How to Display a Pandas Row or Series Vertically in Python

Introduction

When working with pandas, you may sometimes want to inspect one row of a DataFrame. By default, pandas displays a row horizontally, which can be difficult to read when the DataFrame has many columns.

A simple way to improve readability is to display the row vertically. This can be done using the transpose operator .T.

Create an example DataFrame

First, let’s create a simple pandas DataFrame:

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import pandas as pd

data = {
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["New York", "Paris", "London"],
    "job": ["Data Analyst", "Developer", "Scientist"],
    "salary": [70000, 85000, 95000]
}

df = pd.DataFrame(data)

df

Output:

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name  age      city           job  salary
0    Alice   25  New York  Data Analyst   70000
1      Bob   30     Paris     Developer   85000
2  Charlie   35    London     Scientist   95000

Select one row from the DataFrame

You can select one row using .iloc[].

For example, to select the first row:

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row = df.iloc[0]

row

Output:

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name             Alice
age                 25
city          New York
job       Data Analyst
salary           70000
Name: 0, dtype: object

In this case, row is a pandas Series.

You can check its type with:

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type(row)

Output:

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pandas.core.series.Series

Display a pandas Series vertically

A pandas Series is already displayed vertically in many cases.

For example:

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display(row)

This is useful when you want to inspect the values of a single row in a readable format.

Convert the row to a DataFrame

If you want a cleaner table-style display, you can convert the Series to a DataFrame:

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pd.DataFrame(row)

Output:

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0
name            Alice
age                25
city         New York
job      Data Analyst
salary          70000

This displays the values vertically, with the original column names shown as the index.

Display one DataFrame row vertically using .T

Another common method is to first create a one-row DataFrame and then transpose it with .T.

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pd.DataFrame([row]).T

Output:

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0
name            Alice
age                25
city         New York
job      Data Analyst
salary          70000

The .T stands for transpose. It switches rows and columns.

This means that:

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pd.DataFrame([row])

creates a DataFrame with one horizontal row:

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name  age      city           job  salary
0  Alice   25  New York  Data Analyst   70000

Then:

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pd.DataFrame([row]).T

turns that row into a vertical display.

Display a specific row directly

You can also select and display a row vertically in one line:

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pd.DataFrame([df.iloc[0]]).T

For the second row:

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pd.DataFrame([df.iloc[1]]).T

For the third row:

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pd.DataFrame([df.iloc[2]]).T

Use display() in a Jupyter Notebook

In a Jupyter Notebook, it is often better to use display() instead of print():

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display(pd.DataFrame([df.iloc[0]]).T)

This gives a cleaner HTML table output in the notebook.

Rename the value column

By default, the transposed DataFrame may have a column named 0. You can rename it to something clearer:

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row_vertical = pd.DataFrame([df.iloc[0]]).T
row_vertical.columns = ["value"]

display(row_vertical)

Output:

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value
name              Alice
age                  25
city           New York
job        Data Analyst
salary            70000

This is often easier to read.

Display a row with column names and values

Another clean approach is to reset the index:

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row_vertical = pd.DataFrame([df.iloc[0]]).T.reset_index()
row_vertical.columns = ["column", "value"]

display(row_vertical)

Output:

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column         value
0    name         Alice
1     age            25
2    city      New York
3     job  Data Analyst
4  salary         70000

This format is very useful when you want a readable two-column table.

Print the row vertically as plain text

If you want to print the result as plain text, use .to_string():

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print(pd.DataFrame([df.iloc[0]]).T.to_string())

This is useful when working outside Jupyter Notebook or when debugging in a terminal.

Prevent pandas from truncating the output

If your row contains many columns or long text values, pandas may truncate the display.

You can force pandas to show all rows and full column values using:

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with pd.option_context(
    "display.max_rows", None,
    "display.max_columns", None,
    "display.max_colwidth", None
):
    display(pd.DataFrame([df.iloc[0]]).T)

This is helpful when inspecting large DataFrames with many columns.

Complete reproducible example

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import pandas as pd

data = {
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["New York", "Paris", "London"],
    "job": ["Data Analyst", "Developer", "Scientist"],
    "salary": [70000, 85000, 95000]
}

df = pd.DataFrame(data)

row_vertical = pd.DataFrame([df.iloc[0]]).T
row_vertical.columns = ["value"]

display(row_vertical)

Output:

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value
name              Alice
age                  25
city           New York
job        Data Analyst
salary            70000

Summary

To display a pandas row vertically, you can use:

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pd.DataFrame([df.iloc[0]]).T

In a Jupyter Notebook, use:

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display(pd.DataFrame([df.iloc[0]]).T)

For a cleaner output with a custom column name:

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row_vertical = pd.DataFrame([df.iloc[0]]).T
row_vertical.columns = ["value"]

display(row_vertical)

Displaying a pandas row vertically is a simple way to improve readability, especially when your DataFrame contains many columns.

References