Create a Pandas DataFrame from List of Dicts (original) (raw)

Last Updated : 11 Jul, 2025

Pandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It is generally the most commonly used Pandas object. Pandas DataFrame can be created in multiple ways using Python. Let’s discuss how to create a Pandas DataFrame from the List of Dictionaries.

Create a Pandas DataFrame from List of Dictionaries

Below are the ways by which we can create a Pandas DataFrame from list of dicts:

Create a Pandas DataFrame from List of Dictionaries Using from_records()

Pandas from_records() function of DataFrame changes structured data or records into DataFrames. It converts a structured ndarray, tuple or dict sequence, or DataFrame into a DataFrame object.

Python3 `

import pandas as pd

Initialise data to lists.

data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list'}, {'Geeks':10, 'For': 20, 'geeks': 30}]

df = pd.DataFrame.from_records(data,index=['1', '2']) print(df)

`

**Output

   Geeks    For geeks

1 dataframe using list
2 10 20 30

Convert List of Dictionaries to a Pandas DataFrame Using pd.DataFrame.from_dict()

The DataFrame.from dict() method in Pandas builds DataFrame from a dictionary of the dict or array type. By using the dictionary's columns or indexes and allowing for Dtype declaration, it builds a DataFrame object.

Python3 `

import pandas as pd

Initialise data to lists.

data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list'}, {'Geeks':10, 'For': 20, 'geeks': 30}]

df = pd.DataFrame.from_dict(data) print(df)

`

**Output

   Geeks    For geeks

0 dataframe using list
1 10 20 30

Create a Pandas DataFrame from List of Dictionaries Using pd.json_normalize

Pandas have a nice inbuilt function called **json_normalize****()** to flatten the simple to moderately semi-structured nested JSON structures to flat tables.

Python3 `

import pandas as pd

Initialise data to lists.

data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list'}, {'Geeks':10, 'For': 20, 'geeks': 30}]

df=pd.json_normalize(data) print(df)

`

**Output

   Geeks    For geeks

0 dataframe using list
1 10 20 30

Convert List of Dictionaries to a Pandas DataFrame Using pd.DataFrame

**Example 1: As we know while creating a data frame from the dictionary, the keys will be the columns in the resulted Dataframe. When we create Dataframe from a list of dictionaries, matching keys will be the columns and corresponding values will be the rows of the Dataframe. If there are no matching values and columns in the dictionary, then the NaN value will be inserted into the resulting Dataframe.

Python3 `

Python code demonstrate how to create

Pandas DataFrame by lists of dicts without matching key-value pair

import pandas as pd

Initialise data to lists.

data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list', 'Portal': 10000}, {'Geeks':10, 'For': 20, 'geeks': 30}]

Creates DataFrame.

df = pd.DataFrame(data)

Print the data

df

`

**Output

   Geeks    For geeks   Portal

0 dataframe using list 10000.0
1 10 20 30 NaN

**Example 2: Creating a Dataframe by explicitly providing user-defined values for both index and columns

Python3 `

import pandas as pd

Initialise data to lists.

data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list'}, {'Geeks': 10, 'For': 20, 'geeks': 30}]

With two column indices, values same

as dictionary keys

df1 = pd.DataFrame(data, index=['ind1', 'ind2'], columns=['Geeks', 'For'])

With two column indices with

one index with other name

df2 = pd.DataFrame(data, index=['indx', 'indy'])

print for first data frame

print(df1, "\n")

Print for second DataFrame.

print(df2)

`

**Output

      Geeks    For

ind1 dataframe using
ind2 10 20
Geeks For geeks
indx dataframe using list
indy 10 20 30