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samples/python/example_csv.py

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+import csv
+import pprint
+import collections
+from typing import List, Iterable, Optional, Union, Dict
+
+from presidio_analyzer import AnalyzerEngine, BatchAnalyzerEngine, RecognizerResult, DictAnalyzerResult
+from presidio_anonymizer import AnonymizerEngine
+from presidio_anonymizer.entities import EngineResult
+
+"""
+Example implementing a CSV analyzer
+
+This example shows how to use the Presidio Analyzer and Anonymizer
+to detect and anonymize PII in a CSV file.
+It uses the BatchAnalyzerEngine to analyze the CSV file, and the
+BatchAnonymizerEngine to anonymize the requested columns.
+Note that currently BatchAnonymizerEngine is not part of the anonymizer package,
+and is defined in this and the batch_processing notebook.
+https://github.com/microsoft/presidio/blob/main/docs/samples/python/batch_processing.ipynb
+
+Content of csv file:
+id,name,city,comments
+1,John,New York,called him yesterday to confirm he requested to call back in 2 days
+2,Jill,Los Angeles,accepted the offer license number AC432223
+3,Jack,Chicago,need to call him at phone number 212-555-5555
+
+"""
+
+
+class CSVAnalyzer(BatchAnalyzerEngine):
+
+    def analyze_csv(
+        self,
+        csv_full_path: str,
+        language: str,
+        keys_to_skip: Optional[List[str]] = None,
+        **kwargs,
+    ) -> Iterable[DictAnalyzerResult]:
+
+        with open(csv_full_path, 'r') as csv_file:
+            csv_list = list(csv.reader(csv_file))
+            csv_dict = {header: list(map(str, values)) for header, *values in zip(*csv_list)}
+            analyzer_results = self.analyze_dict(csv_dict, language, keys_to_skip)
+            return list(analyzer_results)
+
+
+class BatchAnonymizerEngine(AnonymizerEngine):
+    """
+    Class inheriting from the AnonymizerEngine and adding additional functionality 
+    for anonymizing lists or dictionaries.
+    """
+
+    def anonymize_list(
+        self,
+        texts:List[Union[str, bool, int, float]], 
+        recognizer_results_list: List[List[RecognizerResult]], 
+        **kwargs
+    ) -> List[EngineResult]:
+        """
+        Anonymize a list of strings.
+
+        :param texts: List containing the texts to be anonymized (original texts)
+        :param recognizer_results_list: A list of lists of RecognizerResult,
+        the output of the AnalyzerEngine on each text in the list.
+        :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method
+        """
+        return_list = []
+        if not recognizer_results_list:
+            recognizer_results_list = [[] for _ in range(len(texts))]
+        for text, recognizer_results in zip(texts, recognizer_results_list):
+            if type(text) in (str, bool, int, float):
+                res = self.anonymize(text=str(text), analyzer_results=recognizer_results, **kwargs)
+                return_list.append(res.text)
+            else:
+                return_list.append(text)
+
+        return return_list
+
+    def anonymize_dict(self, analyzer_results: Iterable[DictAnalyzerResult], **kwargs) -> Dict[str, str]:
+
+        """
+        Anonymize values in a dictionary.
+
+        :param analyzer_results: Iterator of `DictAnalyzerResult` 
+        containing the output of the AnalyzerEngine.analyze_dict on the input text.
+        :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method
+        """
+
+        return_dict = {}
+        for result in analyzer_results:
+
+            if isinstance(result.value, dict):
+                resp = self.anonymize_dict(analyzer_results = result.recognizer_results, **kwargs)
+                return_dict[result.key] = resp
+
+            elif isinstance(result.value, str):
+                resp = self.anonymize(text=result.value, analyzer_results=result.recognizer_results, **kwargs)
+                return_dict[result.key] = resp.text
+
+            elif isinstance(result.value, collections.abc.Iterable):
+                anonymize_respones = self.anonymize_list(texts=result.value,
+                                                         recognizer_results_list=result.recognizer_results, 
+                                                         **kwargs)
+                return_dict[result.key] = anonymize_respones 
+            else:
+                return_dict[result.key] = result.value
+        return return_dict
+
+
+if __name__ == "__main__":
+
+    analyzer = CSVAnalyzer()
+    analyzer_results = analyzer.analyze_csv('./csv_sample_data/sample_data.csv',
+                                            language="en")
+    pprint.pprint(analyzer_results)
+
+    anonymizer = BatchAnonymizerEngine()
+    anonymized_results = anonymizer.anonymize_dict(analyzer_results)
+    pprint.pprint(anonymized_results)