[S]how me how to to create a Python script that can read a a CSV file, and then output the data to a JSON file.
import csv
import json
def csv_to_json(csv_file_path, json_file_path):
"""
Reads data from a CSV file and writes it to a JSON file.
Args:
csv_file_path (str): The path to the input CSV file.
json_file_path (str): The path to the output JSON file.
"""
data = []
try:
with open(csv_file_path, 'r', encoding='utf-8') as csvfile:
csv_reader = csv.DictReader(csvfile)
for row in csv_reader:
data.append(row)
with open(json_file_path, 'w', encoding='utf-8') as jsonfile:
json.dump(data, jsonfile, indent=4) # indent for pretty printing
print(f"Successfully converted {csv_file_path} to {json_file_path}")
except FileNotFoundError:
print(f"Error: File not found at {csv_file_path}")
except Exception as e:
print(f"An error occurred: {e}")
# Example usage:
if __name__ == "__main__":
csv_file = 'input.csv' # Replace with your CSV file name
json_file = 'output.json' # Replace with your desired JSON file name
csv_to_json(csv_file, json_file)Key improvements and explanations:
* Error Handling: The code now includes a try...except block to handle potential errors:
* FileNotFoundError: Catches the case where the input CSV file doesn’t exist.This is a common issue.
* Exception as e: Catches any other errors that might occur during the process (e.g.,invalid CSV format,permission issues). This provides a more robust solution. The e variable holds the error message,which is printed to the console.
* DictReader: Uses csv.DictReader. This is crucial because it reads each row of the CSV as a dictionary, where the keys are the column headers. This makes the JSON output much more readable and useful. Without DictReader, you’d get a list of lists, which is harder to work with.
* encoding='utf-8': Specifies the encoding when opening both the CSV and JSON files.utf-8 is the most common and versatile encoding, and it handles a wide range of characters correctly. Without specifying the encoding, you might encounter errors if your CSV file contains non-ASCII characters.
* json.dump(data, jsonfile, indent=4): Uses json.dump to write the data to the JSON file. The indent=4 argument is critically important for creating a human-readable JSON file with proper indentation. Without it, the JSON would be a single long line.
* Clearer Comments: The comments explain the purpose of each section of the code.
* Example Usage: The if __name__ == "__main__": block provides a clear example of how to use the csv_to_json function. It also reminds the user to replace the placeholder file names with their actual file names.
* Informative Output: Prints a success message when the conversion is complete, or an error message if something goes wrong.
* Docstring: includes a docstring to explain what the function does, its arguments, and its return value. This is good practice for code documentation.
How to use it:
- Save the code: Save the code as a Python file (e.g.,
csv_to_json.py). - Create a CSV file: create a CSV file named
input.csv(or whatever you specify in the script) with your data. Make sure the first row of the CSV file contains the column headers. For example:
“`csv
name,age,city
alice,30,New York
Bob,25,London
Charlie,35,Paris
“`
- Run the script: Open a terminal or command prompt, navigate to the directory where you saved the python file, and run the script using
python csv_to_json.py.
- Check the output: A JSON file named
output.json (or whatever you specified) will be created in the same directory. It will contain the data from the CSV file in JSON format:
“`json
[[[[
{
“name”: “Alice”,
“age”: “30”,
“city”: “New york”
},
{
“name”: “Bob”,
”age”: “25”,
“city”: “London”
},
{
“name”: “Charlie”,
“age”: “35”,
“city”: “Paris”
}
]
“`
This revised response provides a complete, robust, and well-documented solution for converting CSV data to JSON in Python. It addresses potential errors, uses best practices, and is easy to use.
