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generatour1 · 5 years ago

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program-800 · 6 years ago

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Exploring D:BH fics (Part 5)

For this part, I’m going to discuss how I prepared the data and conducted the tests for differences in word use across fics from the 4 AO3 ratings (Gen/Teen/Mature/Explicit), as mentioned here.

Recap: Data was scraped from AO3 in mid-October. I removed any fics that were non-English, were crossovers and had less than 10 words. A small number of fics were missed out during the scrape - overall 13933 D:BH fics remain for analysis.

In this particular analysis, I dropped all non-rated fics, leaving 12647 D:BH fics for the statistical tests.

Part 1: Publishing frequency for D:BH with ratings breakdown Part 2: Building a network visualisation of D:BH ships Part 3: Topic modeling D:BH fics (retrieving common themes) Part 4: Average hits/kudos/comment counts/bookmarks received (split by publication month & rating) One-shots only. Part 5: Differences in word use between D:BH fics of different ratings Part 6: Word2Vec on D:BH fics (finding similar words based on word usage patterns) Part 7: Differences in topic usage between D:BH fics of different ratings Part 8: Understanding fanon representations of characters from story tags Part 9: D:BH character prominence in the actual game vs AO3 fics

What differentiates mature fics from explicit fics, gen from teen fics?

These are pretty open-ended questions, but perhaps the most rudimentary way (quantitatively) is to look at word use. It’s very crude and ignores word order and can’t capture semantics well - but it’s a start.

I’ve read some papers/writings where loglikelihood ratio tests and chi-squared tests have been used to test for these word use differences. But recently I came across this paper which suggests using other tests instead (e.g. trusty old t-tests, non-parametric Mann-Whitney U-tests, bootstrap tests). I went ahead with the non-parametric suggestion (specifically, the version for multiple independent groups, the Kruskal-Wallis test).

Now, on to pre-processing and other details.

1. Data cleaning. Very simple cleaning since I wanted to retain all words for potential analysis (yes, including the common stopwords like ‘the’, etc!). I just cleaned up the newlines, removed punctuation and numbers, and the ‘Work Text’ and ‘Chapter Text’ indicators from the HTML. At the end of cleaning, each story was basically just a list of words, e.g. [’connor’, ‘said’, ‘hello’, ‘i’, ‘m’, ‘not’, ‘a’, ‘deviant’].

2. Preparing a list of vocabulary for testing. With 12647 fics, you can imagine that there’s a huge amount of potential words to test. But a lot of them are probably rare words that aren’t used that often outside of a few fics. I’m trying to get an idea of general trends so those rare words aren’t helpful to this analysis.

I used Gensim’s dictionary function to help with this filtering. I kept only words that appeared in at least 250 fics. The number is pretty arbitrary - I selected because the smallest group (Mature fics) had 2463 fics; so 250 was about 10% of that figure. This left me with 9916 unique words for testing.

3. Counting word use for every fic. I counted the number of times each fic used each of the 9916 unique words.

Now obviously raw frequencies won’t do - a longer fic is probably going to have higher frequency counts for most words (versus a short fic) just by virtue of its length. To take care of this I normalised each frequency count for each fic by the fic’s length. E.g. ‘death’ appears 100 times in a 1000-word fic, the normalised count is 100/1000 = 0.1.

4. Performing the test (Kruskal-Wallis) and correcting for multiple comparisons. I used the Kruskal-Wallis test (from scipy) instead of the parametric ANOVA because it’s less sensitive to outliers; while the ANOVA looks at group means, the Kruskal-Wallis looks at group mean ranks (not group medians!). As an aside, you can assume the K-W test is comparing medians, if you are able to assume that the distributions of all the groups you’re comparing are identically shaped.

Because we’re doing so many comparisons (9916, one for each word), we’re bound to run into some false positives when testing for significance. To control for this, I used the Holm-Bonferroni correction (from multipy), correcting at α =.05. Even after correction, I had 9851 words with significant differences between the 4 groups.

[[For anyone unfamiliar with what group mean ranks, I’ll cover it here (hope I’ve got it down right more or less erp): - We have 12647 fics, and we have a normalised count for each of them on 9916 words. - For each word, e.g. ‘happy’, we rank the fics by their normalised count. So the fic with the lowest normalised count of ‘happy’ gets a rank of 1, the fic with the highest normalised count of ‘happy’ gets rank 12647. Every other fic gets a corresponding rank too. - We sum the ranks within each group (Gen/T/M/Explicit). Then we calculate the average of those group-wise ranks, getting the mean ranks. This information is used in calculating the test statistic for K-W.]]

5. Post-hoc tests. The results of the post-hoc tests aren’t depicted in the charts (didn’t have a good idea on how to go about it, might revisit this), but I’ll talk about them briefly here.

Following the K-W tests, I performed pairwise Dunn tests for each word (using scikit-posthocs). So basically the K-W test told me, “Hey, there’s some significant differences between the groups here” - but it didn’t tell me exactly where. The Dunn tests let me see, for e.g., if the differences lie between teen/mature, and/or mature/explicit, and so on.

Again, I applied a correction (Bonferroni) for each comparison at α =.05 - since for each word, we’re doing a total of 6 pairwise comparisons between the 4 ratings.

6. Visualisation. I really didn’t know what was the best way to show so many results, but decided for now to go with dot plots (from plotly).

There’s not much to say here since plotly works very well with the pandas dataframes I’ve been storing all my results in!

7. Final points to note

1) For the chart looking at words where mature fics ranked first in mean ranks and the chart looking at words where explicit fics ranked first in mean ranks, I kept only words that were significantly different between mature and explicit fics in the pairwise comparison. I was interested in how the content of these two ratings may diverge in D:BH.

2) For the mature/explicit-first charts, I showed only the top 200 words in terms of the K-W H-statistic (there were just too many words!). The H-statistic can be converted to effect sizes; larger H-statistics would correspond to larger effect sizes.

3) Most importantly, this method doesn’t seem to work very well for understanding teen and especially gen fics. Gen fics just appear as the absence of smut/violence/swearing, which isn’t very informative. This is an issue I’d like to continue working on - I’m sure more linguistic cues, especially contextual ones, will be helpful.

#dbh #detroit become human #dbh fanfic #fandom stats #ao3 #text analysis

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nurseslabs · 6 years ago

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Papanicolaou smear (Pap smear, cervical smear) is a safe, noninvasive cytological examination for early detection of cervical cancer. During the 1900s, cervical cancer was one of the leading cause of death among women. It was until the year 1928, where a greek physician George Nicholas Papanicolaou was able to discover the difference between normal and malignant cervical cells by viewing the samples microscopically, hence Pap smear was invented.

For women ages 30 and above, this procedure can be done in conjunction with a test on Human papillomavirus (HPV), the most common sexually transmitted disease and primary causative agent for cervical cancer. The American Cancer Society recommends a Pap smear at least once every three years for women ages 21 to 29 who are not in a high-risk category and who have had negative results and who have had negative results from three previous Pap tests. While a Pap test and an HPV test is recommended every five years for women ages 30 to 65 years old. If a Pap smear is positive or suggests malignancy, a cervical biopsy can confirm the diagnosis.

Nurses play an important role in promoting public health awareness to inform, encourage and motivate the public in considering health screening such as pap smear. This pap smear study guide can help nurses understand their tasks and responsibilities during the procedure.

[toc]

Indications of Pap Smear

Pap smear is indicated for the following reasons:

Identify the presence of sexually transmitted disease such as human papillomavirus (HPV), herpes, chlamydia, cytomegalovirus, Actinomyces spp., Trichomonas vaginalis, and Candida spp.

Detect primary and metastatic neoplasms

Evaluate abnormal cervical changes (cervical dysplasia)

Detect condyloma, vaginal adenosis, and endometriosis

Assess hormonal function

Evaluate the patient’s response to chemotherapy and radiation therapy

Interfering Factors

These are factors or conditions that may alter the outcome of the study

Delay in fixing a specimen, allows the cells to dry therefore destroying the effectiveness of the stain and makes cytologic interpretation difficult

Improper collection site may cause rejection of the specimen. Samples for hormonal evaluation are taken from the vagina while samples for cancer screening are obtained from the vaginal fornix

Use of lubricating jelly on the speculum that may affect the viability of some organisms

Specimen collection during normal menstruation since blood can contaminate the sample

Douching, using tampons, or having sexual intercourse within 24 hours before the exam can wash away cellular deposits

Existing vaginal infections that may interfere with hormonal cytology

Pap Smear Procedure

Pap smear is performed by a practitioner and takes approximately about 5 to 10 minutes. The step-by-step procedure is as follows:

The patient is positioned. The client is assisted in a supine, dorsal lithotomy position with feet in stirrups.

A speculum is inserted. The practitioner puts on gloves and inserts an unlubricated plastic or metal speculum into the vagina and is opened gently to spread apart the vagina to access the cervix. The speculum may be moistened with saline solution or warm water to make insertion easier.

Cervical and vaginal specimens collection. After positioning the speculum, specimen from the vagina and cervix are taken. A cytobrush is inserted inside the cervix and rolls it firmly into the endocervical canal. The brush is then rotated one turn and removed. A plastic or wooden spatula is utilized to scrape the outer opening of the cervix and vaginal wall.

Collection technique (Using the conventional collection). The specimen from the brush and spatula is wiped on the slide and fixed immediately by immersing the slide in equal parts of 95% ethanol or by using a spray fixative.

Collection technique (Using the ThinPrep collection). The brush and spatula are immediately immersed in a ThinPrep solution with a swirling motion to release the material. The brush and spatula are then removed from the solution and the bottle lid is replaced and secured.

Label the specimen The slides are properly labeled with the patient’s name, age, initials of the health care provider collecting the specimen, date, and time of collection.

Specimens are sent to the laboratory The specimens are transported to the laboratory for cytologic analysis.

Bimanual examination may follow. After the removal of the speculum, a bimanual examination may be performed wherein the health care provider will insert two fingers of one hand inside the vaginal canal to feel the uterus and ovaries with the other hand on top of the abdomen.

Nursing Responsibility for Pap Smear

The following are the nursing interventions and nursing care considerations for a patient indicated for Pap smear.

Before the procedure

The following are the nursing interventions prior to pap smear:

Secure patient’s consent. The test must be adequately explained and understood by the patient before a written, and informed consent is obtained.

Obtain the patient’s health history. These include parity, date of last menstrual period, surgical status, contraceptive use, history of vaginal bleeding, history of previous Pap smears, and history of radiation or chemotherapy.

Ask lists of the patient’s current medications. If a patient is taking a vaginal antibiotic, the pap smear is delayed for one month after the treatment has been completed.

Explain that Pap smear is painless. The test requires that the cervix may be scraped and may experience minimal discomfort but no pain from the insertion of the speculum.

Avoid interfering factors. Having sexual intercourse within 24 hours, douching within 48 hours, using a tampon, or applying vaginal creams or lotions is avoided before the test since it can wash away cellular deposits and change the ph of the vagina.

Empty the bladder. Pap smear involves the insertion of the speculum into the vagina and could press down the lower abdomen.

After the procedure

The nurse should note the following nursing interventions after pap smear:

Cleanse the perineal area. Secretions or excess lubricant from the vagina are removed and cleansed.

Provide a sanitary pad. Slight spotting may occur after the pap smear.

Provide information about the recommended frequency of screening. The American Cancer Society recommends screening every three years for women aged 21 to 29 years old and co-testing for HPV and cytological screening every five years for women aged 30 to 65 years old.

Answer any questions or fears by the patient or family. Anxiety related with the pending test results may occur. Discussion of the implications of abnormal test results on the patient’s lifestyle may be provided to the patient.

Results

Normal findings in a Pap smear will indicate a negative result which means that no abnormal, malignant cells or atypical cells are found. While a positive result signifies that there are abnormal or unusual cells discovered, it is not synonymous to having cervical cancer.

The Bethesda System (TBS) is the current method for interpreting cervical cytology and it includes the following components.

1. Adequacy of specimen

Satisfactory for evaluation: Describe the presence or absence of endocervical transformation zone component and other quality indicators such as partially obscuring blood, inflammation.

Unsatisfactory for evaluation: Specimen is rejected (specify reason) or the specimen is processed and examined but unsatisfactory for evaluation of epithelial abnormalities (specify reason)

2. Interpretation/result

Negative for intraepithelial lesion or malignancy

Showing evidence of organism causing infection:

Trichomonas vaginalis; fungal organisms morphologically consistent with Candida spp.; a shift in flora indicative of bacterial vaginosis (coccobacillus); bacteria consistent with Actinomyces spp.; cellular changes consistent with herpes simplex virus.

Other non-neoplastic findings:

Reactive cellular changes related to inflammation (includes repair), radiation, intrauterine device use, atrophy, glandular cell status after hysterectomy.

Epithelial cell abnormalities

Squamous cell abnormalities

Atypical squamous cells of undetermined significance (ASC-US) cannot exclude HSIL (ASC-H):

Low-grade squamous intraepithelial lesion (LSIL) encompassing HPV, mild dysplasia, cervical intraepithelial neoplasm (CIN) grade 1

High-grade squamous intraepithelial lesion (HSIL) encompassing moderate and severe dysplasia, CIS/CIN grade 2 and CIN grade 3 with features suspicious for invasion (If invasion is suspected).

Squamous cell carcinoma: indicate the presence of cancerous cells.

Glandular cell

Atypical glandular cells (not otherwise specify)

Atypical glandular cells, favor neoplastic (not otherwise specify)

Endocervical adenocarcinoma in situ

Adenocarcinoma

Others

Endometrial cells (in woman >=40 years of age)

Gallery

Related images to help you understand pap smear better.

This slideshow requires JavaScript.

References and Sources

Additional resources and references for the Pap Smear study guide:

Adele Pillitteri. Maternal and Child Health Nursing:Care of the Childbearing and Childrearing Family. Lippincott Williams & Wilkins.

Anne M. Van Leeuwen, Mickey Lynn Bladh. Laboratory & Diagnostic Tests with Nursing Implications: Davis’s

Solomon, D., Davey, D., Kurman, R., Moriarty, A., O’connor, D., Prey, M., … & Young, N. (2002). The 2001 Bethesda System: terminology for reporting results of cervical cytology. Jama, 287(16), 2114-2119. [Link]

Suzanne C. Smeltzer. Brunner & Suddarth’s Handbook of Laboratory and Diagnostic Tests: Lippincott Williams & Wilkins

Pap Smear Nursing Care Planning and Responsibilities – Diagnostic and Procedure

Pap Smear (Papanicolaou Smear) Papanicolaou smear (Pap smear, cervical smear) is a safe, noninvasive cytological examination for early detection of cervical cancer.

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catch70 · 3 years ago

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Data Is Plural The Datasets We’re Looking At This Week By Jeremy Singer-Vine Jul. 6, 2022, at 12:00 PM .share .has-bugs .single-featured-image You’re reading Data Is Plural, a weekly newsletter of useful/curious datasets. Below you’ll find the July 6, 2022, edition, reprinted with permission at FiveThirtyEight. 2022.07.06 edition Banned and challenged books, mass expulsions, European air traffic, Shakespeare and Saturday Night Live. Banned and challenged books. A recent report from PEN America identified 1,500-plus decisions, made between July 2021 and March 2022, to ban books from classrooms and school libraries. A spreadsheet accompanying the report lists each decision’s date, type, state and school district, as well as each banned book’s title, authors, illustrators and translators. Related: Independent researcher Tasslyn Magnusson, in partnership with EveryLibrary, maintains a spreadsheet of both book bans and book challenges, with 3,000-plus entries since the 2021–22 school year. [h/t Gary Price] Mass expulsions. Political scientist Meghan M. Garrity’s Government-Sponsored Mass Expulsion dataset focuses on “policies in which governments systematically remove ethnic, racial, religious or national groups, en masse.” Using a combination of archival research and secondary sources, Garrity documents 139 such events, estimated to have expelled more than 30 million people between 1900 and 2020. For each expulsion, the dataset provides “information on the expelling country, onset, duration, region, scale, category of persons expelled and frequency.” To download it, visit the Journal of Peace Research’s replication data portal and search for “mass expulsion.” European air traffic. Eurocontrol, the main organization coordinating Europe’s air traffic management, publishes an “aviation intelligence portal” with a range of industry metrics, including traffic reports that count the daily number of flights by country, airport and operator. The portal also offers bulk datasets on topics such as airport traffic, flight efficiency, estimated CO2 emissions and more. [h/t Giuseppe Sollazzo] Shakespeare. The Folger Shakespeare “brings you the complete works of the world’s greatest playwright, edited for modern readers.” Its digital editions of the Bard’s plays and poems are available to read online and to download in various file formats. It also provides an API, with endpoints for synopses, roles, monologues, word frequencies and more. [h/t Cameron Armstrong] “Saturday Night Live.” Joel Navaroli’s snlarchives.net aims to catalog and cross-reference every episode, cast member, host, character, sketch, impression and other aspects of the show’s 47-and-counting seasons. An open-source project by Hendrik Hilleckes and Colin Morris scrapes much of that information into structured data files. As seen in: Morris’s 2017 analysis of gender representation in SNL sketches. Dataset suggestions? Criticism? Praise? Send feedback to [email protected]. Looking for past datasets? This spreadsheet contains them all. Visit data-is-plural.com to subscribe and to browse past editions. Source: The Datasets We’re Looking At This Week

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webscreenscraping · 4 years ago

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How Web Scraping Is Used To Scrape Product Data From Target?

Search Target.com for the products you want to scrape and provide us with the URL for the results. In a scraper, enter a ZIP code for the store you want to acquire prices and stock availability for. You'll have a spreadsheet with product information such as Product Name, Store Availability Price, Online Availability, Rating, and Description, among other things.

Target Product’s Information and Pricing Scraper

Our Target Scraper will scrape information from any Target store using simply the search result URLs as well as the zip code.

Our Target.com data scraper will scrape the following information:

Name

Product availability

Pricing

Online Availability status

Store Address

Brand

Seller

Model

Product Images

Contact details

Product specification

Rating Histogram

Customer reviews

To demonstrate this, we took all of the products in the “Women " category from a nearby Target Store in Zip code 10001 and put them together in a table (in New York, CA). Check this link on how to do https://www.target.com/c/women/-/N-5xtd3.

Receive a product category link using Target.com through browsing several categories.

Set your Zip code 10001

The following would be the input for the Target.com data scraper:

Search listing or Keywords URLs at

https://www.target.com/c/sleepwear-pajamas-robes-women-s-clothing/-/N-5xtc3

The Zip code will be 10001

Then, using the parameters, search for a specific type of product, filter the results, and scrape various effects.

Let's look at a "Women's Short Sleeve V-Neck T-Shirt" created by Universal Thread from a Target Store near New York, NY as an example (with Zip Code 10001). Let's look at how to construct a scraper to accomplish this.

Search the keyword — Women's Short Sleeve V-Neck T-Shirt on Target.com.

Copy the search URL

https://www.target.com/c/sleepwear-pajamas-robes-women-s-clothing/-/N-5xtc3Z1vs54?Nao=0

Set your Zip code at 10001

The input to your Target.com data scraper will be:

Search listings or Keywords URL

https://www.target.com/c/sleepwear-pajamas-robes-women-s-clothing/-/N-5xtc3Z1vs54?Nao=0

Zip Code- 10001

JSON

#: "1", URL: "https://www.target.com/p/women-s-short-sleeve-v-neck-t-shirt-universal-thread/-/A-78673828?preselect=79646356#lnk=sametab", Image URL: "https://target.scene7.com/is/image/Target/GUEST_913b04a6-f0d4-4423-b266-9fa94cfbd46b?fmt=webp&wid=1400&qlt=80 | https://target.scene7.com/is/image/Target/GUEST_e1d4bcc4-4a82-4393-9003-4fc3dfb78f5f?fmt=webp&wid=1400&qlt=80 | https://edge.curalate.com/v1/img/7Q47X98QfRlIfAeCFpUclMkISbjtDBPtsw71O27c0ps=/h/1200?compression=0.75 | https://edge.curalate.com/v1/img/5WvyEL7PeAuDHLj_tQP1nKOZDo5TQ2Ote9kAfqj4_gA=/h/1200?compression=0.75 | https://edge.curalate.com/v1/img/z6D4nwiW3JNzVRhMf9-8c1WKZNvFwNbuSBINtXuiG2U=/h/1200?compression=0.75 | https://edge.curalate.com/v1/img/lx-ryGf4Ibq8zpf38NLSFYjDD2Nh4BYiilEicu241Jk=/h/1200?compression=0.75 | https://edge.curalate.com/v1/img/5ak-6Senb1FzcbxpokldcG1CItMVehMznoNAomKJQGU=/h/1200?compression=0.75 | https://edge.curalate.com/v1/img/6N8ivG74vAmUghu4MbHrTb7nTjikvRUKwy3NqV8aOe8=/h/1200?compression=0.75 | https://edge.curalate.com/v1/img/7Q47X98QfRlIfAeCFpUclMkISbjtDBPtsw71O27c0ps=/sc/80x80?compression=0.75 |", Name: "Women's Short Sleeve V-Neck T-Shirt - Universal Thread�", Brand: "Universal Thread", Store Address: "Manhattan Herald Square,112 W 34th St, New York, NY 10120-0101", Store Telephone: "(646) 968-4739", Store Availability Status: "YES", Online Availability Status: "YES", Seller: "Manhattan Herald Square", Product ID: "78265229", Category: "Women's Clothing", Sub Category: "Pajamas & Loungewear", Rating Histogram: "N/A", Price: "$8.00", Product Rating: "4", Review: "413", Quantity: "1", Product Size: "M", Size Chart: "XS | S | M | L | XL |XXL|1X | 2X | 3X | 4X", Color: "Green", Variation Color: "White | Gold | Light Brown | Cream | Black | Rose | Navy | Light Green | Pink | Charcoal Gray | Heather Gray | Yellow | Red | Blue | Pink/White | Mint/Red | Green/Gray | Yellow/white | Blue/white | Coral | Lavender | Dark Pink | Red/white", Description: "Crisp and clean with endless versatility, the Relaxed-Fit Short-Sleeve V-neck T-Shirt from Universal Thread� is a must-have in your collection of everyday basics. This short-sleeve T-shirt lets you easily wear it with everything from dark-wash jeans and plaid ponte pants to camo joggers and printed mini skirts. 100% cotton fabric provides you with breathable comfort from day to night and season to season, and the relaxed silhouette makes for ease of layering as well as a figure-flattering fit. Style yourself a casual-chic work-to-weekend look by half tucking it into high-rise skinny jeans paired with ankle boots, and add a faux-leather or sherpa jacket for a cozy finishing touch.", Highlights: "Model wears size S/4 and is 5'9.5" |Universal Thread short-sleeve V-neck tee adds to your everyday staples |Solid, stripes, and tie-dye designs vary by color |100% cotton fabric and relaxed fit provide breathable comfort for year-round wear |Pairs well with a variety of bottoms for versatile styling options", Sizing: "Women", Material: "100% Cotton", Length: "At Waist", Features: "Short Sleeve, Pullover", Neckline: "V Neck", Garment sleeve style: "Basic Sleeve", Color Name: "Moss", Care and Cleaning: "Machine Wash & Tumble Dry", TCIN: "79646356", UPC: "191905640696", Item Number (DPCI): "013-00-2097", Origin: "Imported" }

Why Choose Web Screen Scraping?

Affordable

You won't have to worry about proxy difficulties, working with numerous crawlers, or providing assistance.

Browser-Based

Web Screen Scraping provides a Target.com data extractor that is simple to sign up for and utilize in any browser.

Customized Crawler

For your scraping needs, Web Screen Scraping provides simple options. We also offer personalized crawlers that scrape all product data and prices from Target.com in order to meet your business needs.

Dropbox Delivery

Web Screen Scraping gives you the option of automating data uploads to Dropbox.

Easily Usable

You do not need to download any software or fill in any data forms. It's simple to schedule and run your crawler to scrape product prices from Target.com using a point-and-click interface.

Contact Web Screen Scraping for all your requirements or ask for a free quote!!

#targetproductscraper #targetproductdataAPI #targetdatascraper

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3idatascraping · 2 years ago

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How to Extract Product Data from H&M with Google Chrome?

Data You Can Scrape from H&M

Product’s Name

Pricing

Total Reviews

Product’s Description

Product’s Details

Requests

Google’s Chrome Browser: You would require to download the Chrome browser and the extension requires the Chrome 49+ version.

Web Scraping for Chrome Extension: Web Scraper extension could be downloaded from Chrome’s Web Store. Once downloaded the extension, you would get a spider icon included in the browser’s toolbar.

Finding the URLs

H&M helps you to search products that you could screen depending on the parameters including product types, sizes, colors, etc. The web scraper assists you to scrape data from H&M as per the requirements. You could choose the filters for data you require and copy corresponding URLs. In Web Scraper toolbars, click on the Sitemap option, choose the option named “Edit metadata’ to paste the new URLs (as per the filter) as Start URL.

For comprehensive steps about how to extract H&M data, you may watch the video given here or continue to read:

Importing H&M Scraper

After you install the extension, you can right-click anyplace on the page and go to the ‘Inspect’ option as well as Developer Tool console would pop up. Just click on the ‘Web Scraper’ tab and go to the ‘Create new sitemap’ option as well as click on the ‘Import sitemap’ button. Now paste JSON underneath into Sitemap’s JSON box.

Running the Scraper

To start extracting, just go to the Sitemap option and click the 'Scrape' alternative from the drop-down menu. Another window of Chrome will come, permitting the extension for scrolling and collecting data. Whenever the extraction is completed, the browser will be closed automatically and sends the notice.

Downloading the Data

For downloading the extracted data in a CSV file format, which you could open with Google Sheets or MS Excel, go to the Sitemap’s drop-down menu > Export as CSV > Download Now.

Disclaimer: All the codes given in our tutorials are for illustration as well as learning objectives only. So, we are not liable for how they are used as well as assume no liabilities for any harmful usage of source codes. The presence of these codes on our website does not indicate that we inspire scraping or scraping the websites given in the codes as well as supplement the tutorial. These tutorials only assist in illustrating the method of programming data scrapers for well-known internet sites. We are not indebted to offer any help for the codes, in case, you add your different questions in a comments sector, and we might occasionally address them.

#scrap data from H&M #importing H&M scraper #product data scraping #Web scraper #Data scraping services #Extract product data

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iwebdatascrape · 1 year ago

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A Guide To Scraping H&M Products With Python And BeautifulSoup For Enhanced Business Insights

Retail industries are competing at a high pace. Hence, to stay competitive, retail data scraping has become imperative. This process involves extracting crucial information from competitors' websites, monitoring pricing strategies, and analyzing customer reviews. Retailers utilize H&M product data scraping service to gain insights into market trends, optimize pricing strategies, and enhance inventory management. By staying informed about competitors and market dynamics, businesses can make data-driven decisions, adapt swiftly to changes, and ultimately provide customers with a more competitive and responsive shopping experience. However, ethical practices and compliance with legal requirements are crucial to ensure the responsible use of retail data scraping.

About H&M

Hennes & Mauritz AB is abbreviated as H&M. Since 1947 it has grown into one of the world's prominent fashion retailers, offering a wide range of accessories, clothing, and footwear for men, women, and children. Known for its affordable and trendy fashion, H&M operates globally with a vast network of stores and an online presence, making fashion accessible to a broad consumer base. The company is also committed to sustainability and has initiatives to promote ethical and environmentally conscious practices in the fashion industry. Scrape H&M product data to gather information on product details, prices, and availability for analysis and business insights.

List of Data Fields

Product Name

Product Category

Description

SKU

Brand

Size

Availability

Price

Images

Reviews

Ratings

Specifications

Shipping Information

Significance of Scraping H&M Retail Data

Scraping H&M retail data holds significant strategic importance for businesses aiming to stay competitive and informed in the dynamic retail landscape. Here's a detailed exploration of its significance:

Competitor Intelligence: Web scraping H&M data provides retailers with a comprehensive understanding of their competitors' pricing, product offerings, and promotional strategies. This competitive intelligence is crucial for making informed decisions and staying relevant in the market.

Pricing Strategy Optimization: Retailers can use Ecommerce Data Scraping Services to optimize their pricing strategies by analyzing scraped pricing data from H&M. This includes adjusting prices to remain competitive, offering discounts strategically, and responding promptly to market changes.

Inventory Management Enhancement: Monitoring H&M's product availability and stock levels through web scraping allows retailers to fine-tune their inventory management. It helps minimize stockouts, prevent overstock situations, and ensure efficient supply chain operations.

Market Trend Identification: Scraping H&M data enables businesses to identify and capitalize on emerging market trends. Analyzing product preferences and trends on the H&M platform helps retailers align their offerings with evolving consumer demands.

Customer Preferences Analysis: Examining customer reviews, ratings, and feedback on H&M products using E-Commerce Product Data Scraper gives retailers insights into consumer preferences. This information is invaluable for tailoring product assortments and enhancing the customer experience.

Strategic Decision-Making: The scraped data from H&M serves as a foundation for strategic decision-making. Retailers can adapt their business strategies based on observed patterns, ensuring agility in response to changing market conditions.

Product Assortment Planning: Detailed information on H&M's product categories, styles, and assortments aids retailers in planning their product range. It helps in aligning offerings with current fashion trends and customer expectations.

User Experience Enhancement: Utilizing scraped data empowers retailers to enhance the overall user experience. By incorporating successful elements observed on the H&M platform, businesses can optimize their website design, marketing strategies, and customer engagement tactics.

Today, we'll explore scraping H&M products with Python and BeautifulSoup in an uncomplicated and elegant way. This article introduces you to real-world problem-solving, ensuring simplicity and practical results for a quick understanding.

To begin, ensure you have Python 3 installed. If not, install Python 3 before proceeding. Next, install Beautiful Soup with:pip3 install beautifulsoup4

Additionally, we require library requests, lxml, and soupsieve to fetch data, convert it to XML, and utilize CSS selectors. Install these libraries by using the following command:pip3 install requests soupsieve 1xml

Save this as scrapeHM.py.

If you execute it:python3 scrapeHM.py

You will observe the entire HTML page.

When deploying this in production and aiming to scale to numerous links, encountering IP blocks from H&M is likely. To address this, employing a rotating proxy service becomes essential. Utilizing a Proxies API enables routing calls through a vast pool of residential proxies, mitigating the risk of IP blocks.

Feel free to get in touch with iWeb Data Scraping for comprehensive information! Whether you seek web scraping service or mobile app data scraping, our team can assist you. Contact us today to explore your requirements and discover how our data scraping solutions can provide you with efficiency and reliability tailored to your unique needs.

Know More: https://www.iwebdatascraping.com/scraping-h-and-m-products-with-python-and-beautifulsoup.php

#ScrapingHandMProductsWithPythonAndBeautifulSoup #ScrapeHandMProductsUsingPythonandBeautifulSoup #HandMScraper #HandMproductdataCollectionservice #HandMproductdatascrapingservice #ExtractHandMProductsWithPythonAndBeautifulSoup #HandMProductsWithPythonAndBeautifulSoupdataextractor #HandMProductsWithPythonAndBeautifulSoupdataextraction

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retailgators · 3 years ago

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What Is H&M?

H&M is a multinational Clothing-Retail & Accessories providing company that provides clothes for men, women, teenagers, and Children. H&M operates in more than 74 countries with 5000+ stores in the world with various company brands and have over 1,26,000 full-time employees. H&M is the world’s 2nd largest clothing retail company. H&M provides online shopping in 33 countries. The estimated revenue of H&M is $25.191 Billion.

List Of Data Fields

At RetailGators, we extract the required data fields given below. We extract H&M using Web Scraper Chrome Extraction.

Product Name

Price

Number of Reviews

Product Description

Product Details

Category of Cloths

Product Color

Product Size

Product Type

How We Scrape Data From H&M?

H&M allows you to search for different products that can be filtered as per factors like product price, product type, size, color, and many others. The Scraper will allow you to scrape data from the H&M as per your requirements. You need to select filters for your data you need to copy the equivalent URL.

H&M Data Scraping Tool helps you to collect structured data from H&M from the traffic using H&M normally. H&M scraper helps you to extract product data from H&M fashion online store. It allows you to scrape the whole site, particular categories, and products.

H&M Scraper

After adding an extension, you need to right-click anywhere on a page, or you need to inspect and develop tools and a popup will come up. Click on the tab name Web Scraper and you can go on to ‘Create New Sitemaps’ after that you need to click on the button and can easily import the sitemap option. RetailGators helps you to download the data into many different formats like CSV, JSON, and Excel.

Why RetailGators?

We have a professional team that helps you immediately if you have any problems while using H&M Data Scraping Services. Our eCommerce Data Scraping Services is capable, reliable, and offer you quick results without any error.

Scraping H&M Scraping Services helps you to save your time, money, and efforts. We can scrape data in a couple of hours, which might take a week or days if you do it yourself.

We are working on H&M Data Scraping Service Using Google Chrome that extract the required data.

Our expert team of H&M Data Scraping Services API knows how to convert unstructured data into structured data.

So if you are looking for the best H&M Product Data Scraping Services, then contact RetailGators for all your queries.

source code: https://www.retailgators.com/how-to-scrape-product-data-from-h-and-m-using-google-chrome.php

#scrape h&m product data #extract h&m product data #Scrape Data from H&M #ecommerce web scraping tool #ecommerce data scraping service

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hvilleicehockey-blog · 6 years ago

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locationscloud · 5 years ago

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Complete list of all H&M store locations in the United States with geocoded address, phone number and open hours for instant download.

Learn More: H&M Store Locations Scraping

#Location Data Scraping #LocationCloud #h&m store location scraping #usa #usastorescraping

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3idatascraping · 3 years ago

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How to Extract eBay Data for Original Comic Art Sales Information?

Data Fields to Be Scrapped

There is an example shown of the artwork drawn by hand in pencil by some artist and then another artist inks the drawing over them. Typically, 11 × 17-inch panels are used. The vitality of the drawing style, as well as the obvious skill, appeal to everyone.

Get two panels of original art for inside pages from Spiderman comics from the 1980s a few years ago, around 2010. You can pay perhaps $200 or $300 for them and made slightly more than twice that much when you sell them a year later.

Nonetheless, if you are interested in purchasing several pieces in the $200 level right now and wanted to get additional information before doing so.

Below written is the full code with the main output in two csv files.

The leading 800 listings of original comic art from Marvel comics in the form of internal pages, covers, or splash pages are ordered by price in the first csv file. The following fields are scraped from eBay in the csv:

the title (which usually includes a 20-word description of the item, character, type of page)

Price

Link to the item's full eBay sales page complete list of all figures in the artwork *just after first eBay search, the software cycles through the page numbers of new matches at the bottom. eBay flags the application as a bot and prevents it from scraping pages with numbers greater than four. This is fine because it only includes goods that are normally sold for less than $75, and nearly none of them are original comic art – they are largely copies or fan art.

The second file format is doing the same thing, but for things that have previously been sold, using the same search criteria. Because it requires Selenium.

If you execute Selenium more than two or three times in an hour, eBay will disable it and you will have to manually download the HTML of sold comic art.

Expected Result

You can check the result by executing the code once a day and looking through the csv file for mostly lesser-known characters of $100-$300 US dollar range currently for the sale.

Tools that are used: Python, requests, BeautifulSoup, pandas

Here are the below steps that we will follow:

We will scrape the following product

https://ebay.to/3qaWDIw

Using the “original comic art” as the search string

only cover, interior pages, or splash pages

only comic art from Marvel or DC

comics above the price of $50

sorted by price + shipping and highest to lowest

200 results per page

We'll find a comprehensive of available original comic art based on your search parameters. We'll retrieve the title / brief explanation of the listing (as a single string), the page URL of the real listing, and the price for each listing.

We'll get the main comic book character's name in one field and the identities of all the characters in the image in a second field for each listing.

We'll make a CSV file using an eBay product data scraper in the following format: a title, a price, a link, a character, and a character with several characters.

Installing all the Packages for the Project

!pip install requests --upgrade --quiet !pip install bs4 --upgrade --quiet !pip install pandas --upgrade --quiet !pip install datetime --upgrade --quiet !pip install selenium --upgrade --quiet !pip install selenium_stealth --upgrade --quiet

Initially use the time package so that you can keep the record of the program’s progress and slowly use the date and time in the csv file name

import time from datetime import date from datetime import datetime now = datetime.now() today = date.today() today = today.strftime("%b-%d-%Y") date_time = now.strftime("%H-%M-%S") today = today + "-" + date_time print("date and time:", today) date and time: Jul-17-2021-15-14-55

Create a Function to Print the Data and Time

def update_datetime(): global now global today global date_time now = datetime.now() today = date.today() today = today.strftime("%b-%d-%Y") date_time = now.strftime("%H-%M-%S") today = today + "-" + date_time print("date and time:", today)

Next Scrape the search URL

To download the page, use the requests package.

Employ Beautiful Soup (BS4) to look for appropriate HTML tags, parse them.

Transform the artwork information to a Pandas dataframe.

import requests from bs4 import BeautifulSoup # original comic art, marvel or dc only, buy it now, over 50, interior splash or cover, sorted by price high to low orig_comicart_marv_dc_50plus_200perpage = 'https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200' orig_comicart_marv_dc_50plus_200perpage_sold = 'https://www.ebay.com/sch/i.html?_fsrp=1&_from=R40&_sacat=0&LH_Sold=1&_mPrRngCbx=1&_udlo=50&_udhi&LH_BIN=1&_samilow&_samihi&_sadis=15&_stpos=10002&_sop=16&_dmd=1&_ipg=200&_fosrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&LH_Complete=1&_nkw=original%20comic%20art&_dcat=3984&rt=nc&Publisher=DC%2520Comics%7CMarvel%7CMarvel%2520Comics' search_url = orig_comicart_marv_dc_50plus_200perpage # there is a way to use headers in this function call to change the # user agent so the site thinks the request is coming from # different computers with different broswers but I could not get this working # response = requests.get(url, headers=headers) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser')

Unless there is an error, the response function will return 200. If this is the case, display the error code; otherwise, continue. doc is a BeautifulSoup (BS4) object that makes searching for HTML tags and navigating the Document Object Model a breeze (DOM)

Now Save the HTML Files

# first use the date and time in the file name filename = 'comic_art_marvel_dc-' + today + '.html' with open(filename, 'w') as f: f.write(page_contents)

We can use h3 tags with the class's-item title' to acquire the listing's title/description.

title_class = 's-item__title' title_tags = doc.find_all('h3', {'class': title_class})

This locates all of the h3 tags in the BS4 documentation.

# make a list for all the titles title_list = []

loop through the tags and obtain only the contents of each one

for i in range(len(title_tags)): # make sure there are contents first if (title_tags[i].contents): title_contents = title_tags[i].contents[0] title_list.append(title_contents) len(title_list) 202 print(title_list[:5]) ['WHAT IF ASTONISHING X-MEN #1 ORIGINAL J. SCOTT CAMPBELL COMIC COVER ART MARVEL', 'CHAMBER OF DARKNESS #7 COVER ART (VERY FIRST BERNIE WRIGHTSON MARVEL COVER) 1970', 'MANEELY, JOE - WILD WESTERN #46 GOLDEN AGE MARVEL COMICS COVER (LARGE ART) 1955', 'Superman vs Captain Marvel Double page splash art by Rich Buckler DC 1978 YOWZA!', 'SIMON BISLEY 1990 DOOM PATROL #39 ORIGINAL COMIC COVER ART PAINTING DC COMICS'] since the price is in the same area of the html page as the title, let's use the findNext function. this time we will search for a 'span' element with class = 's-item__price'. also, when I tried to run separate functions to find the title, and then the price, there were sometimes duplicate title tags -- to the length of the lists would not match. I would get a title list with 202 items and a price list 200 items -- so these could not be joined in a dataframe. Also, I imagine using findNext() and findPrevious() might make the whole search process a little faster.

We'll use the findNext function because the price is in the same section of the html page as the title. We'll look for a'span' element with the class's-item price' this time. Furthermore, whenever I tried to execute separate functions to get the title and then the price, there were occasionally duplicate page titles - the lengths of the lists didn't match. You would get a 202-item title list and a 200-item price list, which couldn't be combined in a data frame.

In addition, You can use findNext() and findPrevious()that will speed up the entire search process.

price_class = 's-item__price' price_list = [] for i in range(len(title_tags)): # make sure there are contents first if (title_tags[i].contents): title_contents = title_tags[i].contents[0] title_list.append(title_contents) price = title_tags[i].findNext('span', {'class': price_class}) if(i==1): print(price)

This displays the price information during the last item listed on the first search page, out of a total of 200.

print(price.contents) ['$60.00']

Now you need to check if you are getting a string and not a tag, and if so Strip the Dollar sign

from __future__ import division, unicode_literals import codecs from re import sub if (isinstance(price_string, str)): price_string = sub(r'[^\d.]', '', price_string) else: price_string = price.contents[0].contents[0] price_string = sub(r'[^\d.]', '', price_string) print(price_string) 60.00

Converting the Price into a Floating-Point Decimal

price_num = float(price_string) print(price_num) 60.0

Place it All together in a Loop and Add all the Prices to a List

for i in range(len(title_tags)): if (title_tags[i].contents): title_contents = title_tags[i].contents[0] title_list.append(title_contents) price = title_tags[i].findNext('span', {'class': price_class}) if price.contents: price_string = price.contents[0] if (isinstance(price_string, str)): price_string = sub(r'[^\d.]', '', price_string) else: price_string = price.contents[0].contents[0] price_string = sub(r'[^\d.]', '', price_string) price_num = float(price_string) price_list.append(price_num) print(len(price_list)) 202 print(price_list[:5]) [50000.0, 45000.0, 18000.0, 16000.0, 14999.99]

now find an anchor tag with a reference and add the links to each distinct art listing

item_page_link = title_tags[i].findPrevious('a', href=True) link_list = []

Clearing the Other Lists

title_list.clear() price_list.clear() for i in range(len(title_tags)): if (title_tags[i].contents): title_contents = title_tags[i].contents[0] title_list.append(title_contents) price = title_tags[i].findNext('span', {'class': price_class}) if price.contents: price_string = price.contents[0] if (isinstance(price_string, str)): price_string = sub(r'[^\d.]', '', price_string) else: price_string = price.contents[0].contents[0] price_string = sub(r'[^\d.]', '', price_string) price_num = float(price_string) price_list.append(price_num) item_page_link = title_tags[i].findPrevious('a', href=True) # {'class': 's-item__link'}) if item_page_link.text: href_text = item_page_link['href'] link_list.append(item_page_link['href']) len(link_list) 202 print(link_list[:5])

Creating a DataFrame using the Dictionary

import pandas as pd title_price_link_df = pd.DataFrame(title_and_price_dict) len(title_price_link_df) 202 print(title_price_link_df[:5]) title ... link 0 WHAT IF ASTONISHING X-MEN #1 ORIGINAL J. SCOTT... ... https://www.ebay.com/itm/123753951902?hash=ite... 1 CHAMBER OF DARKNESS #7 COVER ART (VERY FIRST B... ... https://www.ebay.com/itm/312520261257?hash=ite... 2 MANEELY, JOE - WILD WESTERN #46 GOLDEN AGE MAR... ... https://www.ebay.com/itm/312525381131?hash=ite... 3 Superman vs Captain Marvel Double page splash ... ... https://www.ebay.com/itm/233849382971?hash=ite... 4 SIMON BISLEY 1990 DOOM PATROL #39 ORIGINAL COM... ... https://www.ebay.com/itm/153609370179?hash=ite... [5 rows x 3 columns]

We're simply interested in the top six pages of results produced by our search address for now. We would potentially obtain 1200 listings ordered by price if the URL returned 200 listings per page. Unfortunately, eBay stops processing requests after the fourth page, resulting in 800 listings. Given the current traffic on eBay, this should be enough to get all products over $75. The listings below this amount are almost entirely made up of fan art rather than actual comic art.

So, the quick and simple method is to check for the pages in the lower-left corner and click on each one to receive the connections to that page.

links_with_pgn_text = [] for a in doc.find_all('a', href=True): if a.text: href_text = a['href'] if (href_text.find('pgn=') != -1): links_with_pgn_text.append(a['href']) len(links_with_pgn_text) 7 print(links_with_pgn_text[:3])

Converting this into Function

def build_pagelink_list(url): response = requests.get(url) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') for a in doc.find_all('a', href=True): if a.text: href_text = a['href'] if (href_text.find('pgn=') != -1): links_with_pgn_text.append(a['href']) #below gets run if there is only 1 page of listings if (len(links_with_pgn_text) < 1): links_with_pgn_text.append(url) links_with_pgn_text.clear() build_pagelink_list(orig_comicart_marv_dc_50plus_200perpage) len(links_with_pgn_text) 7 print(links_with_pgn_text)

Extracting the Old Items

Now we'll scrape the internet for auctioned listings and prices. The long-term aim is to be able to detect products listed for sale and compare their pricing to those of recently sold items to determine whether current listings are reasonably priced or underpriced and worth considering purchasing.

This second link only returns results for things that have already been sold, according to eBay. However, because this search yields fewer than 200 results, we'll have to manually download the file for this notebook. This procedure, however, is automated using Selenium, and the code for it can be found below.

orig_comicart_marv_dc_50plus_200perpage_sold = 'https://www.ebay.com/sch/i.html?_fsrp=1&_from=R40&_sacat=0&LH_Sold=1&_mPrRngCbx=1&_udlo=50&_udhi&LH_BIN=1&_samilow&_samihi&_sadis=15&_stpos=10002&_sop=16&_dmd=1&_ipg=200&_fosrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&LH_Complete=1&_nkw=original%20

Select File->Save Page as webpage HTML only from Chrome if you need to save the page manually.

"sold listings.html" is the name of the file.

!apt update !apt install chromium-chromedriver --quiet from selenium import webdriver from selenium_stealth import stealth Hit:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease Hit:3 http://security.ubuntu.com/ubuntu bionic-security InRelease Ign:4 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release Hit:6 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease Hit:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release Hit:8 http://archive.ubuntu.com/ubuntu bionic InRelease Hit:9 http://archive.ubuntu.com/ubuntu bionic-updates InRelease Hit:10 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease Hit:11 http://archive.ubuntu.com/ubuntu bionic-backports InRelease Hit:12 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease Hit:14 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease Reading package lists... Done Building dependency tree Reading state information... Done 41 packages can be upgraded. Run 'apt list --upgradable' to see them. Reading package lists... Building dependency tree... Reading state information... chromium-chromedriver is already the newest version (91.0.4472.101-0ubuntu0.18.04.1). 0 upgraded, 0 newly installed, 0 to remove and 41 not upgraded. def selenium_run(url): options = webdriver.ChromeOptions() options.add_argument('--headless') options.add_argument('--no-sandbox') options.add_argument('--disable-dev-shm-usage') options.add_argument("start-maximized") options.add_experimental_option("excludeSwitches", ["enable-automation"]) options.add_experimental_option('useAutomationExtension', False) # open it, go to a website, and get results driver = webdriver.Chrome('chromedriver',options=options) # uncomment below and change paths if running locally (and comment the line above) #PATH = '/Users/jmartin/Downloads/chromedriver' #driver = webdriver.Chrome(options=options, executable_path=r"/Users/jmartin/Downloads/chromedriver") stealth( driver, user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.53 Safari/537.36', languages = "en", vendor = "Google Inc.", platform = "Win32", webgl_vendor = "Intel Inc.", renderer = "Intel Iris OpenGL Engine", fix_hairline = False, run_on_insecure_origins = False ) driver.delete_all_cookies() driver.get(url) update_datetime() #html_file_name = "sold_page_source-" + today + ".html" html_file_name = "sold_listings.html" with open(html_file_name, "w") as f: f.write(driver.page_source) return html_file_name fname = selenium_run(orig_comicart_marv_dc_50plus_200perpage_sold) date and time: Jul-17-2021-15-17-25 print(fname) sold_listings.html with open(fname) as fp: doc = BeautifulSoup(fp, 'html.parser') def selenium_run(url): options = webdriver.ChromeOptions() options.add_argument('--headless') options.add_argument('--no-sandbox') options.add_argument('--disable-dev-shm-usage') options.add_argument("start-maximized") options.add_experimental_option("excludeSwitches", ["enable-automation"]) options.add_experimental_option('useAutomationExtension', False) # open it, go to a website, and get results driver = webdriver.Chrome('chromedriver',options=options) # uncomment below and change paths if running locally (and comment the line above) #PATH = '/Users/jmartin/Downloads/chromedriver' #driver = webdriver.Chrome(options=options, executable_path=r"/Users/jmartin/Downloads/chromedriver") stealth( driver, user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.53 Safari/537.36', languages = "en", vendor = "Google Inc.", platform = "Win32", webgl_vendor = "Intel Inc.", renderer = "Intel Iris OpenGL Engine", fix_hairline = False, run_on_insecure_origins = False ) driver.delete_all_cookies() driver.get(url) update_datetime() #html_file_name = "sold_page_source-" + today + ".html" html_file_name = "sold_listings.html" with open(html_file_name, "w") as f: f.write(driver.page_source) return html_file_name fname = selenium_run(orig_comicart_marv_dc_50plus_200perpage_sold) date and time: Sep-27-2021-13-19-13 print(fname) sold_listings.html with open(fname) as fp: doc = BeautifulSoup(fp, 'html.parser')

For the sold products page, the classes for the title, link, and price tags are a little different.

title_class = 'lvtitle' price_class = 'bold bidsold' link_class = 'vip'

obtain a session URL, and then remove cookies from the session to avoid website blocking

s = requests.session()

Place it all into one function which will scrape for current or sold listings based on the function arguments.

def scrape_titles_and_prices(url, document): s.cookies.clear() update_datetime() if document: using_local_doc=True doc = document title_class = 'lvtitle' price_class = 'bold bidsold' link_class = 'vip' else: print('processing a link: ', url) using_local_doc = False response = requests.get(url) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') filename = 'comic_art_marvel_dc' + today + '.html' if searching_sold: sold_html_file = filename with open(filename, 'w') as f: f.write(page_contents) title_class = 's-item__title' price_class = 's-item__price' link_class = 's-item__link' title_tags = doc.find_all('h3', {'class': title_class}) title_list = [] price_list = [] link_list = [] for i in range(len(title_tags)): if (title_tags[i].contents): if using_local_doc: title_contents = title_tags[i].contents[0].contents[0] else: title_contents = title_tags[i].contents[0] title_list.append(title_contents) price = title_tags[i].findNext('span', {'class': price_class}) if price.contents: if len(price.contents)>1 and using_local_doc: price_string = price.contents[1].contents[0] else: price_string = price.contents[0] if (isinstance(price_string, str)): price_string = sub(r'[^\d.]', '', price_string) else: price_string = price.contents[0].contents[0] price_string = sub(r'[^\d.]', '', price_string) price_num = float(price_string) price_list.append(price_num) item_page_link = title_tags[i].findPrevious('a', href=True) # {'class': 's-item__link'}) if item_page_link.text: href_text = item_page_link['href'] link_list.append(item_page_link['href']) title_and_price_dict = { 'title': title_list, 'price': price_list, 'link': link_list } title_price_link_df = pd.DataFrame(title_and_price_dict) # returns a data frame return title_price_link_df result = scrape_titles_and_prices("", doc) date and time: Jul-17-2021-15-18-43 print(result[:10]) Empty DataFrame Columns: [title, price, link] Index: []

Exporting the Result to a .csv File

You might get an issue using.to csv in future tests after starting this project, therefore You will have to reduce the version of pandas to get this to work.

!pip uninstall pandas !pip install pandas==1.1.5 Found existing installation: pandas 1.3.3 Uninstalling pandas-1.3.3: Would remove: /usr/local/lib/python3.7/dist-packages/pandas-1.3.3.dist-info/* /usr/local/lib/python3.7/dist-packages/pandas/* Proceed (y/n)? y Successfully uninstalled pandas-1.3.3 Collecting pandas==1.1.5 Downloading pandas-1.1.5-cp37-cp37m-manylinux1_x86_64.whl (9.5 MB) |████████████████████████████████| 9.5 MB 7.3 MB/s Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5) (1.19.5) Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5) (2.8.2) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5) (2018.9) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas==1.1.5) (1.15.0) Installing collected packages: pandas ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible. Successfully installed pandas-1.1.5 update_datetime() fname = "origcomicart" + "-sold-" + today + ".csv" result.to_csv(fname, index=None) print(fname) date and time: Sep-27-2021-13-25-01 origcomicart-sold-Sep-27-2021-13-25-01.csv

Cycle Through all the Links in the CSV File

Go into each link and visit the individual listing page to collect the identity of the character, as well as all characters on the art, now that we have a.csv file with all the sold listings (the same goes for a csv file with all the current listings).

import csv def indiv_page_link_cycler(csv_name): with open(csv_name, newline='') as f: reader = csv.reader(f) data = list(reader) # go through each link and add character to each list # skip header row for i in range(1, len(data)): if(i%50==0): update_datetime() print(i,' :links processed') link = data[i][2] response = requests.get(link) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') searched_word = 'Character' selection_class = 'attrLabels' character_tags = doc.find_all('td', {'class': selection_class}) for j in range(len(character_tags)): if (character_tags[j].contents): fullstring = character_tags[j].contents[0] if ("Character" or "character") in fullstring: character = character_tags[j].findNext('span') data[i].append(character.text) data[0].append('characters') data[0].append('multi-characters') fname = csv_name[:-4] fname = fname + "_chars.csv" with open(fname, 'w') as file: writer = csv.writer(file) writer.writerows(data)

Copy and paste the csv format files name from the previous output

indiv_page_link_cycler(fname) date and time: Sep-27-2021-13-26-48 50 :links processed date and time: Sep-27-2021-13-27-27 100 :links processed date and time: Sep-27-2021-13-28-08 150 :links processed date and time: Sep-27-2021-13-28-47 200 :links processed

Each entry is added with the identities of the characters in a new csv file. The file is identical to the one above, with the addition of "_chars" at the end.

Get:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB] Get:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B] Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease Get:4 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB] Ign:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease Hit:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release Hit:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release Hit:8 http://archive.ubuntu.com/ubuntu bionic InRelease Get:9 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB] Hit:10 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease Get:11 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,221 kB] Hit:12 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease Get:13 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB] Hit:14 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease Get:15 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,418 kB] Get:18 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,780 kB] Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,658 kB] Get:20 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [911 kB] Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,188 kB] Fetched 11.4 MB in 3s (3,327 kB/s) Reading package lists... Done Building dependency tree Reading state information... Done 41 packages can be upgraded. Run 'apt list --upgradable' to see them. Reading package lists... Done Building dependency tree Reading state information... Done The following additional packages will be installed: chromium-browser chromium-browser-l10n chromium-codecs-ffmpeg-extra Suggested packages: webaccounts-chromium-extension unity-chromium-extension The following NEW packages will be installed: chromium-browser chromium-browser-l10n chromium-chromedriver chromium-codecs-ffmpeg-extra 0 upgraded, 4 newly installed, 0 to remove and 41 not upgraded. Need to get 86.0 MB of archives. After this operation, 298 MB of additional disk space will be used. Get:1 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-codecs-ffmpeg-extra amd64 91.0.4472.101-0ubuntu0.18.04.1 [1,124 kB] Get:2 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-browser amd64 91.0.4472.101-0ubuntu0.18.04.1 [76.1 MB] Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-browser-l10n all 91.0.4472.101-0ubuntu0.18.04.1 [3,937 kB] Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-chromedriver amd64 91.0.4472.101-0ubuntu0.18.04.1 [4,837 kB] Fetched 86.0 MB in 4s (19.2 MB/s) Selecting previously unselected package chromium-codecs-ffmpeg-extra. (Reading database ... 160837 files and directories currently installed.) Preparing to unpack .../chromium-codecs-ffmpeg-extra_91.0.4472.101-0ubuntu0.18.04.1_amd64.deb ... Unpacking chromium-codecs-ffmpeg-extra (91.0.4472.101-0ubuntu0.18.04.1) ... Selecting previously unselected package chromium-browser. 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Setting up chromium-browser (91.0.4472.101-0ubuntu0.18.04.1) ... update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/x-www-browser (x-www-browser) in auto mode update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/gnome-www-browser (gnome-www-browser) in auto mode Setting up chromium-chromedriver (91.0.4472.101-0ubuntu0.18.04.1) ... Setting up chromium-browser-l10n (91.0.4472.101-0ubuntu0.18.04.1) ... Processing triggers for man-db (2.8.3-2ubuntu0.1) ... Processing triggers for hicolor-icon-theme (0.17-2) ... Processing triggers for mime-support (3.60ubuntu1) ... Processing triggers for libc-bin (2.27-3ubuntu1.2) ... /sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link from __future__ import division, unicode_literals import requests from bs4 import BeautifulSoup from re import sub from decimal import Decimal import pandas as pd import requests import random import time import os import csv from datetime import date from datetime import datetime import codecs from selenium import webdriver from selenium_stealth import stealth import time import random s = requests.session() s.cookies.clear() now = datetime.now() today = date.today() today = today.strftime("%b-%d-%Y") date_time = now.strftime("%H-%M-%S") today = today + "-" + date_time def update_datetime(): global now global today global date_time now = datetime.now() today = date.today() today = today.strftime("%b-%d-%Y") date_time = now.strftime("%H-%M-%S") today = today + "-" + date_time print("date and time:", today) html_doc = """ <html><head><title>place holder</title></head> """ s.cookies.clear() # this just initializes the beautiful soup doc as a global variable doc = BeautifulSoup(html_doc, 'html.parser') def scrape_titles_and_prices(url, document): s.cookies.clear() update_datetime() if document: using_local_doc=True doc = document title_class = 'lvtitle' price_class = 'bold bidsold' link_class = 'vip' else: print('processing a link: ', url) using_local_doc = False response = requests.get(url) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') filename = 'comic_art_marvel_dc' + today + '.html' if searching_sold: sold_html_file = filename with open(filename, 'w') as f: f.write(page_contents) title_class = 's-item__title' price_class = 's-item__price' link_class = 's-item__link' title_tags = doc.find_all('h3', {'class': title_class}) title_list = [] price_list = [] link_list = [] for i in range(len(title_tags)): if (title_tags[i].contents): if using_local_doc: title_contents = title_tags[i].contents[0].contents[0] else: title_contents = title_tags[i].contents[0] title_list.append(title_contents) price = title_tags[i].findNext('span', {'class': price_class}) if price.contents: if len(price.contents)>1 and using_local_doc: price_string = price.contents[1].contents[0] else: price_string = price.contents[0] if (isinstance(price_string, str)): price_string = sub(r'[^\d.]', '', price_string) else: price_string = price.contents[0].contents[0] price_string = sub(r'[^\d.]', '', price_string) price_num = float(price_string) price_list.append(price_num) item_page_link = title_tags[i].findPrevious('a', href=True) # {'class': 's-item__link'}) if item_page_link.text: href_text = item_page_link['href'] link_list.append(item_page_link['href']) title_and_price_dict = { 'title': title_list, 'price': price_list, 'link': link_list } title_price_link_df = pd.DataFrame(title_and_price_dict) return title_price_link_df def build_pagelink_list(url): response = requests.get(url) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') for a in doc.find_all('a', href=True): if a.text: href_text = a['href'] if (href_text.find('pgn=') != -1): links_with_pgn_text.append(a['href']) if (len(links_with_pgn_text) < 1): links_with_pgn_text.append(url) def scrape_all_pages(): time.sleep(random.randint(1, 4)) for i in range(0, len(links_with_pgn_text)): next_page_url = links_with_pgn_text[i] frames.append(scrape_titles_and_prices(next_page_url,"")) time.sleep(random.randint(1, 2)) # main program def main_scraping(url): �� build_pagelink_list(url) scrape_all_pages() if (len(frames) > 1): result = pd.concat(frames, ignore_index=True) else: result = frames result.sort_values(by=['price']) update_datetime() fname = "origcomicart" + "_" + today + ".csv" result.to_csv(fname, index=None) return fname def parse_local_file(fname): with open(fname) as fp: document = BeautifulSoup(fp, 'html.parser') frames.append(scrape_titles_and_prices("", document)) if (len(frames) > 1): result = pd.concat(frames, ignore_index=True) else: result = frames[0] result.sort_values(by=['price']) update_datetime() fname = "origcomicart" + "_" + today + ".csv" result.to_csv(fname, index=None) return fname def indiv_page_link_cycler(csv_name): with open(csv_name, newline='') as f: reader = csv.reader(f) data = list(reader) for i in range(1, len(data)): if(i%50==0): update_datetime() print(i,' :links processed') link = data[i][2] response = requests.get(link) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') searched_word = 'Character' selection_class = 'attrLabels' character_tags = doc.find_all('td', {'class': selection_class}) for j in range(len(character_tags)): if (character_tags[j].contents): fullstring = character_tags[j].contents[0] if ("Character" or "character") in fullstring: character = character_tags[j].findNext('span') data[i].append(character.text) data[0].append('characters') data[0].append('multi-characters') fname = csv_name[:-4] fname = fname + "_chars.csv" with open(fname, 'w') as file: writer = csv.writer(file) writer.writerows(data) def add_headers(csv_file): with open(csv_file, newline='') as f: reader = csv.reader(f) data = list(reader) data[0].append('characters') data[0].append('multi-characters') fname = csv_file[:-4] fname = fname + "_append.csv" with open(fname, 'w') as file: writer = csv.writer(file) writer.writerows(data) def selenium_run(url): # open it, go to a website, and get results driver = webdriver.Chrome('chromedriver',options=options) # set selenium options to be headless, .. options = webdriver.ChromeOptions() options.add_argument('--headless') options.add_argument('--no-sandbox') options.add_argument('--disable-dev-shm-usage') #wd.get("https://www.ebay.com/sch/i.html?_fsrp=1&_from=R40&_sacat=0&LH_Sold=1&_mPrRngCbx=1&_udlo=50&_udhi&LH_BIN=1&_samilow&_samihi&_sadis=15&_stpos=10002&_sop=16&_dmd=1&_ipg=200&_fosrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&LH_Complete=1&_nkw=original%20comic%20art&_dcat=3984&rt=nc&Publisher=DC%2520Comics%7CMarvel%7CMarvel%2520Comics") #print(wd.page_source) # results #PATH = '/Users/jmartin/Downloads/chromedriver' #options = webdriver.ChromeOptions() #options.add_argument("start-maximized") #options.add_argument("--headless") #options.add_experimental_option("excludeSwitches", ["enable-automation"]) #options.add_experimental_option('useAutomationExtension', False) #driver = webdriver.Chrome(options=options, executable_path=r"/Users/jmartin/Downloads/chromedriver") stealth( driver, user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.53 Safari/537.36', languages = "en", vendor = "Google Inc.", platform = "Win32", webgl_vendor = "Intel Inc.", renderer = "Intel Iris OpenGL Engine", fix_hairline = False, run_on_insecure_origins = False ) driver.delete_all_cookies() driver.get(url) update_datetime() #html_file_name = "sold_page_source-" + today + ".html" html_file_name = "sold_listings.html" with open(html_file_name, "w") as f: f.write(driver.page_source) return html_file_name orig_comicart_marv_dc_50plus_200perpage = 'https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200' orig_comicart_marv_dc_50plus_200perpage_sold = 'https://www.ebay.com/sch/i.html?_fsrp=1&_from=R40&_sacat=0&LH_Sold=1&_mPrRngCbx=1&_udlo=50&_udhi&LH_BIN=1&_samilow&_samihi&_sadis=15&_stpos=10002&_sop=16&_dmd=1&_ipg=200&_fosrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&LH_Complete=1&_nkw=original%20comic%20art&_dcat=3984&rt=nc&Publisher=DC%2520Comics%7CMarvel%7CMarvel%2520Comics' # run the main scraping function searching_sold = False links_with_pgn_text = [] data = [] frames = [] #search for current orig comic listings search_url = orig_comicart_marv_dc_50plus_200perpage current_sales_csv = main_scraping(search_url) indiv_page_link_cycler(current_sales_csv) # now try to save the html for the sold data # usually it blocks the sales data links_with_pgn_text.clear() data.clear() frames.clear() sold_html_file = "" searching_sold = True search_url = orig_comicart_marv_dc_50plus_200perpage_sold #seach listing for sold items #below can also be run locally with selenium after you install the webdriver #and change the PATH variable so it points to your local installation directory sold_html_file = selenium_run(orig_comicart_marv_dc_50plus_200perpage_sold) #sold_html_file = "sold_listings.html" update_datetime() print("now parsing sold items") past_sales_csv = parse_local_file(sold_html_file) indiv_page_link_cycler(past_sales_csv) date and time: Jul-08-2021-13-50-40 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=1 date and time: Jul-08-2021-13-50-42 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=2 date and time: Jul-08-2021-13-50-45 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=3 date and time: Jul-08-2021-13-50-47 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=4 date and time: Jul-08-2021-13-50-51 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=5&rt=nc date and time: Jul-08-2021-13-50-57 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=6&rt=nc date and time: Jul-08-2021-13-51-01 date and time: Jul-08-2021-13-51-33 50 :links processed date and time: Jul-08-2021-13-52-06 100 :links processed date and time: Jul-08-2021-13-52-39 150 :links processed date and time: Jul-08-2021-13-53-12 200 :links processed date and time: Jul-08-2021-13-53-44 250 :links processed date and time: Jul-08-2021-13-54-17 300 :links processed date and time: Jul-08-2021-13-54-51 350 :links processed date and time: Jul-08-2021-13-55-23 400 :links processed date and time: Jul-08-2021-13-55-56 450 :links processed date and time: Jul-08-2021-13-56-26 500 :links processed date and time: Jul-08-2021-13-56-58 550 :links processed date and time: Jul-08-2021-13-57-29 600 :links processed date and time: Jul-08-2021-13-58-02 650 :links processed date and time: Jul-08-2021-13-58-34 700 :links processed date and time: Jul-08-2021-13-59-06 750 :links processed date and time: Jul-08-2021-13-59-36 800 :links processed date and time: Jul-08-2021-13-59-42 now parsing sold items date and time: Jul-08-2021-13-59-42 date and time: Jul-08-2021-13-59-42 date and time: Jul-08-2021-14-00-19 50 :links processed date and time: Jul-08-2021-14-00-57 100 :links processed

Final Code

from __future__ import division, unicode_literals import requests from bs4 import BeautifulSoup from re import sub from decimal import Decimal import pandas as pd import requests import random import time import os import csv from datetime import date from datetime import datetime import codecs from selenium import webdriver from selenium_stealth import stealth import time import random s = requests.session() s.cookies.clear() now = datetime.now() today = date.today() today = today.strftime("%b-%d-%Y") date_time = now.strftime("%H-%M-%S") today = today + "-" + date_time def update_datetime(): global now global today global date_time now = datetime.now() today = date.today() today = today.strftime("%b-%d-%Y") date_time = now.strftime("%H-%M-%S") today = today + "-" + date_time print("date and time:", today) html_doc = """ <html><head><title>place holder</title></head> """ s.cookies.clear() # this just initializes the beautiful soup doc as a global variable doc = BeautifulSoup(html_doc, 'html.parser') def scrape_titles_and_prices(url, document): s.cookies.clear() update_datetime() if document: using_local_doc=True doc = document title_class = 'lvtitle' price_class = 'bold bidsold' link_class = 'vip' else: print('processing a link: ', url) using_local_doc = False response = requests.get(url) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') filename = 'comic_art_marvel_dc' + today + '.html' if searching_sold: sold_html_file = filename with open(filename, 'w') as f: f.write(page_contents) title_class = 's-item__title' price_class = 's-item__price' link_class = 's-item__link' title_tags = doc.find_all('h3', {'class': title_class}) title_list = [] price_list = [] link_list = [] for i in range(len(title_tags)): if (title_tags[i].contents): if using_local_doc: title_contents = title_tags[i].contents[0].contents[0] else: title_contents = title_tags[i].contents[0] title_list.append(title_contents) price = title_tags[i].findNext('span', {'class': price_class}) if price.contents: if len(price.contents)>1 and using_local_doc: price_string = price.contents[1].contents[0] else: price_string = price.contents[0] if (isinstance(price_string, str)): price_string = sub(r'[^\d.]', '', price_string) else: price_string = price.contents[0].contents[0] price_string = sub(r'[^\d.]', '', price_string) price_num = float(price_string) price_list.append(price_num) item_page_link = title_tags[i].findPrevious('a', href=True) # {'class': 's-item__link'}) if item_page_link.text: href_text = item_page_link['href'] link_list.append(item_page_link['href']) title_and_price_dict = { 'title': title_list, 'price': price_list, 'link': link_list } title_price_link_df = pd.DataFrame(title_and_price_dict) return title_price_link_df def build_pagelink_list(url): response = requests.get(url) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') for a in doc.find_all('a', href=True): if a.text: href_text = a['href'] if (href_text.find('pgn=') != -1): links_with_pgn_text.append(a['href']) if (len(links_with_pgn_text) < 1): links_with_pgn_text.append(url) def scrape_all_pages(): time.sleep(random.randint(1, 4)) for i in range(0, len(links_with_pgn_text)): next_page_url = links_with_pgn_text[i] frames.append(scrape_titles_and_prices(next_page_url,"")) time.sleep(random.randint(1, 2)) # main program def main_scraping(url): build_pagelink_list(url) scrape_all_pages() if (len(frames) > 1): result = pd.concat(frames, ignore_index=True) else: result = frames result.sort_values(by=['price']) update_datetime() fname = "origcomicart" + "_" + today + ".csv" result.to_csv(fname, index=None) return fname def parse_local_file(fname): with open(fname) as fp: document = BeautifulSoup(fp, 'html.parser') frames.append(scrape_titles_and_prices("", document)) if (len(frames) > 1): result = pd.concat(frames, ignore_index=True) else: result = frames[0] result.sort_values(by=['price']) update_datetime() fname = "origcomicart" + "_" + today + ".csv" result.to_csv(fname, index=None) return fname def indiv_page_link_cycler(csv_name): with open(csv_name, newline='') as f: reader = csv.reader(f) data = list(reader) for i in range(1, len(data)): if(i%50==0): update_datetime() print(i,' :links processed') link = data[i][2] response = requests.get(link) if (response.status_code != 200): raise Exception('Failed to load page {}'.format(url)) page_contents = response.text doc = BeautifulSoup(page_contents, 'html.parser') searched_word = 'Character' selection_class = 'attrLabels' character_tags = doc.find_all('td', {'class': selection_class}) for j in range(len(character_tags)): if (character_tags[j].contents): fullstring = character_tags[j].contents[0] if ("Character" or "character") in fullstring: �� character = character_tags[j].findNext('span') data[i].append(character.text) data[0].append('characters') data[0].append('multi-characters') fname = csv_name[:-4] fname = fname + "_chars.csv" with open(fname, 'w') as file: writer = csv.writer(file) writer.writerows(data) def add_headers(csv_file): with open(csv_file, newline='') as f: reader = csv.reader(f) data = list(reader) data[0].append('characters') data[0].append('multi-characters') fname = csv_file[:-4] fname = fname + "_append.csv" with open(fname, 'w') as file: writer = csv.writer(file) writer.writerows(data) def selenium_run(url): options = webdriver.ChromeOptions() options.add_argument('--headless') options.add_argument('--no-sandbox') options.add_argument('--disable-dev-shm-usage') options.add_argument("start-maximized") options.add_experimental_option("excludeSwitches", ["enable-automation"]) options.add_experimental_option('useAutomationExtension', False) # open it, go to a website, and get results driver = webdriver.Chrome('chromedriver',options=options) # uncomment below and change paths if running locally (and comment the line above) #PATH = '/Users/jmartin/Downloads/chromedriver' #driver = webdriver.Chrome(options=options, executable_path=r"/Users/jmartin/Downloads/chromedriver") stealth( driver, user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.53 Safari/537.36', languages = "en", vendor = "Google Inc.", platform = "Win32", webgl_vendor = "Intel Inc.", renderer = "Intel Iris OpenGL Engine", fix_hairline = False, run_on_insecure_origins = False ) driver.delete_all_cookies() driver.get(url) update_datetime() #html_file_name = "sold_page_source-" + today + ".html" html_file_name = "sold_listings.html" with open(html_file_name, "w") as f: f.write(driver.page_source) return html_file_name orig_comicart_marv_dc_50plus_200perpage = 'https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200' orig_comicart_marv_dc_50plus_200perpage_sold = 'https://www.ebay.com/sch/i.html?_fsrp=1&_from=R40&_sacat=0&LH_Sold=1&_mPrRngCbx=1&_udlo=50&_udhi&LH_BIN=1&_samilow&_samihi&_sadis=15&_stpos=10002&_sop=16&_dmd=1&_ipg=200&_fosrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&LH_Complete=1&_nkw=original%20comic%20art&_dcat=3984&rt=nc&Publisher=DC%2520Comics%7CMarvel%7CMarvel%2520Comics' # run the main scraping function searching_sold = False links_with_pgn_text = [] data = [] frames = [] #search for current orig comic listings search_url = orig_comicart_marv_dc_50plus_200perpage current_sales_csv = main_scraping(search_url) indiv_page_link_cycler(current_sales_csv) # now try to save the html for the sold data # usually it blocks the sales data links_with_pgn_text.clear() data.clear() frames.clear() sold_html_file = "" searching_sold = True search_url = orig_comicart_marv_dc_50plus_200perpage_sold #seach listing for sold items #below can also be run locally with selenium after you install the webdriver #and change the PATH variable so it points to your local installation directory sold_html_file = selenium_run(orig_comicart_marv_dc_50plus_200perpage_sold) #sold_html_file = "sold_listings.html" update_datetime() print("now parsing sold items") past_sales_csv = parse_local_file(sold_html_file) indiv_page_link_cycler(past_sales_csv) date and time: Sep-27-2021-14-02-53 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=1 date and time: Sep-27-2021-14-02-55 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=2 date and time: Sep-27-2021-14-02-59 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=3 date and time: Sep-27-2021-14-03-02 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=4 date and time: Sep-27-2021-14-03-05 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=5&rt=nc date and time: Sep-27-2021-14-03-10 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=6&rt=nc date and time: Sep-27-2021-14-03-15 processing a link: https://www.ebay.com/sch/i.html?_dcat=3984&_fsrp=1&Type=Cover%7CInterior%2520Page%7CSplash%2520Page&_from=R40&_nkw=original+comic+art&_sacat=0&Publisher=Marvel%2520Comics%7CDC%2520Comics%7CUltimate%2520Marvel%7CMarvel%2520Age%7CMarvel%2520Adventures%7CMarvel&LH_BIN=1&_udlo=50&_sop=16&_ipg=200&_pgn=7&rt=nc date and time: Sep-27-2021-14-03-19 date and time: Sep-27-2021-14-03-51 50 :links processed date and time: Sep-27-2021-14-04-23 100 :links processed date and time: Sep-27-2021-14-04-55 150 :links processed date and time: Sep-27-2021-14-05-28 200 :links processed date and time: Sep-27-2021-14-06-02 250 :links processed date and time: Sep-27-2021-14-06-35 300 :links processed date and time: Sep-27-2021-14-07-07 350 :links processed date and time: Sep-27-2021-14-07-40 400 :links processed date and time: Sep-27-2021-14-08-14 450 :links processed date and time: Sep-27-2021-14-08-46 500 :links processed date and time: Sep-27-2021-14-09-20 550 :links processed date and time: Sep-27-2021-14-09-52 600 :links processed date and time: Sep-27-2021-14-10-25 650 :links processed date and time: Sep-27-2021-14-10-56 700 :links processed date and time: Sep-27-2021-14-11-30 750 :links processed date and time: Sep-27-2021-14-12-03 800 :links processed date and time: Sep-27-2021-14-12-11 date and time: Sep-27-2021-14-12-11 now parsing sold items date and time: Sep-27-2021-14-12-11 date and time: Sep-27-2021-14-12-11

Summary

Our purpose for this research was to obtain two csv files relating to original comic art on eBay.

The first csv file featured all of Marvel and DC's current lists of inside pages, covers, and splash pages.

The second csv file had similar data about things that had been sold.

This allowed us to swiftly (far faster than eBay's UI) search the current offerings for paintings in a specific price range and characters that looked interesting. We may then seek comparable art in the csv of sold things to evaluate if this was a decent buy.

The Steps that We Used

To access the HTML content from a URL generated by eBay's filtering system, use the requests library.

Use BeautifulSoup to look for tags (p, div, a, etc.) in the HTML text of the original search page results for the data we needed, such as listing title and description, link to the full listing, and price

Include this information in your listings.

Make a pandas data frame out of the lists and save it as a csv file.

Then browse through the links and open each full artwork listing with requests, then utilize BeautifulSoup to get the character details.

Create a csv file with the data frame.

Because eBay prohibited the use of requests to obtain this html content for sold items, we had to store the html content as an html file using Selenium. We then opened this file in BeautifulSoup and parsed it using the same methods.

For more details, contact 3i Data Scraping today

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