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1 | 1 | # An Overview of Python's Datatable package |
2 | 2 | Python library for efficient multi-threaded data processing, with the support for out-of-memory datasets. |
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4 | | -If you are an R user, chances are that you have already been using the data.table package. Data.table is an extension of the data.frame package in R. It's also the go-to package for R users when it comes to the fast aggregation of large data (including 100GB in RAM). |
5 | | -The R's data.table package is a very versatile and a high-performance package due to its ease of use, convenience and programming speed. It is a fairly famous package in the R community with over 400k downloads per month and almost 650 CRAN and Bioconductor packages using it(source). |
| 4 | +If you are an R user, chances are that you have already been using the [data.table](https://cran.r-project.org/web/packages/data.table/data.table.pdf) package. Data.table is an extension of the data.frame package in R. It's also the go-to package for R users when it comes to the fast aggregation of large data (including 100GB in RAM). |
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| 6 | +The R's data.table package is a very versatile and a high-performance package due to its ease of use, convenience and programming speed. It is a fairly famous package in the R community with over 400k downloads per month and almost 650 CRAN and Bioconductor packages using it [source](https://github.com/Rdatatable/data.table/wiki). |
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6 | 8 | So, what is in it for the Python users? Well, the good news is that there also exists a Python counterpart to thedata.table package called datatable which has a clear focus on big data support, high performance, both in-memory and out-of-memory datasets, and multi-threaded algorithms. In a way, it can be called as data.table's younger sibling. |
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