If the behavior for a certain platform needs to be modified, you can use a format like compilerOpts.osx or compilerOpts.linux to provide platform-specific values to the options.
#.klib fike code
This command will produce a png.klib compiled library and png-build/kotlin directory containing Kotlin source code for the library.
#.klib fike install
Install libgit2 and prepare stubs for the git library:Ĭinterop -def png.def -compiler-option -I/usr/local/include -o png
#.klib fike windows
For example, POSIX on Linux/macOS platforms, Win32 on Windows platform, or Apple frameworks on macOS/iOS are available this way. Note that in many cases there's no need to use custom interoperability library creation mechanisms described below, as for APIs available on the platform standardized bindings called platform libraries could be used. Interoperability with Swift/Objective-C is provided too and covered in Objective-C interop. The generated stubs can be imported into an IDE for the purpose of code completion and navigation. The interoperability tool analyses C headers and produces a "natural" mapping of the types, functions, and constants into the Kotlin world. Run the Kotlin/Native compiler on an application to produce the final executable. Use the cinterop tool to produce Kotlin bindings. def file describing what to include into bindings. The following workflow is expected when interacting with the native library:Ĭreate a. So Kotlin/Native comes with a cinterop tool, which can be used to quickly generate everything needed to interact with an external library. In the case of a native platform, the most important interoperability target is a C library.
#.klib fike software
For major changes or feedback, please open an issue first to discuss what you would like to change.Kotlin/Native follows the general tradition of Kotlin to provide excellent existing platform software interoperability. 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 is a Python library for importing, cleaning, analyzing and preprocessing data.
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