Convert a Column to Row Name/Index in Pandas (original) (raw)

Last Updated : 03 Oct, 2025

If a DataFrame has a column whose values naturally identify each row (like IDs, names, or timestamps), you can make that column the row index. **For example, if your DataFrame has a column Name with values ["Akash", "Geeku", "Pankaj"], after converting it to the index, these names become the row labels instead of the default 0, 1, 2.

Consider this DataFrame which we will use in all examples:

Python `

import pandas as pd

df = pd.DataFrame({"Person": ["Zoe", "Max", "Liam", "Nora", "Owen"], "Dept": ["Sales", "IT", "HR", "Finance", "IT"], "Score": [88, 92, 75, 85, 90]}) print(df)

`

Output

Person Dept Score 0 Zoe Sales 88 1 Max IT 92 2 Liam HR 75 3 Nora Finance 85 4 Owen IT 90

Now let’s explore methods one by one

Using set_index()

DataFrame.set_index(col) is the standard, explicit way to make a column the index. It returns a new DataFrame (or modifies in-place with inplace=True) and can drop the column from the columns list immediately.

**Example: This example uses set_index() to make "Person" the index.

Python `

d = df.copy() r = d.set_index("Person") print(r)

`

**Output

Dept Score
Person
Zoe Sales 88
Max IT 92
Liam HR 75
Nora Finance 85
Owen IT 90

**Explanation:

• **d = df.copy(): keep original unchanged.
• **r = d.set_index("Person"): create a new DataFrame with "Person" as the index; by default the "Person" column is removed from columns.

Using pop() and assign to index

df.index = df.pop(col) removes the column and assigns its values to the index in one step concise and efficient for in-place updates.

**Example: This example sets the index by popping "Person" from the DataFrame.

Python `

d = df.copy() d.index = d.pop("Person") print(d)

`

**Output

Dept Score
Person
Zoe Sales 88
Max IT 92
Liam HR 75
Nora Finance 85
Owen IT 90

**Explanation:

• **d = df.copy(): copy the original.
• **d.index = d.pop("Person") pop returns the column (removing it), and we assign those values to d.index.

Assign column values directly to index

You can assign df.index = df[col]. This sets the index but does not remove the original column you’ll often want to drop() it afterwards.

**Example: This example assigns "Person" as index while keeping the column (then shows how to drop it).

Python `

d = df.copy() d.index = d["Person"] print("After assigning index (column still present):\n", d)

d2 = d.drop(columns="Person") print("After dropping the original column:\n", d2)

`

**Output

After assigning index (column still present):
Person Dept Score
Person
Zoe Zoe Sales 88
Max Max IT 92
Liam Liam HR 75
Nora Nora Finance 85
Owen Owen IT 90

After dropping the original column:
Dept Score
Person
Zoe Sales 88
Max IT 92
Liam HR 75
Nora Finance 85
Owen IT 90

**Explanation:

• **d.index = d["Person"] index set to column values, but column remains.
• **d.drop(columns="Person") optional remove the original column if you don’t need it.