本次Python代写(Ptyhon Assignment helper)主要要求使用python程序编码来实现数据库的增删改查和Excel数据处理，并且使用NumPy来对这些数据进行分析和展现。

Data Analysis and Visualisation

Assignment Description

 This assignment will test your ability to use python to interact with databases, excel workbooks, perform data analysis using NumPy, and use matplotlib to generate graphs. Raw data will be provided and will need to be stored in a database. You will then need to write scripts to query the database and pull out subsets of the data. These subsets will then need to be further analysed and have graphs procedurally generated to display relevant information. All of the investigations should yield results that you can present in a professional report.

It is important to note that submission of this assignment is a requirement for passing the course. Late submissions will be marked according to Griffith University’s assessment policy. 10% of the overall mark will be deducted for each business day late. After 5 days, no submissions will be accepted.Submission

Requirements

This assignment must be submitted online via [email protected] under the assessment page. Your submission should include;

• A word document (preferred), PDF Is also acceptable, other formats are not allowed. This should contain your results presented as a scientific
• .py files containing your code. You should submit a separate .py file for each problem.
• A readme.txt file for your scripts

Problem Statement

 As more and more industries are becoming data driven, being able to process a large volume of raw data and produce a concise and insightful summary is becoming more and more important. As a data consultant for a government agency, you are tasked with processing some food inspection and health violation data and producing a report summarising some of the information

The raw data is provided in 2 excel spreadsheets: (A) Inspections.xlsx and (B) Violations.xlsx. You will need to complete the below tasks and present your results in a report.

For each python script, you should handle the case where the script has already been run and therefore the data already exists. This could mean checking to see if the table already existed in the database, or a specific workbook/worksheet already exists. In each case, you should decide what to do (display error ? Create a book/sheet with a different name ? Delete the existing version and re-run the script ?).

You should write a readme.txt file to accompany your scripts. Prepare a brief usage guide, any requirements and assumptions, and document what each script does and any other important info (for example; how does each script deal with database tables/excel sheets already existing).

Task 1 – Access the workbooks and create a database

 Create a Python script (db_create.py) to perform the following tasks:

• Open the excel
• Create a SQLite database with two tables, one for each excel file. Each column in the excel files should correspond to a column in the tables. Make sensible decisions for attribute
• Import the data from the excel files to the corresponding tables in the

Task 2 – Query the database

Create a Python script (sql_food.py) to perform the following tasks:

• List the distinctive businesses that have had at least 1 violation ordered alphabetically to the console and then write their name, address, zip code and city into a new database table called “Previous Violations”.
• Print a count of the violations for each business that has at least 1 violation to the console along with their name ordered by the number of violations. *SQL Hint: Group By*

Task 3 – Excel via Python

Create a Python script (excel_food.py) to perform the following tasks:

• Create a new workbook named “ViolationTypes.xlsx”.
• Create a sheet named “Violations Types”.
• Query the database and calculate the number of each type of violation based on violation code.
• Write the relevant data into the worksheet you created. This should show the total number of violations, then list how that is broken down by violation code, including the description of the violation code. For example:

Task 4 – Numpy in Python

Create a Python script (numpy_food.py) to perform the following tasks:

• Query the database and retrieve data to calculate the average violations for every month between July 2015 and December 2017 for each
• Use MatPlotLib to plot the follow data over time:
  • The violations per month for the postcode(s) with the highest total violations
  • The violations per month for the postcode(s) with the greatest variance (difference) between the lowest and highest number of violations for all
  • The average violations per month for ALL of California (all postcodes combined)
  • The violations per month for all McDonalds and Burger Kings. This will require a new query as it is not grouped by postal
• For 7810ICT students:
  • Query the Violations table and retrieve all distinct violation codes and descriptions
  • Using Regular Expressions (NOT SQL), filter the resulting data to print out a list of all violation codes and descriptions that involve the word ‘food’. You should add this violation listing as an appendix to your

Task 5 – Report

After developing all of your scripts, you need to take the information and present it in a brief executive-style report. You may use the provided template as is, modify it, or come up with your own. Do not simply paste data from the console/excel into each section in the template – present the data in a professional manner.

Marking

This assignment is worth 30% of your final grade. The assignment will be marked out of 100 and marks will be allocated as follows:

• Task 1 – Creating the database and importing the data (20 marks)
• Task 2 – Querying the database (15 marks)
• Task 3 – Excel in Python (20 marks)
• Task 4 – NumPy and Matplotlib (25 marks)
• Task 5 – Report – (20 marks)

Additionally, marks will be deducted for incorrect:

• Poor English/grammar
• Lack of comments in the code
• Poor presentation