This is a set of python scripts that can be used to analyze the sqlite database also included in this repository. The goal of the analysis was to study performance metrics for the product over time (see more info in the pdf report).
========== Some general notes: This code is not optimal: 1) Lack of error handling 2) No thought given to scalability; basically written just for this or equivalently small exercises 3) Some best practices ignored. Example: I've used python string substitutions instead of the DB-API's replace function. 4) Several of the scripts could have been combined, with functionality further specified by user input, to create more general tools
========== Filename: read_database.py
Function: Opens connection to sqlite database, basic reusable functions for general reading of info
Execution: import read_database
========== Filename: add_requests_new.py
Function: adds requests_new table referenced in writeup
Execution: python add_requests_new.py <DB_FILENAME> 1
==========
Filename: users_basic_info.py
Function: Show basic info about the users in the dataset, using plots and output to the console
Execution:
To get information about the users' gender:
python users_basic_info.py <DB_FILENAME> 1
To get information about the number of consumers vs providers:
python users_basic_info.py <DB_FILENAME> 2
To get information (and a plot) about the number of consumer users in a given location:
python users_basic_info.py <DB_FILENAME> 3
To get information (and plots) about the number of consumer users in a given category and meta-category:
python users_basic_info.py <DB_FILENAME> 4
========== Filename: requests_basic_info.py
Function: Show basic info about the requests in the dataset, using plots and output to the console
Execution:
For instructions, type: python requests_basic_info.py help
========== Filename: invites_basic_info.py
Function: Show basic info about the invites in the dataset, using plots and output to the console
Execution:
For instructions, type: python invites_basic_info.py help
========== Filename: quotes_basic_info.py
Function: Show basic info about the quotes in the dataset, using plots and output to the console
Execution:
To generate two plots of the number of quotes vs time (daily and weekly binning):
python quotes_basic_info.py <DB_FILENAME> 1
To generate two plots of the number of quotes vs time (daily and weekly binning) for a given meta-category:
python quotes_basic_info.py <DB_FILENAME> 2 <META-CATEGORY ID>
========== Filename: analysis.py
Function: Analysis of quoting metrics and change in number of unique category ids over time
Execution:
Plots of the linear fit to the trend of the weekly distributions:
python analysis.py trend
Plots of power spectral density for the daily distributions:
Number of quotes vs time:
python analysis.py period_qvt
Number of unique category ids vs time:
python analysis.py period_cat
Invite to quote rate vs time:
python analysis.py period_i2q
Plots of coherence:
Coherence of quotes vs time with categories vs time:
python analysis.py co_qvt_cat
Coherence of invite-to-quote-rate with categories vs time:
python analysis.py co_i2q_cat
To reproduce the dataframe that I reference in the report:
python data_analysis.py <DB_FILENAME> 1
To add an updated requests table ('requests_new') including meta_category_id as an attribute:
python data_analysis.py <DB_FILENAME> 2
NOTE: Don't do this more than once, there is nothing built in to drop the table if it already exists
Example: python data_analysis.py invite_dataset_ff829852.sqlite 1
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