![]() Once Movie Artist,Documentary,"February 09, 2020",79,4.1,Hindi Title,Genre,Premiere,Runtime,IMDB Score,Languageĭiscuss According Model,Horror,"February 09, 2020",107,2.6,Japanese writerow () for n in range ( 1, 100 ): writer. uniform ( 1.0, 5.0 ), 1 ) def generate_movie (): return with open ( 'movie_data.csv', 'w' ) as csvfile : writer = csv. randrange ( 50, 150 ) def get_movie_rating (): return round ( random. date_time_this_decade (), "%B %d, %Y" ) def get_movie_len (): return random. join ( capitalized_words ) def get_movie_date (): return datetime. ![]() words () capitalized_words = list ( map ( capitalize, words )) return ' '. add_provider ( LanguageProvider ) # Some of this is a bit verbose now, but doing so for the sake of completionĭef get_movie_name (): words = fake. choice () class LanguageProvider ( BaseProvider ): def language ( self ): return random. The source code is pretty easy to understand as well.Īnd if you like what you see here, or on my Medium blog, and would like to see more of such helpful technical posts in the future, consider supporting me on Patreon and Github.From faker import Faker from faker.providers import BaseProvider import random import csv class GenereProvider ( BaseProvider ): def movie_genre ( self ): return random. So do head over to the package’s Github repo, take a look around, and take it for a spin. You can generate home phone and email, work phone and email, home address, work address, interests, profiles, credit cards, license plate numbers, and a lot more. You can generate any number of customers or friends (swing how ever you swing) very easily with a complete offline and online profile for each person. Nonetheless, this is definitely a very handy and fun package to have in your arsenal. But to generate one million customer records with first name, last name, email, phone, etc., it took almost 350 seconds on a 2019 16-inch base model MacBook Pro. It’s definitely easy to generate the data. See what I did there?Īctually, I’m not sure about the “quickly” part of my last sentence. I don’t know when you’d ever use, but you call Faker’s bs() any time you want. That’s supposed to be the company’s catch phrase.Īnd I kid you not, there’s a method called bs(). Providers of Horizontal value-added knowledge userĪs you can see from the output above, we provide some great horizontal value-added knowledge user. ![]() You can generate a whole company with for example: The company I just created! You can see now how easy it is to generate large amounts of fake customers, for testing of course. (faker.prefix_female(), faker.name_female(), faker.suffix_female(), faker.phone_number(), pany_email(), faker.address()) ![]() You can call me at %s, or email me at %s, or visit my home at %s" % Print("My name is %s %s %s, I'm a female. (faker.prefix_male(), faker.name_male(), faker.suffix_male(), faker.phone_number(), faker.ascii_company_email(), faker.address()) (faker.prefix_nonbinary(), faker.name_nonbinary(), faker.suffix_nonbinary(), faker.phone_number(), faker.ascii_free_email(), faker.address()) ![]() Print("My name is %s %s %s, I'm a gender neutral person. And the code I used to get this output is the following: from faker import Faker Looking at this, it’s amazing how realistic it looks. This is the output of a simple Python script that I wrote to generate fake customer data, or fake people. You can call me at 543.024.8936, or email me at or visit my home at 5144 Rubio Island You can call me at (276)611-1727, or email me at or visit my home at 7409 Peterson Locks Apt. You can call me at 001-09, or email me at or visit my home at 2703 Fitzpatrick Squares Suite 785 Linda Dunn III, I'm a gender neutral person. Faker = Faker(locale='en_US') Let’s look at what it can do firstīefore we dive into the code, let’s have a look at what it can do for us first. ![]()
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