drops duplicate rows: This is a straightforward drop of entirely duplicate rows.Other examples are “download_date” or indicator variables which are identical for all entries. This comes in handy when columns such as “year” are included while you’re just looking at a single year. removes single-valued columns: As the name states, this removes columns in which each cell contains the same value.The default is to drop columns and rows with more than 90% of the values missing. dropping empty and virtually empty columns: You can use the parameters drop_threshold_cols and drop_threshold_rows to adjust the dropping to your needs.This also checks for and fixes duplicate column names, which you sometimes get when reading data from a file.
With this insight, we can go ahead and start cleaning the data. A quick way to accomplish this is to use klib’s missing value visualization, which is as simple as this: It’s critical to assess data quality before beginning to work on a dataset.
HADISE KLIB INSTALL
Install klib using pip: pip install -upgrade klibĪlternatively, to install with conda run: conda install -c conda-forge klib Key Features of this library: The klib package provides a set of very simple functions with sensible default values that can be used on almost any DataFrame to assess data quality, gain insight, perform cleaning operations, and visualize data, resulting in a Pandas DataFrame that is much lighter and easier to work with. But recently a library is introduced, which will do all the things such as importing, cleaning, analyzing, and preprocessing data. This data preprocessing step is a necessary and time-consuming process. Whenever you do data-related projects, you have to take care of the data preprocessing step, because your model will work well, when your data is well prepared.
ColumnSelector () # selects numerical or categorical columns, ideal for a Feature Union or Pipeline - klib. 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.
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