Dummy data: generating random text and numerical data into one csv/excel file?

Question

So I'm trying to generate dummy data that contains 3 columns: sq. feet, price and Borough. For the first two, which are purely numerical this is fine. I have 50,000 rows of data for both on a spreadsheet. However, when I add Borough and specify random values from a list I receive the following output:

       Sq. feet    Price  Borough
0           112   345382        5
1           310   901500        5
2           215   661033        5
3           147  1038431        5
4           212   296497        5

I have not used a package associated with numerical generation like np.random.randint

Instead I used "Borough" : random.randrange(len(word))

Where have I gone wrong?

My code below

import random

import pandas as pd
import numpy as np

WORDS = ["Chelsea", "Kensington", "Westminster", "Pimlico", "Bank", "Holborn", "Camden", "Islington", "Angel", "Battersea", "Knightsbridge", "Bermondsey", "Newham"]
word = random.choice(WORDS)
np.random.seed(1)
data3 = pd.DataFrame({"Sq. feet" : np.random.randint(low=75, high=325, size=50000),
                     "Price" : np.random.randint(low=200000, high=1250000, size=50000),
                      "Borough" : random.randrange(len(word))
                     })

df = pd.DataFrame(data3)
df.to_csv("/Users/thomasmcnally/PycharmProjects/real_estate_dummy_date/realestate.csv", index=False)

print(df)

I'm expecting a random line of word values from the WORDS [], instead the return value is just the number 5. It's obviously meaningless making another module just for the text-based data and printing them in different files.

Answer 1

I guess you want to generate a list of 50,000 random choices from WORDS - which itself could usefully be renamed BOROUGHS:

import random
import pandas as pd
import numpy as np

SIZE = 50_000
BOROUGHS = ["Chelsea", "Kensington", "Westminster", "Pimlico", "Bank", "Holborn", "Camden", "Islington", "Angel", "Battersea", "Knightsbridge", "Bermondsey", "Newham"]

np.random.seed(1)
data3 = pd.DataFrame({"Sq. feet" : np.random.randint(low=75, high=325, size=SIZE),
  "Price" : np.random.randint(low=200000, high=1250000, size=SIZE),
  "Borough" : [random.choice(BOROUGHS) for _ in range(SIZE)]
})

df = pd.DataFrame(data3)
df.to_csv("realestate.csv", index=False)
print(df)

Output

       Sq. feet    Price      Borough
0           112   345382      Pimlico
1           310   901500    Battersea
2           215   661033      Holborn
3           147  1038431  Westminster
4           212   296497      Holborn
...         ...      ...          ...
49995       252  1065034      Holborn
49996       117   752615      Holborn
49997       238   803058       Camden
49998       147  1163555         Bank
49999       269   888623  Westminster

---

Aside... wherever you have a number repeated all over your code, like your 50,000, t's generally a good idea to make it a variable and declare it up at the top, then it can be changed without causing a maintenance nightmare for some poor future programmer looking all over for every occurrence of 50,000.

This construct is called a "list comprehension" if you want to learn about them:

[random.choice(BOROUGHS) for _ in range(SIZE)]