In today's big data era, the value of data can be imagined. Sometimes in order to test, we need to simulate the real environment, but we can't directly use the real data, so we need to make some data.

Compared with Excel, I still think Python makes such "virtual" data more time-saving and labor-saving.

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Mobile phone name: the boss asked to simulate a batch of data for project experiments. Because some real data can not be displayed, I need to simulate some data, including: name, location, address, phone number, ID number, date of birth, email, etc.

Of course, this batch of data must be written into Excel and handed over to the boss at one time. So, such a demand, will you do it?

Actual combat: simulate 1w pieces of data written into Excel

Before talking about the foundation, let's go directly to the actual combat to let you experience how to write the generated simulation data into the Excel file.

from faker import Faker
import pandas as pd
 
fake = Faker(["zh_CN"])
Faker.seed(0)

def get_data():
    key_list = ["full name","Detailed address","Province","cell-phone number","ID number","date of birth","mailbox"]
    name = fake.name()
    address = fake.address()
    province = address[:3]
    number = fake.phone_number()
    id_card = fake.ssn()
    birth_date = id_card[6:14]
    email = fake.email()
    info_list = [name,address,province,number,id_card,birth_date,email]
    person_info = dict(zip(key_list,info_list))
    return person_info


df = pd.DataFrame(columns=["full name","Detailed address","Province","cell-phone number","ID number","date of birth","mailbox"])
for i in range(10000):
    person_info = [get_data()]
    df1 = pd.DataFrame(person_info)
    df = pd.concat([df,df1])
df.to_excel("Analog data.xlsx",index=None)

The results are as follows:

Python library explanation

How to use such an easy-to-use Python library?

We can directly use the following code to complete the installation of this library.

pip install Faker -i https://pypi.tuna.tsinghua.edu.cn/simple/

Before using, use the following code to import this library.

from faker import Faker

Before writing to Excel, let's talk about the usage of each function.

1. Generate name

fake = Faker(locale='zh_CN')name = fake.name()name

The results are as follows:

2. Generate detailed address

address = fake.address()address

The results are as follows:

3. Province of generation

province = address[:3]province

The results are as follows:

Because the result of this function is different every time, I use slicing to generate provinces. Of course, there are also specific functions to generate provinces.

fake.province()

The results are as follows:

4. Generate mobile phone number

number = fake.phone_number()number

The results are as follows:

5. generate ID number.

id_card = fake.ssn()id_card

The results are as follows:

6. Date of birth

birth_date = id_card[6:14]birth_date

The results are as follows:

7. Generate mailbox

email = fake.email()email

The results are as follows:

supplement

Of course, faker library can not only help us generate the above information, but also many other methods can be used. These methods are divided into the following categories:

• Address address

• person: gender, name, etc

• barcode class

• Color color class

• Company category: company name, email, company name prefix, etc

• credit_card bank card category: card number, validity period, type, etc

• currency

• date_time date class: date, year, month, etc

• File class: file name, file type, file extension, etc

• internet class

• Job job

• lorem random number false text

• misc miscellaneous class

• phone_number mobile phone number category: mobile phone number and operator number segment

• Python data

• profile character description information: name, gender, address, company, etc

• ssn social security code (ID number)

• user_agent user agent

For the use of these methods, we directly refer to faker's official website, which is very convenient to use.

faker.readthedocs.io/en/master/providers.html

1. address

fake.country()  # country fake.city()  # city fake.city_suffix()  # Suffix of city,City or county fake.address()  # address fake.street_address()  # street fake.street_name()  # Street name fake.postcode()  # Zip code fake.latitude()  # dimension fake.longitude()  # longitude

2. person

fake.name() # full name fake.last_name() # surname fake.first_name() # name fake.name_male() # Male name fake.last_name_male() # Male surname fake.first_name_male() # Male name fake.name_female() # Female name

3. color

fake.hex_color() # 16 Color of hexadecimal representation fake.rgb_css_color() # css Useful rgb colour fake.rgb_color()  # express rgb Color string fake.color_name() # Color name fake.safe_hex_color()  #Security hex color fake.safe_color_name() # Safety color name

4. company

fake.company() # Company name fake.company_suffix() # Company name suffix

5. credit_card bank credit card

fake.credit_card_number(card_type=None) # Card number
fake.credit_card_provider(card_type=None) # Card provider
fake.credit_card_security_code(card_type=None)# Card security password
fake.credit_card_expire() # Validity of card
fake.credit_card_full(card_type=None) # Complete card information

6. date_time date

fake.date_time(tzinfo=None)
fake.iso8601(tzinfo=None) # Date of output in iso8601 standard
fake.date_time_this_month(before_now=True, after_now=False, tzinfo=None) # A date of the month
fake.date_time_this_year(before_now=True, after_now=False, tzinfo=None) # Date of the year
fake.date_time_this_decade(before_now=True, after_now=False, tzinfo=None)  # A date in this year
fake.date_time_this_century(before_now=True, after_now=False, tzinfo=None)  # A date in this century
fake.date_time_between(start_date="-30y", end_date="now", tzinfo=None)  # A random time between two times
fake.timezone() # time zone
fake.time(pattern="%H:%M:%S") # Time (customizable format)
fake.am_pm() # Random morning and afternoon
fake.month() # Random month
fake.month_name() # Random month name
fake.year() # Random year
fake.day_of_week() # Random day of the week
fake.day_of_month() # One day in a random month
fake.time_delta() # Random time delay
fake.date_object()  # Random Date object
fake.time_object() # Random time object
fake.unix_time() # Random unix time (timestamp)
fake.date(pattern="%Y-%m-%d") # Random date (customizable format)
fake.date_time_ad(tzinfo=None)  # Random date after AD

7. file

fake.file_name(category="image", extension="png") # File name (specify file type and suffix)
fake.file_name() # Randomly generate various types of files
fake.file_extension(category=None) # file extension
fake.mime_type(category=None) # mime-type

8. internet

fake.ipv4(network=False)  # ipv4 address
fake.ipv6(network=False)  # ipv6 address
fake.uri_path(deep=None) # uri path
fake.uri_extension() # uri extension
fake.uri() # uri
fake.url() # url
fake.image_url(width=None, height=None)  # Picture url
fake.domain_word() # Domain name subject
fake.domain_name() # domain name
fake.tld() # Domain suffix 
fake.user_name() # user name
fake.user_agent() # UA
fake.mac_address() # MAC address
fake.safe_email() # Secure mailbox
fake.free_email() # Free email
fake.company_email()  # Company email
fake.email() # mailbox

9. job

fake.job()#Job position

10. lorem random number of fake articles

fake.text(max_nb_chars=200) # Randomly generate an article
fake.word() # Random words
fake.words(nb=3)  # Randomly generate a few words
fake.sentence(nb_words=6, variable_nb_words=True)  # Randomly generate a sentence
fake.sentences(nb=3) # Randomly generate several sentences
fake.paragraph(nb_sentences=3, variable_nb_sentences=True)  # Randomly generate a text (string)
fake.paragraphs(nb=3)  # Randomly generate several paragraphs of text (list)

11. phone_number phone number

fake.phone_number() # phone number
fake.phonenumber_prefix() # Operator section, top three mobile phone numbers

12. ssn social security code (ID card)

fake.ssn() # Generate ID number randomly (18 bits)

13. user_agent user agent

fake.user_agent()

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