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