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ax = df_par.plot(x='%SKU', y='%CumSum', figsize = (20,7.5))
plt.xlabel('Percentage of SKU (%)',fontsize=15)
plt.ylabel('Percentage of Boxes Ordered (%)',fontsize=15)
plt.title('Pareto Analysis using Cumulative Sum of Boxes Prepared (%) = f(%SKU)', fontsize = 15)
plt.show()

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# > 80% Volume
df_par80 = df_par[df_par['%CumSum'] > 80].copy()
perc_sku80 = df_par80['%SKU'].min()
perc_sum80 = df_par80['%CumSum'].min()

# 20% SKU
df_sku20 = df_par[df_par['%SKU'] > 20].copy()
perc_sku20 = df_sku20['%SKU'].min()
perc_sum20 = df_sku20['%CumSum'].min()

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DATE FORMAT	ORDER_NUMBER	SKU	PCS
01/01/2017	835220	290731	1.0
01/01/2017	835220	373272	1.0
01/01/2017	835214	362101	1.0
01/01/2017	835215	215515	1.0
01/01/2017	835215	287995	1.0
01/01/2017	835215	310751	1.0
01/01/2017	835215	331543	1.0
01/01/2017	835212	159789	1.0
01/01/2017	835209	309851	1.0

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import pandas as pd
from pulp import * # pip install pulp
import matplotlib.pyplot as plt
from itertools import chain, repeat

def ncycles(iterable, n):
    "Returns the sequence elements n times"
    return chain.from_iterable(repeat(tuple(iterable), n))

# Staff needs per Day

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# Initialize Model
model = LpProblem("Minimize Staffing", LpMinimize)

# Create Variables
start_jours = ['Shift: ' + i for i in jours]
x = LpVariable.dicts('shift_', n_days, lowBound=0, cat='Integer')

# Define Objective
model += lpSum([x[i] for i in n_days])

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# Solve Model
model.solve()

# The status of the solution is printed to the screen
print("Status:", LpStatus[model.status])

# How many workers per day ?
dct_work = {}
for v in model.variables():
    dct_work[int(v.name[-1])] = int(v.varValue)

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# Count Machines
machines_count = 1 + max(task[0] for job in jobs_data for task in job)
print("{:,} machines".format(machines_count))
all_machines = range(machines_count)

# Computes horizon dynamically as the sum of all durations.
horizon = sum(task[1] for job in jobs_data for task in job)
print("Total Time using Naive Solution: {} min".format(horizon))

# Named tuple to store information about created variables

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# Add Constraints: No Overlapping of tasks for the same machine
# Create and add disjunctive constraints.
for machine in all_machines:
    model.AddNoOverlap(machine_to_intervals[machine])

# Precedences inside a job: End Time of Step n is before starting time of Step n+1
for job_id, job in enumerate(jobs_data):
    for task_id in range(len(job) - 1):
        model.Add(all_tasks[job_id, task_id +
                            1].start >= all_tasks[job_id, task_id].end)