diff --git a/modin/core/io/text/text_file_dispatcher.py b/modin/core/io/text/text_file_dispatcher.py
index 36039fbcea7..abb89de5ff0 100644
--- a/modin/core/io/text/text_file_dispatcher.py
+++ b/modin/core/io/text/text_file_dispatcher.py
@@ -32,7 +32,10 @@
 from modin.config import NPartitions
 from modin.core.io.file_dispatcher import FileDispatcher, OpenFile
 from modin.core.io.text.utils import CustomNewlineIterator
-from modin.core.storage_formats.pandas.utils import compute_chunksize
+from modin.core.storage_formats.pandas.utils import (
+    compute_chunksize,
+    compute_num_partitions,
+)
 from modin.utils import _inherit_docstrings
 
 ColumnNamesTypes = Tuple[Union[pandas.Index, pandas.MultiIndex]]
@@ -1112,8 +1115,7 @@ def _read(cls, filepath_or_buffer, **kwargs):
                 else:
                     avg_line_len = lines_len // i
                     appprox_nlines = cls.file_size(f) // avg_line_len
-            num_partitions = max(1, (appprox_nlines * len(column_names)) // 64_000)
-            num_partitions = min(NPartitions.get(), num_partitions)
+            num_partitions = compute_num_partitions(len(column_names), appprox_nlines)
 
             f.seek(old_pos)
 
diff --git a/modin/core/storage_formats/pandas/utils.py b/modin/core/storage_formats/pandas/utils.py
index b8f267b3150..4837edd35f1 100644
--- a/modin/core/storage_formats/pandas/utils.py
+++ b/modin/core/storage_formats/pandas/utils.py
@@ -57,6 +57,26 @@ def compute_chunksize(axis_len, num_splits, min_block_size=None):
     return max(chunksize, min_block_size)
 
 
+def compute_num_partitions(ncols, nrows):
+    """
+    Compute the number of partitions for a frame with the specified dimension.
+
+    Parameters
+    ----------
+    ncols : int
+        The number of frame columns.
+    nrows : int
+        The number of frame rows.
+
+    Returns
+    -------
+    int
+        The number of partitions.
+    """
+    num_partitions = max(1, (ncols * nrows) // 64_000)
+    return min(NPartitions.get(), num_partitions)
+
+
 def split_result_of_axis_func_pandas(axis, num_splits, result, length_list=None):
     """
     Split pandas DataFrame evenly based on the provided number of splits.