Create a Pandas DataFrame from List of Dicts

Given a list of dictionaries, the task is to create a Pandas DataFrame from it. For Example:

Input: [{'name': 'Alison, 'age': 25}, {'name': 'Jake', 'age': 30}]
Output:
name age
0 Alison 25
1 Jake 30

Using pd.DataFrame

The DataFrame is created from a list of dictionaries where each dictionary represents a row and keys become column names. If any value is missing, Pandas fills it with NaN and may convert numbers to float.

import pandas as pd
data = [{'name': 'Jake', 'age': 25},
        {'name': 'Martin', 'age': 30}]
df = pd.DataFrame(data)
print(df)

     name  age
0    Jake   25
1  Martin   30

Using pd.DataFrame.from_dict()

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
data = [{'name': 'Jake', 'age': 25},
        {'name': 'Martin', 'age': 30}]
df = pd.DataFrame.from_dict(data)
print(df)

     name  age
0    Jake   25
1  Martin   30

Using from_records()

from_records() method is used to create a DataFrame from structured data like a list of dictionaries. It directly converts each record into a row.

import pandas as pd
data = [{'name': 'Jake', 'age': 25},
        {'name': 'Martin', 'age': 30}]
df = pd.DataFrame.from_records(data)
print(df)

     name  age
0    Jake   25
1  Martin   30

Using pd.json_normalize

json_normalize() function is used to convert JSON-like or semi-structured data into a flat DataFrame.

import pandas as pd
data = [{'name': 'Jake', 'age': 25},
        {'name': 'Martin', 'age': 30}]
df = pd.json_normalize(data)
print(df)