Pandas: create new column in df with random integers from range

Question

I have a pandas data frame with 50k rows. I’m trying to add a new column that is a randomly generated integer from 1 to 5.

If I want 50k random numbers I’d use:

df1['randNumCol'] = random.sample(xrange(50000), len(df1))

but for this I’m not sure how to do it.

Side note in R, I’d do:

sample(1:5, 50000, replace = TRUE)

Any suggestions?

Asked By: screechOwl

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Source

Answer 1

One solution is to use numpy.random.randint:

import numpy as np
df1['randNumCol'] = np.random.randint(1, 6, df1.shape[0])

Or if the numbers are non-consecutive (albeit slower), you can use this:

df1['randNumCol'] = np.random.choice([1, 9, 20], df1.shape[0])

In order to make the results reproducible you can set the seed with numpy.random.seed (e.g. np.random.seed(42))

Answered By: Matt

Answer 2

To add a column of random integers, use randint(low, high, size). There’s no need to waste memory allocating range(low, high) which is what that used to do in Python 2.x; that could be a lot of memory if high is large.

df1['randNumCol'] = np.random.randint(0,5, size=len(df1))

Notes:

when we’re just adding a single column, size is just an integer. In general if we want to generate an array/dataframe of randint()s, size can be a tuple, as in Pandas: How to create a data frame of random integers?)
in Python 3.x range(low, high) no longer allocates a list (potentially using lots of memory), it produces a range() object
use random.seed(...) for determinism and reproducibility

Answered By: smci

Answer 3

An option that doesn’t require an additional import for numpy:

df1['randNumCol'] = pd.Series(range(1,6)).sample(int(5e4), replace=True).array

Answered By: shortorian

Pandas: create new column in df with random integers from range

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