I have a DataFrame with a format like this (simplified)
a b 43
a c 22
I would like this to be split up in the following way.
a b 20
a b 20
a b 1
a b 1
a b 1
a c 20
a c 1
a c 1
Where I have as many rows as the number divides by 20, and then as many rows as the remainder. I have a solution that basically iterates over the rows and fills up a dictionary which can then be converted back to Dataframe but I was wondering if there is a better solution.
You can use floor divison with modulo first and then create new DataFrame by constructor with numpy.repeat.
Last need numpy.concatenate with list comprehension for C:
a,b = df.C // 20, df.C % 20
#print (a, b)
cols = ['A','B']
df = pd.DataFrame({x: np.repeat(df[x], a + b) for x in cols})
df['C'] = np.concatenate([[20] * x + [1] * y for x,y in zip(a,b)])
print (df)
A B C
0 a b 20
0 a b 20
0 a b 1
0 a b 1
0 a b 1
1 a c 20
1 a c 1
1 a c 1
ValueError: operands could not be broadcast together with shape (2,) (3810,) on trying this at the np.repeat line.
df = pd.DataFrame(dict(A=['a', 'a'], B=['b', 'c'], C=[43, 22])) ?Setup
Consider the dataframe df
df = pd.DataFrame(dict(A=['a', 'a'], B=['b', 'c'], C=[43, 22]))
df
A B C
0 a b 43
1 a c 22
m = np.array([20, 1])
dm = list(zip(*np.divmod(df.C.values, m[0])))
# [(2, 3), (1, 2)]
rep = [sum(x) for x in dm]
new = np.concatenate([m.repeat(x) for x in dm])
df.loc[df.index.repeat(rep)].assign(C=new)
A B C
0 a b 20
0 a b 20
0 a b 1
0 a b 1
0 a b 1
1 a c 20
1 a c 1
1 a c 1
