If you are dealing with data where duplicates add value, consider setting drop_duplicates=False. 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.Some column name examples: Yards.Gained -> yards_gained PlayAttempted -> play_attempted Challenge.Replay -> challenge_replay This also checks for and fixes duplicate column names, which you sometimes get when reading data from a file. cleaning the column names: This unifies the column names by formatting them, splitting, among others, CamelCase into camel_case, removing special characters as well as leading and trailing white-spaces and formatting all column names to lowercase_and_underscore_separated.
Klib.data_cleaning() performs a number of steps, among them:
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