Randomly select elements from string in a dataframe - python-3.x

Question

I have dataframe with 7 string columns:
bul; age; gender; hh; pn; freq_pn; rcrds_to_select
1; 2; 5; 1; ['35784905', '40666303', '47603805', '68229102'];4;3
2; 3; 3; 3; ['06299501', '07694901', '35070201'];3;2
In the last column I have the number of id's from "pn" column that I need to select randomly. Example: in the first row I have 4 id's ['35784905', '40666303', '47603805', '68229102'] and I need to select 3 random id's and remove the not selected one. There can be rows with only one id. I came to the conclusion that I need to turn the values in tuples and store them in another column ('pnTuple'). I don't know if this is the right way.
mass_grouped3['pnTuple'] = [tuple(x) for x in mass_grouped3['pn'].values]
I think random.shuffle will do the job, but have no idea how to implement it in my script. I was thinking something like this, but is not working:
for row in mass_grouped3['pnTuple']:
list = list(mass_grouped3['pnTuple'])
whitelist = random.shuffle(list)
Any ideas how to do this selection are appreciated.

Answer 1

You want to randomly select 1 from every row and make the rest 0. Here's one approach. Sample the indices and based on indices assign 1. i.e
idx = pd.DataFrame(np.stack(np.where(df==1))).T.groupby(0).apply(lambda x: x.sample(1)).values
# array([[0, 2],
# [1, 1],
# [2, 0],
# [3, 3]])
ndf = pd.DataFrame(np.zeros(df.shape),columns=df.columns)
ndf.values[idx[:,0],idx[:,1]] = 1
W1 W2 W3 W4
0 0 0 1 0
1 1 0 0 0
2 1 0 0 0
3 0 1 0 0

Answer 2

Welcome to StackOverflow! Hope this helps
Lets go step by step
First lets construct our random function that can select 3
>>> import random
>>> random.choices(['35784905', '40666303', '47603805', '68229102'], k=3)
['68229102', '40666303', '35784905']
I have a sample data frame, df with columns with same data as yours
>>> df
a b
0 12 [35784905, 40666303, 47603805, 68229102]
1 12 [06299501, 07694901, 35070201]
>>> df['b']
0 [35784905, 40666303, 47603805, 68229102]
1 [06299501, 07694901, 35070201]
Name: b, dtype: object
>>> df['b'].map(lambda alist: random.choices(alist, k=3) if len(alist) > 3 else alist)
0 [35784905, 68229102, 35784905]
1 [06299501, 07694901, 35070201]
Name: b, dtype: object
>>> df['b'] = df['b'].map(lambda alist: random.choices(alist, k=3) if len(alist) > 3 else alist)
Using pandas map operation to apply this data transformation to whole columns
Note: We are using a lambda function lambda alist: random.choices(alist, k=3) if len(alist) > 3 else alist to ensure that each list has more than 3 items, and only then apply this operation.
It might be a little new, but this a standard way of writing code in python. Learn more about Python, lambda function and pandas for some time.