Fast method to create nested list with different types: numpy, pandas or list concatenation?

I am trying to accelerate the code below that produces a list of lists with different types for each column. I originally created pandas dataframe and then converted it to list, but this seems to be fairly slow. How can I create this list faster, by say an order of magnitude? All columns are constant except one.

import pandas as pd
import numpy as np
import time
import datetime

def overflow_check(x):
    # in SQL code the column is decimal(13, 2)
    p=13
    s=3
    max_limit = float("9"*(p-s) + "." + "9"*s)
    #min_limit =  0.01 #float("0" + "." + "0"*(s-2) + '1')
    #min_limit = 0.1
    if np.logical_not(isinstance(x, np.ndarray)) or len(x) < 1:
        raise Exception("Non-numeric or empty array.")
    else:
        #print(x)
        return x * (np.abs(x) < max_limit) + np.sign(x)* max_limit * (np.abs(x) >= max_limit)

def list_creation(y_forc):
    
    
    backcast_length = len(y_forc)
    
    backcast = pd.DataFrame(data=np.full(backcast_length, 2),
                            columns=['TypeId'])


    backcast['id2'] = None
    backcast['Daily'] = 1
    backcast['ForecastDate'] = y_forc.index.strftime('%Y-%m-%d')
    backcast['ReportDate'] = pd.to_datetime('today').strftime('%Y-%m-%d')
    backcast['ForecastMethodId'] = 1
    backcast['ForecastVolume'] = overflow_check(y_forc.values)
    backcast['CreatedBy'] = 'test'
    backcast['CreatedDt'] = pd.to_datetime('today')

  
    return backcast.values.tolist()

i=pd.date_range('05-01-2010', '21-05-2018', freq='D')
x=pd.DataFrame(index=i, data = np.random.randint(0, 100, len(i)))

t=time.perf_counter()
y =list_creation(x)
print(time.perf_counter()-t)

This should be a bit faster, it just directly creates the list:

def list_creation1(y_forc):
zipped = zip(y_forc.index.strftime('%Y-%m-%d'), overflow_check(y_forc.values)[:,0])
t = pd.to_datetime('today').strftime('%Y-%m-%d')
t1 =pd.to_datetime('today')
return [
[2, None, 1, i, t,
1, v, 'test', t1] 
for i,v in zipped
]
%%timeit
list_creation(x)
> 29.3 ms ± 468 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%%timeit
list_creation1(x)
> 17.1 ms ± 517 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Edit: one of the large issues with the slowness is the time it takes to go from datetime to specified format. if we can get rid of that by phrasing it as the following:

def list_creation1(i, v):
zipped = zip(i, overflow_check(np.array([[_x] for _x in v]))[:,0])
t = pd.to_datetime('today').strftime('%Y-%m-%d')
t1 =pd.to_datetime('today')
return [
[2, None, 1, i, t,
1, v, 'test', t1] 
for i,v in zipped
]
start = datetime.datetime.strptime("05-01-2010", "%d-%m-%Y")
end = datetime.datetime.strptime("21-05-2018", "%d-%m-%Y")
i = [(start + datetime.timedelta(days=x)).strftime("%d-%m-%Y") for x in range(0, (end-start).days)]
x=np.random.randint(0, 100, len(i))

Then this is now a lot faster:

%%timeit
list_creation1(i, x)
> 1.87 ms ± 24.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)