![]() ![]() cat_pipe () # provides common operations for preprocessing of categorical data - klib. num_pipe () # provides common operations for preprocessing of numerical data - klib. feature_selection_pipe () # provides common operations for feature selection - klib. train_dev_test_split () # splits a dataset and a label into train, optionally dev and test sets - klib. preprocess # functions for data preprocessing (feature selection, scaling. pool_duplicate_subsets () # pools a subset of columns based on duplicate values with minimal loss of information klib. mv_col_handling () # drops features with a high ratio of missing values based on their informational content - klib. drop_missing () # drops missing values, also called in ".data_cleaning()" - klib. ![]() convert_datatypes () # converts existing to more efficient dtypes, also called inside ".data_cleaning()" - klib. data_cleaning () # performs datacleaning (drop duplicates & empty rows/columns, adjust dtypes.) on a dataset - klib. ![]() clean # functions for cleaning datasets - klib. missingval_plot () # returns a figure containing information about missing values klib. dist_plot () # returns a distribution plot for every numeric feature - klib. corr_plot () # returns a color-encoded heatmap, ideal for correlations - klib. corr_mat () # returns a color-encoded correlation matrix - klib. ![]() cat_plot () # returns a visualization of the number and frequency of categorical features. describe # functions for visualizing datasets - klib. Only with the help of macros can we write a generic container, which can compete with type-specific containers in efficiency after initialization.Import klib klib. Since C language has no template feature, macros can only be used to achieve efficient paradigm programming. #KLIB LIBRARY PYTHON CODE#As a result, the source code is a bit difficult to read and not conducive to subsequent debugging. In order to use these data structures, we need to first expand the instance methods in the macro. In order to realize the paradigm of containers, klib makes extensive use of C macros. datar is a re-imagining of APIs of data manipulation libraries in python (currently only pandas supported) so that you can manipulate your data with it like with dplyr in R. In order for me to understand better, I am able to read carefully through translation. Therefore, the first step in learning klib is to read its documentation. Step 1: Read the documentationĪn excellent project will inevitably have an excellent document to introduce the project, otherwise it can only be used for self-entertainment and not widely used. For example, kindle reads like candle, so the k, function name, and structure name here are all acceptable Think it is c, which means this is a c language library. Regarding the k in the name of klib, I thought of the previous English teacher saying that k and c are pronounced similarly in English. Future versions will include model creation and optimization to provide an end-to-end solution. seqtk main use of the two header files, khash.h and kseq.h, and I found these two files klib, whereas klib provide a more detailed introduction to the document, so I went directly to the school klib. klib is a Python library for importing, cleaning, analyzing and preprocessing data. #KLIB LIBRARY PYTHON HOW TO#So for C language, how should I use an existing wheel?Ī few days ago, I translated a blog by Li Heng Dashen about the introduction of seqtk code, so I decided to use seqtk as the starting point to introduce how to use wheels made by others. #KLIB LIBRARY PYTHON INSTALL#For Python and R, use an existing tool, usually install it first, and then learn how to call the function by looking at the help document. ![]()
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