Displaying 1 to 6 from 6 results

textclean - Tools for cleaning and normalizing text data

  •    R

textclean is a collection of tools to clean and normalize text. Many of these tools have been taken from the qdap package and revamped to be more intuitive, better named, and faster. Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards, 2001, doi:10.1006/csla.2001.0169) or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms. The replace_emoticon() function replaces emoticons with word equivalents. Other R packages provide some of the same functionality (e.g., english, gsubfn, mgsub, stringi, stringr, qdapRegex). textclean differs from these packages in that it is designed to handle all of the common cleaning and normalization tasks with a single, consistent, pre-configured toolset (note that textclean uses many of these terrific packages as a backend). This means that the researcher spends less time on munging, leading to quicker analysis. This package is meant to be used jointly with the textshape package, which provides text extraction and reshaping functionality. textclean works well with the qdapRegex package which provides tooling for substring substitution and extraction of pre-canned regular expressions. In addition, the functions of textclean are designed to work within the piping of the tidyverse framework by consistently using the first argument of functions as the data source. The textclean subbing and replacement tools are particularly effective within a dplyr::mutate statement.

Data-Wrangling-with-Python - Simplify your ETL processes with these hands-on data sanitation tips, tricks, and best practices

  •    Jupyter

Data is the new Oil and it is ruling the modern way of life through incredibly smart tools and transformative technologies. But oil does not come out in its final form from the rig. It has to be refined through a complex processing network. Similarly, data needs to be curated, massaged and refined to be used in intelligent algorithms and consumer products. This is called wrangling and (according to Forbes) all the good data scientists spend almost 60-80% of their time on this, each day, every project. It involves scraping the raw data from multiple sources (including web and database tables), imputing, formatting, transforming – basically making it ready, to be used flawlessly in the modeling process. This course aims to teach you all the core ideas behind this process and to equip you with the knowledge of the most popular tools and techniques in the domain. As the programming framework, we have chosen Python, the most widely used language for data science. We work through real-life examples, not toy datasets. At the end of this course, you will be confident to handle a myriad array of sources to extract, clean, transform, and format your data for the great machine learning app you are thinking of building. Hop on and be the part of this exciting journey.

datatools - A set of tools for working with JSON, CSV and Excel workbooks

  •    Go

datatools provides a variety of command line programs for working with data in different formats as well as to ease Posix shell scripting (e.g. writing scripts that run under Bash). The tools are group as data, strings and scripting. Command line utilities for simplifying work with CSV, JSON, TOML, YAML, Excel Workbooks and plain text files or content.