Add columns to pandas data frame with for-loop - python-3.x

Question

The code block below produces the this table:
Trial Week Branch Num_Dep Tot_dep_amt
1 1 1 4 4200
1 1 2 7 9000
1 1 3 6 4800
1 1 4 6 5800
1 1 5 5 3800
1 1 6 4 3200
1 1 7 3 1600
. . . . .
. . . . .
1 1 8 5 6000
9 19 40 3 2800
Code:
trials=10
dep_amount=[]
branch=41
total=[]
week=1
week_num=[]
branch_num=[]
dep_num=[]
trial_num=[]
weeks=20
df=pd.DataFrame()
for a in range(1,trials):
print("Starting trial", a)
for b in range(1,weeks):
for c in range(1,branch):
depnum = int(np.round(np.random.normal(5,2,1)/1)*1)
acc_dep=0
for d in range(1,depnum):
dep_amt=int(np.round(np.random.normal(1200,400,1)/200)*200)
acc_dep=acc_dep+dep_amt
temp = pd.DataFrame.from_records([{'Trial': a, 'Week': b, 'branch': c,'Num_Dep': depnum, 'Tot_dep_amt':acc_dep }])
df = pd.concat([df, temp])
df = df[['Trial', 'Week', 'branch', 'Num_Dep','Tot_dep_amt']]
df=df.reset_index()
df=df.drop('index',axis=1)
I would like to be able to break branches apart in the for-loop and instead have the resultant df represented with headers:
Trial Week Branch_1_Num_Dep Branch_1_Tot_dep_amount Branch_2_Num_ Dep .....etc
I know this could be done by generating the DF and performing an encoding, but for this task I would like it to be generated in the for loop if possible?

Answer 1

In order to achieve this with minimal changes to your code, you can do something like the following:
df = pd.DataFrame()
for a in range(1, trials):
print("Starting trial", a)
for b in range(1, weeks):
records = {'Trial': a, 'Week': b}
for c in range(1, branch):
depnum = int(np.round(np.random.normal(5, 2, 1) / 1) * 1)
acc_dep = 0
for d in range(1, depnum):
dep_amt = int(np.round(np.random.normal(1200, 400, 1) / 200) * 200)
acc_dep = acc_dep + dep_amt
records['Branch_{}_Num_Dep'.format(c)] = depnum
records['Branch_{}_Tot_dep_amount'.format(c)] = acc_dep
temp = pd.DataFrame.from_records([records])
df = pd.concat([df, temp])
df = df.reset_index()
df = df.drop('index', axis=1)
Overall it seems that what you are doing can be done in more elegant and faster ways. I would recommend taking a look to vectorization as a concept (e.g. here).