---
title: "dataframe-concat (tc)"
description: pandas 的 concat() 把多個 DataFrame 拼在一起 — 7 個例子。
url: "https://aaronchan.com/zh/kb/dataframe-concat/"
updated: "2026-09-15"
category: "Code-Python (tc)"
tags:
  - python
---

# dataframe-concat (tc)

pandas 的 `concat()` 用來把多個 DataFrame 拼在一起。以下 7 個例子涵蓋常見用法：上下／左右堆疊、欄位對齊、重複欄位、多層 key。

## Example 1：基本 concat — 將兩個 DataFrame 上下堆疊（axis=0，預設值）

 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：用 axis=1 左右並排

 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：欄位不同時 concat — 缺少的位置補上 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 後 reset_index — 整理重複的索引

 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：axis=1 且兩邊都有 'B' 欄 — 結果出現兩個 'B' 欄位

 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：先重新命名欄位，避免重複

 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：用 keys 加上頂層標籤（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
```