less memory usage when writing a dataframe.

Instead of converting the whole DataFrame into a list of dicts
(which can double the memory usage), uses a generator to iterate
on the rows of the DataFrame converting each row one after the other.

pandavro/__init__.py

@@ -1,6 +1,6 @@
 from collections import OrderedDict
 from pathlib import Path
-from typing import Optional, Iterable
+from typing import Any, Dict, Generator, Iterable, Optional
 
 import fastavro
 import numpy as np
@@ -276,18 +276,24 @@ def from_avro(file_path_or_buffer, schema=None, na_dtypes=False, **kwargs):
     return read_avro(file_path_or_buffer, schema, na_dtypes=na_dtypes, **kwargs)
 
 
-def __preprocess_dicts(l):
-    "Preprocess list of dicts inplace for fastavro compatibility"
-    for d in l:
-        for k, v in d.items():
+def __to_fastavro_records(df: pd.DataFrame) -> Generator[Dict[str, Any], None, None]:
+    "Converts a DataFrame to a fastavro record compatible iterable."
+    def preprocess_dict(record: Dict[str, Any]) -> Dict[str, Any]:
+        "Preprocess a dict inplace for fastavro record compatibility."
+        for k, v in record.items():
             # Replace pd.NA with None so fastavro can write it
             if v is pd.NA:
-                d[k] = None
+                record[k] = None
             # Convert some Pandas dtypes to normal Python dtypes
-            for key, value in PANDAS_TO_PYTHON_TYPES.items():
-                if isinstance(v, key):
-                    d[k] = value(v)
-    return l
+            for pandas_type, converter in PANDAS_TO_PYTHON_TYPES.items():
+                if isinstance(v, pandas_type):
+                    record[k] = converter(v)
+	    return record
+
+    record: Dict[str, Any] = dict()
+    for idx, data in df.iterrows():
+        yield preprocess_dict(data.to_dict(into=record))
+        record.clear()
 
 
 def to_avro(file_path_or_buffer, df, schema=None, append=False,
@@ -305,18 +311,16 @@ def to_avro(file_path_or_buffer, df, schema=None, append=False,
         times_as_micros: If True (default), save datetimes with microseconds resolution. If False, save with millisecond
             resolution instead.
         kwargs: Keyword arguments to fastavro.writer
-
     """
     if schema is None:
         schema = schema_infer(df, times_as_micros)
 
     open_mode = 'wb' if not append else 'a+b'
 
-    records = __preprocess_dicts(df.to_dict('records'))
-
     if isinstance(file_path_or_buffer, Path):
         file_path_or_buffer = str(file_path_or_buffer)
 
+    records = __to_fastavro_records(df)
     if isinstance(file_path_or_buffer, str):
         with open(file_path_or_buffer, open_mode) as f:
             fastavro.writer(f, schema=schema,