Creating random samples with same number of instances for each element

Question

In one part of my project , I need to create a random month-names and store them into a data-frame column. currently I am using the following snippet: First, Creating a data-frame of predefined size:

df = pd.DataFrame(index=range(size))

then creating 120 random Time-Stamp and storing them into ['Timestamp'] column:

df["Timestamp"] = [ pd.Timestamp(2017, np.random.randint(1,13), 1) for _ in range(120) ]

at the end extracting the Months and stroing them into ['STD_Months'] column :

df["STD_Months"] = df["Timestamp"].apply(lambda x: x.strftime('%B'))

this creates random months but with different quantity , I mean we may have 10 January out of 120 samples , 14 May , 8 December etc(Not equal quantity)

How can I modify my code to have the same quantity of random samples(10 instances of each month name:10 January , 10 February , .... ,10 December)

John Coleman · Accepted Answer · 2017-08-26 11:54:00Z

1

One way is to create a non-random list and then shuffle it:

import random

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
months *= 10
random.shuffle(months)

Then just use months as the column.

answered Aug 26, 2017 at 11:54

John Coleman

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1 Comment

Nima Over a year ago

Thanks John, I was also thinking about a simple solution like this @John Coleman

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