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.
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