![]() Both modes of operation are explained in this article. Compression and decompression can both be done as a one-off operations, or by splitting the data into chunks like you'd seem from a stream of data. The most important functionalities included in this library are compression and decompression. The main use of the zlib library is in applications that require compression and decompression of arbitrary data, whether it be a string, structured in-memory content, or files. Another important benefit of this compression mechanism is that it doesn't expand the data. It is a lossless compression format (which means you don't lose any data between compression and decompression), and has the advantage of being portable across different platforms. The zlib compression format is free to use, and is not covered by any patent, so you can safely use it in commercial products as well. ![]() The data format used by the library is specified in the RFC 1950 to 1952, which is available at. For major changes or feedback, please open an issue first to discuss what you would like to change.The Python zlib library provides a Python interface to the zlib C library, which is a higher-level abstraction for the DEFLATE lossless compression algorithm. Pull requests and ideas, especially for further functions are welcome. Klib.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. Klib.dist_plot(df) # default representation of a distribution plot, other settings include fill_range, histogram. rr_plot(df, target= 'wine') # default representation of correlations with the feature column rr_plot(df, split= 'neg') # displaying only negative correlations rr_plot(df, split= 'pos') # displaying only positive correlations, other settings include threshold, cmap. klib.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. klib.pool_duplicate_subsets(df) # pools subset of cols based on duplicates with min. klib.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() nvert_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 rr_plot(df) # returns a color-encoded heatmap, ideal for correlations rr_mat(df) # returns a color-encoded correlation matrix klib.cat_plot(df) # returns a visualization of the number and frequency of categorical features # scribe - functions for visualizing datasets Use the package manager pip to install klib.Īlternatively, to install this package with conda run: Explanations on key functionalities can be found on Medium / TowardsDataScience in the examples section or on YouTube (Data Professor). Klib is a Python library for importing, cleaning, analyzing and preprocessing data. ![]()
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