parquet sampling function developed in data_utils.py; Added sample_nr…

…ows argument in ParquetData class; Added test_len_sampled_data in test_parquet_data.py

dataprofiler/data_readers/data_utils.py

@@ -1,5 +1,6 @@
 """Contains functions for data readers."""
 import json
+import random
 import re
 import urllib
 from collections import OrderedDict
@@ -20,6 +21,7 @@
 )
 
 import dateutil
+import numpy as np
 import pandas as pd
 import pyarrow.parquet as pq
 import requests
@@ -435,52 +437,114 @@ def read_csv_df(
     return data
 
 
-def read_parquet_df(
+def sample_parquet(
     file_path: str,
+    sample_nrows: int,
     selected_columns: Optional[List[str]] = None,
     read_in_string: bool = False,
 ) -> Tuple[pd.DataFrame, pd.Series]:
     """
-    Return an iterator that returns one row group each time.
+    Read parquet file, sample n row from it and return a dataframe.
 
     :param file_path: path to the Parquet file.
     :type file_path: str
+    :param sample_nrows: number of rows being sampled
+    :type sample_nrows: int
+    :param selected_columns: columns need to be read
+    :type selected_columns: list
+    :param read_in_string: return as string type
+    :type read_in_string: bool
     :return:
-    :rtype: Iterator(pd.DataFrame)
+    :rtype:
     """
-    parquet_file = pq.ParquetFile(file_path)
-    data = pd.DataFrame()
-    for i in range(parquet_file.num_row_groups):
+    # read parquet file into table
+    table = pq.read_table(file_path, columns=selected_columns)
+
+    # sample
+    n_rows = table.num_rows
+    if n_rows > sample_nrows:
+        select = np.array([False] * n_rows)
+        select[random.sample(range(n_rows), sample_nrows)] = True
+    else:
+        select = np.array([True] * n_rows)
+    out = table.filter(select).to_pandas()
 
-        data_row_df = parquet_file.read_row_group(i).to_pandas()
+    # Convert all the unicode columns to utf-8
+    types = out.apply(lambda x: pd.api.types.infer_dtype(x.values, skipna=True))
 
-        # Convert all the unicode columns to utf-8
-        types = data_row_df.apply(
-            lambda x: pd.api.types.infer_dtype(x.values, skipna=True)
-        )
+    mixed_and_unicode_cols = types[types == "unicode"].index.union(
+        types[types == "mixed"].index
+    )
 
-        mixed_and_unicode_cols = types[types == "unicode"].index.union(
-            types[types == "mixed"].index
+    for col in mixed_and_unicode_cols:
+        out[col] = out[col].apply(
+            lambda x: x.encode("utf-8").strip() if isinstance(x, str) else x
         )
+        out[col] = out[col].apply(
+            lambda x: x.decode("utf-8").strip() if isinstance(x, bytes) else x
+        )
+    original_df_dtypes = out.dtypes
+    if read_in_string:
+        out = out.astype(str)
+
+    return out, original_df_dtypes
 
-        for col in mixed_and_unicode_cols:
-            data_row_df[col] = data_row_df[col].apply(
-                lambda x: x.encode("utf-8").strip() if isinstance(x, str) else x
+
+def read_parquet_df(
+    file_path: str,
+    sample_nrows: Optional[int] = None,
+    selected_columns: Optional[List[str]] = None,
+    read_in_string: bool = False,
+) -> Tuple[pd.DataFrame, pd.Series]:
+    """
+    Return an iterator that returns one row group each time.
+
+    :param file_path: path to the Parquet file.
+    :type file_path: str
+    :return:
+    :rtype: Iterator(pd.DataFrame)
+    """
+    if sample_nrows is None:
+        parquet_file = pq.ParquetFile(file_path)
+        data = pd.DataFrame()
+        for i in range(parquet_file.num_row_groups):
+
+            data_row_df = parquet_file.read_row_group(i).to_pandas()
+
+            # Convert all the unicode columns to utf-8
+            types = data_row_df.apply(
+                lambda x: pd.api.types.infer_dtype(x.values, skipna=True)
             )
-            data_row_df[col] = data_row_df[col].apply(
-                lambda x: x.decode("utf-8").strip() if isinstance(x, bytes) else x
+
+            mixed_and_unicode_cols = types[types == "unicode"].index.union(
+                types[types == "mixed"].index
             )
 
-        if selected_columns:
-            data_row_df = data_row_df[selected_columns]
+            for col in mixed_and_unicode_cols:
+                data_row_df[col] = data_row_df[col].apply(
+                    lambda x: x.encode("utf-8").strip() if isinstance(x, str) else x
+                )
+                data_row_df[col] = data_row_df[col].apply(
+                    lambda x: x.decode("utf-8").strip() if isinstance(x, bytes) else x
+                )
 
-        data = pd.concat([data, data_row_df])
+            if selected_columns:
+                data_row_df = data_row_df[selected_columns]
 
-    original_df_dtypes = data.dtypes
-    if read_in_string:
-        data = data.astype(str)
+            data = pd.concat([data, data_row_df])
 
-    return data, original_df_dtypes
+        original_df_dtypes = data.dtypes
+        if read_in_string:
+            data = data.astype(str)
+        return data, original_df_dtypes
+    else:
+        data, original_df_dtypes = sample_parquet(
+            file_path,
+            sample_nrows,
+            selected_columns=selected_columns,
+            read_in_string=read_in_string,
+        )
+        return data, original_df_dtypes
 
 
 def read_text_as_list_of_strs(

dataprofiler/data_readers/parquet_data.py

@@ -61,6 +61,7 @@ def __init__(
         self._data_formats["json"] = self._get_data_as_json
         self._selected_data_format: str = options.get("data_format", "dataframe")
         self._selected_columns: List[str] = options.get("selected_columns", list())
+        self._sample_nrows: Optional[int] = options.get("sample_nrows", None)
 
         if data is not None:
             self._load_data(data)
@@ -83,6 +84,11 @@ def selected_columns(self) -> List[str]:
         """Return selected columns."""
         return self._selected_columns
 
+    @property
+    def sample_nrows(self) -> Optional[int]:
+        """Return sample_nrows."""
+        return self._sample_nrows
+
     @property
     def is_structured(self) -> bool:
         """Determine compatibility with StructuredProfiler."""
@@ -100,7 +106,10 @@ def _load_data_from_str(self, data_as_str: str) -> pd.DataFrame:
     def _load_data_from_file(self, input_file_path: str) -> pd.DataFrame:
         """Return data from file."""
         data, original_df_dtypes = data_utils.read_parquet_df(
-            input_file_path, self.selected_columns, read_in_string=True
+            input_file_path,
+            selected_columns=self.selected_columns,
+            read_in_string=True,
+            sample_nrows=self.sample_nrows,
         )
         self._original_df_dtypes = original_df_dtypes
         return data

dataprofiler/tests/data_readers/test_parquet_data.py

@@ -181,6 +181,21 @@ def test_len_data(self):
             self.assertEqual(input_file["count"], len(data), msg=input_file["path"])
             self.assertEqual(input_file["count"], data.length, msg=input_file["path"])
 
+    def test_len_sampled_data(self):
+        """
+        Validate that length called on ParquetData with sample_nrows option is
+        appropriately determining the length value.
+        """
+
+        for input_file in self.file_or_buf_list:
+            data = Data(input_file["path"], options={"sample_nrows": 100})
+            self.assertEqual(
+                min(100, input_file["count"]), len(data), msg=input_file["path"]
+            )
+            self.assertEqual(
+                min(100, input_file["count"]), data.length, msg=input_file["path"]
+            )
+
     def test_file_encoding(self):
         """Tests to ensure file_encoding set to None"""
         for input_file in self.file_or_buf_list: