{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Soner Yıldırım",
  "title_listed": "I Spent an Hour on a Data Preprocessing Task Before Asking Gemini",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/i-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini/",
  "publish_date": "2026-06-23",
  "corpus_stage": "",
  "notes": "",
  "id": "81eb60777195",
  "collected_at": "2026-10-02T09:26:12",
  "title": "I Spent an Hour on a Data Preprocessing Task Before Asking Gemini | Towards Data Science",
  "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# I Spent an Hour on a Data Preprocessing Task Before Asking Gemini\nHow Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nJune 23, 20266 min read\n![Photo by National Institute of Allergy and Infectious Diseases on Unsplash](../images/000.jpg)\n[Image](https://unsplash.com/photos/a-purple-and-green-string-sculpture-on-a-white-surface-oc12eprOeoI) by National Institute of Allergy and Infectious Diseases via Unsplash\nAs data scientists, we spend a significant amount of time on data preparation for downstream tasks. Whether it involves data cleaning, handling missing values, feature engineering, data preprocessing, or post processing, this phase requires a lot of time.\nSo, I was working on this post-processing task where I needed to create a new column in a Pandas DataFrame by extracting values from an existing column, based on the data from two other columns. \nI could have directly asked an LLM to write the code (which I usually do) but this time I wanted to do it myself. It was early in the morning and I had a fresh mind so I was in the mood to handle some complex data operations.\nHere is what I had to do. I had a DataFrame with `predicted_categories`, `pred_category_id`, and `text_predicted_probs` columns.\nThe values in the `predicted_categories` column are lists of five categories in \"category_id\" - \"category_description\" format. \npython\n\n```\n['80814001 - Freze Uçları', \n '13003106 - Freze', \n '80805004 - Sanayi Makineleri', \n '13003144 - Torna Makinesi', \n '13003195 - Kumpas']\n```\n\nThe `text_predicted_probs` column has the predicted probabilities of these five categories in order. \npython\n\n```\n[0.943, 0.018, 0.008, 0.006, 0.004]\n```\n\nHence, the first value in the `text_predicted_probs` is the probability of the first category in the `predicted_categories`, and so on.\nThe `pred_category_id` column shows the predicted category id from another model . What I need is the predicted probability of the category in the `pred_category_id` column.\nI need to get the order of the `pred_category_id` in the `predicted_categories` column and then take its value from the `test_predicted_probs` column.\nThe drawing below demonstrates what I want to achieve:\n![](../images/001.png)\nIn this drawing, I want to get the probability of category 13003106, which is the second item in the list and its corresponding probability value is 0.018.\nIf we asked Gemini, or another advanced model, we'll probably get the answer in seconds. But, I wanted to do it on my own first and then ask Gemini.\nLet's start with reading the dataset into a Pandas DataFrame.\npython\n\n```\nimport pandas as pd\n\nresults = pd.read_csv(\"prediction_results.csv\")\n```\n\nThe values in the `predicted_categories` column are lists of strings with category ids and category names:\npython\n\n```\nresults.loc[0, \"predicted_categories\"]\n# output: \"['80814001 - Freze Uçları', '13003106 - Freze', '80805004 - Sanayi Makineleri', '13003144 - Torna Makinesi', '13003195 - Kumpas']\"\n```\n\nIt's a list but saved as a string so we first convert it to a list object using the `literal_eval` function in the built-in `ast` module of Python:\npython\n\n```\nast.literal_eval(results.loc[0, \"predicted_categories\"])\n# output:\n['80814001 - Freze Uçları',\n '13003106 - Freze',\n '80805004 - Sanayi Makineleri',\n '13003144 - Torna Makinesi',\n '13003195 - Kumpas']\n```\n\nTo extract the category ids, we can split each string in this list at the \"-\" character and then select the first part after splitting. Since we have a list with five categories, we should do this operation in a list comprehension as follows:\npython\n\n```\n[category.split(\"-\")[0].strip() \nfor category in ast.literal_eval(results.loc[0, \"predicted_categories\"])]\n# output:\n['80814001', '13003106', '80805004', '13003144', '13003195']\n```\n\nWe've done it for a single value (i.e. one row). In order to do the same operation to the entire `predicted_categories` column, we can use a list comprehension. It will be a list comprehension inside another list comprehension (i.e. nested list comprehension):\npython\n\n```\nresults.loc[:, \"predicted_category_ids\"] = [\n    [category.split(\"-\")[0].strip() for category in ast.literal_eval(predicted_categories)] \n    for predicted_categories in results[\"predicted_categories\"]\n]\n```\n\nWe now have category ids extracted from the predicted_categories column:\n![](../images/body-2e46d3ae832caceba304.png)\nThe next step is to check the order of the categories in the predicted category id lists. We will then use this order to extract the predicted probability of the category.\nPython list object has an index method, which returns the index (i.e. order) of the item in the list.\npython\n\n```\nresults.loc[0, \"predicted_category_ids\"]\n# output:\n['80814001', '13003106', '80805004', '13003144', '13003195']\n\n\nresults.loc[0, \"predicted_category_ids\"].index(\"13003106\")\n# output:\n2\n```\n\nOnce I find the index of a predicted category id, I can use it to get the probability of this category id from the `text_predicted_probs` column:\n![](../images/body-da61addc9eeb75dc0b89.png)\nWhat we need to do:\n  1. Get the index of `pred_category_id` in the `predicted_category_ids`\n  2. Use this index to extract the relevant value from `text_predicted_probs`\n\n\nThese steps can be done in a single operation by zipping these three columns. Let's test it on the first row:\npython\n\n```\nfor i, j, k in zip(results[\"pred_category_id\"][:1], results[\"predicted_category_ids\"][:1], results[\"text_predicted_probs\"][:1]):\n    print(j.index(str(i))) # get the index of pred_category_id in predicted_category_ids\n    print(ast.literal_eval(k)[j.index(str(i))]) # get the value at this index in text_predicted_probs\n\n# output:\n0\n0.943\n```\n\nWe can confirm the output in the screenshot above. The `pred_category_id` is 80814001, which is the first item (i.e. index = 0) in the `predicted_category_ids` and the first probability value is 0.943.\nThe lists in the `text_predicted_probs` column are saved as string as well so we apply the `literal_eval` function to convert them to a list object.\nWe can do this operation on the entire DataFrame using a list comprehension as follow:\npython\n\n```\nresults.loc[:, \"pred_category_prob\"] = [\n    float(ast.literal_eval(k)[j.index(str(i))]) if str(i) in j else 0\n    for i, j, k in zip(results[\"pred_category_id\"], results[\"predicted_category_ids\"], results[\"text_predicted_probs\"])\n]\n```\n\nWe basically converted the for loop in the previous code block to a list comprehension. I've only added a check \"`if str(i) in j else 0`\" because there are some rows with a `pred_category_id` that does not exist in `predicted_category_ids`. In such cases, we set the probability value to 0.\nThe task is now completed as we can see below:\n![](../images/body-653cdb5af6a022d09a33.png)\n## How did Gemini do this?\nAfter working about an hour (including breaks) I completed the task. Then, I wanted to ask Gemini to see how it would handle the same task.\nI wrote a prompt explaining my goal and included a small sample of the data.\nHere is what Gemini produced on its first attempt:\npython\n\n```\nimport pandas as pd\n\n# 1. Create the sample DataFrame\ndata = {\n    'predicted_categories': [\n        ['80814001 - Freze Uçları', '13003106 - Freze', '80805004 - Sanayi Makineleri', '13003144 - Torna Makinesi', '13003195 - Kumpas']\n    ],\n    'pred_category_id': [80814001],\n    'text_predicted_probs': [\n        [0.943, 0.018, 0.008, 0.006, 0.004]\n    ]\n}\n\ndf = pd.DataFrame(data)\n\n# 2. Define a function to extract the probability matching the category ID\ndef get_matching_prob(row):\n    # Convert ID to string for matching\n    target_id_str = str(row['pred_category_id'])\n    \n    # Iterate through the categories to find the matching index\n    for index, category in enumerate(row['predicted_categories']):\n        if category.startswith(target_id_str):\n            # Return the corresponding probability from the same index\n            return row['text_predicted_probs'][index]\n            \n    # Return None (or 0) if no match is found to prevent the code from crashing\n    return None\n\n# 3. Create the new column\ndf['pred_category_prob'] = df.apply(get_matching_prob, axis=1)\n\n# Display the result\nprint(df[['pred_category_id', 'pred_category_prob']])\n```\n\nIt first created a sample DataFrame using the data I provided in the prompt. Then, it wrote a function that does exactly what I explained.\nIt took me about an hour to write the code myself. Gemini completed the exact same task in seconds based on a prompt that took me only a few minutes to write. \nHowever, there is a minor issue in Gemini's code. It uses the `apply` function, which is not vectorized. In case of working with large datasets, using the apply function (basically a for loop) can increase the run time. \nBut the solution is simple. I asked Gemini to write a vectorized version of it and here is what I got:\npython\n\n```\nimport pandas as pd\n\n# Sample DataFrame\ndata = {\n    'predicted_categories': [\n        ['80814001 - Freze Uçları', '13003106 - Freze', '80805004 - Sanayi Makineleri'],\n        ['13003144 - Torna Makinesi', '13003195 - Kumpas']\n    ],\n    'pred_category_id': [80814001, 13003195],\n    'text_predicted_probs': [\n        [0.943, 0.018, 0.008],\n        [0.6, 0.4]\n    ]\n}\ndf = pd.DataFrame(data)\n\n# 1. Explode both list columns simultaneously to maintain alignment between category and probability\ndf_exploded = df.explode(['predicted_categories', 'text_predicted_probs'])\n\n# 2. Extract the numeric ID from the category string using vectorized regex\ndf_exploded['extracted_id'] = df_exploded['predicted_categories'].str.extract(r'^(\\d+)').astype(float)\n\n# 3. Filter for rows where the extracted ID matches the target 'pred_category_id'\nmatched = df_exploded[df_exploded['extracted_id'] == df_exploded['pred_category_id']]\n\n# 4. Dedup the index (safety net in case an ID appears twice within the same list)\nmatched = matched[~matched.index.duplicated(keep='first')]\n\n# 5. Map the extracted probability column back to the original DataFrame using the index\ndf['pred_category_prob'] = matched['text_predicted_probs']\n\ndf\n```\n\nThe second solution was perfectly fine and looked simpler than the code I wrote.\nSo, I spent about an hour on a task that an LLM could have completed in less than five minutes. However, if I didn't know how Pandas worked, I would have accepted the first solution, which was not the optimal one. It is a perfect example of how LLMs can increase productivity, but only if you actually know what you're doing.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-11c1a04e02d8a14729da.png)\nWritten By\n## [Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[See all from Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Gemini](https://towardsdatascience.com/tag/gemini/)[Pandas](https://towardsdatascience.com/tag/pandas/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F&title=I%20Spent%20an%20Hour%20on%20a%20Data%20Preprocessing%20Task%20Before%20Asking%20Gemini)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F&title=I%20Spent%20an%20Hour%20on%20a%20Data%20Preprocessing%20Task%20Before%20Asking%20Gemini)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F&text=I%20Spent%20an%20Hour%20on%20a%20Data%20Preprocessing%20Task%20Before%20Asking%20Gemini)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](../images/002.jpg)](https://towardsdatascience.com/why-are-language-models-everywhere-36d9961dd9e1/)\n## [Why are language models everywhere?](https://towardsdatascience.com/why-are-language-models-everywhere-36d9961dd9e1/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nThe answer lies in the 75 years of NLP history\n[Ajay Halthor](https://towardsdatascience.com/author/dataemporium/)\nMay 26, 20236 min read\n  * [![](../images/003.jpeg)](https://towardsdatascience.com/how-to-control-a-robot-with-python/)\n## [How to Control a Robot with Python](https://towardsdatascience.com/how-to-control-a-robot-with-python/)\n[Robotics](https://towardsdatascience.com/category/robotics/)\n3D simulations and movement control with PyBullet\n[Mauro Di Pietro](https://towardsdatascience.com/author/maurodp/)\nOctober 23, 20259 min read\n  * [![](../images/004.jpeg)](https://towardsdatascience.com/build-a-serverless-website-using-amazon-s3-and-route-53-c741fae6ef8d/)\n## [Build a serverless website using Amazon S3 and Route 53](https://towardsdatascience.com/build-a-serverless-website-using-amazon-s3-and-route-53-c741fae6ef8d/)\n[Web Development](https://towardsdatascience.com/category/programming/web-development/)\nHow to host a scalable website in under 15 minutes\n[André Ribeiro](https://towardsdatascience.com/author/andrefsr/)\nJuly 4, 20217 min read\n  * [![](../images/005.jpg)](https://towardsdatascience.com/mongodb-92370a2641ad/)\n## [MongoDB](https://towardsdatascience.com/mongodb-92370a2641ad/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nFrom Installation to Implementation: Part 6\n[Mike Wolfe](https://towardsdatascience.com/author/miketechgame/)\nApril 10, 20218 min read\n  * [![](../images/006.jpg)](https://towardsdatascience.com/i-built-a-podcast-clipping-app-in-one-weekend-using-vibe-coding/)\n## [I Built a Podcast Clipping App in One Weekend Using Vibe Coding](https://towardsdatascience.com/i-built-a-podcast-clipping-app-in-one-weekend-using-vibe-coding/)\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\nRapid prototyping with Replit, AI agents, and minimal manual coding\n[Katy Hagerty](https://towardsdatascience.com/author/katyhagerty19/)\nMarch 23, 202611 min read\n  * [![](../images/007.png)](https://towardsdatascience.com/automated-customer-segmentation-2f9cec9df4df/)\n## [Automated customer segmentation](https://towardsdatascience.com/automated-customer-segmentation-2f9cec9df4df/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nWith Python and UbiOps\n[Anouk Dutrée](https://towardsdatascience.com/author/aadutree/)\nNovember 9, 202110 min read\n  * [![](../images/008.jpeg)](https://towardsdatascience.com/best-universities-to-study-data-science-65f9a12a0342/)\n## [Best Universities to Study Data Science](https://towardsdatascience.com/best-universities-to-study-data-science-65f9a12a0342/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nNot all Data Science degrees are the same, so find out which programme is right for you.\n[Renato Boemer](https://towardsdatascience.com/author/boemer/)\nJanuary 25, 20217 min read\n  * [![](../images/009.jpeg)](https://towardsdatascience.com/how-to-replace-values-in-pandas-609ba7a031c9/)\n## [How to Replace Values in Pandas](https://towardsdatascience.com/how-to-replace-values-in-pandas-609ba7a031c9/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nLearn how to use methods for values replacement in Pandas Dataframes\n[Gustavo Santos](https://towardsdatascience.com/author/gustavorsantos/)\nDecember 8, 20213 min read\n  * [![](../images/body-63beb50f664a055ed739.jpg)](https://towardsdatascience.com/latest-picks-four-deep-learning-papers-to-read-in-february-2021-8afc74b334f6/)\n## [Latest picks: Four Deep Learning Papers to Read in February 2021](https://towardsdatascience.com/latest-picks-four-deep-learning-papers-to-read-in-february-2021-8afc74b334f6/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nYour daily dose of data science\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nFebruary 1, 20211 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ \"Productivity Tips, Data Career Insights, and Other Recent Must-Reads\")\n\n\n",
  "metadata": {
    "title": "I Spent an Hour on a Data Preprocessing Task Before Asking Gemini | Towards Data Science",
    "description": "How Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions",
    "keywords": null,
    "author": "Soner Yıldırım",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "I Spent an Hour on a Data Preprocessing Task Before Asking Gemini",
    "og:description": "How Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions",
    "og:url": "https://towardsdatascience.com/i-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2026/06/national-institute-of-allergy-and-infectious-diseases-oc12eprOeoI-unsplash-scaled-1.jpg",
    "og:image:width": "2560",
    "og:image:height": "2560",
    "og:image:type": "image/jpeg",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "Soner Yıldırım",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "6 min read",
    "article:published_time": "2026-06-23T10:30:07.000Z",
    "article:modified_time": "2026-06-23T10:30:07.000Z",
    "article:author": "https://towardsdatascience.com/author/sonery/"
  },
  "paywall": {
    "listed_paid": false,
    "detected": false,
    "marker": "",
    "public_only": false,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 202,
    "error": ""
  },
  "code_blocks": [
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_000.txt",
      "chars": 138
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_001.txt",
      "chars": 36
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_002.txt",
      "chars": 69
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_003.txt",
      "chars": 181
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_004.txt",
      "chars": 201
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_005.txt",
      "chars": 178
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_006.txt",
      "chars": 204
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_007.txt",
      "chars": 185
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_008.txt",
      "chars": 339
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_009.txt",
      "chars": 233
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_010.txt",
      "chars": 1158
    },
    {
      "language": "txt",
      "file": "attachments/81eb60777195/code/block_011.txt",
      "chars": 1214
    }
  ],
  "images": [
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:40:57Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:40:57Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:40:57Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:40:57Z"
    }
  ],
  "audio": [],
  "videos": [
    {
      "url": "https://www.youtube.com/c/TowardsDataScience",
      "kind": "channel_reference",
      "status": "reference_only"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/sonery/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/artificial-intelligence/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/gemini/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/pandas/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/python/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/why-are-language-models-everywhere-36d9961dd9e1/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/dataemporium/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/how-to-control-a-robot-with-python/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/robotics/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/maurodp/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/build-a-serverless-website-using-amazon-s3-and-route-53-c741fae6ef8d/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/programming/web-development/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/photos/a-purple-and-green-string-sculpture-on-a-white-surface-oc12eprOeoI"
    },
    {
      "type": "external",
      "href": "https://www.facebook.com/sharer/sharer.php?title=I+Spent+an+Hour+on+a+Data+Preprocessing+Task+Before+Asking+Gemini&u=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/shareArticle?mini=true&title=I+Spent+an+Hour+on+a+Data+Preprocessing+Task+Before+Asking+Gemini&url=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F"
    },
    {
      "type": "external",
      "href": "https://x.com/share?text=I+Spent+an+Hour+on+a+Data+Preprocessing+Task+Before+Asking+Gemini&url=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F"
    },
    {
      "type": "external",
      "href": "https://www.youtube.com/c/TowardsDataScience"
    },
    {
      "type": "external",
      "href": "https://www.threads.net/@towardsdatascience"
    },
    {
      "type": "external",
      "href": "https://bsky.app/profile/towardsdatascience.com"
    },
    {
      "type": "external",
      "href": "https://thenewstack.io"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh"
    },
    {
      "type": "external",
      "href": "https://insightmediagroup.io/creator-network"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-20T17:25:28Z",
  "source_url": "https://towardsdatascience.com/i-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini/",
  "delivery_status": "unverified",
  "discovery_status": "http_403",
  "missing_reason": "源页面未重新核验:http_403；已按保留正文补采图片",
  "repair_error": "",
  "actual_video_files": [],
  "expected_saved_body_images": 4,
  "image_discovery": "saved_markdown",
  "source_body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# I Spent an Hour on a Data Preprocessing Task Before Asking Gemini\nHow Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nJune 23, 20266 min read\n![Photo by National Institute of Allergy and Infectious Diseases on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/06/national-institute-of-allergy-and-infectious-diseases-oc12eprOeoI-unsplash-scaled-1.jpg)\n[Image](https://unsplash.com/photos/a-purple-and-green-string-sculpture-on-a-white-surface-oc12eprOeoI) by National Institute of Allergy and Infectious Diseases via Unsplash\nAs data scientists, we spend a significant amount of time on data preparation for downstream tasks. Whether it involves data cleaning, handling missing values, feature engineering, data preprocessing, or post processing, this phase requires a lot of time.\nSo, I was working on this post-processing task where I needed to create a new column in a Pandas DataFrame by extracting values from an existing column, based on the data from two other columns. \nI could have directly asked an LLM to write the code (which I usually do) but this time I wanted to do it myself. It was early in the morning and I had a fresh mind so I was in the mood to handle some complex data operations.\nHere is what I had to do. I had a DataFrame with `predicted_categories`, `pred_category_id`, and `text_predicted_probs` columns.\nThe values in the `predicted_categories` column are lists of five categories in \"category_id\" - \"category_description\" format. \npython\n\n```\n['80814001 - Freze Uçları', \n '13003106 - Freze', \n '80805004 - Sanayi Makineleri', \n '13003144 - Torna Makinesi', \n '13003195 - Kumpas']\n```\n\nThe `text_predicted_probs` column has the predicted probabilities of these five categories in order. \npython\n\n```\n[0.943, 0.018, 0.008, 0.006, 0.004]\n```\n\nHence, the first value in the `text_predicted_probs` is the probability of the first category in the `predicted_categories`, and so on.\nThe `pred_category_id` column shows the predicted category id from another model . What I need is the predicted probability of the category in the `pred_category_id` column.\nI need to get the order of the `pred_category_id` in the `predicted_categories` column and then take its value from the `test_predicted_probs` column.\nThe drawing below demonstrates what I want to achieve:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/06/image-274-1024x546.png)\nIn this drawing, I want to get the probability of category 13003106, which is the second item in the list and its corresponding probability value is 0.018.\nIf we asked Gemini, or another advanced model, we'll probably get the answer in seconds. But, I wanted to do it on my own first and then ask Gemini.\nLet's start with reading the dataset into a Pandas DataFrame.\npython\n\n```\nimport pandas as pd\n\nresults = pd.read_csv(\"prediction_results.csv\")\n```\n\nThe values in the `predicted_categories` column are lists of strings with category ids and category names:\npython\n\n```\nresults.loc[0, \"predicted_categories\"]\n# output: \"['80814001 - Freze Uçları', '13003106 - Freze', '80805004 - Sanayi Makineleri', '13003144 - Torna Makinesi', '13003195 - Kumpas']\"\n```\n\nIt's a list but saved as a string so we first convert it to a list object using the `literal_eval` function in the built-in `ast` module of Python:\npython\n\n```\nast.literal_eval(results.loc[0, \"predicted_categories\"])\n# output:\n['80814001 - Freze Uçları',\n '13003106 - Freze',\n '80805004 - Sanayi Makineleri',\n '13003144 - Torna Makinesi',\n '13003195 - Kumpas']\n```\n\nTo extract the category ids, we can split each string in this list at the \"-\" character and then select the first part after splitting. Since we have a list with five categories, we should do this operation in a list comprehension as follows:\npython\n\n```\n[category.split(\"-\")[0].strip() \nfor category in ast.literal_eval(results.loc[0, \"predicted_categories\"])]\n# output:\n['80814001', '13003106', '80805004', '13003144', '13003195']\n```\n\nWe've done it for a single value (i.e. one row). In order to do the same operation to the entire `predicted_categories` column, we can use a list comprehension. It will be a list comprehension inside another list comprehension (i.e. nested list comprehension):\npython\n\n```\nresults.loc[:, \"predicted_category_ids\"] = [\n    [category.split(\"-\")[0].strip() for category in ast.literal_eval(predicted_categories)] \n    for predicted_categories in results[\"predicted_categories\"]\n]\n```\n\nWe now have category ids extracted from the predicted_categories column:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/06/image-299-1024x183.png)\nThe next step is to check the order of the categories in the predicted category id lists. We will then use this order to extract the predicted probability of the category.\nPython list object has an index method, which returns the index (i.e. order) of the item in the list.\npython\n\n```\nresults.loc[0, \"predicted_category_ids\"]\n# output:\n['80814001', '13003106', '80805004', '13003144', '13003195']\n\n\nresults.loc[0, \"predicted_category_ids\"].index(\"13003106\")\n# output:\n2\n```\n\nOnce I find the index of a predicted category id, I can use it to get the probability of this category id from the `text_predicted_probs` column:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/06/image-298-1024x251.png)\nWhat we need to do:\n  1. Get the index of `pred_category_id` in the `predicted_category_ids`\n  2. Use this index to extract the relevant value from `text_predicted_probs`\n\n\nThese steps can be done in a single operation by zipping these three columns. Let's test it on the first row:\npython\n\n```\nfor i, j, k in zip(results[\"pred_category_id\"][:1], results[\"predicted_category_ids\"][:1], results[\"text_predicted_probs\"][:1]):\n    print(j.index(str(i))) # get the index of pred_category_id in predicted_category_ids\n    print(ast.literal_eval(k)[j.index(str(i))]) # get the value at this index in text_predicted_probs\n\n# output:\n0\n0.943\n```\n\nWe can confirm the output in the screenshot above. The `pred_category_id` is 80814001, which is the first item (i.e. index = 0) in the `predicted_category_ids` and the first probability value is 0.943.\nThe lists in the `text_predicted_probs` column are saved as string as well so we apply the `literal_eval` function to convert them to a list object.\nWe can do this operation on the entire DataFrame using a list comprehension as follow:\npython\n\n```\nresults.loc[:, \"pred_category_prob\"] = [\n    float(ast.literal_eval(k)[j.index(str(i))]) if str(i) in j else 0\n    for i, j, k in zip(results[\"pred_category_id\"], results[\"predicted_category_ids\"], results[\"text_predicted_probs\"])\n]\n```\n\nWe basically converted the for loop in the previous code block to a list comprehension. I've only added a check \"`if str(i) in j else 0`\" because there are some rows with a `pred_category_id` that does not exist in `predicted_category_ids`. In such cases, we set the probability value to 0.\nThe task is now completed as we can see below:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/06/image-300-1024x214.png)\n## How did Gemini do this?\nAfter working about an hour (including breaks) I completed the task. Then, I wanted to ask Gemini to see how it would handle the same task.\nI wrote a prompt explaining my goal and included a small sample of the data.\nHere is what Gemini produced on its first attempt:\npython\n\n```\nimport pandas as pd\n\n# 1. Create the sample DataFrame\ndata = {\n    'predicted_categories': [\n        ['80814001 - Freze Uçları', '13003106 - Freze', '80805004 - Sanayi Makineleri', '13003144 - Torna Makinesi', '13003195 - Kumpas']\n    ],\n    'pred_category_id': [80814001],\n    'text_predicted_probs': [\n        [0.943, 0.018, 0.008, 0.006, 0.004]\n    ]\n}\n\ndf = pd.DataFrame(data)\n\n# 2. Define a function to extract the probability matching the category ID\ndef get_matching_prob(row):\n    # Convert ID to string for matching\n    target_id_str = str(row['pred_category_id'])\n    \n    # Iterate through the categories to find the matching index\n    for index, category in enumerate(row['predicted_categories']):\n        if category.startswith(target_id_str):\n            # Return the corresponding probability from the same index\n            return row['text_predicted_probs'][index]\n            \n    # Return None (or 0) if no match is found to prevent the code from crashing\n    return None\n\n# 3. Create the new column\ndf['pred_category_prob'] = df.apply(get_matching_prob, axis=1)\n\n# Display the result\nprint(df[['pred_category_id', 'pred_category_prob']])\n```\n\nIt first created a sample DataFrame using the data I provided in the prompt. Then, it wrote a function that does exactly what I explained.\nIt took me about an hour to write the code myself. Gemini completed the exact same task in seconds based on a prompt that took me only a few minutes to write. \nHowever, there is a minor issue in Gemini's code. It uses the `apply` function, which is not vectorized. In case of working with large datasets, using the apply function (basically a for loop) can increase the run time. \nBut the solution is simple. I asked Gemini to write a vectorized version of it and here is what I got:\npython\n\n```\nimport pandas as pd\n\n# Sample DataFrame\ndata = {\n    'predicted_categories': [\n        ['80814001 - Freze Uçları', '13003106 - Freze', '80805004 - Sanayi Makineleri'],\n        ['13003144 - Torna Makinesi', '13003195 - Kumpas']\n    ],\n    'pred_category_id': [80814001, 13003195],\n    'text_predicted_probs': [\n        [0.943, 0.018, 0.008],\n        [0.6, 0.4]\n    ]\n}\ndf = pd.DataFrame(data)\n\n# 1. Explode both list columns simultaneously to maintain alignment between category and probability\ndf_exploded = df.explode(['predicted_categories', 'text_predicted_probs'])\n\n# 2. Extract the numeric ID from the category string using vectorized regex\ndf_exploded['extracted_id'] = df_exploded['predicted_categories'].str.extract(r'^(\\d+)').astype(float)\n\n# 3. Filter for rows where the extracted ID matches the target 'pred_category_id'\nmatched = df_exploded[df_exploded['extracted_id'] == df_exploded['pred_category_id']]\n\n# 4. Dedup the index (safety net in case an ID appears twice within the same list)\nmatched = matched[~matched.index.duplicated(keep='first')]\n\n# 5. Map the extracted probability column back to the original DataFrame using the index\ndf['pred_category_prob'] = matched['text_predicted_probs']\n\ndf\n```\n\nThe second solution was perfectly fine and looked simpler than the code I wrote.\nSo, I spent about an hour on a task that an LLM could have completed in less than five minutes. However, if I didn't know how Pandas worked, I would have accepted the first solution, which was not the optimal one. It is a perfect example of how LLMs can increase productivity, but only if you actually know what you're doing.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657090323_hn276a.png)\nWritten By\n## [Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[See all from Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Gemini](https://towardsdatascience.com/tag/gemini/)[Pandas](https://towardsdatascience.com/tag/pandas/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F&title=I%20Spent%20an%20Hour%20on%20a%20Data%20Preprocessing%20Task%20Before%20Asking%20Gemini)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F&title=I%20Spent%20an%20Hour%20on%20a%20Data%20Preprocessing%20Task%20Before%20Asking%20Gemini)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fi-spent-an-hour-on-a-data-preprocessing-task-before-asking-gemini%2F&text=I%20Spent%20an%20Hour%20on%20a%20Data%20Preprocessing%20Task%20Before%20Asking%20Gemini)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/05/0cu0xnlNteFfUClBu-scaled.jpg)](https://towardsdatascience.com/why-are-language-models-everywhere-36d9961dd9e1/)\n## [Why are language models everywhere?](https://towardsdatascience.com/why-are-language-models-everywhere-36d9961dd9e1/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nThe answer lies in the 75 years of NLP history\n[Ajay Halthor](https://towardsdatascience.com/author/dataemporium/)\nMay 26, 20236 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/10/image-2.jpeg)](https://towardsdatascience.com/how-to-control-a-robot-with-python/)\n## [How to Control a Robot with Python](https://towardsdatascience.com/how-to-control-a-robot-with-python/)\n[Robotics](https://towardsdatascience.com/category/robotics/)\n3D simulations and movement control with PyBullet\n[Mauro Di Pietro](https://towardsdatascience.com/author/maurodp/)\nOctober 23, 20259 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/07/1VFOcTRCmJs8_k-1WLiynmA-scaled.jpeg)](https://towardsdatascience.com/build-a-serverless-website-using-amazon-s3-and-route-53-c741fae6ef8d/)\n## [Build a serverless website using Amazon S3 and Route 53](https://towardsdatascience.com/build-a-serverless-website-using-amazon-s3-and-route-53-c741fae6ef8d/)\n[Web Development](https://towardsdatascience.com/category/programming/web-development/)\nHow to host a scalable website in under 15 minutes\n[André Ribeiro](https://towardsdatascience.com/author/andrefsr/)\nJuly 4, 20217 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/04/0RqPR72WLnPB-xvXW-scaled.jpg)](https://towardsdatascience.com/mongodb-92370a2641ad/)\n## [MongoDB](https://towardsdatascience.com/mongodb-92370a2641ad/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nFrom Installation to Implementation: Part 6\n[Mike Wolfe](https://towardsdatascience.com/author/miketechgame/)\nApril 10, 20218 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/ChatGPT-Image-Mar-19-2026-05_00_42-PM.jpg)](https://towardsdatascience.com/i-built-a-podcast-clipping-app-in-one-weekend-using-vibe-coding/)\n## [I Built a Podcast Clipping App in One Weekend Using Vibe Coding](https://towardsdatascience.com/i-built-a-podcast-clipping-app-in-one-weekend-using-vibe-coding/)\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\nRapid prototyping with Replit, AI agents, and minimal manual coding\n[Katy Hagerty](https://towardsdatascience.com/author/katyhagerty19/)\nMarch 23, 202611 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/0n4S3wp2UH5RUmSUI.png)](https://towardsdatascience.com/automated-customer-segmentation-2f9cec9df4df/)\n## [Automated customer segmentation](https://towardsdatascience.com/automated-customer-segmentation-2f9cec9df4df/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nWith Python and UbiOps\n[Anouk Dutrée](https://towardsdatascience.com/author/aadutree/)\nNovember 9, 202110 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/01/188bn3kQDgwU1FUZxcuhodg-scaled.jpeg)](https://towardsdatascience.com/best-universities-to-study-data-science-65f9a12a0342/)\n## [Best Universities to Study Data Science](https://towardsdatascience.com/best-universities-to-study-data-science-65f9a12a0342/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nNot all Data Science degrees are the same, so find out which programme is right for you.\n[Renato Boemer](https://towardsdatascience.com/author/boemer/)\nJanuary 25, 20217 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/12/1-ZcYx1lZ3HebWE4vG9i6sQ-scaled.jpeg)](https://towardsdatascience.com/how-to-replace-values-in-pandas-609ba7a031c9/)\n## [How to Replace Values in Pandas](https://towardsdatascience.com/how-to-replace-values-in-pandas-609ba7a031c9/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nLearn how to use methods for values replacement in Pandas Dataframes\n[Gustavo Santos](https://towardsdatascience.com/author/gustavorsantos/)\nDecember 8, 20213 min read\n  * [![](https://towardsdatascience.com/images/fallback-image.jpg)](https://towardsdatascience.com/latest-picks-four-deep-learning-papers-to-read-in-february-2021-8afc74b334f6/)\n## [Latest picks: Four Deep Learning Papers to Read in February 2021](https://towardsdatascience.com/latest-picks-four-deep-learning-papers-to-read-in-february-2021-8afc74b334f6/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nYour daily dose of data science\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nFebruary 1, 20211 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ \"Productivity Tips, Data Career Insights, and Other Recent Must-Reads\")\n\n\n",
  "body_base_path": "attachments/81eb60777195/pages/",
  "body_markdown_path": "attachments/81eb60777195/pages/archived-body.md",
  "local_html": "attachments/81eb60777195/pages/archived-body.html",
  "body_source": "prior_capture"
}