{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Dario Radečić",
  "title_listed": "Pandas vs. Polars - Time to Switch?",
  "keywords": "Pandas,Polars,性能",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/pandas-vs-polars-time-to-switch-932d62e7e829/",
  "publish_date": "2024-04",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "b9bfd2f30739",
  "collected_at": "2026-10-01T21:16:23",
  "title": "Pandas vs. Polars - Time to Switch? | Towards Data Science",
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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# Pandas vs. Polars - Time to Switch?\nLooking to speed up your data processing pipelines up to 10 times? Maybe it's time to say goodbye to Pandas.\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nApril 7, 20247 min read\n![Photo by Hans-Jurgen Mager on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/0EV0ULplSYdYbjY8X-scaled.jpg)\nPhoto by [Hans-Jurgen Mager](https://unsplash.com/@hansjurgen007?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nIn a world where compute time is billed by the second, it's only logical to minimize it as much as you can. And then some.\nPython's vast data processing ecosystem is great for beginners, but challenging to scale up as dataset size grows. **Parallel processing** , **query optimization** , and **lazy evaluation** are all things unheard of in Pandas, but are concepts you must wrap your head around if you want to use Python in large-scale production environments.\nEnter [Polars](https://pola.rs/). It's a Python library written from the ground up with performance in mind. Polars has a multi-threaded query engine written in Rust, which means you should expect to see blazingly fast data processing times, even [30–50 times faster](https://github.com/pola-rs/tpch) than Pandas.\nToday you'll see how Polars compares to Pandas in a series of 4 benchmarks performed on a CSV file with 11 million rows.\nBut first, let's go over the reasons why you should even consider Polars as a Pandas alternative.\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## Pandas vs. Polars - Why Should You Consider Polars as a Data Professional\nIf Python was the first programming language you learned, I'll guess your first data processing framework was Pandas. Nothing wrong with that, especially on a beginner level.\nBut as soon as datasets get large, you'll run into performance limitations.\nThe biggest one is the **single-threaded** nature of Pandas. Datasets are getting bigger by the day, so leveraging only one CPU core doesn't sound reasonable. Parallel and/or distributed processing are your friends.\nAdding insult to injury, Pandas requires you to **store the entire data frame in memory** , which isn't efficient at best, and downright impossible when working with larger-than-memory datasets.\nLastly, Pandas doesn't offer **order of operation optimization**. This means the data processing operations will be applied in the order in which you declared them, which is rarely optimal. You don't know best and that's okay.\n### Polars to the Rescue\nPolars is a Python and Rust based library for processing large volumes of data fast.\nIt's written in Rust (a programming language that has C/C++ performance) and offers data processing benefits Pandas simply can't match:\n  * **Parallel computing** - You can utilize all available CPU cores on your machine.\n  * **Query optimization** - Drastically reduces memory allocations by using query planning and lazy evaluation. You'll see the latter in action in one of today's benchmarks.\n  * **File scanning** - Allows you to work with larger-than-memory datasets by only reading their schema, and not storing the entire thing in memory.\n  * **Improved copy-on-write** - Polars uses Apache Arrow to improve storage performance.\n\n\nAnd much, much more.\n**The best part?** Switching from Pandas to Polars requires minimal code changes, as their APIs are quite similar.\nYou now know which shortcomings Polars addresses. The next step is to see it in action, so let's get right to it.\n## Benchmark Prerequisite - How to Create a Large Dataset in Python\nFirst things first, you'll need some data. The following snippet creates a random data frame containing made-up sales amounts across 125 years (hourly intervals) and 10 office locations.\nThat should give you around **11 million rows** to play with:\npython\n\n```\nimport uuid\nimport numpy as np\nimport pandas as pd\nimport polars as pl\nfrom datetime import datetime\n\ndate_ranges = pd.date_range(start=datetime(1900, 1, 1), end=datetime(2025, 12, 31), freq=\"h\")\nlocations = [\"Italy\", \"France\", \"Spain\", \"Portugal\", \"Greece\", \"Germany\", \"Austria\", \"Norway\", \"Sweeden\", \"Poland\"]\nlocation_multiplier = [0.7, 1.2, 0.9, 0.5, 0.3, 1.0, 1.4, 1.9, 2.1, 1.4]\n\ndata = []\n\nprint(\"Started constructing data...\")\n\nfor dt in date_ranges:\n    for i, loc in enumerate(locations):\n        data.append({\n            \"id\": str(uuid.uuid4()),\n            \"date\": dt,\n            \"location\": loc,\n            \"sales\": np.random.randint(low=1, high=50) * location_multiplier[i]\n        })\n\nprint(\"Finished constructing data!\")\nprint(\"Converting to dataframe...\")\n\ndata = pd.DataFrame(data)\n\nprint(\"Finished converting to dataframe!\")\nprint(\"Saving to CSV...\")\n\ndata.to_csv(\"data.csv\", index=False)\n\nprint(\"Saved to CSV!\")\nprint(\"Done!\")\n```\n\nIf you're wondering, the string UUID column is here only to make the dataset larger and slower to read.\nLet's print the shape and data sample, just to verify everything looks good:\npython\n\n```\nprint(data.shape)\nprint()\ndata.sample(10)\n```\n\nThese are the results:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1LhrEfbUHlMI0alcsBl45-w.png)\nLooks good! You have a dataset of 11.04 million rows that takes around 800MB of disk space. Not a whole lot, but should be enough to see if there are significant differences between Pandas and Polars.\nIf you want more data, simply play around with the date aggregation frequency. Swapping hours for minutes will get you over 660 million rows. Good luck with that.\n## Pandas vs. Polars - Data Processing Benchmarks\nI've run each benchmark **3 times** and averaged the results. The hardware of choice is an M2 MacBook Pro with 16GB of RAM and 12 CPU cores. Library versions are as follows:\n  * `pandas` - 2.2.1\n  * `polars` - 0.20.18\n\n\n### Benchmark #1 - Reading a CSV File\nThe first task of almost any data pipeline is getting the data. The file format of the day is CSV, and both Pandas and Polars have an identical `read_csv()` function you can use.\npython\n\n```\n# PANDAS\ndf_pd = pd.read_csv(\"data.csv\")\n\n# POLARS\ndf_pl = pl.read_csv(\"data.csv\")\n```\n\nThe API might be the same, but the behind the scenes implementation surely isn't:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1BDUBif-Tr9Pu3XICU2sHLQ.png)\nPolars is almost 10 times faster than Pandas when reading an 800MB CSV file from disk.\n### Benchmark #2 - Reading and Preprocessing a CSV File\nDataset reading is usually immediately followed by data preprocessing, such as data type conversions and filtering. That's the use case I'll test here.\nThe goal is to read the CSV file once again, convert the string date column representation to actual datetime objects, keep only the rows for the Italy office location, and remove unnecessary columns.\nImplementation between Pandas and Polars looks a lot different at first, but a seasoned Pythonista should have no trouble navigating between the two:\npython\n\n```\n# PANDAS\ndf_pd_italy = pd.read_csv(\"data.csv\")\ndf_pd_italy[\"date\"] = pd.to_datetime(df_pd_italy[\"date\"])\ndf_pd_italy = df_pd_italy[df_pd_italy[\"location\"] == \"Italy\"]\ndf_pd_italy = df_pd_italy[[\"date\", \"sales\"]]\n\n# POLARS\ndf_pl_italy = (\n    pl.read_csv(\"data.csv\")\n      .with_columns(pl.col(\"date\").str.to_date(\"%Y-%m-%d %H:%M:%S\"))\n      .filter(pl.col(\"location\") == \"Italy\")\n      .select(pl.col([\"date\", \"sales\"]))\n)\n```\n\nPolars is faster once again:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1n4Jt4xZQZskQEfpsn619dA.png)\nOnly 5 times faster now, but still significant.\n### Benchmark #3 - Group By\nOne of the most common data operations is grouping. The idea is to condense the information and show summary statistics, such as totals and averages, across different groups.\nThe below code snippet groups the data by year and location and then calculates the total and average sales. The resulting data frames are sorted by date in ascending order, and by total sales in descending order:\npython\n\n```\n# PANDAS\n(df_pd\n    .groupby([df_pd[\"date\"].dt.year, df_pd[\"location\"]])\n    .agg(\n        total_sales=(\"sales\", \"sum\"),\n        avg_sales=(\"sales\", \"mean\")\n    )\n    .reset_index()\n    .sort_values(by=[\"date\", \"total_sales\"], ascending=[True, False])\n)\n\n# POLARS\n(df_pl\n    .group_by([pl.col(\"date\").dt.year(), pl.col(\"location\")])\n    .agg(\n        pl.sum(\"sales\").alias(\"total_sales\"),\n        pl.mean(\"sales\").alias(\"avg_sales\")\n    )\n    .sort(by=[\"date\", \"total_sales\"], descending=[False, True])     \n)\n```\n\nThere are ever so slight differences between Pandas and Polars APIs, mostly shown in function and parameter names. But really, this shouldn't be a deciding factor.\nLet's take a look at the results:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1x9ZqreYXpapAAScSI450aw.png)\nPolars is faster yet again, but not by a huge margin. This grouping operation didn't take much time in the first place, so that's the main reason why.\n### Benchmark #4 - The Complete Pipeline\nComparing Pandas and Polars among individual operations is fine, but most data professionals only care about the total time it takes for their data pipeline to finish.\nThat's where Polars packs an extra punch. It ships with a **lazily evaluated** data frame version called _LazyFrames_. The idea behind them is to set up a computation graph and let Polars optimize the order of execution.\nYou don't have to lift a finger, but note how you're now using the `scan_csv()` function instead of `read_csv()`. Long story short, this function creates a LazyFrame that references the file's data instead of reading it from disk. A caveat is that you have to call the `collect()` function manually, as no data processing will happen before you do - hence, _lazy_ :\npython\n\n```\n# PANDAS\ndf_pd = pd.read_csv(\"data.csv\")\ndf_pd[\"date\"] = pd.to_datetime(df_pd[\"date\"])\n\ndf_pd_res = (\n    df_pd\n        .groupby([df_pd[\"date\"].dt.year, df_pd[\"location\"]])\n        .agg(\n            total_sales=(\"sales\", \"sum\"),\n            avg_sales=(\"sales\", \"mean\")\n        )\n        .reset_index()\n        .sort_values(by=[\"date\", \"total_sales\"], ascending=[True, False])\n)\n\n# POLARS\ndf_pl_res = (\n    pl.read_csv(\"data.csv\")\n        .with_columns(pl.col(\"date\").str.to_date(\"%Y-%m-%d %H:%M:%S\"))\n        .group_by([pl.col(\"date\").dt.year(), pl.col(\"location\")])\n        .agg(\n            pl.sum(\"sales\").alias(\"total_sales\"),\n            pl.mean(\"sales\").alias(\"avg_sales\")\n        )\n        .sort(by=[\"date\", \"total_sales\"], descending=[False, True])\n)\n\n# POLARS WITH LAZY EVALUATION\ndf_pl_lazy_query = (\n    pl.scan_csv(\"data.csv\")\n        .with_columns(pl.col(\"date\").str.to_date(\"%Y-%m-%d %H:%M:%S\"))\n        .group_by([pl.col(\"date\").dt.year(), pl.col(\"location\")])\n        .agg(\n            pl.sum(\"sales\").alias(\"total_sales\"),\n            pl.mean(\"sales\").alias(\"avg_sales\")\n        )\n        .sort(by=[\"date\", \"total_sales\"], descending=[False, True])\n)\n\ndf_pl_lazy_res = df_pl_lazy_query.collect()\n```\n\nBut the additional function call is worth it:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1kQSvtA9WrW7dzp7qLLNonw.png)\nPolars turns out to be almost 5 times faster for the entire pipeline when compared to Pandas. If you use its lazy API, the difference jumps to almost 12 times.\nIt's impressive how much of performance gains you can get with minimal code changes.\n* * *\n## Summing up Pandas vs. Polars\nWorking with Polars is lightning fast when compared to Pandas. Every operation shown today was faster, and it turned out you could speed up the entire data processing pipeline 12 times by switching from Pandas to lazily evaluated Polars.\nIf that's the case, **why are most data professionals still using Pandas?** Well, it comes down to familiarity and a broader ecosystem. Pandas is probably the first data processing framework most people have learned, and there's more awareness around it. Change only gets harder with time.\nPandas is also a superb choice for small to medium-sized datasets. But when data gets larger than memory, it's not even close. Polars wins by a mile.\n_Have you replaced Pandas with Polars in your workspace? What challenges did you encounter?_ Please share in the comment section.\n**Read next:**\n> [**How to Use OpenAI ChatGPT API... In SQL**](https://towardsdatascience.com/how-to-use-openai-chatgpt-api-in-sql-9b60d2526a9e)\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. 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