First post! That’s my husband he knows about data…

@LuisRomaUSA

2 жыл бұрын

He knows a lot of good stuff about data 😁. His the first non-introductory Python KZreadr I have found so far 🎉

@venvanman

2 жыл бұрын

aww this is cute

@sketch1625

2 жыл бұрын

Guess he's really in a "pickle" now.

@foobarAlgorithm

2 жыл бұрын

Awww now you guys need a The DataCouple channel if you both do data science! Love your content

@Arpan_Gupta

Жыл бұрын

Nice work Mr. ROB

Man, I thought this video is a clickbait, but it was awesome. Thank you!

You are my new favorite KZreadr, Sir. I'm learning more from you than anyone else, by a country mile!

As always, awesome video...a real eye opener on most efficient file formats. I have only used pickle as compression, but will now investigate feather and parquet. Thanks for putting this together for all of us.

@robmulla

Жыл бұрын

Glad it was helpful! I use parquet all the time now and will never go back.

A major design objective of feather is to be able to be read by R. If you are doing pandas-type data science stuff, this is a significant advantage.

@robmulla

Жыл бұрын

Great point. The R package called "arrow" can read in both parquet and feather files.

Very good video :). One note: pickle files can be compressed. If you compress them, they become much smaller but reading and writing becomes slower. Overall parquet und feather are still much better.

@robmulla

2 жыл бұрын

Good point! There are many ways to save/compress that I probably didn't cover. Thanks for watching the video.

Very clear, very structured, and the details are intuitive to understand!

Very clear and insightful explanation, thanks Rob, keep it up!

@robmulla

Жыл бұрын

Thanks Gustavo. I’ll try my best.

Thanks Rob, awesome information! Learning a lot from your channel. Keep it up!

@robmulla

Жыл бұрын

Isn’t learning fun?! Thanks for watching.

One really cool feature of .read_parquet() is that it passes through additional parameters for whichever backend you're using. For example the filters parameter in pyarrow allows you to filter data at read, potentially making it even faster: df = pd.read_parquet("myfile.parquet", filters=[('col_name', '

@robmulla

Жыл бұрын

Whoa. That is really cool. I didn't realize you could do that. I've used athena which allows you to query parquet files using standard SQL and it's really nice.

@juanm555

Жыл бұрын

Athena is amazing when backed with parquet files, I've used it in order to be able to read through 600M+ records that were in those parquets easily

@incremental_failure

11 ай бұрын

That's the real use case for parquet. Feather doesn't have this.

Rob, you did it again...keep'em coming, good job!

@robmulla

Жыл бұрын

Thanks!

I saw people mentioned feather on Kaggle sometimes, but had no clue what they were talking about. Finally, I got answers to many questions in my mind. Thank you!

@robmulla

Жыл бұрын

Yes. Feather and parquet formats are awesome for when you want to quickly read and write data to disk. Glad the video helped you learn!

as someone moving into datascience this is such a great explainer! thank you

Excellent tutorial Rob. Subscribed!

@robmulla

Жыл бұрын

Thanks so much for the feedback. Thanks for subscribing!

learnt something new today. Thank you Rob for this useful & informative video.

@robmulla

2 жыл бұрын

Learn something new every day and before long you will be teaching others!

This was the first video from the channel that randomly appeared in my feed. I clicked, I watched - I liked and subscribed :D. This video plant a seed into my mind, some others inspired me to try. So few days later I got running playground environment in the docker. I'm not data scientist but tips and tricks from your videos could be useful for any developer. I used to code before to check some datasets, but with pandas and jupiter notebook it way more faster. Thank You for sharing your experience !

@robmulla

Жыл бұрын

Wow, I really appreciate this feedback. Glad you found it helpful and got some code working yourself. Share with friends and keep an eye out for new videos dropping soon!

Amazing! Got one new member. Thanks, Rob! 😉

@robmulla

Жыл бұрын

Glad you liked it. Thanks for commenting!

Excellent as usual Rob...very very useful indeed

@robmulla

2 жыл бұрын

Thank you sir!

Exactly what I needed to know, and to the point. Thanks. As Einstein said, 'Everything should be as simple as possible, and no simpler!'

@robmulla

Жыл бұрын

That’s a great quote. Glad you found this helpful.

Hi Rob. I'm from Argentina, you are the best!!!

Great summary of data types. Thanks

@robmulla

2 жыл бұрын

Thanks for the feedback! Glad you found it helpful.

Very engaging and clear. Thanks!

@robmulla

Жыл бұрын

Thanks for watching. 🙌

I've learned a great deal with this video. Thank you!

@robmulla

Жыл бұрын

Thanks so much for the feedback. Glad you learned from it!

Very informative video! Subscribed :)

@robmulla

Жыл бұрын

Glad it helped! 🙏

Amazing. Congrats for the video

@robmulla

Жыл бұрын

Glad you like the video. Thanks for watching.

Huge thanks for sharing 🍀

@robmulla

Жыл бұрын

Glad you liked it? Thanks for the comment.

This is excellent, thank you man

@robmulla

Жыл бұрын

Glad it helped!

super clear and useful! Subscribed

@robmulla

Жыл бұрын

Awesome, thank you!

Great video!! Small things matter the most. Thanks

@robmulla

Жыл бұрын

Absolutely! Thanks.

really good video! thank you

Hey Guy, nice job. Congratulations! Thanks for video.

@robmulla

Жыл бұрын

Thanks for watching Humberto.

Rob, You're a natural communicator (or you worked really hard at acquiring that skill) - most effective. I follow you on twitch and I'm currently going through your youtube content to come up to speed. Thanks for sharing your time and experience. Have you thought about aggregating your content into a book as a companion to your content - something like "Data Analysis Using Python/Pandas - No BS, Just Good Stuff" ?

@robmulla

Жыл бұрын

Hey. Thanks for the kind words. I’ve never considered myself a naturally good communicator and it’s a skill I’m still working in but I appreciate your positive feedback. The book idea is great, maybe sometime in the future….

I really love it man, thank you. You saved a life

@robmulla

Жыл бұрын

Thanks! Maybe not saved a life, but saved a few minutes of compute time!

Great! Thank you for this very helpful video.

@robmulla

9 ай бұрын

Glad it was helpful!

Awesome informations ! Thankyou for this.

@robmulla

2 жыл бұрын

Glad you liked it!

super awesome tricks, thank you

@robmulla

Жыл бұрын

Glad you like them! Thanks for watching.

thanks rob, its help me a lot for beginner like me to realize there is weakness in csv format 😉

Lol this video changed my life :D Thank you so much.

Really useful video - thanks. I was just searching for some Pandas videos for some light upskilling on the weekend, so this was a great find.

@robmulla

Жыл бұрын

Glad I could help! Check out my other videos on pandas too if you liked this one.

Great video and content.

Was very useful, thanks much

@robmulla

Жыл бұрын

Thanks! Glad you learned something new.

awesome ! thank you for this tutorial

@robmulla

Жыл бұрын

You're very welcome! Share with a friend.

Thank u very much for sharing such useful skills! 😉Subscribed!

@robmulla

Жыл бұрын

Anytime! Glad you liked it.

Great video. Thanks

@robmulla

Жыл бұрын

You are welcome!

It's useful for me, thanks a lot!

@robmulla

Жыл бұрын

Happy to hear that!

This is good to know. I`m going into web development now, so I usually use JSON format for serialization... I`m still new to python so I didn`t know about parquet and feather. Thank you!

@robmulla

Жыл бұрын

Glad you found it helpful. Share it with anyone else you think would benefit!

I looked this up, and it's a pretty cool format, I kinda guessed that it could be a column-based storage strategy when you said that we can efficiently get only select columns, but after I looked it up and found it to be true, it felt very exciting. Anyways, hats off to Google's engineers for thinking out of the box on this, the number of things we can do just by storing data as column-lines rather than row-lines is a lot. Of course, the trade-off is that it's very expensive to modify column-wise data, so this is more useful for static datasets that require multi-dim analysis

Useful. Thanks.

This blew my mind, duuude

@robmulla

Жыл бұрын

Happy to hear that! Share with others so their minds can be blown too!

Hey this was very useful to me thank you for sharing!!

@robmulla

Жыл бұрын

So glad you found it useful.

This content is really awesome

@robmulla

Жыл бұрын

Appreciate that!

Fantastic video

@robmulla

Жыл бұрын

Fantastic comment. 😎

Great stuff! Thanks for sharing.

@robmulla

Жыл бұрын

Glad you enjoyed it!

@chrisogonas

Жыл бұрын

@@robmulla 👍

Very good and informative video

@robmulla

Жыл бұрын

So nice of you. Thanks for the feedback.

12:28 "When your data set gets very large." - Me working with 800GB json files: :) Good video regardless, i might give them a test sometime.

@robmulla

Жыл бұрын

Haha. It’s all relative. When your data can’t fit in local ram you need to start using things like spark.

Very good stuff. The essence of information.

@robmulla

Жыл бұрын

Glad you liked it!

@ozymet

Жыл бұрын

@@robmulla I saw few more videos, insta sub. Thank you. Glad to find you.

Good tips on speeding up large file read and write

@robmulla

Жыл бұрын

Glad you liked it! Thanks for the feedback.

Nice video. I'm going to rewrite the storage on the parquet

@robmulla

Жыл бұрын

You should! Parquet is awesome.

Thanks a lot, just brought down my database backup size to MBs.

@robmulla

Жыл бұрын

Glad it helped. That’s a huge improvement!

Informative video! I've heard about feather and pickle, but never used them. I think I should give feather and parquet a try! I'd like to get some materials on machine learning and data science that are not introductory - something for middle and senior engineers :)

@robmulla

2 жыл бұрын

Glad you found it useful. I’ll try to make some more ML videos in the near future.

thanks very helpful

@robmulla

Жыл бұрын

Glad it helped

This video greatly helped me. I didn't know so many ways to dump a DataFrame. I then did a further test, and found the compression option plays a big role: df.to_pickle(FILE_NAME, compression='xz') -> 288M df.to_pickle(FILE_NAME, compression='bz2') -> 322M df.to_pickle(FILE_NAME, compression='gzip') -> 346M df.to_pickle(FILE_NAME, compression='zip') -> 348M df.to_pickle(FILE_NAME, compression='infer') -> 679M # default compression df.to_parquet(FILE_NAME, compression='brotli') -> 334M df.to_parquet(FILE_NAME, compression='gzip') -> 355M df.to_parquet(FILE_NAME, compression='snappy') -> 423M # default compression df.to_feather(FILE_NAME) -> 500M

@robmulla

Жыл бұрын

Nice findings! Thanks for sharing. Funny that compressing parquet still works. I didn't know that.

@DeathorGloryNow

Жыл бұрын

@@robmulla Actually if you check the docs parquet files are snappy compressed by default. You have to explicitly say `compression=None` to not compress it. Snappy is the default because it adds very little time to read/write with modest compression and low CPU usage while still maintaining the very nice columnar properties (as you showed in the video). It is also the default for Spark. Other compressions like gzip get it smaller but at a much more significant cost to speed. I'm not sure this is still the case but in the past they also broke some of the nice properties because it is compressing the entire object.

Thanks, great comp. One thing about Parquet - it has some limitations in what chars column names can take, I spent quite some time renaming col names 1 year ago - perhaps that has fallen away by now.

@robmulla

Жыл бұрын

Good point! I've noticed this too. Definately a limitation that makes it sometimes unusable. Thanks for watching!

Parquet really saved me ) Around one year data, each day is appr 2GB (csv format). Parquet is both compact and fast. But have to use filtering and load only necessary columns “on demand”.

Great Video!!!!!!!!!!!

@robmulla

Жыл бұрын

Glad you enjoyed it

interesting to learn the existence of parquet and feather files. nothing beats csv for portability and ease of use

@robmulla

Жыл бұрын

Yea, for small/medium files CSV gets the job done.

I really hope you make a video about Data Cleaning in Python soon. Thanks a lot for all your awesome tutorials

@robmulla

2 жыл бұрын

I'll try my best. Thanks for the feedback!

amazing info

@robmulla

Жыл бұрын

Thanks!

Thanks!

@robmulla

Жыл бұрын

Whoa. Thanks Aff. 🙏

Thank you

@robmulla

Жыл бұрын

Anytime!

Thank you very much 😂, I got something totally new to me.

@robmulla

Жыл бұрын

Happy to hear it.

Great video - It would have been good to at least mention the downsides to pickle and also the built in compatibility with zip files. Haven't come across feather before, will try it out

@robmulla

Жыл бұрын

Great point! I did forget to mention that pandas will auto-unzip. I still like parquet the best.

@Zoltag00

Жыл бұрын

@@robmulla - Agreed, parquet has some serious benefits You know it also supports a compression option? Use it with gzip to see your parquet file get even smaller (and you only need to use it on write)

On the first pass when you timeit the csv writing you time both the writing to csv and generating the dataset. So you are likely having biased results as you only time the writing with other format. (Sure it does not change the final message, just want to point it out) Also with timeit, you can use the -o flag of timeit to output the result to a variable, and this can help you to for example make a plot of the times.

@robmulla

2 жыл бұрын

Good point about timing the dataframe generation. It should be negligable but fair to note. Also great tip on using -o. I didn't know about that! It looks like from the docs it writes the entire stdout, so it would need to be parsed. ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit Still a handy tip. Thanks!

great comparison. What about HDF5 format? Is it in anyway better?

Very nice bro

@robmulla

Жыл бұрын

Thanks. Hope you learned something!

Experiment add the compression "Brotli" at the file create. The file size reduce considerably and the read is more fast a lot. Example: to save file: from pyarrow import csv, parquet parse_options = csv.ParseOptions(delimiter=delimiter) data_arrow = csv.read_csv(temp_file, parse_options=parse_options, read_options=csv.ReadOptions(autogenerate_column_names=autogenerate_column_names, encoding=encoding)) parquet.write_table(data_arrow, parquet_file + '.brotli', compression='BROTLI') to read file: pd.read_parquet(file, engine='pyarrow')

@robmulla

Жыл бұрын

Oh. Very cool I need to check that out.

Thanks for the great benchmark. In R / Python hybrid environment I sometimes use `csv.gz` or `tsv.gz` to address the size issue with CSV but retain the ability to quickly pipe these through line based processors. It would be interesting to see how gzipped flat files perform. I do agree that parquet/feather is a better way to go for many reasons, they are superior especially from the data engineering point of view.

@robmulla

Жыл бұрын

I do the same with gzipped CSV files. Good idea about making a comparison. I’ll add it to the list of potential future videos.

Great videos! Thank you for posting them. I wonder if feather is faster to read a >2G file.tsv than csv in chunks.

@robmulla

2 жыл бұрын

Thanks for watching Ondina! I think it would depend on the data types within the >2G file. I think the only difference between tsv and csv is a comma ',' vs tab '\t' seperator between values. Hope that helps.

Hello! Very interesting! Thank you! Can you please tell me is any limitation for a DF to save in parquet in terms of number of columns? Excel allow around 16-17k columns to save! Thank you for the answer!

stumbled on to this awesome video and absolutely loved it. Just out of curiosity - what tool are you using for making Jupyter notebook with themes especially dark theme?

@robmulla

2 ай бұрын

Glad you enjoyed the video. I have a different video that covers my jupyter setup including theme: kzread.info/dash/bejne/Z6SaksGboLHIm9o.html

great stuff

@robmulla

2 жыл бұрын

Thank you sir!

Thanks

@robmulla

Жыл бұрын

Welcome

Hey Rob, this was a really nice video! Can you please make a tutorial where you try to write this data to a database? Maybe sqlite or postgres? And explain bottlenecks? (Optional: with or without using an ORM).

@robmulla

Жыл бұрын

I was actually working on just this type of video and even looking at stuff like duckdb where you can write SQL on parquet files.

Thank you for the video! I've basically never heard of parquet or feather and don't really know what type of file those are. I assume it's not an easy format to share with stakeholders for example. Is there a way to link those types of file to a database or perhaps import them in a data vizualisation tool (such as PowerBI or Tableau)?

@robmulla

Жыл бұрын

Thanks for watching Jonathan. Glad you found the video useful. You are correct these file formats are more common for storage within systems that read the data via code and not sharing with stakeholders. CSV and excel still dominates for that type of thing.

Great comparing, thanks, not sure if feather/pickle files i'm creating from Julia's script use some compression - none that i'm specifying out of the box .. but happens that the pickle files always end up being 1/2 the size smaller than the feather ones. (havent compared those 2 to a parquet made file)

Very nice explanation. Can you compare Dask and PySpark ?

I’m working on a little project and I have a csv file that’s 15GB. If I get what you’re telling me, I could turn it into a parquet file and save tons of memory space and time?

Just wow!!!!

@robmulla

Жыл бұрын

Thanks!

Nice video! Thank you. What about hdf5 format? Thanks!

@robmulla

Жыл бұрын

Thanks! I haven’t used Hdf5 much but I’d be interested to hear how it compares.

In addition to everything, parquet is the native file format to spark and can fully support spark‘s lazy computing (spark will only ever read the columns and rows that are needed for the desired output). If you ever prep really big data for spark, parquet is the way to go.

@robmulla

Жыл бұрын

That’s a great point. Same with polars!

@riessm

Жыл бұрын

@@robmulla Need to have a closer look at polars then! 🙂

Another awesome video. It has become my favorite channel. Only regret is that I found it too late. Small correction. It should be 0.3s 0.08s for parquet files. You mistakenly wrote 0.3ms and 0.08ms while converting. Thanks.

@robmulla

Жыл бұрын

Apprecate that you are finding my videos helpful. Good catch on finding that typo!

@Jay-og6nj

Жыл бұрын

i was going to comment that, but decided to check first, least should have caught that. Good video.

Nice video. How does the performance and storage size of parquet, feather compare to hdf/pytables?

@robmulla

Жыл бұрын

Great question. I have no idea! I need to learn more about how they compare.

Hi Rob! I love our channel. It is very helpfull. I would like to ask you a question: is HDF5 any better than all the options you showed in the video?

@robmulla

Жыл бұрын

Good question. I didn't cover it because I thought it's an older, lesser used format.

@leonjbr

Жыл бұрын

@@robmulla so the answer is no?

@robmulla

Жыл бұрын

@@leonjbr The answer is - I don't know but probably not. 😁

@leonjbr

Жыл бұрын

@@robmulla ok thanks.

@CoolerQ

Жыл бұрын

I don't know about "better" but HDF5 is a very popular data format in science.

Fantastic video as always. What are disadvantages of json? I use json because it can easily be passed to the front end.

@robmulla

Жыл бұрын

Great question. I don’t use json much. It isn’t common for tabular/relational data and more for unstructured web based stuff I believe. It probably is pretty slow to read/write large dataset I’m guessing.

Cool. Would be nice to compare with storing data to an sql base (Postgres for example).

@robmulla

Жыл бұрын

Great suggestion! This video only covers storing to flat files, but comparison of different relational databases is a great idea for a future video.

keep uploading videos please!!

@robmulla

Жыл бұрын

Thanks Sbg! I'm planning on it!

Nice.

@robmulla

Жыл бұрын

Thanks! Glad you liked the video.

진짜 parquet는 혁명임... 저장용량은 확 줄이고 나중에 다시 데이터 불러올 때의 속도는 확 높이는 최고의 데이터 포맷

@robmulla

Жыл бұрын

I agree. Parquet is great!

Hey, that IDE / code editor is so cool! What is it? It looks similar to VS Code. But I don't know how to do that kind of tricks.

@robmulla

Жыл бұрын

I have a whole video on this. It's jupyterlab with the solarized dark theme. Check out my jupyter tutorial for the full details!

I'm really interested in the comparison against hdf file. My guess is that it's gonna be the fastest to read, however it prolly takes up more space.

@robmulla

Жыл бұрын

I’m not sure. But I think feather files are pretty fast.

@YeWangRDFZ

Жыл бұрын

@@robmulla Hey Rob thanks for the reply. I had the impression that hdf maps the data taken in ram so there wont be much conversion once its read in the ram but I could be wrong. Also it would be interesting to investigate how feather works. I'll do some benchmarking on my m1mac and maybe get back to you.