![]() ![]() For major changes or feedback, please open an issue first to discuss what you would like to change. Pull requests and ideas, especially for further functions are welcome. cat_plot ( data, top = 4, bottom = 4 ) # representation of the 4 most & least common values in each categorical columnįurther examples, as well as applications of the functions in klib.clean() can be found here. dist_plot ( df ) # default representation of a distribution plot, other settings include fill_range, histogram. corr_plot ( df, target = 'wine' ) # default representation of correlations with the feature column corr_plot ( df, split = 'neg' ) # displaying only negative correlations corr_plot ( df, split = 'pos' ) # displaying only positive correlations, other settings include threshold, cmap. missingval_plot ( df ) # default representation of missing values in a DataFrame, plenty of settings are available loss of information Examplesįind all available examples as well as applications of the functions in klib.clean() with detailed descriptions here. pool_duplicate_subsets ( df ) # pools subset of cols based on duplicates with min. ![]() mv_col_handling ( df ) # drops features with high ratio of missing vals based on informational content - klib. drop_missing ( df ) # drops missing values, also called in data_cleaning() - klib. convert_datatypes ( df ) # converts existing to more efficient dtypes, also called inside data_cleaning() - klib. clean_column_names ( df ) # cleans and standardizes column names, also called inside data_cleaning() - klib. data_cleaning ( df ) # performs datacleaning (drop duplicates & empty rows/cols, adjust dtypes.) - klib. missingval_plot ( df ) # returns a figure containing information about missing values # klib.clean - functions for cleaning datasets - klib. dist_plot ( df ) # returns a distribution plot for every numeric feature - klib. corr_plot ( df ) # returns a color-encoded heatmap, ideal for correlations - klib. corr_mat ( df ) # returns a color-encoded correlation matrix - klib. cat_plot ( df ) # returns a visualization of the number and frequency of categorical features - klib. DataFrame ( data ) # scribe - functions for visualizing datasets - klib. Usage import klib import pandas as pd df = pd. Use the package manager pip to install klib.Īlternatively, to install this package with conda run: Additionally, there are great introductions and overviews of the functionality on PythonBytes or on YouTube (Data Professor). Explanations on key functionalities can be found on Medium / TowardsDataScience and in the examples section. klib artifact and provides a set of methods for declaring and configuring them. This property represents a collection of native binaries built for this target in addition to the default. The actual native artifact is a klib ultimately, but it's all managed with gradle and dependency metadata.Klib is a Python library for importing, cleaning, analyzing and preprocessing data. To declare final native binaries such as executables or shared libraries, use the binaries property of a native target. I then publish that whole thing as a multiplatform library. The cinterops sets up where the def files are and params. The native and interop config live in the multiplatform config. I just created an example but it's not public yet, so this is the best one I have off hand: There was a separate plugin for that last year, but you definitely don't want to be using that. Konan is the name of the native platform/compiler. In Link #2 it says that platform plugin is deprecated. If you're building a klib separately, you're creating some extra steps (probably). In general, you'll want to use the multiplatform plugin. ![]()
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