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ECON2040 – COMPUTATIONAL ECONOMICS
创建日期：2024-06-08 15:41:17
所属分类：经济Economics
标签： ECON2040
COMPUTATIONAL ECONOMICS

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ECON2040 – COMPUTATIONAL ECONOMICS

Coursework 2
• This coursework consists of three questions and is worth 35% of the overall mark for ECON2040.
• The deadline for submission is 18:00 GMT on Wednesday 13 December 2023.
• Standard University policies and procedures will be followed for late submission, extensions,
and academic integrity. (See the Module Syllabus and Programme Handbook for details.)
• Submission is via Blackboard. Your answers should be composed of three parts: a report (type-
set using Word/LaTex and saved in pdf format), containing your analysis/summary statis-
tics/output (e.g. tables and figures), AND csv files containing your data output AND your
Python and R scripts, containing the code that you used to obtain your results.
– You should submit your report via TurnItIn on Blackboard in a file called ECON2040CW2_ID.pdf,
where ID is your student ID number, for example ECON2040CW2_12345678.pdf. In the As-
signments folder, click on Coursework 2 – Report Submission to submit your report.
– Please make sure that your answers are typeset, and that the output is well-organised and
clear and that tables and figures are appropriately labelled.
– You should not include Python/R code used in your analysis in your report, but you
must submit a separate Python and R scripts via Blackboard containing your code called
ECON2040CW2_ID.py and ECON2040CW2_ID.R, where ID is your student ID number, for
example ECON2040CW2_12345678.R. In the Assignments folder, click on Coursework 2 –
Code Submission to submit your code.
– Please also upload your CSV files called ECON2040CW2_Sales_ID.csv and ECON2040CW2_WDI_ID.csv,
where ID is your student ID number, for example ECON2040CW2_Sales_12345678.csv. In
the Assignments folder, click on Coursework 2 – CSV Submission to upload your data.
– Your Python and R scripts should include comments throughout to explain what you are
doing and each section should be properly labelled.
• In answering the questions, please keep in mind the Grade Descriptors for Year 2 as posted on
Blackboard at the start of the module.
• It is the policy of the Department of Economics that coursework is anonymous, therefore please
do not put your name on any part of your report.
1
1. The Sales Data.zip contains 12 csv files with the information about the sales of a U.S. electronic
goods store chain throughout 12 months of a year. Every line contains information about a
product sold by one of the stores in a given month. It has the following columns:
• Order ID identifies a specific sales order. There may be several lines with the same Order
ID, which means that all those goods were part of the same order.
• Product gives the name of the product sold.
• Quantity Ordered provides information about how many items of this product were sold
in this order.
• Price Each lists the price at which each item was sold.
• Order Date gives the date of the sale.
• Purchase Address provides the address of the store where sale took place.
Some of the rows may contain missing information or processing errors, so you may need to do
some data cleaning. Use Python’s Pandas library to analyse this data in order to answer the
following questions:
(a) Merge all the data into one Pandas dataframe. How many observations are there altogether?
(b) Calculate and plot the total value of sales by month. In what month were the total sales
the highest and the lowest? What were they equal to?
(c) In what city were the total annual sales the highest? Be careful, since in the U.S., there
may be several cities with the same name in different states.
(d) The company wants to order some online advertising, and wants to know at what time of
the day most of the sales usually happen. Plot the distribution of sales by hour.
(e) Which are the items that are most likely to be sold together?

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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