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Create a Pandas DataFrame from List of Dicts

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.

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

• Using from_records()
• Using pd.DataFrame.from_dict()
• Using pd.json_normalize
• Using pd.DataFrame

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.

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

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.

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

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

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

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.

# 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 

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

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