final project for pfch
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google_spreadsheets
not_using_this_stuff
2010_12_02_auction.py
2010_12_02_auction.txt
2011_may_auction.txt
2011_may_candlestick.csv
2011_may_converted.csv
2011_may_converted.py
2011_may_converted_matt.py
2011_may_decade_tally.csv
2011_may_decades_prices.py
2011_may_dp_analysis.csv
2011_may_dp_analysis_by_price.csv
2011_may_for_candlestick.py
2011_may_pxd_totals.csv
2011_may_pxd_totals.py
2011_may_pxd_viz.txt
2011_may_pxd_viz_again.csv
2011_may_results.json
2011_may_results.py
2011_may_results_with_dates.json
2011_may_sep_date_from_title.py
2011_may_tally_decades.py
2011_may_unsold_counter.py
2011_may_unsold_to_zero.py
2011_may_viz_price_date_csv.py
2012_may_auction.txt
2012_may_candlestick.csv
2012_may_decade_tally.csv
2012_may_decades_prices.py
2012_may_dp_analysis.csv
2012_may_dp_analysis_by_price.csv
2012_may_for_candlestick.py
2012_may_pxd_totals.csv
2012_may_pxd_totals.py
2012_may_results.json
2012_may_results.py
2012_may_results_with_dates.json
2012_may_sep_date_from_title.py
2012_may_tally_decades.py
2012_may_unsold_counter.py
2012_may_viz_again.csv
2012_may_viz_price_date.py
2012_nov_auction.txt
2012_nov_candlestick.csv
2012_nov_decade_tally.csv
2012_nov_decades_prices.py
2012_nov_dp_analysis.csv
2012_nov_dp_analysis_by_price.csv
2012_nov_for_candlestick.py
2012_nov_pxd_totals.csv
2012_nov_pxd_totals.py
2012_nov_results.json
2012_nov_results.py
2012_nov_results_with_dates.json
2012_nov_sep_date_from_title.py
2012_nov_tally_decades.py
2012_nov_unsold_counter.py
2012_nov_viz_again.csv
2012_nov_viz_price_date.py
2013_nov_auction.txt
2013_nov_candlestick.csv
2013_nov_decade_tally.csv
2013_nov_decades_prices.py
2013_nov_dp_analysis.csv
2013_nov_dp_analysis_by_price.csv
2013_nov_for_candlestick.py
2013_nov_pxd_totals.csv
2013_nov_pxd_totals.py
2013_nov_results.json
2013_nov_results.py
2013_nov_results_with_dates.json
2013_nov_sep_date_from_title.py
2013_nov_tally_decades.py
2013_nov_unsold_counter.py
2013_nov_viz_again.csv
2013_nov_viz_price_date.py
2014_feb_viz_again.csv
2014_feb_viz_price_date.py
2014_june_viz_again.csv
2014_june_viz_price_date.py
2014_photo_auction_presentation.key
2015_info_vis_data_clean.xlsx
2015_info_viz_data_super_clean.csv
2015_viz_values_only.xlsx
README.txt
README.txt.rtf
Workbook1.csv
all_charts.pdf
feb_2014_auction.txt
feb_2014_candlestick.csv
feb_2014_decade_tally.csv
feb_2014_decades_prices.py
feb_2014_dp_analysis.csv
feb_2014_dp_analysis_by_price.csv
feb_2014_for_candlestick.py
feb_2014_pxd_totals.csv
feb_2014_pxd_totals.py
feb_2014_results.json
feb_2014_results.py
feb_2014_results_with_dates.json
feb_2014_sep_date_from_title.py
feb_2014_tally_decades.py
feb_2014_unsold_counter.py
june_2014_auction.txt
june_2014_candlestick.csv
june_2014_decade_tally.csv
june_2014_decades_prices.py
june_2014_dp_analysis.csv
june_2014_dp_analysis_by_price.csv
june_2014_for_candlestick.py
june_2014_pxd_totals.csv
june_2014_pxd_totals.py
june_2014_results.json
june_2014_results.py
june_2014_results_with_dates.json
june_2014_sep_date_from_title.py
june_2014_tally_decades.py
june_2014_unsold_counter.py
~$2015_info_vis_data_clean.xlsx

README.txt

This is a project I worked on for the course "Programming for Cultural Heritage" at Pratt SILS in the fall of 2014.
I became interested in the Dreweatts Auction House because I am QA’ing the website for my job at the Frick Art Reference Library’s Web Archiving Initiative. 

As a student of art history/library science and as an archivist, I am passionate about the history of photography and will eventually be writing my art history master's thesis on the subject. I want to investigate the circumstances that have led to the establishment of photography as an "accepted high art form" and that conception's subsequent influence on the sales of photography at auction.  I am interested in seeing general trends, such aswhether works created during certain decades went for higher prices or how many works from a decade went unsold.

I selected 6 auctions that were photography based:
2011, May
2012, May
2012, November
2013, November
2014, February
2014, June

I scraped the URLS for each lot in the auction that was a photography based work. I gathered information about: artist; date the work was created; the price in GB Pounds for which the lot sold (or whether it was Unsold); the title of the work. This information wrote out to JSON files.  From there I used the date created to generate a decade in which the work was created. I continued to parse out the data.

The (year)_(month)_for_candlestick.py files use the data from the json files to perform several calculations: the total amount of money spent on each decade; the total number of works available for auction in a decade; the total number of works sold by decade; the highest price per decade; the lowest price per decade; the average price per decade; the median per decade.  

I still have one issue to solve.  I have not been able to get the median to calculate outside of the writerow.