Master Fast CSV, JSON, and XML Handling in Python – Code Samples Included

Python's flexibility and ease of use make it a top choice for data processing and machine learning, especially with its powerful libraries.

CSV Data

CSV is the most common way to store tabular data, widely used in Kaggle competitions. Python's built‑in csv module can read and write CSV files, typically loading data into a list of lists.

import csv

filename = "my_data.csv"

fields = []
rows = []
# Reading csv file 
with open(filename, 'r') as csvfile:
    csvreader = csv.reader(csvfile)
    fields = csvreader.next()
    for row in csvreader:
        rows.append(row)
# Printing out the first 5 rows 
for row in rows[:5]:
    print(row)

Writing CSV files is equally straightforward: define field names and rows, create a writer object, and output the data.

import csv

fields = ['Name', 'Goals', 'Assists', 'Shots']
rows = [
    ['Emily', '12', '18', '112'],
    ['Katie', '8', '24', '96'],
    ['John', '16', '9', '101'],
    ['Mike', '3', '14', '82']
]

filename = "soccer.csv"

with open(filename, 'w+') as csvfile:
    csvwriter = csv.writer(csvfile)
    csvwriter.writerow(fields)
    csvwriter.writerows(rows)

Using pandas, a CSV can be turned into a list of dictionaries, which can then be saved as JSON or converted to XML with dicttoxml.

import pandas as pd
from dicttoxml import dicttoxml
import json

# Building our dataframe
data = {'Name': ['Emily', 'Katie', 'John', 'Mike'],
        'Goals': [12, 8, 16, 3],
        'Assists': [18, 24, 9, 14],
        'Shots': [112, 96, 101, 82]}

df = pd.DataFrame(data, columns=data.keys())

# Convert to dictionary and save as JSON
data_dict = df.to_dict(orient="records")
with open('output.json', "w+") as f:
    json.dump(data_dict, f, indent=4)

# Convert to XML and save
xml_data = dicttoxml(data_dict).decode()
with open("output.xml", "w+") as f:
    f.write(xml_data)

JSON Data

JSON offers a concise, readable format that mirrors Python dictionaries. The built‑in json module makes reading and writing JSON trivial.

import json
import pandas as pd

# Read JSON file into a Python dictionary
with open('data.json') as f:
    data_listofdict = json.load(f)

# Load the same data with pandas
data_df = pd.read_json('data.json', orient='records')

# Write dictionary back to JSON with pretty formatting
with open('new_data.json', 'w+') as json_file:
    json.dump(data_listofdict, json_file, indent=4, sort_keys=True)

# Export pandas DataFrame to JSON
export = data_df.to_json('new_data.json', orient='records')

XML Data

XML is more verbose than CSV or JSON, requiring more storage and bandwidth, but it provides features like namespaces and schema validation.

import xml.etree.ElementTree as ET
import xmltodict
import json

tree = ET.parse('output.xml')
xml_data = tree.getroot()

xmlstr = ET.tostring(xml_data, encoding='utf8', method='xml')

data_dict = dict(xmltodict.parse(xmlstr))
print(data_dict)

with open('new_data_2.json', 'w+') as json_file:
    json.dump(data_dict, json_file, indent=4, sort_keys=True)