Return filter func and csv type

README.md

@@ -56,10 +56,16 @@ With the default inputs, this function outputs Pandas Dataframes after streaming
     - Defaults to False
 - initial_snapshot
     - Boolean value, defaults to False
+- return_first_result_function
+    - Custom filtering function that accepts parsed data passed as an argument and returns as either True or False
+    - Gets called when return_first_result is True and a block has applicable events/txs
+    - If function resolves to True, the polling function returns the data from the block
+    - If function resolves to False, the polling function continues iteration
 - return_type
     - Specifies the type of value to return
     - Passing "df" returns the data in a pandas DataFrame
     - Passing "dict" returns in the format {"data": [], "module_name": String, "data_block": int, error: str | None}
+    - Passing "csv" returns in the format {"data": String(CSV), "module_name": String, "data_block": int, error: str | None}
 
 The result here is the default `SubstreamOutput` object, you can access both the `data` and `snapshots` dataframes by doing:
 

substreams/substream.py

@@ -166,12 +166,13 @@ def poll(
         start_block: int,
         end_block: int,
         return_first_result: bool = False,
+        return_first_result_function: Optional[callable] = None,
         initial_snapshot: bool = False,
         return_type: str = "df"
     ):
 
         return_dict_interface = {"data": [], "module_name": output_module, "data_block": str(start_block), "error": None}
-        valid_return_types = ["dict", "df"]
+        valid_return_types = ["dict", "df", "csv"]
         results = []
         raw_results = defaultdict(lambda: {"data": list(), "snapshots": list()})
 
@@ -221,16 +222,25 @@ def poll(
                     if len(parsed) > 0:
                         parsed = [dict(item, **{'block':data["clock"]["number"]}) for item in parsed]
                         if return_first_result is True:
-                            break
+                            if callable(return_first_result_function):
+                                func_result = return_first_result_function(parsed)
+                                if func_result is True:
+                                    break
+                                else:
+                                    continue
+                            else:
+                                break
                     elif int(return_dict_interface["data_block"]) + 1 == end_block:
                         results = return_dict_interface
 
             if return_first_result is True and parsed:
-                return_dict_interface["data"] = parsed
                 if return_type == "dict":
-                    results = return_dict_interface
+                    return_dict_interface["data"] = parsed
                 if return_type == "df":
-                    results = pd.DataFrame(parsed)
+                    return_dict_interface["data"] = pd.DataFrame(parsed)
+                if return_type == "csv":
+                    return_dict_interface["data"] = pd.DataFrame(parsed).to_csv(index=False)
+                results = return_dict_interface
             if return_first_result is False and raw_results:
                 result = SubstreamOutput(module_name=output_module)
                 data_dict: dict = raw_results.get(output_module)
@@ -242,6 +252,9 @@ def poll(
                 if return_type == "dict":
                     return_dict_interface["data"] = results.to_dict()
                     results = return_dict_interface
+                if return_type == "csv":
+                    return_dict_interface["data"] = results.to_csv(index=False)
+                    results = return_dict_interface
         except Exception as err:
             error_to_pass = err
             if isinstance(err, Exception):