scikit-learn (next to numpy) is the one library I use in every single project at work. Every time I consider switching away from python I am faced with the fact that I'd lose access to this workhorse of a library.
Of course it's not all sunshine and rainbows - I had my fair share of rummaging through its internals - but its API design is a de-facto standard for a reason.
My only recurring gripe is that the serialization story (basically just pickling everything) is not optimal.
Scikit-Learn Version 1.0
21–30 of 106 posts
Re: Scikit-Learn Version 1.0
#22Earlier quoted context omitted.
I'm with you on sklearn, the DL libraries and Numpy, but Pandas and Matplotlib are poor, poor relations of the tools available in the R ecosystem (dplyr/ggplot etc).
If you're doing data science aren't sklearn, DL, and numpy getting you 90% of the way there anyway? Even if R has better "versions" of pandas/matplotlib (not conceding that point) it's not exactly central to the job of data science.
As a working data scientist I'd say it's completely the opposite: a good tabular data manipulation package is the single most valuable tool in my tool box. And R's packages (either data.table or dplyr) are definitely way better than pandas. There's no comparison.
I would be hard-pressed to find a working data scientist whose definition of data science is "that thing you do with sklearn, Deep Learning and Numpy".
Re: Scikit-Learn Version 1.0
#23Excellent library for train_test_split. Jokes aside. This next to Numpy, Pandas Jupyter and Matplotlib + the DL libraries are the reason Python is the powerhouse it is for Data Science.
I'm with you on sklearn, the DL libraries and Numpy, but Pandas and Matplotlib are poor, poor relations of the tools available in the R ecosystem (dplyr/ggplot etc).
They need seaborn too, whoch makes the python side a lot stronger
Re: Scikit-Learn Version 1.0
#24Earlier quoted context omitted.
If you're doing data science aren't sklearn, DL, and numpy getting you 90% of the way there anyway? Even if R has better "versions" of pandas/matplotlib (not conceding that point) it's not exactly central to the job of data science.
As a working data scientist I'd say it's completely the opposite: a good tabular data manipulation package is the single most valuable tool in my tool box. And R's packages (either data.table or dplyr) are definitely way better than pandas. There's no comparison. I would be hard-pressed to find a working data scientist whose definition of data science is "that thing you do with sklearn, Deep Learning and Numpy".
> "Data science is that thing where you do sklearn, Deep Learning and Numpy" is not a working data scientist's perspective.
It could be. It's such a broad job title and it looks so different across different companies and teams that the main tool for one data scientist might be something that another data scientist never has to touch. Different data science jobs prioritise different tools, that's all.
Re: Scikit-Learn Version 1.0
#25scikit-learn (next to numpy) is the one library I use in every single project at work. Every time I consider switching away from python I am faced with the fact that I'd lose access to this workhorse of a library. Of course it's not all sunshine and rainbows - I had my fair share of rummaging through its internals - but its API design is a de-facto standard for a reason. My only recurring gripe is that the serializat…
I recently ran into this issue as well. Serialization of sklearn random forests results in absolutely massive files. I had to switch to lightgbm, which is 100x faster to load from a save file and about 20x smaller.
Re: Scikit-Learn Version 1.0
#26Earlier quoted context omitted.
I'm with you on sklearn, the DL libraries and Numpy, but Pandas and Matplotlib are poor, poor relations of the tools available in the R ecosystem (dplyr/ggplot etc).
If you're doing data science aren't sklearn, DL, and numpy getting you 90% of the way there anyway? Even if R has better "versions" of pandas/matplotlib (not conceding that point) it's not exactly central to the job of data science.
maybe we are casualties of the vague definition of "data science," but in my experience numpy is too low-level for most of what I consider DS, and pandas/matplotlib are _much_ more central than sklearn or pytorch. Even if your definition only encompasses deep learning research, surely plotting is still indispensable?
I'll also add my vote for the superiority of data.table and ggplot2 to any Python alternatives. the bloat and verbosity of pandas is a daily struggle
Re: Scikit-Learn Version 1.0
#27Earlier quoted context omitted.
If you're doing data science aren't sklearn, DL, and numpy getting you 90% of the way there anyway? Even if R has better "versions" of pandas/matplotlib (not conceding that point) it's not exactly central to the job of data science.
As a working data scientist I'd say it's completely the opposite: a good tabular data manipulation package is the single most valuable tool in my tool box. And R's packages (either data.table or dplyr) are definitely way better than pandas. There's no comparison. I would be hard-pressed to find a working data scientist whose definition of data science is "that thing you do with sklearn, Deep Learning and Numpy".
Tabular data is great for many usecases, but saying that image, audio, and video analysis is not data science seems like a weird variant of gatekeeping to me.
Re: Scikit-Learn Version 1.0
#28Earlier quoted context omitted.
As a working data scientist I'd say it's completely the opposite: a good tabular data manipulation package is the single most valuable tool in my tool box. And R's packages (either data.table or dplyr) are definitely way better than pandas. There's no comparison. I would be hard-pressed to find a working data scientist whose definition of data science is "that thing you do with sklearn, Deep Learning and Numpy".
Tabular data is great for many usecases, but saying that image, audio, and video analysis is not data science seems like a weird variant of gatekeeping to me.
Of course, my post doesn't imply such a silly statement.
Re: Scikit-Learn Version 1.0
#29Earlier quoted context omitted.
As a working data scientist I'd say it's completely the opposite: a good tabular data manipulation package is the single most valuable tool in my tool box. And R's packages (either data.table or dplyr) are definitely way better than pandas. There's no comparison. I would be hard-pressed to find a working data scientist whose definition of data science is "that thing you do with sklearn, Deep Learning and Numpy".
> "Data science is that thing where you do sklearn, Deep Learning and Numpy" is not a working data scientist's perspective. It could be. It's such a broad job title and it looks so different across different companies and teams that the main tool for one data scientist might be something that another data scientist never has to touch. Different data science jobs prioritise different tools, that's all.
Right, so defining data science as 90% sklearn+DL+numpy is just as silly as saying that it's 90% table manipulation. That's exactly my point.
Still, if anyone here has managed to find a data science job in which tabular data management is not a sizable piece of what you do, I'd like to know some details!
Re: Scikit-Learn Version 1.0
#30Earlier quoted context omitted.
If you're doing data science aren't sklearn, DL, and numpy getting you 90% of the way there anyway? Even if R has better "versions" of pandas/matplotlib (not conceding that point) it's not exactly central to the job of data science.
maybe we are casualties of the vague definition of "data science," but in my experience numpy is too low-level for most of what I consider DS, and pandas/matplotlib are _much_ more central than sklearn or pytorch. Even if your definition only encompasses deep learning research, surely plotting is still indispensable? I'll also add my vote for the superiority of data.table and ggplot2 to any Python alternatives. the b…
Just curious. In which way is data.table superior to pandas? Really interested about it! From my personal experience pandas is just sometimes a bit slow.