Convert a List of Dictionaries to a Pandas DataFrame in Python


In data analysis applications, one possible way to store data in Python is in a list of dictionaries. But what if you want to perform more complex operations on your data, like sorting, filtering, or statistical analysis? That's where the powerful Pandas library comes in, and more specifically, the DataFrame object. In this byte, we're going to learn how to convert a list of dictionaries to a DataFrame in Python.

Why Convert a List of Dictionaries to a DataFrame?

So why should you convert your list of dictionaries to a DataFrame? Isn't it just another data structure? Well, yes, but it's a very powerful one. A DataFrame is a two-dimensional labeled data structure with columns potentially of different types. It's similar in many ways to a database table or an Excel spreadsheet. It's designed to handle a lot of data, and it comes with quite a few built-in methods for data manipulation and analysis.

One of the key benefits of using a DataFrame is its flexibility. It can handle both homogenous and heterogenous data. This means you can store different data types in different columns of the same DataFrame.

Converting a List of Dictionaries to a DataFrame

Converting a list of dictionaries to a DataFrame is surprisingly easy, thanks to how intuitive Pandas is. First, we need to import the pandas library. If you haven't installed it yet, you can do so with the command pip install pandas.

$ pip install pandas

Next, let's create a list of dictionaries. For this example, we'll use a simple list of dictionaries where each dictionary represents a person with their name and age.

people = [
    {"name": "Alice", "age": 25},
    {"name": "Bob", "age": 30},
    {"name": "Charlie", "age": 35}

To convert this list to a DataFrame, we simply pass it to the pd.DataFrame() function.

import pandas as pd

df = pd.DataFrame(people)


The output will look like this:

      name  age
0    Alice   25
1      Bob   30
2  Charlie   35

As you can see, each dictionary in the list has been converted to a row in the DataFrame, and the keys of the dictionaries have become the column names.

Working with Different Data Types

Remember when we mentioned that DataFrames can handle different data types in different columns? Let's put that to the test. Suppose we have a list of dictionaries where each dictionary represents a book. Each book has a title, an author, a publication year, and a price.

books = [
    {"title": "To Kill a Mockingbird", "author": "Harper Lee", "year": 1960, "price": 7.99},
    {"title": "1984", "author": "George Orwell", "year": 1949, "price": 8.99},
    {"title": "The Great Gatsby", "author": "F. Scott Fitzgerald", "year": 1925, "price": 10.99}

We can convert this list to a DataFrame in exactly the same way as before.

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df = pd.DataFrame(books)


The output will look like this:

                   title               author  year  price
0  To Kill a Mockingbird           Harper Lee  1960   7.99
1                   1984        George Orwell  1949   8.99
2       The Great Gatsby  F. Scott Fitzgerald  1925  10.99

As you can see, the DataFrame has handled the different data types without any issues. The year column contains integers, the price column contains floats, and the title and author columns contain strings. This is one of the reasons why DataFrames are so powerful and flexible for data manipulation and analysis. Many data conversion tasks like this just work.

Alternative Methods for Conversion

While using the DataFrame constructor is the most common way to convert a list of dictionaries to a DataFrame in Pandas, there are a few alternative methods that can be helpful in certain situations.

One such method is using the json_normalize function from the module. This function is designed to handle deeply nested JSON-like data and can be a lifesaver when dealing with complex and messy data structures.

from pandas import json_normalize

data = [
    {'name': 'John', 'age': 28, 'job': 'Teacher'},
    {'name': 'Mike', 'age': 30, 'job': 'Engineer'},
    {'name': 'Emily', 'age': 22, 'job': 'Doctor'}

df = json_normalize(data)


This will output:

    name  age       job
0   John   28   Teacher
1   Mike   30  Engineer
2  Emily   22    Doctor

The use-case for this function is when you're working with JSON-like data.

Another alternative method is to use the from_records function of the DataFrame. This function can help when your data is in a structured format like a structured NumPy array or a list of tuples.

data = [
    ('John', 28, 'Teacher'),
    ('Mike', 30, 'Engineer'),
    ('Emily', 22, 'Doctor')

df = pd.DataFrame.from_records(data, columns=['name', 'age', 'job'])


The output will be the same as the previous methods. All we had to do for this to work is specify the column names.


In this Byte, we explored how to convert a list of dictionaries to a DataFrame in Pandas. We started with the most common method using the DataFrame constructor, and then looked at a couple of alternative methods - json_normalize and from_records. In order to master data manipulation in Python, you need to understand your underlying data structures and knowing how to leverage powerful tools like Pandas. As Clive Humby once said, "Data is the new oil, but if unrefined it cannot really be used." So keep refining your data skills!

Last Updated: September 18th, 2023
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