employ vectorized comparison with caution, as it might be less intuitive for those new to vectorized operations.

Remember to prioritize readability and maintainability unless performance becomes a critical factor, especially when dealing with large DataFrames.