feat: Add component CSVDocumentCleaner for removing empty rows and columns (#8816)

* Initial commit for csv cleaner

* Add release notes

* Update lineterminator

* Update releasenotes/notes/csv-document-cleaner-8eca67e884684c56.yaml

Co-authored-by: David S. Batista <dsbatista@gmail.com>

* alphabetize

* Use lazy import

* Some refactoring

* Some refactoring

---------

Co-authored-by: David S. Batista <dsbatista@gmail.com>

docs/pydoc/config/preprocessors_api.yml

@@ -1,7 +1,7 @@
 loaders:
   - type: haystack_pydoc_tools.loaders.CustomPythonLoader
     search_path: [../../../haystack/components/preprocessors]
-    modules: ["document_cleaner", "document_splitter", "recursive_splitter", "text_cleaner"]
+    modules: ["csv_document_cleaner", "document_cleaner", "document_splitter", "recursive_splitter", "text_cleaner"]
     ignore_when_discovered: ["__init__"]
 processors:
   - type: filter

haystack/components/preprocessors/__init__.py

@@ -2,9 +2,10 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+from .csv_document_cleaner import CSVDocumentCleaner
 from .document_cleaner import DocumentCleaner
 from .document_splitter import DocumentSplitter
 from .recursive_splitter import RecursiveDocumentSplitter
 from .text_cleaner import TextCleaner
 
-__all__ = ["DocumentSplitter", "DocumentCleaner", "RecursiveDocumentSplitter", "TextCleaner"]
+__all__ = ["CSVDocumentCleaner", "DocumentCleaner", "DocumentSplitter", "RecursiveDocumentSplitter", "TextCleaner"]

haystack/components/preprocessors/csv_document_cleaner.py

@@ -0,0 +1,116 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from io import StringIO
+from typing import Dict, List
+
+from haystack import Document, component, logging
+from haystack.lazy_imports import LazyImport
+
+with LazyImport("Run 'pip install pandas'") as pandas_import:
+    import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+
+@component
+class CSVDocumentCleaner:
+    """
+    A component for cleaning CSV documents by removing empty rows and columns.
+
+    This component processes CSV content stored in Documents, allowing
+    for the optional ignoring of a specified number of rows and columns before performing
+    the cleaning operation.
+    """
+
+    def __init__(self, ignore_rows: int = 0, ignore_columns: int = 0) -> None:
+        """
+        Initializes the CSVDocumentCleaner component.
+
+        :param ignore_rows: Number of rows to ignore from the top of the CSV table before processing.
+        :param ignore_columns: Number of columns to ignore from the left of the CSV table before processing.
+
+        Rows and columns ignored using these parameters are preserved in the final output, meaning
+        they are not considered when removing empty rows and columns.
+        """
+        self.ignore_rows = ignore_rows
+        self.ignore_columns = ignore_columns
+        pandas_import.check()
+
+    @component.output_types(documents=List[Document])
+    def run(self, documents: List[Document]) -> Dict[str, List[Document]]:
+        """
+        Cleans CSV documents by removing empty rows and columns while preserving specified ignored rows and columns.
+
+        :param documents: List of Documents containing CSV-formatted content.
+
+        Processing steps:
+        1. Reads each document's content as a CSV table.
+        2. Retains the specified number of `ignore_rows` from the top and `ignore_columns` from the left.
+        3. Drops any rows and columns that are entirely empty (all NaN values).
+        4. Reattaches the ignored rows and columns to maintain their original positions.
+        5. Returns the cleaned CSV content as a new `Document` object.
+        """
+        ignore_rows = self.ignore_rows
+        ignore_columns = self.ignore_columns
+
+        cleaned_documents = []
+        for document in documents:
+            try:
+                df = pd.read_csv(StringIO(document.content), header=None, dtype=object)  # type: ignore
+            except Exception as e:
+                logger.error(
+                    "Error processing document {id}. Keeping it, but skipping cleaning. Error: {error}",
+                    id=document.id,
+                    error=e,
+                )
+                cleaned_documents.append(document)
+                continue
+
+            if ignore_rows > df.shape[0] or ignore_columns > df.shape[1]:
+                logger.warning(
+                    "Document {id} has fewer rows {df_rows} or columns {df_cols} "
+                    "than the number of rows {rows} or columns {cols} to ignore. "
+                    "Keeping the entire document.",
+                    id=document.id,
+                    df_rows=df.shape[0],
+                    df_cols=df.shape[1],
+                    rows=ignore_rows,
+                    cols=ignore_columns,
+                )
+                cleaned_documents.append(document)
+                continue
+
+            # Save ignored rows
+            ignored_rows = None
+            if ignore_rows > 0:
+                ignored_rows = df.iloc[:ignore_rows, :]
+
+            # Save ignored columns
+            ignored_columns = None
+            if ignore_columns > 0:
+                ignored_columns = df.iloc[:, :ignore_columns]
+
+            # Drop rows and columns that are entirely empty
+            remaining_df = df.iloc[ignore_rows:, ignore_columns:]
+            final_df = remaining_df.dropna(axis=0, how="all").dropna(axis=1, how="all")
+
+            # Reattach ignored rows
+            if ignore_rows > 0 and ignored_rows is not None:
+                # Keep only relevant columns
+                ignored_rows = ignored_rows.loc[:, final_df.columns]
+                final_df = pd.concat([ignored_rows, final_df], axis=0)
+
+            # Reattach ignored columns
+            if ignore_columns > 0 and ignored_columns is not None:
+                # Keep only relevant rows
+                ignored_columns = ignored_columns.loc[final_df.index, :]
+                final_df = pd.concat([ignored_columns, final_df], axis=1)
+
+            cleaned_documents.append(
+                Document(
+                    content=final_df.to_csv(index=False, header=False, lineterminator="\n"), meta=document.meta.copy()
+                )
+            )
+        return {"documents": cleaned_documents}

releasenotes/notes/csv-document-cleaner-8eca67e884684c56.yaml

@@ -0,0 +1,6 @@
+---
+features:
+  - |
+    Introduced `CSVDocumentCleaner` component for cleaning CSV documents.
+    - Removes empty rows and columns, while preserving specified ignored rows and columns.
+    - Customizable number of rows and columns to ignore during processing.

test/components/preprocessors/test_csv_document_cleaner.py

@@ -0,0 +1,146 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from haystack import Document
+
+from haystack.components.preprocessors.csv_document_cleaner import CSVDocumentCleaner
+
+
+def test_empty_column() -> None:
+    csv_content = """,A,B,C
+,1,2,3
+,4,5,6
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner()
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == "A,B,C\n1,2,3\n4,5,6\n"
+
+
+def test_empty_row() -> None:
+    csv_content = """A,B,C
+1,2,3
+,,
+4,5,6
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner()
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == "A,B,C\n1,2,3\n4,5,6\n"
+
+
+def test_empty_column_and_row() -> None:
+    csv_content = """,A,B,C
+,1,2,3
+,,,
+,4,5,6
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner()
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == "A,B,C\n1,2,3\n4,5,6\n"
+
+
+def test_ignore_rows() -> None:
+    csv_content = """,,
+A,B,C
+4,5,6
+7,8,9
+"""
+    csv_document = Document(content=csv_content, meta={"name": "test.csv"})
+    csv_document_cleaner = CSVDocumentCleaner(ignore_rows=1)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == ",,\nA,B,C\n4,5,6\n7,8,9\n"
+    assert cleaned_document.meta == {"name": "test.csv"}
+
+
+def test_ignore_rows_2() -> None:
+    csv_content = """A,B,C
+,,
+4,5,6
+7,8,9
+"""
+    csv_document = Document(content=csv_content, meta={"name": "test.csv"})
+    csv_document_cleaner = CSVDocumentCleaner(ignore_rows=1)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == "A,B,C\n4,5,6\n7,8,9\n"
+    assert cleaned_document.meta == {"name": "test.csv"}
+
+
+def test_ignore_rows_3() -> None:
+    csv_content = """A,B,C
+4,,6
+7,,9
+"""
+    csv_document = Document(content=csv_content, meta={"name": "test.csv"})
+    csv_document_cleaner = CSVDocumentCleaner(ignore_rows=1)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == "A,C\n4,6\n7,9\n"
+    assert cleaned_document.meta == {"name": "test.csv"}
+
+
+def test_ignore_columns() -> None:
+    csv_content = """,,A,B
+,2,3,4
+,7,8,9
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner(ignore_columns=1)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == ",,A,B\n,2,3,4\n,7,8,9\n"
+
+
+def test_too_many_ignore_rows() -> None:
+    csv_content = """,,
+A,B,C
+4,5,6
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner(ignore_rows=4)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == ",,\nA,B,C\n4,5,6\n"
+
+
+def test_too_many_ignore_columns() -> None:
+    csv_content = """,,
+A,B,C
+4,5,6
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner(ignore_columns=4)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == ",,\nA,B,C\n4,5,6\n"
+
+
+def test_ignore_rows_and_columns() -> None:
+    csv_content = """,A,B,C
+1,item,s,
+2,item2,fd,
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner(ignore_columns=1, ignore_rows=1)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == ",A,B\n1,item,s\n2,item2,fd\n"
+
+
+def test_zero_ignore_rows_and_columns() -> None:
+    csv_content = """,A,B,C
+1,item,s,
+2,item2,fd,
+"""
+    csv_document = Document(content=csv_content)
+    csv_document_cleaner = CSVDocumentCleaner(ignore_columns=0, ignore_rows=0)
+    result = csv_document_cleaner.run([csv_document])
+    cleaned_document = result["documents"][0]
+    assert cleaned_document.content == ",A,B,C\n1,item,s,\n2,item2,fd,\n"