---
title: "dataframe-concat (en)"
description: pandas concat() sticks multiple DataFrames together — 7 examples.
url: "https://aaronchan.com/kb/dataframe-concat/"
updated: "2026-09-15"
category: "Code-Python (en)"
tags:
  - python
---

# dataframe-concat (en)

pandas `concat()` sticks multiple DataFrames together. These 7 examples cover the common patterns: vertical vs. horizontal stacking, column alignment, duplicate columns, and multi-level keys.

## Example 1: Basic concat — stack two DataFrames vertically (axis=0, the default)

 Copy

```
import pandas as pd

df1 = pd.DataFrame({
    'Name':['Alice', 'Bob'],
    'Score':[80,90]
})

df2 = pd.DataFrame({
    'Name':['Cat', 'David'],
    'Score':[70,85]
})

df3 = pd.DataFrame({
    'Age':[25, 30],
    'City':['HK', 'Sydney']
})

result = pd.concat([df1, df2])
print (result)
```

**output:**

```
    Name  Score
0  Alice     80
1    Bob     90
0    Cat     70
1  David     85
```

---

## Example 2: Concat side by side with axis=1

 Copy

```
import pandas as pd

df1 = pd.DataFrame({
    'Name':['Alice', 'Bob'],
    'Score':[80,90]
})

df2 = pd.DataFrame({
    'Name':['Cat', 'David'],
    'Score':[70,85]
})

df3 = pd.DataFrame({
    'Age':[25, 30],
    'City':['HK', 'Sydney']
})

# result = pd.concat([df1, df2])
result = pd.concat([df1, df3], axis=1)
print (result)
```

**output:**

```
    Name  Score Age    City
0  Alice     80  25      HK
1    Bob     90  30  Sydney
```

---

## Example 3: Concat with different columns — missing values become NaN

 Copy

```
import pandas as pd

df1 = pd.DataFrame({
    'Name':['Alice', 'Bob'],
    'Score':[80,90]
})

df2 = pd.DataFrame({
    'Name':['Cat', 'David'],
    'Score':[70,85]
})

df3 = pd.DataFrame({
    'Age':[25, 30],
    'City':['HK', 'Sydney']
})

df4 = pd.DataFrame({
    'Name':['Eric'],
    'Age':[40]
})

# result = pd.concat([df1, df2])
result = pd.concat([df1, df4], axis=0)
print (result)
```

**output:**

```
    Name  Score   Age
0  Alice   80.0   NaN
1    Bob   90.0   NaN
0   Eric    NaN  40.0
```

---

## Example 4: Concat then reset_index — clean up the duplicated index

 Copy

```
import pandas as pd

df1 = pd.DataFrame({
    'Name':['Alice', 'Bob']
})

df2 = pd.DataFrame({
    'Name':['Cat', 'David']
})

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

**output:**

```
    Name
0  Alice
1    Bob
2    Cat
3  David
```

---

## Example 5: Concat with axis=1 — two 'B' columns appear when both share 'B'

 Copy

```
import pandas as pd

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

df2 = pd.DataFrame({
    'B':[5,6],
    'D':[7,8]
})

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

**output:**

```
   A  B    B    D
0  1  3  5.0  7.0
1  2  4  6.0  8.0
2  3  4  NaN  NaN
```

---

## Example 6: Rename the column first to avoid the duplicate

 Copy

```
import pandas as pd

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

df2 = pd.DataFrame({
    'B':[5,6],
    'D':[7,8]
})

# result = pd.concat([df1, df2], axis=1)
df2_renamed = df2.rename(columns={'B':'B2'})
pd.concat ([df1, df2_renamed], axis=1)
# print (result)
```

**output:**

```
	A	B	B2	D
0	1	3	5	7
1	2	4	6	8
```

---

## Example 7: Concat with keys — a top-level label for each source (multi-index)

 Copy

```
#Concat with multi-Column
import pandas as pd

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

df2 = pd.DataFrame({
    'B':[5,6],
    'D':[7,8]
})

df3 = pd.DataFrame({
    'B':[5,6],
    'D':[7,8]
})

dfNew = pd.concat([df1,df2,df3], axis=1, keys=['df1','df2','df3'])
dfNew
print(dfNew['df1'][dfNew['df1']['B'] > 3]['B'])
```

**output:**

```
	df1	df2	df3
A	B	B	D	B	D
0	1	3	5	7	5	7
1	2	4	6	8	6	8

1    4
Name: B, dtype: int64
```