I know that if I use randn, the following code gives me what I am looking for, but with elements from a normal distribution. But what if I just wanted random integers?
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))
randint works by providing a range, but not an array like randn. So how do I do this with random integers between some range?
numpy.random.randint accepts a third argument (size) , in which you can specify the size of the output array. You can use this to create your DataFrame -
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
Here - np.random.randint(0,100,size=(100, 4)) - creates an output array of size (100,4) with random integer elements between [0,100) .
Demo -
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
which produces:
A B C D
0 45 88 44 92
1 62 34 2 86
2 85 65 11 31
3 74 43 42 56
4 90 38 34 93
5 0 94 45 10
6 58 23 23 60
.. .. .. .. ..
pandas.DataFrame(x, y) when x is a numpy.ndarray (which in this case is a 2d matrix like thing)?The recommended way to create random integers with NumPy these days is to use numpy.random.Generator.integers. (documentation)
import numpy as np
import pandas as pd
rng = np.random.default_rng()
df = pd.DataFrame(rng.integers(0, 100, size=(100, 4)), columns=list('ABCD'))
df
----------------------
A B C D
0 58 96 82 24
1 21 3 35 36
2 67 79 22 78
3 81 65 77 94
4 73 6 70 96
... ... ... ... ...
95 76 32 28 51
96 33 68 54 77
97 76 43 57 43
98 34 64 12 57
99 81 77 32 50
100 rows × 4 columns
You can also use np.random.Generator.choice.
df = pd.DataFrame(np.random.default_rng().choice(100, size=(100, 4)), columns=['A','B','C','D'])
The advantage of this method over integers is that you can choose from any list / array you want. For example, if you want to generate random sample from [2, 5, 10], then
df = pd.DataFrame(np.random.default_rng().choice([2,5,10], size=(100, 4)), columns=['A','B','C','D'])
You can even associate a probability distribution to sample entries. For example, if you want to choose 2 with p=0.8, and 5 with p=0.2, you can do so by, passing p= argument.
df = pd.DataFrame(np.random.default_rng().choice([2,5], p=[.8,.2], size=(100, 4)), columns=['A','B','C','D'])
Also, with the Generator, choice is as fast as integers and faster than randint.
%timeit pd.DataFrame(np.random.default_rng().choice(100, size=(100_000,4)), columns=[*'ABCD'])
# 3.34 ms ± 308 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit pd.DataFrame(np.random.default_rng().integers(0, 100, size=(100_000,4)), columns=[*'ABCD'])
# 3.81 ms ± 708 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit pd.DataFrame(np.random.randint(100, size=(100_000,4)), columns=[*'ABCD'])
# 6.78 ms ± 776 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)