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[NOTE: To keep the code as simple as we can, We WILL NOT ADD any user input Varevecations. Assuming that our user will Enter the right inputs.]

The Story:
last week i was reading about Data Cleaning, Deep Learning and Machine Learning, and for all we need some data to play with and testing our code. There are several sites providing a free (to certain limits) Data-set. But while i was working, it just come to my mind Why not to write a code to generate some Fake data-set.

Before I start:
The main question was “What is the subject of the data” or Data about What? This is the main question that will guide us to write our code, and tha answer was very..very fast 🙂 .. at that moment I was holding my Coffee Mug, and just jump to my mind, Data about Coffee … Coffee Consumption. So we are taking the data of Coffee Consumption from a Coffee-Shop, the coffee-shop is collecting the following data: Date-Time (of the order), Coffee-Name, Coffee-Type, Coffee-size, Sex, Rank. Here is in more details..

Dat-Time: Date and Time of Order, Date format: dd-mm-yyyy, the Time format: hh:mm (24h)

Coffee-Name: Such as: [black, latte, espresso, americano, cappuccino, mocha, lungo, flat white, irish, macchiato,ristretto, iced coffee]

Coffee-Type: 3n1 , pods, grounded

Coffee Size: samll, medium, large, venti  

Sex: The person how order it male, female 

Rank: if the customer drink the coffee in the shop, will ask for a ranks (1,10) 1:bad, 10:Grate

Scope of Work: We will write several functions to generate random data for each attribute we have, saving the data into a list, then combining all the lists in a pandas dataframe df and save the data_set df to a csv file. Later on we can re-call the file using pandas command df.read_csv(file_name) and manipulate the data.

.::.. Coding ..::.
Let’s start with Date-Time Function: def fake_date_time(from_y,to_y): In this function we will use a random.randint() to generate numbers for day’s, months and years, also for hours and minites. The function will take two arguments from_y, to_y and will return a string like this: dd-mm-yyyy hh:mm … here is the code ..

Now,Coffee Name: def fake_coffee_name() : for Coffee Name I create a list of Coffee Names (from the net) and use random.choice(coffee_name_list) to select one from the list. this function is a one-line code .. here it is ..

 # fake_coffee_name() function

def fake_coffee_name() : 
    """
        Function to randomly select one from the list. 
        
        Return coffee_name 
        
     """ 
    
    coffee_name_list = ['black', 'latte', 'espresso', 'americano', 'cappuccino', 
                         'mocha', 'lungo', 'flat white', 'irish', 'macchiato', 'ristretto', 
                         'iced coffee'
                         ] 
    return random.choice(coffee_name_list) 

Here are another two Functions def fake_coffee_type(): and def fake_coffee_size(): Both are using the random.choice to select from a list.. here is the code ..

]

More over, in out dataset we need another two variables sex and rank, both are simple and we don’t need to put them in separate function, we will call the random.choice(‘f’,’m’) to select between Male and Female, and random.randint (1,11) to select a Rank between (1 and 10). Here is the main part of the application, we will use a for loop and append all the returns from the function’s in a list (a list for each attribute) after that we will combine all the list in a dataset using pandas command. Here is the code..

 # Main body of the application

# empty lists of the columns
d_d =[] 
cn_d =[] 
ct_d =[] 
cs_d =[] 
s_d =[] 
r_d = [] 

number_of_rows = 1000 

for x in range(1,number_of_rows): 
    d_d.append(fake_date_time(2000,2022)) 
    cn_d.append(fake_coffee_name()) 
    ct_d.append(fake_coffee_type()) 
    cs_d.append(fake_coffee_size()) 
    s_d.append(random.choice(['f','m'])) 
    r_d.append(random.randint (1,11)) 
    

the_data ={'date_time':d_d, 'c_name':cn_d, 'c_type':ct_d, 'c_size':cs_d, 'sex':s_d, 'rank':r_d } 

df = pd.DataFrame (the_data) 

# to create a CSV file and save the data in it. 
file_name = 'coffee_consumption_v1.csv'
df.to_csv(file_name, index=False)

print(f'\n\n    The data been generated, a file named: {file_name} saved')

Now we have a file called: coffee_consumption_v1.csv saved in the same directory of the .py code file. Here is a sample of the data.

We will stop here, and we will do another post to apply pandas commands over the dataset.

..:: Have Fun with Coding ::.. 🙂

To Download my Python code (.py) files Click-Here
Also the Date-Set coffee_consumption_v1 file (.csv) files is available in the same page.

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By: Ali Radwani