How do I convert a CSV file to JSON in Python?

Use the csv.DictReader class to read each CSV row as a dictionary, collect them in a list, then serialize with json.dumps(). For example: import csv, json; rows = list(csv.DictReader(open('data.csv'))); print(json.dumps(rows, indent=2))

Does Python's CSV reader preserve data types like numbers?

No. csv.DictReader returns every field as a string. You need to manually cast numeric fields (int(), float()) if you want them to appear as numbers in the JSON output. pandas read_csv() does type inference automatically.

What is the easiest way to convert CSV to JSON in Python?

With pandas: import pandas as pd; df = pd.read_csv('data.csv'); print(df.to_json(orient='records', indent=2)). This handles type inference, encoding, and JSON formatting in two lines.

How to Convert CSV to JSON in Python – With Code Examples

Converting a CSV file to JSON in Python takes about five lines of code using the standard library. This article covers the built-in csv module approach (no dependencies required), the pandas shortcut, and a few practical patterns for handling numeric types and nested structures.

Method 1 — csv.DictReader (standard library, no install)

Given a CSV file like this:

name,age,city
Alice,30,London
Bob,25,Paris
Carol,35,Berlin

Here is the conversion:

import csv
import json

with open('data.csv', newline='', encoding='utf-8') as f:
    reader = csv.DictReader(f)
    rows = list(reader)

print(json.dumps(rows, indent=2))

Output:

[
  {
    "name": "Alice",
    "age": "30",
    "city": "London"
  },
  {
    "name": "Bob",
    "age": "25",
    "city": "Paris"
  },
  {
    "name": "Carol",
    "age": "35",
    "city": "Berlin"
  }
]

Notice that age is a string ("30"), not a number (30). The CSV format has no type information — everything is text. See below for how to fix this.

Writing the JSON to a file

with open('data.csv', newline='', encoding='utf-8') as infile, \
     open('output.json', 'w', encoding='utf-8') as outfile:
    rows = list(csv.DictReader(infile))
    json.dump(rows, outfile, indent=2)

Converting numeric fields from string to number

If you know which columns should be numbers, convert them after reading:

import csv
import json

numeric_columns = {'age'}

with open('data.csv', newline='', encoding='utf-8') as f:
    reader = csv.DictReader(f)
    rows = []
    for row in reader:
        for col in numeric_columns:
            try:
                row[col] = int(row[col])
            except (ValueError, KeyError):
                pass
        rows.append(row)

print(json.dumps(rows, indent=2))

Now age appears as 30 (a number) instead of "30" (a string).

Method 2 — pandas (two lines)

If you have pandas installed, the conversion is even shorter, and pandas infers numeric types automatically:

import pandas as pd

df = pd.read_csv('data.csv')
print(df.to_json(orient='records', indent=2))

The orient='records' argument produces a JSON array where each row is an object — the most common format for API consumption. Other orient options:

orient='index' — a dict keyed by row index
orient='columns' — a dict keyed by column name (column-oriented)
orient='values' — a 2D array without column names

Handling special characters and encoding

Always specify encoding='utf-8' when opening CSV files, especially if they contain non-ASCII characters (accented names, Unicode symbols). For CSV files exported from Excel, you may need encoding='utf-8-sig' to handle the byte-order mark:

with open('data.csv', newline='', encoding='utf-8-sig') as f:
    ...

Command-line one-liner

For quick terminal use:

python3 -c "
import csv, json, sys
print(json.dumps(list(csv.DictReader(sys.stdin)), indent=2))
" < data.csv

No-code alternative

If you do not need to script the conversion, the CSV to JSON Converter lets you paste or upload a CSV file and download the JSON output immediately — no Python required.