Keyword - ignore_index¶

The ignore_index parameter resets the index of the concatenated result to a new RangeIndex.

Default Behavior¶

By default, original indices are preserved.

1. Preserved Indices¶

import pandas as pd

df1 = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))

df = pd.concat([df1, df2])
print("ignore_index = False (default)")
print(df)

2. Duplicate Indices¶

Notice indices 0 and 1 appear twice.

3. Potential Issues¶

Duplicate indices can cause problems with loc.

ignore_index=True¶

Create a new sequential index.

1. Reset Index¶

dg = pd.concat([df1, df2], ignore_index=True)
print("ignore_index = True")
print(dg)

2. Unique Indices¶

New RangeIndex from 0 to n-1.

3. Clean Result¶

No duplicate indices; easier to work with.

When to Use¶

Guidelines for ignore_index.

1. Use ignore_index=True When¶

# Original index is meaningless (like auto-generated)
# Combining data from multiple files
# Need unique indices
# Index will be reset anyway

2. Keep Original Index When¶

# Index has meaning (dates, IDs)
# Need to trace data back to source
# Using keys parameter instead

3. Common Pattern¶

# Combine CSVs with ignore_index
all_data = pd.concat([
    pd.read_csv('data_2023.csv'),
    pd.read_csv('data_2024.csv')
], ignore_index=True)

axis=1 Behavior¶

ignore_index works on both axes.

1. Reset Column Names¶

df1 = pd.DataFrame([[1, 2]], columns=['A', 'B'])
df2 = pd.DataFrame([[3, 4]], columns=['C', 'D'])

result = pd.concat([df1, df2], axis=1, ignore_index=True)
print(result)

   0  1  2  3
0  1  2  3  4

2. Column Names Lost¶

Columns become 0, 1, 2, 3 instead of A, B, C, D.

3. Usually Not Wanted¶

For axis=1, typically keep column names.

Comparison with reset_index¶

Two ways to achieve clean indices.

1. concat with ignore_index¶

result = pd.concat([df1, df2], ignore_index=True)

2. reset_index After¶

result = pd.concat([df1, df2]).reset_index(drop=True)

3. Equivalent Results¶

Both produce the same output; ignore_index is cleaner.