Pandas DataFrame Append Deprecation: Modern Alternatives

With the deprecation of pandas DataFrame.append() method in version 1.4.0, you need modern, efficient alternatives to add rows to your DataFrames. This article covers the best replacements that maintain code cleanliness while improving performance.

Why Append() Was Deprecated

The append() method was deprecated because it's inefficient for repeated operations. Each call creates a new DataFrame by copying all data from both the original and new rows, leading to:

• Poor performance with large datasets
• O(n²) time complexity for repeated appends
• Excessive memory usage

Recommended Alternatives

Using pd.concat() for Single Row Addition

The most direct replacement for append() uses pd.concat() with a single-row DataFrame:

import pandas as pd

# Create initial DataFrame
df = pd.DataFrame(columns=['a', 'b'])

# Append a single row using concat
df = pd.concat([
    df, 
    pd.DataFrame.from_records([{'a': 1, 'b': 2}])
], ignore_index=True)

TIP

Use from_records() with a list containing your dictionary to ensure proper DataFrame creation with the correct structure.

Using loc for Index-Based Assignment

For DataFrames with incremental integer indexes, use loc for efficient assignment:

# Direct assignment to the next available index
df.loc[len(df), ['a', 'b']] = 1, 2

# Or using a dictionary
df.loc[len(df)] = {'a': 1, 'b': 2}

WARNING

This method only works when your index is a standard integer range index. For other index types, use concat() instead.

Batch Processing with List Accumulation

The most efficient approach for multiple appends is to collect data in a list and create the DataFrame once:

# Collect all data in a list first
rows_list = []

# Add dictionaries to the list
rows_list.append({'a': 1, 'b': 2})
rows_list.append({'a': 3, 'b': 4})

# Create DataFrame in one operation
df = pd.DataFrame.from_records(rows_list)

This approach avoids the performance pitfalls of repeated DataFrame operations.

Advanced Techniques

Custom Append Function

Create a reusable function for cleaner code:

def append_dict_to_df(df, dict_to_append):
    """Append a dictionary as a new row to DataFrame"""
    return pd.concat([
        df, 
        pd.DataFrame.from_records([dict_to_append])
    ], ignore_index=True)

# Usage
df = append_dict_to_df(df, {'a': 1, 'b': 2})

Chaining Operations with pipe()

For method chaining patterns:

def append_row(df, data):
    return pd.concat([df, pd.DataFrame.from_records([data])], ignore_index=True)

df = (
    pd.DataFrame(columns=['a', 'b'])
    .pipe(append_row, {'a': 1, 'b': 2})
    .pipe(append_row, {'a': 3, 'b': 4})
)

Handling Different Index Types

For non-integer indexes or specific index values:

# With custom index value
df = pd.concat([
    df, 
    pd.DataFrame({'a': 1, 'b': 2}, index=['custom_index'])
])

# Using Series with named index
new_row = pd.Series([1, 2], index=['a', 'b'], name='row_name')
df = pd.concat([df, new_row.to_frame().T])

Performance Comparison

The batch processing method (collecting data in a list) outperforms all other approaches for multiple row additions:

List Accumulation (Recommended)

rows = []
for i in range(1000):
    rows.append({'a': i, 'b': i*2})
df = pd.DataFrame.from_records(rows)

Single concat() Calls

df = pd.DataFrame(columns=['a', 'b'])
for i in range(1000):
    df = pd.concat([df, pd.DataFrame({'a': [i], 'b': [i*2]})])

loc Assignment

df = pd.DataFrame(columns=['a', 'b'])
for i in range(1000):
    df.loc[i] = [i, i*2]

The list accumulation method is significantly faster because it only creates the DataFrame once, avoiding the overhead of repeated concatenation operations.

Migration Guide

Old Code	New Equivalent
df.append(row_dict)	pd.concat([df, pd.DataFrame([row_dict])])
df.append(row_dict, ignore_index=True)	pd.concat([df, pd.DataFrame([row_dict])], ignore_index=True)
Multiple append() calls	Collect data in list, then create DataFrame

Conclusion

While the deprecation of DataFrame.append() requires code changes, the alternatives provide better performance and maintain code readability:

1. Use pd.concat() with from_records() for single-row additions
2. Use loc assignment for integer-indexed DataFrames
3. Use list accumulation for batch operations (most efficient)
4. Create helper functions for cleaner repetitive operations

By adopting these patterns, you'll write more efficient pandas code that avoids the performance pitfalls of the deprecated append() method.