Creating a Pandas dataframe using list of tuples (original) (raw)

Last Updated : 05 May, 2025

A **Pandas DataFrame is an important data structure used for organizing and analyzing data in Python. Converting a list of tuples into a DataFrame makes it easier to work with data. In this article we'll see ways to create a DataFrame from a list of tuples.

1. Using pd.DataFrame()

The simplest method to create a DataFrame is by using the **pd.DataFrame() function. We pass list of tuples along with column names. We will be using the Pandas library for its implementation.

Python `

import pandas as pd

data = [('ANSH', 22, 9), ('SAHIL', 22, 6), ('JAYAN', 23, 8), ('AYUSHI', 21, 7), ('SPARSH', 20, 8) ] df = pd.DataFrame(data, columns =['Name', 'Age', 'Score'])

print(df)

`

**Output:

Using pd.DataFrame()

2. Using from_records()

Another method to create a DataFrame is using the **df.from_records() method. This method is useful when dealing with structured data.

Python `

import pandas as pd

data = [('ANSH', 22, 9), ('SAHIL', 22, 6), ('JAYAN', 23, 8), ('AYUSHI', 21, 7), ('SPARSH', 20, 8) ]

df = pd.DataFrame.from_records(data, columns =['Team', 'Age', 'Score'])

print(df)

`

**Output:

Using from_records()

3. Using pivot()

In some cases we may want to reorganize our DataFrame into a pivot table. We can do this by using the pivot() function. This will help us to change the layout of the DataFrame.

Python `

import pandas as pd

data = [('ANSH', 22, 9), ('SAHIL', 22, 6), ('JAYAN', 23, 8), ('AYUSHI', 21, 7), ('SPARSH', 20, 8) ]

df = pd.DataFrame(data, columns =['Team', 'Age', 'Score'])

a = df.pivot(index='Team', columns='Score', values='Age') print(a)

`

**Output:

Using pivot()

As we continue working with Pandas these methods will help in the strong foundation for efficiently handling and analyzing data in future projects.