DataWrangling | ultimate reference guide to data

by ben519 R Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | DataWrangling Summary

DataWrangling is a R library typically used in Data Science applications. DataWrangling has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Data science is 90% cleaning the data and 10% complaining about cleaning the data. In the realm of data wrangling, data.table from R and pandas from Python dominate. This repo is meant to be a comprehensive, easy to use reference guide on how to do common operations with data.table and pandas, including a cross-reference between them as well as speed comparisons.

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DataWrangling has a low active ecosystem.

It has 220 star(s) with 80 fork(s). There are 33 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DataWrangling is current.

Quality

DataWrangling has no bugs reported.

Security

DataWrangling has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

DataWrangling does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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DataWrangling releases are not available. You will need to build from source code and install.

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DataWrangling Key Features

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DataWrangling Examples and Code Snippets

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Community Discussions

Trending Discussions on DataWrangling

Extract FASTA sequences (with version number) using sequence IDs (without version number) listed in txt file

google Dataprep: number of instances and architecture optimisation

data.table join + update with mult='first' gives unexpected result

QUESTION

Extract FASTA sequences (with version number) using sequence IDs (without version number) listed in txt file

Asked 2020-Nov-27 at 04:06

I would like to extract specific sequences from myfile.fasta based on the ids listed in transcript_id.txt file. My main problem is that my transcript_id.txt file only lists transcripts ids while fasta file also has transcript versions and transcripts listed in transcript_id.txt can have multiple versions in fasta file. I have tried several approach (listed below) but couldn't get what I need.

myfile.fasta

...

ANSWER

Answered 2020-Nov-25 at 15:47

1st solution: Could you please try following. Written and tested with shown samples in GNU awk.

Source https://stackoverflow.com/questions/65007580

QUESTION

google Dataprep: number of instances and architecture optimisation

Asked 2018-Jul-18 at 16:18

I have noticed that every destination in Google dataprep (be it manual or scheduled) spins up a compute engine instance. Limit quota for a normal account is 8 instances max.

look at this flow: dataprep flow

Since datawrangling is composed by multiple layers and you might want to materialize intermediate steps with exports, what is the best approach/ architecture to run dataprep flows?

Option A

run 2 separate flows and schedule them with a 15 min. discrepancy:

first flow will export only the final step
other flow will export intermediate steps only

this way you´re not hitting the quota limit but you´re still calculating early stages of the same flow multiple times

Option B

leave the flow as it is and request for more Compute Engine Quota: Computational effort is the same, I will just have more instances running in parallel instead of sequentially

Option C

each step has his own flow + create reference dataset: this way each flow will only run one single step.

E.g. when I run the job "1549_first_repo" I will no longer calculate the 3 previous steps but only the last one: the transformations between the referenced "5912_first" table and "1549_first_repo".

This last option seems to me the most reasonable as each transformation is run once at most, Am I missing something?

and also, is there a way to run each export sequentially instead of in parallel?

-- EDIT 30. May--

it turns out option C is not the way to go as "referencing" is a pure continuation of the previous flow. You could imagine the flow before the referenced dataset and after the referenced dataset as a single flow.

Still trying to figure out how to achieve modularity without redundantly calculating the same operations.

...

ANSWER

Answered 2018-Jul-18 at 16:18

Both options A and B are good, the difference being the quota increase. If you are expecting to upgrade sooner or later, might as well do it sooner.

And other option, if you are familiar with java or python and Dataflow, is to create a pipeline having a combination of numWorkers, workerMachineType, and maxNumWorkers that fits within your trial limit of 8 cores (or virtual CPUs). Here are the pipeline option and here is a tutorial that can give you a better view of the product.

Source https://stackoverflow.com/questions/50588298

QUESTION

data.table join + update with mult='first' gives unexpected result

Asked 2017-May-02 at 23:13

In the below example, I have a table of users and a table of transactions where one user can have 0, 1, or more transactions. I execute a join+update with mult='first' on the users table to attempt to insert a column indicating the date of the first occurring transaction for each user.

...

ANSWER

Answered 2017-May-02 at 23:13

Reading the documentation for data.table's mult more closely, it says that:

When i is a list (or data.frame or data.table) and multiple rows in x match to the row in i, mult controls which are returned: "all" (default), "first" or "last".

So if there are multiple rows in X (users) that match to i (transactions), then mult will return the first row in X. However, in your case, there aren't multiple rows in X that match to i, rather there are multiple rows in i that match to X.

As @Arun suggested, the best option would be change around your so that mult = "first" is relevant:

Source https://stackoverflow.com/questions/43747571

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DataWrangling

You can download it from GitHub.

Support

I'd like to encourage contributions for this project - it's well suited for it. Also note that I'm much more comfortable using data.table than pandas, so it's likely I've done some suboptimal wrangling in pandas.

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