Convert a Column to Row Name/Index in Pandas

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:

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)

  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.

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.

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).

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.