Processing CSV, JSON, and XML Data in Python

Python's flexibility and extensive libraries make it a top choice for data handling, especially for formats like CSV, JSON, and XML that dominate everyday data exchange.

CSV Data – The article explains reading CSV files with the built‑in csv module, iterating rows, and writing new CSV files using csv.writer. Example code shows opening a file, creating a reader, extracting field names, iterating rows, and printing the first five rows, followed by code that writes a list of rows to a new CSV file.

import csv

filename = "my_data.csv"
fields = []
rows = []
# Reading csv file
with open(filename, 'r') as csvfile:
    # Creating a csv reader object
    csvreader = csv.reader(csvfile)
    # Extracting field names in the first row
    fields = csvreader.next()
    # Extracting each data row one by one
    for row in csvreader:
        rows.append(row)
# Printing out the first 5 rows
for row in rows[:5]:
    print(row)

# Writing CSV
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)

JSON Data – Using Python's json module and pandas, the guide shows how to load JSON into a dictionary, convert it to a DataFrame, and write formatted JSON files with indentation and sorted keys. It also demonstrates converting a DataFrame back to JSON.

import json
import pandas as pd
# Read the data from file
with open('data.json') as f:
    data_listofdict = json.load(f)
# pandas read json
data_df = pd.read_json('data.json', orient='records')
# Write a dictionary 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 DataFrame to JSON
export = data_df.to_json('new_data.json', orient='records')

XML Data – The article introduces Python's built‑in xml.etree.ElementTree module and the third‑party xmltodict library for parsing XML, converting it to a dictionary, and then optionally converting that dictionary to CSV, JSON, or a pandas DataFrame. Sample code parses an XML file, converts it to a dictionary, and writes the result as pretty‑printed JSON.

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)

Overall, the guide provides a concise, code‑first reference for developers who need to manipulate common data interchange formats in Python, illustrating both low‑level module usage and higher‑level library shortcuts.