How to create a dataframe of a particular size containing both continuous and categorical values with a uniform random distribution

So, I'm trying to generate some fake random data of a given dimension size. Essentially, I want a dataframe in which the data has a uniform random distribution. The data consist of both continuous and categorical values. I've written the following code, but it doesn't work the way I want it to be.

import random
import pandas as pd
import time
from datetime import datetime

# declare global variables
adv_name = ['soft toys', 'kitchenware', 'electronics',
            'mobile phones', 'laptops']
adv_loc = ['location_1', 'location_2', 'location_3',
       'location_4', 'location_5']
adv_prod = ['baby product', 'kitchenware', 'electronics',
            'mobile phones', 'laptops']
adv_size = [1, 2, 3, 4, 10]
adv_layout = ['static', 'dynamic']  # advertisment layout type on website

# adv_date, start_time, end_time = []
num = 10 # the given dimension

# define function to generate random advert locations
def rand_shuf_loc(str_lst, num):
    lst = adv_loc
    # using list comprehension
    rand_shuf_str = [item for item in lst for i in range(num)]
    return(rand_shuf_str)
    

# define function to generate random advert names
def rand_shuf_prod(loc_list, num):
    rand_shuf_str = [item for item in loc_list for i in range(num)]
    random.shuffle(rand_shuf_str)
    return(rand_shuf_str)

# define function to generate random impression and click data
def rand_clic_impr(num):
    rand_impr_lst = []
    click_lst = []
    for i in range(num):
        rand_impr_lst.append(random.randint(0, 100))
        click_lst.append(random.randint(0, 100))
    return {'rand_impr_lst': rand_impr_lst, 'rand_click_lst': click_lst}

# define function to generate random product price and discount
def rand_prod_price_discount(num):
    prod_price_lst = []  # advertised product price
    prod_discnt_lst = []  # advertised product discount
    
    for i in range(num):
        prod_price_lst.append(random.randint(10, 100))
        prod_discnt_lst.append(random.randint(10, 100))
    
    return {'prod_price_lst': prod_price_lst, 'prod_discnt_lst': prod_discnt_lst}

def rand_prod_click_timestamp(stime, etime, num):
    prod_clik_tmstmp = []
    frmt = '%d-%m-%Y %H:%M:%S'
        
    for i in range(num):
        rtime = int(random.random()*86400)
    
        hours   = int(rtime/3600)
        minutes = int((rtime - hours*3600)/60)
        seconds = rtime - hours*3600 - minutes*60
    
        time_string = '%02d:%02d:%02d' % (hours, minutes, seconds)
        prod_clik_tmstmp.append(time_string)
        time_stmp = [item for item in prod_clik_tmstmp for i in range(num)]
        
    return {'prod_clik_tmstmp_lst':time_stmp}

def main():
    print('generating data...')
    # print('generating random geographic coordinates...')
    # get the impressions and click data
    impression = rand_clic_impr(num)
    clicks = rand_clic_impr(num)
    product_price = rand_prod_price_discount(num)
    product_discount = rand_prod_price_discount(num)
    prod_clik_tmstmp = rand_prod_click_timestamp("20-01-2018 13:30:00",
                                                 "23-01-2018 04:50:34",num)
    lst_dict = {"ad_loc": rand_shuf_loc(adv_loc, num),
                "prod": rand_shuf_prod(adv_prod, num),
                "imprsn": impression['rand_impr_lst'],
                "cliks": clicks['rand_click_lst'],
                "prod_price": product_price['prod_price_lst'],
                "prod_discnt": product_discount['prod_discnt_lst'],
                "prod_clik_stmp": prod_clik_tmstmp['prod_clik_tmstmp_lst']}
    fake_data = pd.DataFrame.from_dict(lst_dict, orient="index")
    res = fake_data.apply(lambda x: x.fillna(0)
                          if x.dtype.kind in 'biufc'
                          # where 'biufc' means boolean, integer,
                          # unicode, float & complex data types
                          else x.fillna(random.randint(0, 100)
                                        )
                          )
    print(res.transpose())
    res.to_csv("fake_data.csv", sep=",")

# invoke the main function
   
if __name__ == "__main__":
    main()

Problem 1

when I execute the above code snippet, it prints fine but when written to csv format, its horizontally positioned; i.e., it looks like this... How do I position it vertically when writing to csv file? What I want is 7 columns (see lst_dict variable above) with n number of rows?

Problem 2 I dont understand why the random date is generated for the first 50 columns and remaining columns are filled with numerical values?

To answer your first question, replace

print(res.transpose())

with

res.transpose() print(res)  

To answer your second question look at the length of the output of the method

rand_shuf_loc() 

it as well as the other helper functions only produce a list of 50 items.
The creation of res using the method

fake_data.apply  

replaces all nan with a random numeric, so it also applies a numeric to the columns without any predefined values.