Keyword - axis¶

The axis parameter determines whether to concatenate along rows (vertically) or columns (horizontally).

axis=0 Vertical¶

Stack DataFrames on top of each other.

1. Default Behavior¶

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("axis = 0 (default): append top-down using index")
print(df)

2. Row Count Increases¶

print(len(df))  # 4 (2 + 2)

3. Index Concatenation¶

Row indices from both DataFrames are preserved.

axis=1 Horizontal¶

Stack DataFrames side by side.

1. Horizontal Concatenation¶

dg = pd.concat([df1, df2], axis=1)
print("axis = 1: append left-right using columns")
print(dg)

   A  B  A  B
0  1  2  5  6
1  3  4  7  8

2. Column Count Increases¶

print(len(dg.columns))  # 4 (2 + 2)

3. Index Alignment¶

Rows are aligned by index; unmatched indices get NaN.

Index Alignment Behavior¶

How indices are handled differs by axis.

1. Different Row Indices¶

df1 = pd.DataFrame({'A': [1, 2]}, index=[0, 1])
df2 = pd.DataFrame({'B': [3, 4]}, index=[1, 2])

# axis=1 aligns on index
result = pd.concat([df1, df2], axis=1)
print(result)

     A    B
0  1.0  NaN
1  2.0  3.0
2  NaN  4.0

2. Different Columns¶

df1 = pd.DataFrame({'A': [1], 'B': [2]})
df2 = pd.DataFrame({'B': [3], 'C': [4]})

# axis=0 creates union of columns
result = pd.concat([df1, df2])
print(result)

     A  B    C
0  1.0  2  NaN
0  NaN  3  4.0

3. Use join Parameter¶

pd.concat([df1, df2], axis=0, join='inner')  # Only common columns
pd.concat([df1, df2], axis=1, join='inner')  # Only common indices

Practical Examples¶

Common concatenation patterns.

1. Stack Multiple Files¶

# Load and stack CSV files
dfs = [pd.read_csv(f'data_{year}.csv') for year in range(2020, 2024)]
combined = pd.concat(dfs, axis=0, ignore_index=True)

2. Add Calculated Columns¶

original = pd.DataFrame({'A': [1, 2, 3]})
calculated = pd.DataFrame({'B': [10, 20, 30]})

result = pd.concat([original, calculated], axis=1)

3. Build Wide DataFrame¶

# Combine time series side by side
prices = pd.concat([aapl, msft, googl], axis=1)
prices.columns = ['AAPL', 'MSFT', 'GOOGL']