![]() ![]() Care should be taken in choosing non-default str so as to create legal pandas column names. column_separator: "_" (default) The separator to append plus incremental Int to create unique column names. If all columns string lenths are truncated to 10 characters, the first distinct column will be 10 characters and the remaining columns are 12 characters with a column separator of one character). The column separator value and number for duplicate column name does not contribute to total string length. Parameters: X: dataset Keywords: column_length: 3 (default) Truncate all columns names to length. Method chaining will truncate all columns to a given length and append a given separator character with the index of duplicate columns, except for the first distinct column name. ![]() This method mutates the original DataFrame. If column length is shorter, then column length left as is. ![]() def toColumnNamesFixedLen( oX: pd.DataFrame, column_length: int = 3, column_separator: str = "_", inplace: bool = True, verbose: bool = True, ) -> pd.DataFrame: """ Truncate column name to a specific length. Note : The following code snippets show only the docstring. The type of any class instance is the class name. ![]() The type of class instance is the class name. Notice that oXand the return value type is the class name pd.DataFrame. The following docstring uses type hints to show the type of arguments and return values. For me, the need to document the type in your docstring is eliminated.You can put type hints directly into the call signature. Type hints improve your source code readability. Python's significant design philosophy is that its language constructs enable programmers to write clear, logical code for small- and large-scale projects.So why put type hints into your Python code? I have many reasons. Why Type Hints Can Improve Your Python Code ![]()
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