How to create DataFrame from dictionary in Python-Pandas?

Last Updated : 20 Feb, 2025

The task of converting a dictionary into a Pandas DataFrame involves transforming a dictionary into a structured, tabular format where keys represent column names or row indexes and values represent the corresponding data.

Using Default Constructor

This is the simplest method where a dictionary is directly passed to pd.DataFrame(). Here, dictionary keys become column names and values become the corresponding data.

Python

import pandas as pd

d = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Paris']
}
# creating a Dataframe object 
df = pd.DataFrame(d)
print(df)

Output

      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Paris

Table of Content

Using Custom Indexes
Using Simple Dictionary
Selecting Specific Columns
Using Different Orientation (Keys as Indexes)
Using Nested Dictionary

Using Custom Indexes

By default, Pandas assigns numerical row indexes (0,1,2,...). however we can define custom indexes using the index parameter.

Python

import pandas as pd

# dictionary with list object in values
d = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    'Age' : [23, 21, 22, 21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}

# creating a Dataframe object from dictionary with custom indexing
df = pd.DataFrame(d, index = ['a', 'b', 'c', 'd'])
print(df)

Output

        Name  Age University
a      Ankit   23        BHU
b  Aishwarya   21        JNU
c    Shaurya   22         DU
d   Shivangi   21        BHU

Using Simple Dictionary

When the dictionary contains only key-value pairs (instead of lists), we need to convert it into a tabular format using pd.DataFrame(list(dictionary.items())).

Python

import pandas as pd

d = {
    'Ankit' : 22,
    'Golu' : 21,
    'hacker' : 23
    }

# creating a Dataframe object from a list  of tuples of key, value pair
df = pd.DataFrame(list(d.items()))
print(df)

Output

        0   1
0   Ankit  22
1    Golu  21
2  hacker  23

Selecting Specific Columns

We can create a DataFrame using only the required columns from the dictionary.

Python

import pandas as pd

d = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    'Age' : [23, 21, 22, 21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}

# creating a Dataframe object with skipping one column i.e skipping age column.
df = pd.DataFrame(d, columns = ['Name', 'University'])
print(df)

Output

        Name University
0      Ankit        BHU
1  Aishwarya        JNU
2    Shaurya         DU
3   Shivangi        BHU

Using Different Orientation (Keys as Indexes)

By default, dictionary keys act as column names, but we can use them as row indexes by setting orient='index'.

Python

import pandas as pd

# dictionary with list object in values
d = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    'Age' : [23, 21, 22, 21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}

# creating a Dataframe object in which dictionary
# key is act as index value and column value is
# 0, 1, 2...
df = pd.DataFrame.from_dict(d, orient = 'index')
print(df)

Output

                0          1        2         3
Name        Ankit  Aishwarya  Shaurya  Shivangi
Age            23         21       22        21
University    BHU        JNU       DU       BHU

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How to create DataFrame from dictionary in Python-Pandas?

Using Default Constructor

Using Custom Indexes

Using Simple Dictionary

Selecting Specific Columns

Using Different Orientation (Keys as Indexes)

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