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BLOG: The "Polars vs pandas" difference nobody is talking about #843

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Text styling

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@MarcoGorelli MarcoGorelli changed the title WIP: The Polars innovation nobody is talking about The "Polars vs pandas" difference nobody is talking about May 10, 2024
@MarcoGorelli MarcoGorelli changed the title The "Polars vs pandas" difference nobody is talking about BLOG: The "Polars vs pandas" difference nobody is talking about May 26, 2024
@MarcoGorelli MarcoGorelli marked this pull request as draft May 28, 2024 16:47
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---
title: 'The "Polars vs pandas" difference nobody is talking about'
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title: 'The "Polars vs pandas" difference nobody is talking about'
title: 'The Polars vs pandas difference nobody is talking about'


# The "Polars vs pandas" difference nobody is talking about

I attended PyData Berlin 2024 this week, and it was a blast! I met so many colleagues, collaborators, and friends.
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"this week"?

category: [PyData ecosystem]
---

# The "Polars vs pandas" difference nobody is talking about
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# The "Polars vs pandas" difference nobody is talking about
# The Polars vs pandas difference nobody is talking about

Comment on lines 15 to 19
- lazy execution;
- Rust;
- consistent handling of null values;
- multithreading;
- query optimisation.
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- lazy execution;
- Rust;
- consistent handling of null values;
- multithreading;
- query optimisation.
- lazy execution
- Rust
- consistent handling of null values
- multithreading
- query optimisation


If we want a single scalar value per group, we can use a reduction ('mean', 'sum', 'std', ...):
```python
df.group_by('a').agg(pl.sum('b'))
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I'm already a little lost. Is this Polars or pandas?

This isn't too bad, but it involves doing two group-bys, and so is at least twice as slow as it could
be.

Finally, can rely on `GroupBy` caching its groups, in-place mutation of the original dataframe, and the
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Finally, can rely on `GroupBy` caching its groups, in-place mutation of the original dataframe, and the
Finally, we can rely on `GroupBy` caching its groups, in-place mutation of the original dataframe, and the


is a common refrain among Polars users.

There may be a more general lesson here: if you have the courage to do things differently, you may be rewarded.
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This line doesn't really connect for me with anything else in the blog post, seems to come out of nowhere.

There may be a more general lesson here: if you have the courage to do things differently, you may be rewarded.

If you'd like to learn about how to use Polars effectively, or how to solve problems in your organisation
using Polars, Quansight is here to help - you can get in touch [here](https://quansight.com/about-us/#bookacallform).
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I wonder if there's some way we could or should set this off from the rest of the blog post, something like the following (not saying we should do it exactly like this):

And now, a small message from our fellow coworkers at Quansight Consulting... If you'd like to learn about how to use Polars effectively, or how to solve problems in your organisation
using Polars, please get in touch.

@gabalafou gabalafou changed the base branch from develop to main June 18, 2024 17:01
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Note: I changed the base branch of this PR to reflect the new blog publishing process. Once this is approved, it will be merged directly into the main branch and deployed live to the Web.

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