{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Benjamin Nweke",
  "title_listed": "Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/modern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb/",
  "publish_date": "2025-11-21",
  "corpus_stage": "",
  "notes": "",
  "id": "836eecc9eed3",
  "collected_at": "2026-10-02T07:45:29",
  "title": "Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB | 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# Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB\nHow I learned to handle growing datasets without slowing down my entire workflow\n[Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nNovember 21, 20256 min read\n![](../images/000.jpg)\nPhoto by [Rene Böhmer](https://unsplash.com/@qrenep?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/photos/a-very-long-line-of-yellow-lines-on-a-black-background-YeUVDKZWSZ4?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\nIf you have worked with Python for data, you have probably experienced the frustration of waiting minutes for a Pandas operation to finish.\nAt first, everything seems fine, but as your dataset grows and your workflows become more complex, your laptop suddenly feels like it's preparing for lift-off.\nA couple of months ago, I worked on a project analyzing e-commerce transactions with over 3 million rows of data.\nIt was a pretty interesting experience, but most of the time, I watched simple groupby operations that normally ran in seconds suddenly stretch into minutes.\nAt that point, I realized Pandas is amazing, but it is not always enough.\nThis article explores modern alternatives to Pandas, including Polars and DuckDB, and examines how they can simplify and improve the handling of large datasets.\nFor clarity, let me be upfront about a few things before we begin.\nThis article is not a deep dive into Rust memory management or a proclamation that Pandas is obsolete.\nInstead, it is a practical, hands-on guide. You will see real examples, personal experiences, and actionable insights into workflows that can save you time and sanity.\n···\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## Why Pandas Can Feel Slow\nBack when I was on the e-commerce project, I remember working with CSV files over two gigabytes, and every filter or aggregation in Pandas often took several minutes to complete.\nDuring that time, I would stare at the screen, wishing I could just grab a coffee or binge a few episodes of a show while the code ran.\nThe main pain points I encountered were speed, memory, and workflow complexity. \nWe all know how large CSV files consume enormous amounts of RAM, sometimes more than what my laptop could comfortably handle. On top of that, chaining multiple transformations also made code harder to maintain and slower to execute.\nPolars and DuckDB address these challenges in different ways.\nPolars, built in Rust, uses multi-threaded execution to process large datasets efficiently. \nDuckDB, on the other hand, is designed for analytics and executes SQL queries without needing you to load everything into memory.\nBasically, each of them has its own superpower. Polars is the speedster, and DuckDB is kind of like the memory magician.\nAnd the best part? Both integrate seamlessly with Python, allowing you to enhance your workflows without a complete rewrite.\n## Setting Up Your Environment\nBefore we start coding, make sure your environment is ready. For consistency, I used Pandas 2.2.0, Polars 0.20.0, and DuckDB 1.9.0.\nPinning versions can save you headaches when following tutorials or sharing code.\nbash\n\n```\npip install pandas==2.2.0 polars==0.20.0 duckdb==1.9.0\n```\n\nIn Python, import the libraries:\npython\n\n```\nimport pandas as pd\nimport polars as pl\nimport duckdb\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n```\n\nFor example, I will use an e-commerce sales dataset with columns such as order ID, product ID, region, country, revenue, and date. You can download similar datasets from [Kaggle](https://www.kaggle.com/datasets) or generate synthetic data.\n## Loading Data\nLoading data efficiently sets the tone for the rest of your workflow. I remember a project where the CSV file had nearly 5 million rows.\nPandas handled it, but the load times were long, and the repeated reloads during testing were painful. \nIt was one of those moments where you wish your laptop had a “fast forward” button.\nSwitching to Polars and DuckDB completely improved everything, and suddenly, I could access and manipulate the data almost instantly, which honestly made the testing and iteration processes far more enjoyable.\nWith Pandas:\npython\n\n```\ndf_pd = pd.read_csv(\"sales.csv\")\nprint(df_pd.head(3))\n```\n\nWith Polars:\npython\n\n```\ndf_pl = pl.read_csv(\"sales.csv\")\nprint(df_pl.head(3))\n```\n\nWith DuckDB:\npython\n\n```\ncon = duckdb.connect()\ndf_duck = con.execute(\"SELECT * FROM 'sales.csv'\").df()\nprint(df_duck.head(3))\n```\n\nDuckDB can query CSVs directly without loading the entire datasets into memory, making it much easier to work with large files.\n## Filtering Data\nThe problem here is that filtering in Pandas can be slow when dealing with millions of rows. I once needed to analyze European transactions in a massive sales dataset. Pandas took minutes, which slowed down my analysis.\nWith Pandas:\npython\n\n```\nfiltered_pd = df_pd[df_pd.region == \"Europe\"]\n```\n\nPolars is faster and can process multiple filters efficiently:\npython\n\n```\nfiltered_pl = df_pl.filter(pl.col(\"region\") == \"Europe\")\n```\n\nDuckDB uses SQL syntax:\npython\n\n```\nfiltered_duck = con.execute(\"\"\"\n    SELECT *\n    FROM 'sales.csv'\n    WHERE region = 'Europe'\n\"\"\").df()\n```\n\nNow you can filter through large datasets in seconds instead of minutes, leaving you more time to focus on the insights that really matter.\n## Aggregating Large Datasets Quickly\nAggregation is often where Pandas starts to feel slow. Imagine calculating total revenue per country for a marketing report.\nIn Pandas:\npython\n\n```\nagg_pd = df_pd.groupby(\"country\")[\"revenue\"].sum().reset_index()\n```\n\nIn Polars:\npython\n\n```\nagg_pl = df_pl.groupby(\"country\").agg(pl.col(\"revenue\").sum())\n\n```\n\nIn DuckDB:\npython\n\n```\nagg_duck = con.execute(\"\"\"\n    SELECT country, SUM(revenue) AS total_revenue\n    FROM 'sales.csv'\n    GROUP BY country\n\"\"\").df()\n```\n\nI remember running this aggregation on a 10 million-row dataset. In Pandas, it took nearly half an hour. Polars completed the same operation in under a minute.\nThe sense of relief was almost like finishing a marathon and realizing your legs still work.\n## Joining Datasets at Scale\nJoining datasets is one of those things that sounds simple until you are actually knee-deep in the data.\nIn real projects, your data usually lives in multiple sources, so you have to combine them using shared columns like customer IDs.\nI learned this the hard way while working on a project that required combining millions of customer orders with an equally large demographic dataset.\nEach file was big enough on its own, but merging them felt like trying to force two puzzle pieces together while your laptop begged for mercy.\nPandas took so long that I began timing the joins the same way people time how long it takes their microwave popcorn to finish.\nSpoiler: the popcorn won every time.\nPolars and DuckDB gave me a way out.\nWith Pandas:\npython\n\n```\nmerged_pd = df_pd.merge(pop_df_pd, on=\"country\", how=\"left\")\n```\n\nPolars:\npython\n\n```\nmerged_pl = df_pl.join(pop_df_pl, on=\"country\", how=\"left\")\n```\n\nDuckDB:\npython\n\n```\nmerged_duck = con.execute(\"\"\"\n    SELECT *\n    FROM 'sales.csv' s\n    LEFT JOIN 'pop.csv' p\n    USING (country)\n\"\"\").df()\n```\n\n[Joins](https://urbizedge.com/understanding-sql-joins/) on large datasets that used to freeze your workflow now run smoothly and efficiently.\n## Lazy Evaluation in Polars\nOne thing I didn’t appreciate early in my data science journey was how much time gets wasted while running transformations line by line.\nPolars approaches this differently. \nIt uses a technique called lazy evaluation, which essentially waits until you have completed defining your transformations before executing any operations.\nIt examines the entire pipeline, determines the most efficient path, and executes everything simultaneously.\nIt’s like having a friend who listens to your entire order before walking to the kitchen, instead of one who takes each instruction separately and keeps going back and forth.\nThis [TDS article](https://towardsdatascience.com/understanding-lazy-evaluation-in-polars-b85ccb864d0c/) indepthly explains lazy evaluation.\nHere’s what the flow looks like:\nPandas:\npython\n\n```\ndf = df[df[\"amount\"] > 100]\ndf = df.groupby(\"segment\").agg({\"amount\": \"mean\"})\ndf = df.sort_values(\"amount\")\n```\n\nPolars Lazy Mode:\npython\n\n```\nimport polars as pl\n\ndf_lazy = (\n    pl.scan_csv(\"sales.csv\")\n      .filter(pl.col(\"amount\") > 100)\n      .groupby(\"segment\")\n      .agg(pl.col(\"amount\").mean())\n      .sort(\"amount\")\n)\n\nresult = df_lazy.collect()\n\n```\n\nThe first time I used lazy mode, it felt strange not seeing instant results. But once I ran the final `.collect()`, the speed difference was obvious.\nLazy evaluation won’t magically solve every performance issue, but it brings a level of efficiency that Pandas wasn’t designed for.\n···\n## Conclusion and takeaways\nWorking with large datasets doesn’t have to feel like wrestling with your tools. \nUsing Polars and DuckDB showed me that the problem wasn’t always the data. Sometimes, it was the tool I was using to handle it.\nIf there is one thing you take away from this tutorial, let it be this: you don’t have to abandon Pandas, but you can reach for something better when your datasets start pushing their limits. \nPolars gives you speed as well as smarter execution, then DuckDB lets you query huge files like they’re tiny. Together, they make working with large data feel more manageable and less tiring.\nIf you want to go deeper into the ideas explored in this tutorial, the official documentation of [Polars](https://docs.pola.rs/) and [DuckDB](https://duckdb.org/docs/stable/) are good places to start.\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-4ad89a2fc5e69dde007d.jpeg)\nWritten By\n## [Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nBenjamin Nweke is a Computer Science graduate and technical writer exploring AI, machine learning, and data science. He writes about building reliable AI systems, model evaluation, agentic architectures, and the practical challenges that appear when machine learning moves beyond experiments. His work focuses on making technically complex ideas clear, useful, and grounded in real implementation.\n[See all from Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\n[Big Data](https://towardsdatascience.com/tag/big-data/)[Data Analysis](https://towardsdatascience.com/tag/data-analysis/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Polars](https://towardsdatascience.com/tag/polars/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F&title=Modern%20DataFrames%20in%20Python%3A%20A%20Hands-On%20Tutorial%20with%20Polars%20and%20DuckDB)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F&title=Modern%20DataFrames%20in%20Python%3A%20A%20Hands-On%20Tutorial%20with%20Polars%20and%20DuckDB)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F&text=Modern%20DataFrames%20in%20Python%3A%20A%20Hands-On%20Tutorial%20with%20Polars%20and%20DuckDB)\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/001.jpg)](https://towardsdatascience.com/to-really-learn-a-new-topic-take-your-time-7700a35ddce1/)\n## [To Really Learn a New Topic, Take Your Time](https://towardsdatascience.com/to-really-learn-a-new-topic-take-your-time-7700a35ddce1/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nOur weekly selection of must-read Editors' Picks and original features\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nJune 22, 20232 min read\n  * [![](../images/002.jpeg)](https://towardsdatascience.com/linear-regression-in-python-sklearn-vs-excel-6790187dc9ca/)\n## [Linear Regression in Python: Sklearn vs Excel](https://towardsdatascience.com/linear-regression-in-python-sklearn-vs-excel-6790187dc9ca/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nIs Microsoft excel a good alternative for a quick and approximate linear regression prediction business case? I think yes, but let's do a...\n[Kaushik Choudhury](https://towardsdatascience.com/author/kaushikthoughts/)\nAugust 6, 20206 min read\n  * [![](../images/003.png)](https://towardsdatascience.com/data-for-good-somalia-drought-management-e882355a98e0/)\n## [Data for Good - Somalia Drought Management](https://towardsdatascience.com/data-for-good-somalia-drought-management-e882355a98e0/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nApplied data science and analytics to tackle global warming induced environmental challenges. End-to-end project with real world rainfall...\n[Mohamed A. Warsame](https://towardsdatascience.com/author/warsamewords/)\nMarch 25, 202114 min read\n  * [![](../images/004.jpg)](https://towardsdatascience.com/5-quick-and-easy-tips-to-become-an-efficient-data-scientist-in-both-python-and-r-5cf51f6396a3/)\n## [5 Quick and Easy Tips to Become an Efficient Data Scientist in Both Python and R](https://towardsdatascience.com/5-quick-and-easy-tips-to-become-an-efficient-data-scientist-in-both-python-and-r-5cf51f6396a3/)\n[Programming](https://towardsdatascience.com/category/programming/)\nHaving difficulty transitioning between using Python and R? Try these tips to make the transition smoother\n[Madison Hunter](https://towardsdatascience.com/author/madison13/)\nNovember 8, 20229 min read\n  * [![](../images/005.jpeg)](https://towardsdatascience.com/pandas-tips-i-wish-i-knew-before-ef4ea6a39e1a/)\n## [Pandas tips I wish I knew before](https://towardsdatascience.com/pandas-tips-i-wish-i-knew-before-ef4ea6a39e1a/)\n[Programming](https://towardsdatascience.com/category/programming/)\nHow does pivot work? What is the main pandas building block? And more ...\n[Roman Orac](https://towardsdatascience.com/author/romanorac/)\nApril 1, 20204 min read\n  * [![](../images/body-63beb50f664a055ed739.jpg)](https://towardsdatascience.com/streamlit-color-picker-widget-an-easy-way-to-change-chart-colours-7929973393ea/)\n## [The Streamlit Colour Picker: An Easy Way to Change Chart Colours on Your Streamlit Dashboard](https://towardsdatascience.com/streamlit-color-picker-widget-an-easy-way-to-change-chart-colours-7929973393ea/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nProvide Your Dashboard Users with Greater Flexibility\n[Andy McDonald](https://towardsdatascience.com/author/andymcdonaldgeo/)\nDecember 20, 20225 min read\n  * [![](../images/007.png)](https://towardsdatascience.com/from-business-student-to-data-scientist-in-tech-eb7bbaab1784/)\n## [From Business Student to Data Scientist in Tech](https://towardsdatascience.com/from-business-student-to-data-scientist-in-tech-eb7bbaab1784/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA timeline of how I went from being scared of maths to becoming a full-fledged Data Scientist at a renowned Tech firm\n[Khouloud El Alami](https://towardsdatascience.com/author/elalamik/)\nJuly 3, 202310 min read\n  * [![](../images/008.jpg)](https://towardsdatascience.com/comparing-documents-with-similarity-metrics-e486bc678a7d/)\n## [Comparing Documents With Similarity Metrics](https://towardsdatascience.com/comparing-documents-with-similarity-metrics-e486bc678a7d/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nAn initial look into how math is leveraged to compare text in NLP\n[Aashish Nair](https://towardsdatascience.com/author/aashishnair/)\nJanuary 17, 20224 min read\n  * [![](../images/009.jpg)](https://towardsdatascience.com/data-science-career-switch-is-masters-degree-worth-it-d2d23e87b3de/)\n## [Data Science Career Switch: Is Master's Degree Worth It?](https://towardsdatascience.com/data-science-career-switch-is-masters-degree-worth-it-d2d23e87b3de/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nPart 1: Mode of Learning (Online vs Physical)\n[Nathan Timothy Handoko](https://towardsdatascience.com/author/nathanthandoko/)\nJanuary 11, 20226 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/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\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\n\n",
  "metadata": {
    "title": "Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB | Towards Data Science",
    "description": "How I learned to handle growing datasets without slowing down my entire workflow",
    "keywords": null,
    "author": "Benjamin Nweke",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB",
    "og:description": "How I learned to handle growing datasets without slowing down my entire workflow",
    "og:url": "https://towardsdatascience.com/modern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/11/rene-bohmer-YeUVDKZWSZ4-unsplash-scaled-1.jpg",
    "og:image:width": "2560",
    "og:image:height": "1707",
    "og:image:type": "image/jpeg",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "Benjamin Nweke",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "6 min read",
    "article:published_time": "2025-11-21T12:00:08.000Z",
    "article:modified_time": "2025-11-21T12:00:08.000Z",
    "article:author": "https://towardsdatascience.com/author/benjaminnweke/"
  },
  "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": 200,
    "error": ""
  },
  "code_blocks": [
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_000.txt",
      "chars": 55
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_001.txt",
      "chars": 105
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_002.txt",
      "chars": 54
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_003.txt",
      "chars": 54
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_004.txt",
      "chars": 102
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_005.txt",
      "chars": 46
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_006.txt",
      "chars": 57
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_007.txt",
      "chars": 104
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_008.txt",
      "chars": 65
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_009.txt",
      "chars": 64
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_010.txt",
      "chars": 129
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_011.txt",
      "chars": 61
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_012.txt",
      "chars": 60
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_013.txt",
      "chars": 122
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_014.txt",
      "chars": 109
    },
    {
      "language": "txt",
      "file": "attachments/836eecc9eed3/code/block_015.txt",
      "chars": 215
    }
  ],
  "images": [
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:07:10Z"
    },
    {
      "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": "2026-10-20T18:07:10Z"
    },
    {
      "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": "2026-10-20T18:07:10Z"
    },
    {
      "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": "2026-10-20T18:07:10Z"
    }
  ],
  "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/benjaminnweke/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/understanding-lazy-evaluation-in-polars-b85ccb864d0c/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/big-data/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-analysis/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/polars/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/python/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/to-really-learn-a-new-topic-take-your-time-7700a35ddce1/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/towardsdatascience/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/linear-regression-in-python-sklearn-vs-excel-6790187dc9ca/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/kaushikthoughts/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/data-for-good-somalia-drought-management-e882355a98e0/"
    },
    {
      "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/@qrenep"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/photos/a-very-long-line-of-yellow-lines-on-a-black-background-YeUVDKZWSZ4"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/ai-data-scientist"
    },
    {
      "type": "external",
      "href": "https://www.kaggle.com/datasets"
    },
    {
      "type": "external",
      "href": "https://urbizedge.com/understanding-sql-joins/"
    },
    {
      "type": "external",
      "href": "https://docs.pola.rs/"
    },
    {
      "type": "external",
      "href": "https://duckdb.org/docs/stable/"
    },
    {
      "type": "external",
      "href": "https://www.facebook.com/sharer/sharer.php?title=Modern+DataFrames+in+Python%3A+A+Hands-On+Tutorial+with+Polars+and+DuckDB&u=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/shareArticle?mini=true&title=Modern+DataFrames+in+Python%3A+A+Hands-On+Tutorial+with+Polars+and+DuckDB&url=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F"
    },
    {
      "type": "external",
      "href": "https://x.com/share?text=Modern+DataFrames+in+Python%3A+A+Hands-On+Tutorial+with+Polars+and+DuckDB&url=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%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": "2026-10-20T17:33:16Z",
  "source_url": "https://towardsdatascience.com/modern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb/",
  "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# Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB\nHow I learned to handle growing datasets without slowing down my entire workflow\n[Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nNovember 21, 20256 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/11/rene-bohmer-YeUVDKZWSZ4-unsplash-scaled-1.jpg)\nPhoto by [Rene Böhmer](https://unsplash.com/@qrenep?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/photos/a-very-long-line-of-yellow-lines-on-a-black-background-YeUVDKZWSZ4?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\nIf you have worked with Python for data, you have probably experienced the frustration of waiting minutes for a Pandas operation to finish.\nAt first, everything seems fine, but as your dataset grows and your workflows become more complex, your laptop suddenly feels like it's preparing for lift-off.\nA couple of months ago, I worked on a project analyzing e-commerce transactions with over 3 million rows of data.\nIt was a pretty interesting experience, but most of the time, I watched simple groupby operations that normally ran in seconds suddenly stretch into minutes.\nAt that point, I realized Pandas is amazing, but it is not always enough.\nThis article explores modern alternatives to Pandas, including Polars and DuckDB, and examines how they can simplify and improve the handling of large datasets.\nFor clarity, let me be upfront about a few things before we begin.\nThis article is not a deep dive into Rust memory management or a proclamation that Pandas is obsolete.\nInstead, it is a practical, hands-on guide. You will see real examples, personal experiences, and actionable insights into workflows that can save you time and sanity.\n···\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## Why Pandas Can Feel Slow\nBack when I was on the e-commerce project, I remember working with CSV files over two gigabytes, and every filter or aggregation in Pandas often took several minutes to complete.\nDuring that time, I would stare at the screen, wishing I could just grab a coffee or binge a few episodes of a show while the code ran.\nThe main pain points I encountered were speed, memory, and workflow complexity. \nWe all know how large CSV files consume enormous amounts of RAM, sometimes more than what my laptop could comfortably handle. On top of that, chaining multiple transformations also made code harder to maintain and slower to execute.\nPolars and DuckDB address these challenges in different ways.\nPolars, built in Rust, uses multi-threaded execution to process large datasets efficiently. \nDuckDB, on the other hand, is designed for analytics and executes SQL queries without needing you to load everything into memory.\nBasically, each of them has its own superpower. Polars is the speedster, and DuckDB is kind of like the memory magician.\nAnd the best part? Both integrate seamlessly with Python, allowing you to enhance your workflows without a complete rewrite.\n## Setting Up Your Environment\nBefore we start coding, make sure your environment is ready. For consistency, I used Pandas 2.2.0, Polars 0.20.0, and DuckDB 1.9.0.\nPinning versions can save you headaches when following tutorials or sharing code.\nbash\n\n```\npip install pandas==2.2.0 polars==0.20.0 duckdb==1.9.0\n```\n\nIn Python, import the libraries:\npython\n\n```\nimport pandas as pd\nimport polars as pl\nimport duckdb\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n```\n\nFor example, I will use an e-commerce sales dataset with columns such as order ID, product ID, region, country, revenue, and date. You can download similar datasets from [Kaggle](https://www.kaggle.com/datasets) or generate synthetic data.\n## Loading Data\nLoading data efficiently sets the tone for the rest of your workflow. I remember a project where the CSV file had nearly 5 million rows.\nPandas handled it, but the load times were long, and the repeated reloads during testing were painful. \nIt was one of those moments where you wish your laptop had a “fast forward” button.\nSwitching to Polars and DuckDB completely improved everything, and suddenly, I could access and manipulate the data almost instantly, which honestly made the testing and iteration processes far more enjoyable.\nWith Pandas:\npython\n\n```\ndf_pd = pd.read_csv(\"sales.csv\")\nprint(df_pd.head(3))\n```\n\nWith Polars:\npython\n\n```\ndf_pl = pl.read_csv(\"sales.csv\")\nprint(df_pl.head(3))\n```\n\nWith DuckDB:\npython\n\n```\ncon = duckdb.connect()\ndf_duck = con.execute(\"SELECT * FROM 'sales.csv'\").df()\nprint(df_duck.head(3))\n```\n\nDuckDB can query CSVs directly without loading the entire datasets into memory, making it much easier to work with large files.\n## Filtering Data\nThe problem here is that filtering in Pandas can be slow when dealing with millions of rows. I once needed to analyze European transactions in a massive sales dataset. Pandas took minutes, which slowed down my analysis.\nWith Pandas:\npython\n\n```\nfiltered_pd = df_pd[df_pd.region == \"Europe\"]\n```\n\nPolars is faster and can process multiple filters efficiently:\npython\n\n```\nfiltered_pl = df_pl.filter(pl.col(\"region\") == \"Europe\")\n```\n\nDuckDB uses SQL syntax:\npython\n\n```\nfiltered_duck = con.execute(\"\"\"\n    SELECT *\n    FROM 'sales.csv'\n    WHERE region = 'Europe'\n\"\"\").df()\n```\n\nNow you can filter through large datasets in seconds instead of minutes, leaving you more time to focus on the insights that really matter.\n## Aggregating Large Datasets Quickly\nAggregation is often where Pandas starts to feel slow. Imagine calculating total revenue per country for a marketing report.\nIn Pandas:\npython\n\n```\nagg_pd = df_pd.groupby(\"country\")[\"revenue\"].sum().reset_index()\n```\n\nIn Polars:\npython\n\n```\nagg_pl = df_pl.groupby(\"country\").agg(pl.col(\"revenue\").sum())\n\n```\n\nIn DuckDB:\npython\n\n```\nagg_duck = con.execute(\"\"\"\n    SELECT country, SUM(revenue) AS total_revenue\n    FROM 'sales.csv'\n    GROUP BY country\n\"\"\").df()\n```\n\nI remember running this aggregation on a 10 million-row dataset. In Pandas, it took nearly half an hour. Polars completed the same operation in under a minute.\nThe sense of relief was almost like finishing a marathon and realizing your legs still work.\n## Joining Datasets at Scale\nJoining datasets is one of those things that sounds simple until you are actually knee-deep in the data.\nIn real projects, your data usually lives in multiple sources, so you have to combine them using shared columns like customer IDs.\nI learned this the hard way while working on a project that required combining millions of customer orders with an equally large demographic dataset.\nEach file was big enough on its own, but merging them felt like trying to force two puzzle pieces together while your laptop begged for mercy.\nPandas took so long that I began timing the joins the same way people time how long it takes their microwave popcorn to finish.\nSpoiler: the popcorn won every time.\nPolars and DuckDB gave me a way out.\nWith Pandas:\npython\n\n```\nmerged_pd = df_pd.merge(pop_df_pd, on=\"country\", how=\"left\")\n```\n\nPolars:\npython\n\n```\nmerged_pl = df_pl.join(pop_df_pl, on=\"country\", how=\"left\")\n```\n\nDuckDB:\npython\n\n```\nmerged_duck = con.execute(\"\"\"\n    SELECT *\n    FROM 'sales.csv' s\n    LEFT JOIN 'pop.csv' p\n    USING (country)\n\"\"\").df()\n```\n\n[Joins](https://urbizedge.com/understanding-sql-joins/) on large datasets that used to freeze your workflow now run smoothly and efficiently.\n## Lazy Evaluation in Polars\nOne thing I didn’t appreciate early in my data science journey was how much time gets wasted while running transformations line by line.\nPolars approaches this differently. \nIt uses a technique called lazy evaluation, which essentially waits until you have completed defining your transformations before executing any operations.\nIt examines the entire pipeline, determines the most efficient path, and executes everything simultaneously.\nIt’s like having a friend who listens to your entire order before walking to the kitchen, instead of one who takes each instruction separately and keeps going back and forth.\nThis [TDS article](https://towardsdatascience.com/understanding-lazy-evaluation-in-polars-b85ccb864d0c/) indepthly explains lazy evaluation.\nHere’s what the flow looks like:\nPandas:\npython\n\n```\ndf = df[df[\"amount\"] > 100]\ndf = df.groupby(\"segment\").agg({\"amount\": \"mean\"})\ndf = df.sort_values(\"amount\")\n```\n\nPolars Lazy Mode:\npython\n\n```\nimport polars as pl\n\ndf_lazy = (\n    pl.scan_csv(\"sales.csv\")\n      .filter(pl.col(\"amount\") > 100)\n      .groupby(\"segment\")\n      .agg(pl.col(\"amount\").mean())\n      .sort(\"amount\")\n)\n\nresult = df_lazy.collect()\n\n```\n\nThe first time I used lazy mode, it felt strange not seeing instant results. But once I ran the final `.collect()`, the speed difference was obvious.\nLazy evaluation won’t magically solve every performance issue, but it brings a level of efficiency that Pandas wasn’t designed for.\n···\n## Conclusion and takeaways\nWorking with large datasets doesn’t have to feel like wrestling with your tools. \nUsing Polars and DuckDB showed me that the problem wasn’t always the data. Sometimes, it was the tool I was using to handle it.\nIf there is one thing you take away from this tutorial, let it be this: you don’t have to abandon Pandas, but you can reach for something better when your datasets start pushing their limits. \nPolars gives you speed as well as smarter execution, then DuckDB lets you query huge files like they’re tiny. Together, they make working with large data feel more manageable and less tiring.\nIf you want to go deeper into the ideas explored in this tutorial, the official documentation of [Polars](https://docs.pola.rs/) and [DuckDB](https://duckdb.org/docs/stable/) are good places to start.\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/1787657271062_x78p7k.jpeg)\nWritten By\n## [Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nBenjamin Nweke is a Computer Science graduate and technical writer exploring AI, machine learning, and data science. He writes about building reliable AI systems, model evaluation, agentic architectures, and the practical challenges that appear when machine learning moves beyond experiments. His work focuses on making technically complex ideas clear, useful, and grounded in real implementation.\n[See all from Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\n[Big Data](https://towardsdatascience.com/tag/big-data/)[Data Analysis](https://towardsdatascience.com/tag/data-analysis/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Polars](https://towardsdatascience.com/tag/polars/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F&title=Modern%20DataFrames%20in%20Python%3A%20A%20Hands-On%20Tutorial%20with%20Polars%20and%20DuckDB)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F&title=Modern%20DataFrames%20in%20Python%3A%20A%20Hands-On%20Tutorial%20with%20Polars%20and%20DuckDB)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fmodern-dataframes-in-python-a-hands-on-tutorial-with-polars-and-duckdb%2F&text=Modern%20DataFrames%20in%20Python%3A%20A%20Hands-On%20Tutorial%20with%20Polars%20and%20DuckDB)\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/2023/06/05QcrIZEz2I7FdFVO-scaled.jpg)](https://towardsdatascience.com/to-really-learn-a-new-topic-take-your-time-7700a35ddce1/)\n## [To Really Learn a New Topic, Take Your Time](https://towardsdatascience.com/to-really-learn-a-new-topic-take-your-time-7700a35ddce1/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nOur weekly selection of must-read Editors' Picks and original features\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nJune 22, 20232 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/1HhGQicu2l-VLk_08_Si9Ow.jpeg)](https://towardsdatascience.com/linear-regression-in-python-sklearn-vs-excel-6790187dc9ca/)\n## [Linear Regression in Python: Sklearn vs Excel](https://towardsdatascience.com/linear-regression-in-python-sklearn-vs-excel-6790187dc9ca/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nIs Microsoft excel a good alternative for a quick and approximate linear regression prediction business case? I think yes, but let's do a...\n[Kaushik Choudhury](https://towardsdatascience.com/author/kaushikthoughts/)\nAugust 6, 20206 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/03/14OoI-m7PBa_BGIG4MkwKmA.png)](https://towardsdatascience.com/data-for-good-somalia-drought-management-e882355a98e0/)\n## [Data for Good - Somalia Drought Management](https://towardsdatascience.com/data-for-good-somalia-drought-management-e882355a98e0/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nApplied data science and analytics to tackle global warming induced environmental challenges. End-to-end project with real world rainfall...\n[Mohamed A. Warsame](https://towardsdatascience.com/author/warsamewords/)\nMarch 25, 202114 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/11/0cGkB7xigCEYgVANb-scaled.jpg)](https://towardsdatascience.com/5-quick-and-easy-tips-to-become-an-efficient-data-scientist-in-both-python-and-r-5cf51f6396a3/)\n## [5 Quick and Easy Tips to Become an Efficient Data Scientist in Both Python and R](https://towardsdatascience.com/5-quick-and-easy-tips-to-become-an-efficient-data-scientist-in-both-python-and-r-5cf51f6396a3/)\n[Programming](https://towardsdatascience.com/category/programming/)\nHaving difficulty transitioning between using Python and R? Try these tips to make the transition smoother\n[Madison Hunter](https://towardsdatascience.com/author/madison13/)\nNovember 8, 20229 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/04/1_gu2ctyUqKGYe2Q6KbgDJA-scaled.jpeg)](https://towardsdatascience.com/pandas-tips-i-wish-i-knew-before-ef4ea6a39e1a/)\n## [Pandas tips I wish I knew before](https://towardsdatascience.com/pandas-tips-i-wish-i-knew-before-ef4ea6a39e1a/)\n[Programming](https://towardsdatascience.com/category/programming/)\nHow does pivot work? What is the main pandas building block? And more ...\n[Roman Orac](https://towardsdatascience.com/author/romanorac/)\nApril 1, 20204 min read\n  * [![](https://towardsdatascience.com/images/fallback-image.jpg)](https://towardsdatascience.com/streamlit-color-picker-widget-an-easy-way-to-change-chart-colours-7929973393ea/)\n## [The Streamlit Colour Picker: An Easy Way to Change Chart Colours on Your Streamlit Dashboard](https://towardsdatascience.com/streamlit-color-picker-widget-an-easy-way-to-change-chart-colours-7929973393ea/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nProvide Your Dashboard Users with Greater Flexibility\n[Andy McDonald](https://towardsdatascience.com/author/andymcdonaldgeo/)\nDecember 20, 20225 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1U1g0xPo4XtbiLzyzwuDbAQ.png)](https://towardsdatascience.com/from-business-student-to-data-scientist-in-tech-eb7bbaab1784/)\n## [From Business Student to Data Scientist in Tech](https://towardsdatascience.com/from-business-student-to-data-scientist-in-tech-eb7bbaab1784/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA timeline of how I went from being scared of maths to becoming a full-fledged Data Scientist at a renowned Tech firm\n[Khouloud El Alami](https://towardsdatascience.com/author/elalamik/)\nJuly 3, 202310 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/0D2IBdJ-u-vL3Wf68-scaled.jpg)](https://towardsdatascience.com/comparing-documents-with-similarity-metrics-e486bc678a7d/)\n## [Comparing Documents With Similarity Metrics](https://towardsdatascience.com/comparing-documents-with-similarity-metrics-e486bc678a7d/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nAn initial look into how math is leveraged to compare text in NLP\n[Aashish Nair](https://towardsdatascience.com/author/aashishnair/)\nJanuary 17, 20224 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/0xvh_devdP9-hfmzu-scaled.jpg)](https://towardsdatascience.com/data-science-career-switch-is-masters-degree-worth-it-d2d23e87b3de/)\n## [Data Science Career Switch: Is Master's Degree Worth It?](https://towardsdatascience.com/data-science-career-switch-is-masters-degree-worth-it-d2d23e87b3de/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nPart 1: Mode of Learning (Online vs Physical)\n[Nathan Timothy Handoko](https://towardsdatascience.com/author/nathanthandoko/)\nJanuary 11, 20226 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/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\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\n\n",
  "body_base_path": "attachments/836eecc9eed3/pages/",
  "body_markdown_path": "attachments/836eecc9eed3/pages/archived-body.md",
  "local_html": "attachments/836eecc9eed3/pages/archived-body.html",
  "body_source": "prior_capture"
}