https:\/\/app.zenserp.com\/playground <\/a><\/p>\n\n\n\n# zenserp variables used to search google \/\/ https:\/\/app.zenserp.com\/playground
search_engine = \"google.co.uk\"
device = \"desktop\"
location = \"London,England,United Kingdom\"
geolocation = \"GB\"
language = \"en\"<\/pre>\n\n\n\nThe next step is to get the current working directory using the OS module<\/em> so the script knows where to find zenserp_key.txt<\/em> and brands.txt<\/em><\/p>\n\n\n\n# get the current working directory and print\npath = os.getcwd()\nprint(path)<\/pre>\n\n\n\nOnce the directory has been set, we can read in both files.<\/p>\n\n\n\n
# read in the zenserp.com key to scrape the serps
with open(path +'\/zenserp_key.txt', 'r') as file: # read in the Keywords Everywhere API Key
zenserp_key = file.read()
# read in the list of brands
with open(path +'\/brands.txt', 'r') as file: # read in the Keywords Everywhere API Key
brands = file.read().splitlines()<\/pre>\n\n\n\nNext we need to append the word ‘stockists’ to each brand name so we can search for the related stockist page in Google. I’m doing this using Pandas because it’s familiar. There’s probably 100 more pythonic ways to do this, this is just how I choose to do it.<\/p>\n\n\n\n
# make a temp dataframe to append the word 'stockists'
df = pd.DataFrame(brands, columns=[\"brand\"])
df['stockists'] = \" Stockists\"
df['brand'] = df['brand'] + df['stockists']
total = len(df['brand'])
search_terms = df['brand'].tolist() # dump search term queries to a list (to loop through with the Search Console API)<\/pre>\n\n\n\nNow let’s make some empty lists to store the data ..<\/p>\n\n\n\n
# make empty list and dataframe to store the extracted data
df_final = pd.DataFrame(None)
url_list = []
description_list = []
title_list = []
query_list = []
df_position = []<\/pre>\n\n\n\nNow it’s time to use ZenSERP to search Google for our stockist links. Ths loops through our list of brands, appending the data returning from Google into lists, ready to be inserted into a dataframe once complete.<\/p>\n\n\n\n
count = 0\n\nfor i in search_terms:\n count = count + 1\n print(\"Searching:\", i.strip(), count, \"of\", total)\n headers = {\"apikey\": zenserp_key}\n params = (\n (\"q\", i),\n (\"device\", device),\n (\"search_engine\", search_engine),\n (\"location\", location),\n (\"gl\", geolocation),\n (\"hl\", language),\n (\"apikey\", zenserp_key),\n )\n\n response = requests.get('https:\/\/app.zenserp.com\/api\/v2\/search', headers=headers, params=params);\n\n # Get JSON Data\n d = response.json()\n json_str = json.dumps(d) # dumps the json object into an element\n resp = json.loads(json_str) # load the json to a string\n organic = (resp['organic'])\n\n # get the length of the list to iterate over in the loop\n list_len = len(organic)\n pos_counter = 0\n counter = 0\n while counter != list_len:\n access = (organic[counter])\n pos_counter = pos_counter + 1\n df_position.append(pos_counter)\n try:\n my_url = (access['url'])\n url_list.append(my_url)\n except Exception:\n url_list.append(\"MISSING\")\n pass\n\n try:\n my_description = (access['description'])\n description_list.append(my_description)\n except Exception:\n description_list.append(\"MISSING\")\n pass\n\n try:\n my_title = (access['title'])\n title_list.append(my_title)\n except Exception:\n title_list.append(\"MISSING\")\n pass\n\n query = (resp['query'])\n q_access = (query['q'])\n query_list.append(q_access)\n\n counter = counter +1<\/pre>\n\n\n\nAll of the lists are then added to new columns in the dataframe.<\/p>\n\n\n\n
# add lists to dataframe columns
df_final['query'] = query_list
df_final['url'] = url_list
df_final['title'] = title_list
df_final['description'] = description_list
df_final['position'] = df_position<\/pre>\n\n\n\nThe data is then cleaned to remove any missing values and to just keep the first result from Google.<\/p>\n\n\n\n
# clean the data!
df_final = df_final[df_final.position == 1] # keep position 1 result for each search only
df_final = df_final[~df_final[\"url\"].isin(['MISSING'])]
df_final = df_final[~df_final[\"description\"].isin(['MISSING'])]
df_final = df_final[~df_final[\"title\"].isin(['MISSING'])]
df_final[\"temp_url\"] = df_final.loc[:, [\"url\"]]<\/pre>\n\n\n\nThis removes any result which returned a homepage. The reason? A Homepage isn’t a stockists link page. This reduces the QA time significantly.<\/p>\n\n\n\n
# Remove homepages. Ensures consistency by adding extra \/\/\/\/\/ which are removed so all domains are stripped back\ndf_final[\"temp_url\"] = df_final[\"temp_url\"] + \"\/\/\/\/\/\"\ndf_final[\"temp_url\"] = df_final[\"temp_url\"].str.replace(\"\/\/\/\/\/\/\", \"\")\ndf_final[\"temp_url\"] = df_final[\"temp_url\"].str.replace(\"\/\/\/\/\/\", \"\")\ndf_final['url_depth'] = df_final[\"temp_url\"].str.count(\"\/\")\ndf_final = df_final[~df_final['url_depth'].isin([\"2\"])] # depth 2 = homepage link<\/pre>\n\n\n\nOne final clean up before we export the data. Columns are reordered into a more logical state, with all duplicates values removed.<\/p>\n\n\n\n
# clean and sort the columns
cols = \"query\", \"url\", \"title\", \"description\"
df_final = df_final.reindex(columns=cols)
df_final.drop_duplicates(subset=['url'], keep=\"first\", inplace=True)<\/pre>\n\n\n\nLastly, we export the .csv file, ready for QA-ing. That’s most of the boring work taken care of, time to get some links!<\/p>\n\n\n\n
# export the data
df_final.to_csv(path + '\/brand_links_output.csv')
print(f'\\nCompleted in {time.time() - startTime:.2f} Seconds')<\/pre>\n\n\n\nConclusion<\/h2>\n\n\n\n
Stockist links are one of the greatest tools in your arsenal when building links for an eCommerce site and being able to automate the process makes it that much sweeter. I hope this script saves you a lot of time \/ boredom the next time you’re looking to link build.
If you have any questions, suggestions or queries I’d love to hear them! Ping me on Twitter @LeeFootSEO<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Introduction Links are still an important aspect of any SEO strategy. One of the best types of link for an eCommerce store is from a brand stockist page. These links are gold dust and really move the needle for SEO. The best part is that a stockist link is usually just a quick phone call […]<\/p>\n","protected":false},"author":2,"featured_media":1428,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[12],"tags":[],"acf":[],"yoast_head":"\n
eCommerce Link Building on Autopilot with Python - Professional SEO Consultants | Search Solved<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/searchsolved.co.uk\/ecommerce-link-building-on-autopilot-with-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"eCommerce Link Building on Autopilot with Python - Professional SEO Consultants | Search Solved\" \/>\n<meta property=\"og:description\" content=\"Introduction Links are still an important aspect of any SEO strategy. One of the best types of link for an eCommerce store is from a brand stockist page. These links are gold dust and really move the needle for SEO. 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