Thank you, guys. I have slightly modified the code to fill in data automatically from the .csv files in the google drive. It seems the code works well.

from google.colab import drive
drive.mount('/content/drive')

!pip install pulp
import numpy as np
import pandas as pd
import pulp

#import data
path="/content/drive/MyDrive/Colab Notebooks/LPanelProduction/"

jobmold=pd.read_csv(path+"jobmold.csv")
jobmoldcontent=jobmold.iloc[:,0].tolist()
jn=len(jobmoldcontent)

jobmoldcontentdata=[]
for i in range(jn):
  jobmoldcontentdata.append(jobmoldcontent[i].split('|'))

molddata=[]
for i in range(jn):
  temp=jobmoldcontentdata[i][3]
  molddata.append(int(temp))

def count_distinct_np(arr):
  return len(np.unique(arr))

mn=count_distinct_np(molddata)

moldchangecost=pd.read_csv(path+"moldchangecost.csv")
moldchangecostcontent=moldchangecost.iloc[:,0].tolist()

moldchangecostcontentdata=[]
for i in range(len(moldchangecostcontent)):
  moldchangecostcontentdata.append(moldchangecostcontent[i].split('|'))

moldchangecostdata=[]
for i in range(mn):
  for j in range(mn):
    moldchangecostdata_row=[]
    for k in range(len(moldchangecostcontentdata)):
      temp1=moldchangecostcontentdata[k][1] #prev_mold_ID
      temp2=moldchangecostcontentdata[k][3] #next_mold_ID
      temp3=moldchangecostcontentdata[k][4] #cost
      if int(temp1)==i+1 and int(temp2)==j+1: moldchangecostdata.append(int(temp3))

#main
molds=pd.Series(index=pd.RangeIndex(name='job',start=1,stop=jn+1),
                name='mold',data=molddata)

prev_mold_idx = pd.RangeIndex(name='prev_mold', start=1, stop=mn+1)
next_mold_idx = pd.RangeIndex(name='next_mold', start=1, stop=mn+1)
mold_change_costs = pd.Series(
    index=pd.MultiIndex.from_product((prev_mold_idx, next_mold_idx)),
    name='cost', data=moldchangecostdata,
)

job_change_idx = pd.MultiIndex.from_product(
    (molds.index, molds.index),
    names=('source_job', 'dest_job'),
)

job_changes = pd.Series(
    index=job_change_idx,
    name='job_change',
    data=pulp.LpVariable.matrix(
        name='job_change', indices=job_change_idx, cat=pulp.LpBinary,
    ),
)

mold_change_idx = pd.MultiIndex.from_product((
    pd.RangeIndex(name='prev_job', start=1, stop=len(molds)),
    prev_mold_idx, next_mold_idx,
))
mold_change_idx = mold_change_idx[
    mold_change_idx.get_level_values('prev_mold') != mold_change_idx.get_level_values('next_mold')
]
all_mold_costs = pd.Series(
    index=mold_change_idx,
    name='mold_cost',
    data=pulp.LpVariable.matrix(
        name='mold_cost', indices=mold_change_idx, cat=pulp.LpContinuous, lowBound=0,
    ),
)

prob = pulp.LpProblem(name='job_sequence', sense=pulp.LpMinimize)
prob.setObjective(pulp.lpSum(all_mold_costs))

# Job changes must be assigned exclusively
for source_job, subtotal in job_changes.groupby('source_job').sum().items():
    prob.addConstraint(
        name=f'excl_s{source_job}',
        constraint=1 == subtotal,
    )
for dest_job, subtotal in job_changes.groupby('dest_job').sum().items():
    prob.addConstraint(
        name=f'excl_d{dest_job}',
        constraint=1 == subtotal,
    )

for (prev_job, prev_mold, next_mold), cost in all_mold_costs.items():
    # if both the relevant prev job and next job are assigned, this is the cost
    cost_if_assigned = mold_change_costs[(prev_mold, next_mold)]

    # series of job assignment variables for any source job having 'prev_mold', and dest job 'prev_job'
    prev_job_changes = job_changes.loc[(molds.index[molds == prev_mold], prev_job)]

    # series of job assignment variables for any source job having 'next_mold', and dest job 'next_job'
    next_job = prev_job + 1
    next_job_changes = job_changes.loc[(molds.index[molds == next_mold], next_job)]

    prob.addConstraint(
        name=f'cost_j{prev_job}_j{next_job}_m{prev_mold}_m{next_mold}',
        constraint=cost >= cost_if_assigned*(
            -1 + pulp.lpSum(prev_job_changes) + pulp.lpSum(next_job_changes)
        ),
    )

print(prob)
prob.solve()
assert prob.status == pulp.LpStatusOptimal

print('Job changes:')
job_changes = job_changes.apply(pulp.value).unstack(level='dest_job').astype(np.int8)
print(job_changes)
print()


job_changes.to_csv(path+"jobsequenceresult.csv")

print('All mold costs:')
all_mold_costs = all_mold_costs.apply(pulp.value).unstack(
    level=['prev_mold', 'next_mold'],
).astype(np.int8)
print(all_mold_costs)
print()

print('Job sequence:')
i, j = job_changes.values.T.nonzero()
print(molds.iloc[j])