How can I create a 2D array with random numbers without using NumPy (Python)
3 Answers
You could use the random module and populate a nested list with a list comprehension
import random
low = 0
high = 10
cols = 10
rows = 5
[random.choices(range(low,high), k=cols) for _ in range(rows)]
[[5, 7, 1, 0, 6, 5, 9, 2, 5, 6],
[9, 2, 3, 0, 6, 7, 0, 6, 6, 3],
[2, 7, 9, 2, 4, 5, 5, 9, 9, 4],
[2, 6, 7, 8, 5, 1, 4, 4, 4, 4],
[9, 2, 8, 4, 5, 2, 0, 1, 2, 1]]
For a nested list of floats, you can map each range with float:
choices = list(map(float, range(low,high)))
[random.choices(choices , k=cols) for _ in range(rows)]
[[0.0, 3.0, 9.0, 1.0, 5.0, 3.0, 7.0, 4.0, 2.0, 4.0],
[5.0, 8.0, 7.0, 7.0, 7.0, 2.0, 9.0, 8.0, 2.0, 6.0],
[3.0, 3.0, 1.0, 9.0, 2.0, 8.0, 7.0, 2.0, 9.0, 7.0],
[7.0, 8.0, 1.0, 2.0, 0.0, 6.0, 7.0, 6.0, 0.0, 9.0],
[3.0, 3.0, 3.0, 1.0, 7.0, 8.0, 3.0, 9.0, 2.0, 8.0]]
4 Comments
tjardskleener
Wow, thanks, that works perfectly!! Can I also use float numbers instead of integers?
tjardskleener
You're awesome! But can this also generate random digits after the decimal point? In your code, it will always be 00
yatu
In such case
[[random.random() for i in range(cols)] for _ in range(rows)] @tjardskleenertjardskleener
Can I extend the range of this? It's from 0 to 1
[[random.random() for _ in range(3)] for _ in range(7)]
This generates a 2D array of size [7, 3] with random float in [0, 1) interval.
You use nested list comprehensions. The outer one builds a main list while the inner one builds lists that are used as elements of the main list.
Edit
You can then tweak it for your needs. For example:
import random
import pprint
NUM_ROWS=7
NUM_COLS=3
MAX_VAL=1000.50
MIN_VAL=-MAX_VAL
pprint.pprint([
[random.uniform(MIN_VAL, MAX_VAL) for _ in NUM_COLS]
for _ in NUM_ROWS
])
This prints a list/array/matrix of 7 lines and 3 colums with random floats in [-1000.50, 1000.50) interval:
[[561.3985362160208, -157.9871329592354, -245.7102502320838],
[-817.8786101352823, -528.9769041860632, 102.67728824479877],
[-886.6488625065194, 941.0504221837489, -458.58155555154565],
[6.69525238666165, 919.5903586746183, 66.70453038938808],
[754.3718741592056, -121.25678519054622, -577.7163532922043],
[-352.3158889341157, 254.9985130814921, -365.0937338693691],
[563.0633042715097, 833.2963094260072, -946.6729221921638]]
The resulting array can be indexed with array[line][column].
5 Comments
tjardskleener
Thanks!! Can I also use float numbers instead of integers in a range of -1000.50 to 1000.50?
AlexisBRENON
@tjardskleener Yes, that's easy use
uniform instead of random. [[random.uniform(-1000.50, 1000.50) for _ in range(3)] for _ in range(7)]
Gábor Fekete
random.random() gives you a number between [0,1), so to get values between [-1000.50,1000.50) you need to modify the returned value like this: random.random() * 2001.0 - 1000.5 or use the random.uniform as mentioned by @AlexisBRENON.AlexisBRENON
@GáborFekete The
random.uniform method do this internaly but improve clarity.tjardskleener
You guys are insane! Thank you very much for your help!!
import pandas as pd
from random import randint
outside_size = 10 # How many nested lists to include
inside_size = 10 # How many numbers will be in an inside list
outside_list = [] # The final list
for i in range(0, outside_size, 1):
_list = [] # Create new "inside" (nested) list
for j in range(0, inside_size, 1): # Populate the nested list with random numbers
_list.append(randint(0, 100))
outside_list.append(_list) # Add the inside (nested) list to the outside (final) list
df = pd.DataFrame(outside_list) # Create the dataframe from it
print(df)
OUTPUT:
0 1 2 3 4 5 6 7 8 9
0 63 79 100 42 98 45 80 85 71 98
1 65 38 55 5 49 19 99 87 36 74
2 49 76 71 56 54 30 90 50 96 26
3 31 46 79 38 13 66 10 31 8 59
4 0 98 7 67 87 7 95 79 94 50
5 79 44 86 83 1 79 15 80 31 79
6 86 5 19 78 78 87 77 8 43 90
7 30 59 4 4 68 85 95 34 92 48
8 65 39 28 76 12 59 28 29 15 56
9 34 22 68 57 97 69 59 62 12 29
For speed and brevity of code, you can use a nested list comprehension to get the same effect ...
outside_list = [[randint(0, 100) for j in range(0, inside_size)] for i in range(0, outside_size) ]
df = pd.DataFrame(outside_list)
