# \[ANN\] DataWrangler: Data transformation tools for analytics

**URL:** <https://discourse.julialang.org/t/ann-datawrangler-data-transformation-tools-for-analytics/69490>\
**Category:** Package Announcements\
**Tags:** time-series, regression\
**Created:** [October 9, 2021, 10:30pm UTC](https://discourse.julialang.org/t/ann-datawrangler-data-transformation-tools-for-analytics/69490 "2021-10-09T22:30:08Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![viraltux](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/viraltux/32/15236_2.png) [@viraltux](https://discourse.julialang.org/u/viraltux)\
**Post date:** [October 9, 2021, 10:30pm UTC](https://discourse.julialang.org/t/ann-datawrangler-data-transformation-tools-for-analytics/69490/1 "2021-10-09T22:30:08Z")

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[DataWrangler.lj](https://github.com/viraltux/DataWrangler.jl) has been created in the process of decoupling functionality from [Forecast.jl](https://github.com/viraltux/Forecast.jl). The package contains a collection of basic tools to prepare data for analytics and specially so for time series analysis and regressions.

Next follows a list of the available tools to wrangle data:

- Box-Cox and inverse Box-Cox transformation and estimation: `boxcox` , `iboxcox`
- Data imputation (loess inter/extra-polation, random local density): `impute` , `impute!`
- Data normalization (z-score, min-max, softmax, sigmoid): `normalize` , `normalize!`
- Finite lagged difference and partial difference and its inverse: `d` , `p`
- Outlier detection and removal: `outlie` , `outlie!`

![image](https://global.discourse-cdn.com/julialang/original/3X/f/9/f98fe07a16bd1635178ddcf86a0be57d5f3fc238.png)  
 ![image](https://global.discourse-cdn.com/julialang/original/3X/0/4/04cd47382ee93e0cfde4008a506375de9056efba.png)

**Note** : Although there are still a couple of packages to be finished before the decoupling is completed, this announcement will most likely be the last one before all the decoupled packages are fully integrated in `Forecast.jl v.0.2.0`. This integration might take a while but I believe the packages decoupled so far are useful in their own, hence the announcements.

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**Author:** ![viraltux](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/viraltux/32/15236_2.png) [@viraltux](https://discourse.julialang.org/u/viraltux)\
**Post date:** [October 10, 2021, 10:18am UTC](https://discourse.julialang.org/t/ann-datawrangler-data-transformation-tools-for-analytics/69490/2 "2021-10-10T10:18:07Z")

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Hi @Storopoli , I went through your course at [https://storopoli.io/Computacao-Cientifica/5\_TimeSeries/](https://storopoli.io/Computacao-Cientifica/5_TimeSeries/)  
and it looks like it already has the basic chapters for an introduction to Time Series using Julia, what I might suggest is to add some information about how to prepare data before the chapter **“Maneiras de Modelar Séries Temporais”**.

Although Auto-Arima packages will differentiate and integrate automatically some preparation often needs to be done before that (removing outliers, deal with Heteroscedasticity, impute missing values… etc) and I think students will find it useful.

Great work with those Notebooks!

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**Author:** ![Storopoli](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/storopoli/32/209278_2.png) [@Storopoli](https://discourse.julialang.org/u/Storopoli)\
**Post date:** [October 10, 2021, 1:14pm UTC](https://discourse.julialang.org/t/ann-datawrangler-data-transformation-tools-for-analytics/69490/3 "2021-10-10T13:14:07Z")

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Thanks I will revise this week the lecture and notebook. Since the virtual meeting and recording will be this friday.

I already starred the package, I found it awesome, specially the several normalize options. I was already using something myself ([https://gist.github.com/storopoli/31ef4abdc542dc3a0e1cae2965ed8740](https://gist.github.com/storopoli/31ef4abdc542dc3a0e1cae2965ed8740)) but having a package (and one with so small dependency weight) is truly great.