pandas fill column with random numbers with a total for each row - python-3.x

Question

I've got a pandas dataframe like this:
id foo
0 A col1
1 A col2
2 B col1
3 B col3
4 D col4
5 C col2
I'd like to create four additional columns based on unique values in foo column. col1,col2, col3, col4
id foo col1 col2 col3 col4
0 A col1 75 20 5 0
1 A col2 20 80 0 0
2 B col1 82 10 8 0
3 B col3 5 4 80 11
4 D col4 0 5 10 85
5 C col2 12 78 5 5
The logic for creating the columns is as follows:
if foo = col1 then col1 contains a random number between 75-100 and the other columns (col2, col3, col4) contains random numbers, such that the total for each row is 100
I can manually create a new column and assign a random number, but I'm unsure how to include the logic of sum for each row of 100.
Appreciate any help!

Answer 1

My two cents
d=[]
s=np.random.randint(75,100,size=6)
for x in 100-s:
a=np.random.randint(100, size=3)
b=np.random.multinomial(x, a /a.sum())
d.append(b.tolist())
s=[np.random.choice(x,4,replace= False) for x in np.column_stack((s,np.array(d))) ]
df=pd.concat([df,pd.DataFrame(s,index=df.index)],1)
df
id foo 0 1 2 3
0 A col1 16 1 7 76
1 A col2 4 2 91 3
2 B col1 4 4 1 91
3 B col3 78 8 8 6
4 D col4 8 87 3 2
5 C col2 2 0 11 87

Answer 2

IIUC,
df['col1'] = df.apply(lambda x: np.where(x['foo'] == 'col1', np.random.randint(75,100), np.random.randint(0,100)), axis=1)
df['col2'] = df.apply(lambda x: np.random.randint(0,100-x['col1'],1)[0], axis=1)
df['col3'] = df.apply(lambda x: np.random.randint(0,100-x[['col1','col2']].sum(),1)[0], axis=1)
df['col4'] = 100 - df[['col1','col2','col3']].sum(1).astype(int)
df[['col1','col2','col3','col4']].sum(1)
Output:
id foo col1 col2 col3 col4
0 A col1 92 2 5 1
1 A col2 60 30 0 10
2 B col1 89 7 3 1
3 B col3 72 12 0 16
4 D col4 41 52 3 4
5 C col2 72 2 22 4

Answer 3

My Approach
import numpy as np
def weird(lower, upper, k, col, cols):
first_num = np.random.randint(lower, upper)
delta = upper - first_num
the_rest = np.random.rand(k - 1)
the_rest = the_rest / the_rest.sum() * (delta)
the_rest = the_rest.astype(int)
the_rest[-1] = delta - the_rest[:-1].sum()
key = lambda x: x != col
return dict(zip(sorted(cols, key=key), [first_num, *the_rest]))
def f(c): return weird(75, 100, 4, c, ['col1', 'col2', 'col3', 'col4'])
df.join(pd.DataFrame([*map(f, df.foo)]))
id foo col1 col2 col3 col4
0 A col1 76 2 21 1
1 A col2 11 76 11 2
2 B col1 75 4 10 11
3 B col3 0 1 97 2
4 D col4 5 4 13 78
5 C col2 9 77 6 8

Answer 4

If we subtract the numbers between 75-100 by 75, the problem become generating a table of random number between 0-25 whose each row sums to 25. That can be solve by reverse cumsum:
num_cols = 4
# generate random number and sort them in each row
a = np.sort(np.random.randint(0,25, (len(df), num_cols)), axis=1)
# create a dataframe and attach a last column with values 25
new_df = pd.DataFrame(a)
new_df[num_cols] = 25
# compute the difference, which are our numbers and add to the dummies:
dummies = pd.get_dummies(df.foo) * 75
dummies += new_df.diff(axis=1).fillna(new_df[0]).values
And dummies is
col1 col2 col3 col4
0 76.0 13.0 2.0 9.0
1 1.0 79.0 2.0 4.0
2 76.0 5.0 8.0 9.0
3 1.0 3.0 79.0 10.0
4 1.0 2.0 1.0 88.0
5 1.0 82.0 1.0 7.0
which can be concatenated to the original dataframe.