deepcsv 0.6.0

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

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 list value "[" into real NumPy arrays (fast and lightweight)
• Detects and fixes mixed-type columns by safely converting them to numeric
• Recursively processes all CSV/XLSX files in each directory
• 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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Utilities

read_any(file_path)

Reads any supported file and returns a pandas DataFrame. No need to manually pick the reader.

from deepcsv import read_any

df = read_any('data/users.csv')
df = read_any('reports/sales.xlsx')
df = read_any('warehouse/orders.parquet')

Supported formats: .csv, .txt, .tsv, .xls, .xlsx, .json, .parquet, .pkl, .feather, .db, .sqlite

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clean_values(data_input, ...)

Cleans a DataFrame by removing nulls from specific columns or rows, or dropping rows by index.

from deepcsv import clean_values

# Drop fully-null columns from specific cols
df = clean_values('data.csv', cols=['age', 'salary'])

# Drop rows that have nulls in specific cols
df = clean_values('data.csv', cols=['age', 'salary'], ax_0=True)

# Drop rows by index
df = clean_values(df, index=[0, 5, 12])

# Apply on all columns except some
df = clean_values('data.csv', all_cols_except=['id', 'name'])

Parameters:

Parameter	Type	Default	Description
data_input	str | DataFrame	required	File path or DataFrame
cols	list	None	Columns to apply on
ax_0	bool	False	If True: drop rows with nulls. If False: drop fully-null cols
index	list	None	Row indexes to drop
all_cols_except	list	None	Apply on all columns except these

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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
• read_any(file_path: str) -> pd.DataFrame
• clean_values(data_input, cols=None, ax_0=False, index=None, all_cols_except=None) -> pd.DataFrame

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
• Built-in file reader supporting 10+ formats (read_any)
• Flexible null/index cleaning (clean_values)

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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

Changelog

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[0.6.1]

Added

• Added utils.py easy understand for devs

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