TYP: writers.pyi

pandas/_libs/writers.pyi

@@ -0,0 +1,24 @@
+import numpy as np
+
+# TODO: can make this more specific
+def write_csv_rows(
+    data: list,
+    data_index: np.ndarray,
+    nlevels: int,
+    cols: np.ndarray,
+    writer: object,  # _csv.writer
+) -> None: ...
+
+def convert_json_to_lines(arr: str) -> str: ...
+
+def max_len_string_array(
+    arr: np.ndarray,  # pandas_string[:]
+) -> int: ...
+
+def word_len(val: object) -> int: ...
+
+def string_array_replace_from_nan_rep(
+    arr: np.ndarray,  #  np.ndarray[object, ndim=1]
+    nan_rep: object,
+    replace: object = ...,
+) -> None: ...

pandas/_libs/writers.pyx

@@ -23,7 +23,7 @@ def write_csv_rows(
     Py_ssize_t nlevels,
     ndarray cols,
     object writer
-):
+) -> None:
     """
     Write the given data to the writer object, pre-allocating where possible
     for performance improvements.
@@ -162,7 +162,7 @@ def string_array_replace_from_nan_rep(
     ndarray[object, ndim=1] arr,
     object nan_rep,
     object replace=np.nan
-):
+) -> None:
     """
     Replace the values in the array with 'replacement' if
     they are 'nan_rep'. Return the same array.
@@ -173,5 +173,3 @@ def string_array_replace_from_nan_rep(
     for i in range(length):
         if arr[i] == nan_rep:
             arr[i] = replace
-
-    return arr

pandas/io/formats/csvs.py

@@ -308,4 +308,12 @@ def _save_chunk(self, start_i: int, end_i: int) -> None:
         data = [res.iget_values(i) for i in range(len(res.items))]
 
         ix = self.data_index[slicer]._format_native_types(**self._number_format)
-        libwriters.write_csv_rows(data, ix, self.nlevels, self.cols, self.writer)
+        # error: Argument 4 to "write_csv_rows" has incompatible type
+        # "Sequence[Hashable]"; expected "ndarray"
+        libwriters.write_csv_rows(
+            data,
+            ix,
+            self.nlevels,
+            self.cols,  # type: ignore[arg-type]
+            self.writer,
+        )

pandas/io/pytables.py

@@ -5066,7 +5066,7 @@ def _unconvert_string_array(
     if nan_rep is None:
         nan_rep = "nan"
 
-    data = libwriters.string_array_replace_from_nan_rep(data, nan_rep)
+    libwriters.string_array_replace_from_nan_rep(data, nan_rep)
     return data.reshape(shape)