The evaluate_dtype() function is designed to perform advanced data type evaluations on DataFrame columns. It extends beyond simple type checks by considering unique value distributions and other factors that might affect whether a column should be treated as categorical, numerical, text, or datetime. This detailed error handling ensures that the function robustly handles a variety of input scenarios and guides users in providing correct inputs.
Detailed Error Handling Breakdown
Type Validations
DataFrame Type Check
Confirms the input df is a pandas DataFrame. This check is critical as the function operations are tailored to DataFrame objects.
if not isinstance(df, pd.DataFrame):
raise TypeError("evaluate_dtype(): The 'df' parameter must be a pandas DataFrame.")
List and String Type Checks for col_names
Validates that col_names is a list and its elements are strings, ensuring the columns can be accurately referenced within the DataFrame.
if not isinstance(col_names, list):
raise TypeError("evaluate_dtype(): The 'col_names' parameter must be a list.")
elif not all(isinstance(col, str) for col in col_names):
raise TypeError("evaluate_dtype(): All elements in the 'col_names' list must be strings representing column names.")
Type Checks for Other Parameters
Ensures that max_unique_values_ratio, min_unique_values, string_length_threshold, small_dataset_threshold, and output are of the appropriate types for their intended uses.
if not isinstance(max_unique_values_ratio, (float, int)):
raise TypeError("evaluate_dtype(): The 'max_unique_values_ratio' must be a float or integer.")
# Similar checks for other parameters
Value Validations
DataFrame Emptiness Check
Ensures the DataFrame is not empty, which is necessary for any meaningful data type evaluation.
if df.empty:
raise ValueError("evaluate_dtype(): The input DataFrame is empty.")
Validation of Numerical Ranges and Thresholds
Checks that parameters like max_unique_values_ratio, min_unique_values, etc., are within their logical bounds to prevent runtime errors during calculations.
if max_unique_values_ratio < 0 or max_unique_values_ratio > 1:
raise ValueError("evaluate_dtype(): The 'max_unique_values_ratio' must be between 0 and 1.")
# Similar checks for other numerical parameters
Column Presence Validation
Verifies that each column listed in col_names exists in the DataFrame, preventing errors when accessing non-existent DataFrame columns.
missing_cols = [col for col in col_names if col not in df.columns]
if missing_cols:
raise ValueError(f"evaluate_dtype(): The following columns were not found in the DataFrame: {', '.join(missing_cols)}")
Output Type Check
Ensures the output parameter matches one of the allowed options, guiding the structure of the function's return value.
valid_outputs = ['dict', 'list_n', 'list_c', 'list_d', 'list_t']
if output not in valid_outputs:
raise ValueError(f"evaluate_dtype(): Invalid output '{output}'. Valid options are: {', '.join(valid_outputs)}")
Implementation Summary
The
evaluate_dtype()
function is designed to perform advanced data type evaluations on DataFrame columns. It extends beyond simple type checks by considering unique value distributions and other factors that might affect whether a column should be treated as categorical, numerical, text, or datetime. This detailed error handling ensures that the function robustly handles a variety of input scenarios and guides users in providing correct inputs.Detailed Error Handling Breakdown
Type Validations
df
is a pandas DataFrame. This check is critical as the function operations are tailored to DataFrame objects.col_names
col_names
is a list and its elements are strings, ensuring the columns can be accurately referenced within the DataFrame.max_unique_values_ratio
,min_unique_values
,string_length_threshold
,small_dataset_threshold
, andoutput
are of the appropriate types for their intended uses.Value Validations
max_unique_values_ratio
,min_unique_values
, etc., are within their logical bounds to prevent runtime errors during calculations.col_names
exists in the DataFrame, preventing errors when accessing non-existent DataFrame columns.output
parameter matches one of the allowed options, guiding the structure of the function's return value.Link to Full Code