![]() ![]() Most software packages handle edge cases (U.S. Special care has to be taken when breaking down terms so that logical units are created. We understand these units as words or sentences, but a machine cannot until they’re separated. Tokenization: Tokenization breaks the text into smaller units vs.The following are general steps in text preprocessing: This post will show how I typically accomplish this. In order to maximize your results, it’s important to distill your text to the most important root words in the corpus and clean out unwanted noise. One of the most common tasks in Natural Language Processing (NLP) is to clean text data. In this article we examined how to use skimpy to quickly standardize column names to different common case styles without using regex.Photo by Dmitry Ratushny on Unsplash Cleaning Text For example, we might like to shorten Address to Addr and Description to Desc clean_df = clean_columns(messy_df, case = 'pascal', replace = ) clean_df.columns.tolist() > Conclusionĭealing with messy column names are part and parcel of a data science workflow and can be rather tedious for datasets with large number of columns and different naming formats. This can be useful for correcting misspelled column names or for renaming the column to a standard format. clean_df = clean_columns(messy_df, case = 'const') clean_df.columns.tolist() > Replace StringsĬlean_columns() also provides option to replace string. clean_df = clean_columns(messy_df, case = 'pascal') clean_df.columns.tolist() > Constant CaseĬonstant case style uses the underscore delimiter and changes all characters to upper case. ![]() Pascal case style is similar to camel case except the first character of each string is in upper case. clean_df = clean_columns(messy_df, case = 'camel') clean_df.columns.tolist() > Pascal Case clean_df = clean_columns(messy_df, case = 'kebab') clean_df.columns.tolist() > Camel CaseĬamel case style starts uses upper case characters as delimiter and the first character of each string is in lower case. Kebab case style is similar to snake case except it uses dash delimiter instead of underscore. clean_df = clean_columns(messy_df) clean_df.columns.tolist() > Kebab Case Snake case style replaces the white spaces and symbol delimiters with underscore and converts all characters to lower case. The default format which skimpy standardizes to is the snake case style. These case styles differs in the the casing and delimiter used. The below table shows the resulting column names for different case styles. Skimpy provides a few common formats for standardizing column names. A lesser used function of Skimpy is the clean_columms convenience function which helps to address the issue of messy Pandas column names. However this is not what we are interested today. Skimpy is commonly used for providing summary statistics about variables in a Pandas DataFrame. Regex is commonly used for cleaning up messy column names, however it can be quite tedious to write regex for covering various messy scenarios. Despite the trouble, it is still important to standardize the column names to a common format early in the data cleaning process to better facilitate downstream task. We often receive data from multiple sources with different column naming format and standardizing them can be a hassle. Photo by No Revisions on Unsplash Motivation
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |