I am working in kaggle notebook and wanted to use pandas profiling for EDA but it is giving error. What can be done to use this library in kaggle notebooks?
Even though Kaggle comes with pandas-profiling as default, I would recommend running this command in a cell to use the latest version of the package: %pip install -Uqq pandas-profiling[notebook]
Related
I've installed the pandas_profiling package but, the system is not recognizing the package.
I'm using VS Code for the project.
Hi, I'm trying to install pandas profiling for an ML testing project I've checked multiple Question Related to it on StackOverFlow but could not find any match Or Solution.
I've checked the package and it got successfully installed on the system.
But I'm still getting the Error
FYI:
I've installed multiple packages required for the project, such as NumPy, pandas, etc., and did not face any problems.
Note: If any other info is required let me Know.
import sys
!{sys.executable} -m pip install pandas-profiling
Add the above line to the top of your file
I have an R notebook that I am trying to make into a Jupyter notebook using IRkernel, which I downloaded via Anaconda.
Everything works fine except when I try to use dygraphs, which just won't display the graph.
The only thing close to an answer I could find online is: https://doc.dataiku.com/dss/latest/R/dygraphs.html but I am not able to download the dataiku library for some reason.
I tried using conda install -c r r-dygraphs but it has made no difference.
On the dygraphs website they don't mention anything about using it in a Jupyter notebook so I'm not sure what to do. If it isn't supported on Jupyter, can someone point me to a close alternative to dygraphs that will work on Jupyter? The time series visualisation is really great on it which is why I want to implement it in my Jupyter notebook.
Thanks in advance!
If your error is something like:
ERROR while rich displaying an object: Error in file(file, "rb"): invalid 'description' argument
Then I think it's referring to not being able to plot the dygraphs in Jupyter. I would suggest checking out the "using" section on the documentation for R dygraphs. Although it doesn't strictly mention using dygraphs in Jupyter notebooks, it gives pointers for using them in R Console, R Markdown, and R Shiny. Lately I've been using them in an R shiny application and they've worked flawlessly, just as long as you set them up correctly. Also, just use:
install.packages('dygraphs')
in Jupyter notebooks to ensure successful installation and use. I've noticed that there are inconsistencies when running conda install commands (i.e. whether or not the package will end up working in Jupyter notebooks, RStudio, or both).
I think the problem is not in the install dygraphs, but it depends on jupyter notebook, this one can't appear dynamic graphic such as in RMarkdown.
I am looking to integrate python code into a r notebook (or even a rmarkdown document). I am able to run Python using a python code chunk:
```{python}
```
However I am unable to import a package (e.g. pandas). I have anaconda installed which has pandas installed, however I am unable to import it. Can anyone give me direction as to how I can import packages into r via knitr, preferably using anaconda?
{python, engine.path="/path/to/your/anaconda3/bin/python"}
I want to install a package that is listed in https://cran.r-project.org/web/packages/available_packages_by_name.html as available in CRAN, but when I check in R the install packages menu or the available.packages() command, I can't see the package there.
Do I need to do something different to install those packages? Why aren't those packages available?
The packages I'm interested on are WikipediR ( https://cran.r-project.org/web/packages/WikipediR/index.html ), WikidataR and WikipediaR.
If it matters, I'm using R 2.15.0 in Windows XP.
See the documentation for ?available.packages...
By default, the return value includes only packages whose version and
OS requirements are met by the running version of R, and only gives
information on the latest versions of packages.
In other words... your R 2.15 is likely too old for the package you are looking to download.
You can try to download the package source manually add the package to the package library usually found somewhere like "win-library/2.15/" but like Cory mentioned it is likely that the older version of R does not support the package build.
The advice given so far is a bit incomplete although I do agree you need to update your R version if you want to use these packages. Looks like they don't need compilation so you might have been able to either install from a local copy or drop R code in, but critically they depend on httr which requires R 3.0.0 or above. They were released only relatively recently, so there will be no Windows binaries from back in 2012. (Your copy of R is from 30-Mar-2012.) Look in the DESCRIPTION file which is presented in a nice web format at the CRAN/package listing:
https://cran.r-project.org/web/packages/WikidataR/index.html
Imports: httr, jsonlite, WikipediR
Suggests: testthat, knitr, pageviews
# only one version of these two
https://cran.r-project.org/src/contrib/Archive/WikidataR/WikidataR_1.0.0.tar.gz
https://cran.r-project.org/src/contrib/Archive/WikipediaR/WikipediaR_1.0.tar.gz
# pick one of these
https://cran.r-project.org/src/contrib/Archive/WikipediR/
I am developing an R package, but every time I make some modifications
I have to use R CMD INSTALL to install it and see if the new version
is working.
I would like to know if there is some easier way to develop a package in R.
Specifically I would like to be able to develop the package without having
to install it every time I want to test it.
If you are familiar with Python and setuptools, I would like to achieve the
same effect you get using
python setup.py develop.
Install devtools, then all you need to do is:
require(devtools)
load_all("/wherever/your/package/is")
It reloads all the changed code in .R files, recompiles, links, loads C code and so on.
devtools will also compile your documentation, and run checks.
Nothing else comes close for package development.