5 Best Ways to Load a List of Dicts from a File in Python

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💡 Problem Formulation: Developers often encounter scenarios where they need to read data from a file into a Python program. Specifically, for this article, our input will be a file containing a JSON-like list of dictionaries, and the desired output is a Python list of dictionaries. This data structure is commonly used for configurations, data processing, and more. Our aim is to effectively load this data structure from a file using different methods in Python.

Method 1: Using json.load()

The json.load() method is a straightforward way to deserialize JSON from a file into a Python object. Since JSON’s format closely resembles Python’s native dictionary format, json.load() is a popular option when dealing with JSON data stored in files. It reads and converts the JSON data directly into a Python list of dictionaries, assuming the JSON structure aligns with this format.

Here’s an example:

import json

# Assuming 'data.json' contains a list of dictionaries
with open('data.json', 'r') as file:
    data = json.load(file)
print(data)

Output:

[{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]

This code opens a file called ‘data.json’ in read mode and uses the json.load() function to parse the JSON data into a Python list of dictionaries. The result is printed to the console. This method is efficient and works well with valid JSON formatted files.

Method 2: Using pandas.read_json()

For those working with data analysis, pandas offer a convenient pandas.read_json() function that can read JSON from a file and convert it into a pandas DataFrame. A DataFrame can be easily converted to a list of dictionaries using the .to_dict('records') method. This allows for quick manipulation and analysis of structured data.

Here’s an example:

import pandas as pd

# Assuming 'data.json' contains a list of dictionaries
df = pd.read_json('data.json')
data = df.to_dict('records')
print(data)

Output:

[{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]

This code snippet first reads a file ‘data.json’ using the pandas.read_json() method and then converts the resulting DataFrame into a list of dictionaries with the .to_dict('records') method. While pandas is a powerful library, using it only for loading data may be overkill if you do not require its analytical features.

Method 3: Using csv.DictReader()

When data is stored in a CSV format, Python’s csv module can be used to read and parse the data. The csv.DictReader() function reads each row of the CSV file into a dictionary, with the keys corresponding to the column headers. It is an apt choice for tabular data.

Here’s an example:

import csv

# Assuming 'data.csv' has name and age as the column headers
with open('data.csv', mode='r') as file:
    csv_reader = csv.DictReader(file)
    data = list(csv_reader)
print(data)

Output:

[{'name': 'Alice', 'age': '30'}, {'name': 'Bob', 'age': '25'}]

The code opens ‘data.csv’ in read mode and employs csv.DictReader() to convert each row in the CSV file into a dictionary using the headers as keys. The dictionaries are then stored in a list that is printed out. This method is great for CSV files but does not work for JSON data.

Method 4: Using ast.literal_eval() with a Text File

For data that are not in JSON or CSV format, one could read the file as a plain text and then use ast.literal_eval() from Python’s Abstract Syntax Trees (AST) library to safely evaluate a string containing a Python literal or container display.

Here’s an example:

import ast

# Assuming 'data.txt' contains a Python-style list of dictionaries
with open('data.txt', 'r') as file:
    data = ast.literal_eval(file.read())
print(data)

Output:

[{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]

This code snippet reads the entire content of ‘data.txt’ and then converts the string that is assumed to look like a literal Python list of dictionaries into a Python object using ast.literal_eval(). This method is quite versatile, but less secure compared to json.load(), and may fail with improperly formatted strings.

Bonus One-Liner Method 5: Using List Comprehension with eval()

A one-liner approach to load a list of dictionaries from a file could be to read the file line-by-line and use Python’s eval() function. However, this approach should be used with extreme caution as eval() can execute arbitrary code, which could be a security risk.

Here’s an example:

# Assuming 'data.txt' contains one dictionary per line
with open('data.txt', 'r') as file:
    data = [eval(line) for line in file]
print(data)

Output:

[{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]

This line of code reads each line of ‘data.txt’, evaluating each as a Python expression, and collects the results into a list. While concise, this method is highly insecure and should only be used with trusted data sources.

Summary/Discussion

  • Method 1: json.load(). Ideal for JSON files. Fast and built-in. Requires valid JSON format.
  • Method 2: pandas.read_json(). Best for data manipulation. Requires pandas installation. Possibly unnecessary for simple data loading tasks.
  • Method 3: csv.DictReader(). Specialized for CSV files. Simple and effective for tabular data. Not suitable for JSON.
  • Method 4: ast.literal_eval(). Versatile for text data. Safer than eval. May fail on malformed data.
  • Bonus Method 5: eval(). Very concise one-liner. Unsafe due to the risk of code execution. Use with caution.