# SPDX-FileCopyrightText: 2022-present deepset GmbH # # SPDX-License-Identifier: Apache-2.0 from copy import deepcopy from io import StringIO from typing import Optional 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. Additionally, it provides options to keep document IDs and control whether empty rows and columns should be removed. """ def __init__( self, *, ignore_rows: int = 0, ignore_columns: int = 0, remove_empty_rows: bool = True, remove_empty_columns: bool = True, keep_id: bool = False, ) -> 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. :param remove_empty_rows: Whether to remove rows that are entirely empty. :param remove_empty_columns: Whether to remove columns that are entirely empty. :param keep_id: Whether to retain the original document ID in the output document. 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 self.remove_empty_rows = remove_empty_rows self.remove_empty_columns = remove_empty_columns self.keep_id = keep_id 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. :return: A dictionary with a list of cleaned Documents under the key "documents". 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 (if enabled by `remove_empty_rows` and `remove_empty_columns`). 4. Reattaches the ignored rows and columns to maintain their original positions. 5. Returns the cleaned CSV content as a new `Document` object, with an option to retain the original document ID. """ if len(documents) == 0: return {"documents": []} 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) except Exception as e: logger.exception( "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 final_df = self._clean_df(df=df, ignore_rows=ignore_rows, ignore_columns=ignore_columns) clean_doc = Document( id=document.id if self.keep_id else "", content=final_df.to_csv(index=False, header=False, lineterminator="\n"), blob=document.blob, meta=deepcopy(document.meta), score=document.score, embedding=document.embedding, sparse_embedding=document.sparse_embedding, ) cleaned_documents.append(clean_doc) return {"documents": cleaned_documents} def _clean_df(self, df: "pd.DataFrame", ignore_rows: int, ignore_columns: int) -> "pd.DataFrame": """ Cleans a DataFrame by removing empty rows and columns while preserving ignored sections. :param df: The input DataFrame representing the CSV data. :param ignore_rows: Number of top rows to ignore. :param ignore_columns: Number of left columns to ignore. """ # Get ignored rows and columns ignored_rows = self._get_ignored_rows(df=df, ignore_rows=ignore_rows) ignored_columns = self._get_ignored_columns(df=df, ignore_columns=ignore_columns) final_df = df.iloc[ignore_rows:, ignore_columns:] # Drop rows that are entirely empty if self.remove_empty_rows: final_df = final_df.dropna(axis=0, how="all") # Drop columns that are entirely empty if self.remove_empty_columns: final_df = final_df.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) return final_df @staticmethod def _get_ignored_rows(df: "pd.DataFrame", ignore_rows: int) -> Optional["pd.DataFrame"]: """ Extracts the rows to be ignored from the DataFrame. :param df: The input DataFrame. :param ignore_rows: Number of rows to extract from the top. """ if ignore_rows > 0: return df.iloc[:ignore_rows, :] return None @staticmethod def _get_ignored_columns(df: "pd.DataFrame", ignore_columns: int) -> Optional["pd.DataFrame"]: """ Extracts the columns to be ignored from the DataFrame. :param df: The input DataFrame. :param ignore_columns: Number of columns to extract from the left. """ if ignore_columns > 0: return df.iloc[:, :ignore_columns] return None