#Question 4 use sku.csv and WarehouseLocations.csv##############################################################def warehouse_stats(sku):"""Question 4- Read sku.csv with CSV and create a dictionary of the New SKU Statistics.- The New Sku should be the key, with the corresponding value being an innerdictionary containing the following statistics:- 350 Loc: True if not 0- Warehouse Qty- Forcasted Qty- Items/Day: can be calculated using CuFt/Day divided by Item Cube.This result should be an float rounded to5 decimals places.- In your warehouse dictionary, add an inner dictionary with key Totals whichcontains:- Total Qty in Warehouse as key "Qty": Do Not add to Totals if '350 Loc' is not a valid location.- Number of Valid 350 Loc as key "Valid"Data Cleaning Steps:- In some variates of New Sku #, the Item Cube & CuFt/Day are faulty.Fix the manufacturers mistake. If either is **less than or equal to 0**,Item Cube can be assumed to be 5.0 and CuFt/Day is 10% of theForcasted Qty of the New Sku #. Calculate similarly, with Forcasted Qty dividedby Item Cube, and round to 5 decimal places.- Ensure the New SKU number is valid. Do not add to your new dictionary or calucalte in totalsof the New SKU number of "#N/A"Assumptions:- You can assume all New Sku numbers are unique.- Do not assume values are integers even if they appear to be numbers. You may have to cast to integers or floats. Return your final warehouse_stats dictionaryArgs:sku (csv file)Return:dictLast five elements displayed:>> warehouse_stats('sku.csv').....'63149901': {'350 Loc': True,'Forecasted Qty': '1315','Items/Day': 26.3,'Warehouse Qty': '2304'},'63149902': {'350 Loc': True,'Forecasted Qty': '1458','Items/Day': 29.16,'Warehouse Qty': '3072'},'63149904': {'350 Loc': True,'Forecasted Qty': '1324','Items/Day': 26.48,'Warehouse Qty': '1536'},'63149905': {'350 Loc': True,'Forecasted Qty': '413','Items/Day': 8.26,'Warehouse Qty': '1920'},'Totals': {'Qty': 3861258, 'Valid': 3327}}"""passdef update_warehouse(stats,input_file):''' skuGT SKU New SKU 350 Loc Warehouse Qty Forecasted Quantity Item Cube CuFt/Day26359737 603178580523350617.875100.726483737 6240902502011826488614 6237883501540026393062 6063699221517426483730 6178800701460026487386 6178801701290926410944 607392721231726487394 624090321215611861110711063726139575 5932674326488610 6178801826495972 6178797126488612 6237883326488613 6237883426483731 6178800626410946 6073927326416317 6178792426353200 6037995026438803 6073016926487391 6240902726487393 6240902826299738 6090709626474053 6178800326487385 6178801626470439 6178801426416311 6178792226393064 6063698126468309 6178798526468313 6178800026488615 6237883626416319 6178792626393063 6063698526455859 6216798226353220 6037994626439896 6165221126474055 6178800526393067 6063699426397324 6090709926393066 6063699326468311 6178799926468312 6178798626412381 6126906800000000000000000001000010000031000150141038216907107445679173125706442013547898911080330238217848058725230-44471367109004105601871814677881694211655915689328790350436310272157661322517481908832.62532.62532.62532.62532.62532.62532.6254.532.62532.6251020632.625970832.625797332.625765131.68728632.6257164 29.906270955.0625616732.625613832.6256133 81.2813600628.125582232.62528.12532.62532.62532.62532.62532.62532.62532.62532.6255774565656295379535653364965493846754346408939383911323128.12532.62539007.875387432.625378432.625373232.6253694 22.96872519.218.918.216.115.315.1213.813.212.712.19.99.29.18.21.47.77.6196.47.26.2776.76.76.66.26.15.85.34.84.24.91.24.84.74.63.2 WarehouseLocationsNew SKU Warehouse Location60317858 A262409025 F1462378835 C260636992 E661788007 E861788017 C1660739272 C1062409032 A259326743 DO61788018 A1861787971 C1262378833 D1462378834 E661788006 B1060739273 F1661787924 B1460379950 Co60730169 B262409027 A1062409028 C260907096 E1261788003 DO61788016 F1861788014 E461787922 D1260636981 D1061787985 AO61788000 E1062378836 D461787926 B1260636985 B1262167982 E1660379946 C861652211 D261788005 EO60636994 C1860907099 D1460636993 AO61787999 B1061787986 E861269068 A6

Answered: #Question 4 use sku.csv and…

Computer Networking: A Top-Down Approach (7th Edition)

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ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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#Question 4 use sku.csv and WarehouseLocations.csv#
#############################################################
def warehouse_stats(sku):
"""
Question 4
- Read sku.csv with CSV and create a dictionary of the New SKU Statistics.
- The New Sku should be the key, with the corresponding value being an inner
dictionary containing the following statistics:
- 350 Loc: True if not 0
- Warehouse Qty
- Forcasted Qty
- Items/Day: can be calculated using CuFt/Day divided by
Item Cube.
This result should be an float rounded to
5 decimals places.
- In your warehouse dictionary, add an inner dictionary with key Totals which
contains:
- Total Qty in Warehouse as key "Qty": Do Not add to Totals if
'350 Loc' is not a valid location.
- Number of Valid 350 Loc as key "Valid"
Data Cleaning Steps:
- In some variates of New Sku #, the Item Cube & CuFt/Day are faulty.
Fix the manufacturers mistake. If either is **less than or equal
to 0**,
Item Cube can be assumed to be 5.0 and CuFt/Day is 10% of the
Forcasted Qty of the New Sku #. Calculate similarly, with
Forcasted Qty divided
by Item Cube, and round to 5 decimal places.
- Ensure the New SKU number is valid. Do not add to your new dictionary or
calucalte in totals
of the New SKU number of "#N/A"
Assumptions:
- You can assume all New Sku numbers are unique.
- Do not assume values are integers even if they appear to be numbers. You
may have to cast to integers or floats.

Return your final warehouse_stats dictionary
Args:
sku (csv file)
Return:
dict
Last five elements displayed:
>> warehouse_stats('sku.csv')
.....
'63149901': {'350 Loc': True,
'Forecasted Qty': '1315',
'Items/Day': 26.3,
'Warehouse Qty': '2304'},
'63149902': {'350 Loc': True,
'Forecasted Qty': '1458',
'Items/Day': 29.16,
'Warehouse Qty': '3072'},
'63149904': {'350 Loc': True,
'Forecasted Qty': '1324',
'Items/Day': 26.48,
'Warehouse Qty': '1536'},
'63149905': {'350 Loc': True,
'Forecasted Qty': '413',
'Items/Day': 8.26,
'Warehouse Qty': '1920'},
'Totals': {'Qty': 3861258, 'Valid': 3327}}
"""
pass
def update_warehouse(stats,input_file):
'''

sku
GT SKU New SKU 350 Loc Warehouse Qty Forecasted Quantity Item Cube CuFt/Day
26359737 60317858
0
52
335061
7.875
100.7
26483737 62409025
0
20118
26488614 62378835
0
15400
26393062 60636992
2
15174
26483730 61788007
0
14600
26487386 61788017
0
12909
26410944 60739272
12317
26487394 62409032
12156
11861
11071
10637
26139575 59326743
26488610 61788018
26495972 61787971
26488612 62378833
26488613 62378834
26483731 61788006
26410946 60739273
26416317 61787924
26353200 60379950
26438803 60730169
26487391 62409027
26487393 62409028
26299738 60907096
26474053 61788003
26487385 61788016
26470439 61788014
26416311 61787922
26393064 60636981
26468309 61787985
26468313 61788000
26488615 62378836
26416319 61787926
26393063 60636985
26455859 62167982
26353220 60379946
26439896 61652211
26474055 61788005
26393067 60636994
26397324 60907099
26393066 60636993
26468311 61787999
26468312 61787986
26412381 61269068
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
3
1
0
0
0
15014
10382
16907
10744
567
9173
1257
0
6442
0
13547
8989
11080
3302
3821
7848
0
5872
5230
-4
447
13671
0
9004
10560
18718
14677
8816
9421
16559
15689
3287
9035
0
4363
10272
15766
13225
17481
9088
32.625
32.625
32.625
32.625
32.625
32.625
32.625
4.5
32.625
32.625
10206
32.625
9708
32.625
7973
32.625
7651
31.68
7286
32.625
7164 29.9062
7095
5.0625
6167
32.625
6138
32.625
6133 81.2813
6006
28.125
5822
32.625
28.125
32.625
32.625
32.625
32.625
32.625
32.625
32.625
32.625
5774
5656
5629
5379
5356
5336
4965
4938
4675
4346
4089
3938
3911
32
31
28.125
32.625
3900
7.875
3874
32.625
3784
32.625
3732
32.625
3694 22.9687
25
19.2
18.9
18.2
16.1
15.3
15.1
2
13.8
13.2
12.7
12.1
9.9
9.2
9.1
8.2
1.4
7.7
7.6
19
6.4
7.2
6.2
7
7
6.7
6.7
6.6
6.2
6.1
5.8
5.3
4.8
4.2
4.9
1.2
4.8
4.7
4.6
3.2

WarehouseLocations
New SKU Warehouse Location
60317858 A2
62409025 F14
62378835 C2
60636992 E6
61788007 E8
61788017 C16
60739272 C10
62409032 A2
59326743 DO
61788018 A18
61787971 C12
62378833 D14
62378834 E6
61788006 B10
60739273 F16
61787924 B14
60379950 Co
60730169 B2
62409027 A10
62409028 C2
60907096 E12
61788003 DO
61788016 F18
61788014 E4
61787922 D12
60636981 D10
61787985 AO
61788000 E10
62378836 D4
61787926 B12
60636985 B12
62167982 E16
60379946 C8
61652211 D2
61788005 EO
60636994 C18
60907099 D14
60636993 AO
61787999 B10
61787986 E8
61269068 A6

Expert Solution

Step 1 Introduction

A comma-separated values file which refers to the delimited text file that uses the comma to separate the values. Each line of the file which is the data record. Each record which consists of one or more fields, separated by commas. The use of the comma is used as a field separator is the source of the name for this file format

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#Question 4 use sku.csv and WarehouseLocations.csv# ############################################################# def warehouse_stats(sku): """ Question 4 - Read sku.csv with CSV and create a dictionary of the New SKU Statistics.