deepcsv 0.5.0

Automatically processes data files in directories, converts array-like strings to NumPy arrays, detects and fixes data type issues, and saves results as optimized Parquet files.

deepcsv (v0.5.0)

Ever loaded a CSV file and found your carefully structured lists turned into useless strings?

"['Action', 'Sci-Fi', 'Thriller']"  # This is a string, not a list

deepcsv fixes this automatically.

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The Solution

deepcsv handles these cases automatically:

• Reads CSV/XLSX files or existing DataFrames
• Converts any string value starting with [ into real NumPy arrays (fast and lightweight)
• Detects and fixes mixed-type columns by safely converting them to numeric (float)
• Recursively processes all CSV/XLSX files in subdirectories
• Saves results as Parquet format to preserve types and speed up analysis

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Installation

pip install deepcsv

Usage

Single file processing (process_file)

import deepcsv

df = deepcsv.process_file('path/to/file.csv')

• Accepts str (file path) or pd.DataFrame
• Returns pd.DataFrame with columns converted to arrays

Batch directory processing (process_all_files)

import deepcsv

deepcsv.process_all_files('path/to/folder')

• Processes all .csv and .xlsx files recursively
• Saves converted files as Parquet in: All CSV Files is Converted Here

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What it does

• Auto-detects files in directory and subdirectories
• Converts values like:
  • "['item1', 'item2']" → array(['item1', 'item2']) (NumPy array)
  • Mixed numeric/string columns → single numeric type (float)
• Handles NaN values without breaking
• Stores results in Parquet format for type safety and performance

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Function Signatures

• process_file(data_input: Union[str, pd.DataFrame]) -> pd.DataFrame
• process_all_files(directory_path: str) -> None

Output arrays are NumPy arrays for optimal performance in machine learning workflows.

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Key Features

• Fast NumPy array conversion instead of slow Python lists
• Mixed-type detection with automatic fixes
• Parquet storage for data integrity
• Recursive directory traversal
• Warning messages for transparency

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Notes

• Requires pyarrow for Parquet support
• Only saves files that contain converted array columns

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Requirements

• Python >= 3.7
• pandas
• pyarrow