Next, as part of our\u00a0 example, we will be dealing with CSV data so to iterate over the csv data we need to install a python library called Unicode CSV by running<\/p>\n
pip install unicodecsv<\/pre>\n
\nWorking example to anonymize data from a csv file<\/h3>\nContents of original_data.csv file<\/h5>\n
![\"\"](\"https:\/\/qxf2.com\/blog\/wp-content\/uploads\/2019\/08\/original_data_csv-300x144.png\")
<\/a><\/p>\nConsider we have a CSV file named original_data which has 3 columns namely Id, Name and Email with 5 records for the same as shown in the screenshot above of which we will be anonymizing the cell values for Name and Email columns by making use of Faker library<\/p>\n
Create a file named anonymize_data.py with the following content:<\/p>\n
\"\"\"\r\nThis script will Anonymize the data in original_data.csv file to Anonymized form in anonymized_data csv file\r\n\"\"\"\r\n\r\nimport unicodecsv as csv\r\nfrom faker import Faker\r\nfrom collections import defaultdict\r\n\r\n\r\ndef anonymize():\r\n\t'Anonymizes the given original data to anonymized form'\r\n\t# Load the faker and its providers\r\n\tfaker = Faker()\r\n\r\n\t# Create mappings of names & emails to faked names & emails.\r\n\tnames = defaultdict(faker.name)\r\n\temails = defaultdict(faker.email)\r\n \r\n\twith open(\"original_data.csv\", 'rU') as f: \r\n\t with open(\"anonymized_data.csv\", 'wb') as o :\r\n\t # Use the DictReader to easily extract fields\r\n\t reader = csv.DictReader(f)\r\n\t writer = csv.DictWriter(o, reader.fieldnames)\r\n\t writer.writeheader()\r\n\t for row in reader:\r\n\t row['name'] = names[row['name']]\r\n\t row['email'] = emails[row['email']]\r\n\t writer.writerow(row)\r\n\r\nif __name__ == '__main__':\r\n anonymize()\r\n<\/pre>\nAs you can see in the code you can simply fake a name by using the method – faker.name.\u00a0Each call to method faker.name() yields a different (random) result similarly for emails. Faker object has around 158 different methods all of which generates fake data depending on users need.\u00a0Faker delegates the data generation to providers. The default object provider uses the English locale. Faker supports other locales; they differ in the level of completion. If you wish to use some other locale provider then you can visit –\u00a0Faker Locales<\/a><\/span><\/p>\nYou can run the script with <\/p>\n
python anonymize_data.py<\/pre>\n which would generate an anonymized_data.csv in the same directory of your python script with your anonymized data<\/p>\n
Contents of newly generated anonymized_data.csv<\/h5>\n
![\"\"](\"https:\/\/qxf2.com\/blog\/wp-content\/uploads\/2019\/08\/original_data_csv.png\")
<\/a><\/p>\n