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1000 results for “cosima_meyer”
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I can't present today's package without bias—it was my first #rstats package and taught me a lot about #softwaredevelopment.
📦 overviewR (by Cosima Meyer & Dennis Hammerschmidt)
It’s designed for time-series cross-sectional data but works for a range of datasets in your #EDA.
🔗 https://cosimameyer.github.io/overviewR/
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Looking for more #rstats packages for #EDA? Check out this post: https://bit.ly/eda-in-rstats
#pythonista? Series coming soon!
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@cosima_meyer Truly inspired! Had to do the idea justice.
(this is probably the first use of sf::st_random() to generate pepperonis inside a pizza)
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@cosima_meyer Truly inspired! Had to do the idea justice.
(this is probably the first use of sf::st_random() to generate pepperonis inside a pizza)
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@cosima_meyer Truly inspired! Had to do the idea justice.
(this is probably the first use of sf::st_random() to generate pepperonis inside a pizza)
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@cosima_meyer Truly inspired! Had to do the idea justice.
(this is probably the first use of sf::st_random() to generate pepperonis inside a pizza)
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@cosima_meyer Truly inspired! Had to do the idea justice.
(this is probably the first use of sf::st_random() to generate pepperonis inside a pizza)
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📝 "How to structure your data workflow efficiently using R"
👤 Cosima Meyer (@cosima_meyer)
🔗 https://cosimameyer.com/post/how-to-structure-your-data-workflow-efficiently-using-r/
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This book is golden! (and I think I'll have to read it again because it's so full of information!)
For anyone trying to understand their models, Serg Masís' book "Interpretable Machine Learning with #Python" provides the right mix of theory and practical approaches. It has both a high-level and applied perspective, which I really enjoyed, and gives both practitioners and those new to the field a good and illustrative starting point.
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And while this is true for any modeling approach, I think it's especially relevant as we move to more complex models. In my experience, interpretability is important not only in making models accessible to the end user but also in the development process as we strive to build fair and reliable models that produce the best results.
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And the last package on the list is another really nice solution to get a quick overview of your data:
📦 YData Profiling
A great solution for quickly gaining insights into your data. This tool provides variable-level insights, descriptive statistics, bivariate scatterplots, alerts, and more.
🔗 https://docs.profiling.ydata.ai/
🔗 demo: https://cosimameyer.com/images/single-blog/sweet_viz_penguins.gif------------------
Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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And the last package on the list is another really nice solution to get a quick overview of your data:
📦 YData Profiling
A great solution for quickly gaining insights into your data. This tool provides variable-level insights, descriptive statistics, bivariate scatterplots, alerts, and more.
🔗 https://docs.profiling.ydata.ai/
🔗 demo: https://cosimameyer.com/images/single-blog/sweet_viz_penguins.gif------------------
Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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Going back to interactive #EDA with few-liners, we have
📦 SweetViz (by Francois Bertrand)
This one gives you a really nice (and clean) overview of your data. You have different tabs that unfold and show you what's in your data. Here's the GIF that shows the report: https://cosimameyer.com/images/single-blog/sweet_viz_penguins.gif
🔗 https://pypi.org/project/sweetviz/
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Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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Going back to interactive #EDA with few-liners, we have
📦 SweetViz (by Francois Bertrand)
This one gives you a really nice (and clean) overview of your data. You have different tabs that unfold and show you what's in your data. Here's the GIF that shows the report: https://cosimameyer.com/images/single-blog/sweet_viz_penguins.gif
🔗 https://pypi.org/project/sweetviz/
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Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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The next library has also an equivalent in #rstats - it's called
📦 summarytools (by Chaoran L. and AJ)
It builds on the original idea of summarytools in #rstats and implements my favorite go-to feature: the dfSummary function.
🔗 https://pypi.org/project/summarytools/
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Looking for more hashtag#Python libraries for #EDA? Check out this post: https://lnkd.in/ekVqJM_p
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The next library has also an equivalent in #rstats - it's called
📦 summarytools (by Chaoran L. and AJ)
It builds on the original idea of summarytools in #rstats and implements my favorite go-to feature: the dfSummary function.
🔗 https://pypi.org/project/summarytools/
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Looking for more hashtag#Python libraries for #EDA? Check out this post: https://lnkd.in/ekVqJM_p
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The next one is again something I can't present without being biased because it's the #Python version of #overviewR (though still very much a work in progress).
📦 overviewpy
Like overviewR, this library has a clear focus on cross-sectional time series data and provides two main functions... but more to come!
🔗 https://cosimameyer.github.io/overviewpy/
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Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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The next one is again something I can't present without being biased because it's the #Python version of #overviewR (though still very much a work in progress).
📦 overviewpy
Like overviewR, this library has a clear focus on cross-sectional time series data and provides two main functions... but more to come!
🔗 https://cosimameyer.github.io/overviewpy/
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Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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Now it's time for all #pythonistas and #EDA 🐍
📦 pandas
While pandas is a "Swiss army knife" when it comes to data wrangling in #Python, it also has out-of-the-box capabilities that you can use when exploring your data.
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Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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Now it's time for all #pythonistas and #EDA 🐍
📦 pandas
While pandas is a "Swiss army knife" when it comes to data wrangling in #Python, it also has out-of-the-box capabilities that you can use when exploring your data.
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Looking for more #Python libraries for #EDA? Check out this post: https://bit.ly/eda-in-python
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The last tool on the list is another allrounder 🚀
📦 summarytools (by Dominic Comtois)
I’m a visual person and I like to “see” my data. With a combination of two commands from {summarytools} that’s easy and you get a descriptive overview in your viewer pane in your IDE.
🔗 https://github.com/dcomtois/summarytools
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Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats
#pythonista? A series is coming soon!
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The last tool on the list is another allrounder 🚀
📦 summarytools (by Dominic Comtois)
I’m a visual person and I like to “see” my data. With a combination of two commands from {summarytools} that’s easy and you get a descriptive overview in your viewer pane in your IDE.
🔗 https://github.com/dcomtois/summarytools
---------------------
Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats
#pythonista? A series is coming soon!
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The last tool on the list is another allrounder 🚀
📦 summarytools (by Dominic Comtois)
I’m a visual person and I like to “see” my data. With a combination of two commands from {summarytools} that’s easy and you get a descriptive overview in your viewer pane in your IDE.
🔗 https://github.com/dcomtois/summarytools
---------------------
Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats
#pythonista? A series is coming soon!
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The last tool on the list is another allrounder 🚀
📦 summarytools (by Dominic Comtois)
I’m a visual person and I like to “see” my data. With a combination of two commands from {summarytools} that’s easy and you get a descriptive overview in your viewer pane in your IDE.
🔗 https://github.com/dcomtois/summarytools
---------------------
Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats
#pythonista? A series is coming soon!
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The last tool on the list is another allrounder 🚀
📦 summarytools (by Dominic Comtois)
I’m a visual person and I like to “see” my data. With a combination of two commands from {summarytools} that’s easy and you get a descriptive overview in your viewer pane in your IDE.
🔗 https://github.com/dcomtois/summarytools
---------------------
Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats
#pythonista? A series is coming soon!
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The next #rstats package comes with a full suite of functions to wrangle your data 🛠️
📦 SmartEDA (by Dayanand Ubrangala and others)
I like to use a powerful function to get a brief overview of the data - if you’re looking for more, it can also generate a standardized #HTML report for you.
🔗 https://daya6489.github.io/SmartEDA/
---------------------
Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats#pythonista? A series is coming soon!
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The next #rstats package comes with a full suite of functions to wrangle your data 🛠️
📦 SmartEDA (by Dayanand Ubrangala and others)
I like to use a powerful function to get a brief overview of the data - if you’re looking for more, it can also generate a standardized #HTML report for you.
🔗 https://daya6489.github.io/SmartEDA/
---------------------
Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats#pythonista? A series is coming soon!
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I like the next one because without creating full-blown plots, you get visuals. How cool is that?
📦 skimr (by Elin Waring and others)
As you can see in the screenshot, calling skimr::skim() gives you a wealth of descriptive statistics - all readily accessible in your console.
🔗 https://docs.ropensci.org/skimr/
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Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats#pythonista? A series is coming soon!
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The next package sheds light on your missing data 💡
📦 naniar (by @njtierney, @visnut, @milesmcbain, @colinfay and others)
It's all about highlighting and dealing with missing data 🕵🏼♀️ (and I love the hex-logo - it tells the story so nicely!)
🔗 https://naniar.njtierney.com
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Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats
#pythonista? A series is coming soon!
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The next package for #EDA is
📦 Hmisc (by Frank Harrell & Charles Dupont).
I'm just scratching the surface, but Hmisc::describe() is my go-to for a great tabular overview of descriptive stats.
🔗 https://hbiostat.org/r/hmisc/ 🔗 https://hbiostat.org/r/hmisc/#package-usage-and-examples (workflows)
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Looking for more #rstats packages for #EDA? Check out this post: https://bit.ly/eda-in-rstats
#pythonista? Series coming soon!
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Let's welcome another good friend in #EDA in #rstats
📦 gtsummary (by Daniel Sjoberg (creator and maintainer) as well as contributors)
It gives you nice, publication-ready tables (and goes beyond #EDA here - if you are looking for a way to present regression tables nicely, this one has it for you too!)
🔗 https://www.danieldsjoberg.com/gtsummary/
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Looking for more #rstats packages for #EDA? Check out this blog post: https://bit.ly/eda-in-rstats#pythonista? A series is coming soon!