jiboia-gpu 2.0.0

jiboia gpu is a python package for automatically normalizing DataFrames and optimizing types efficiently.

jiboia-gpu

jiboia-gpu is a Python library designed to normalize data and optimize data types of a DataFrame efficiently using NVIDIA GPUs through the RAPIDS ecosystem.

Requirements

• NVIDIA GPU with CUDA support (CUDA 12+ recommended)
• Compatible cuDF version (>=25.8)
• Python >= 3.9

By Lucas Muffato 🇧🇷

Key Features

String Normalization

• Trims leading and trailing spaces.
• Removes extra whitespace.
• Detects data inconsistencies (e.g., numeric columns containing strings).

Numeric Conversion**

• Converts numeric strings and floats ending with .0 into integers (int8, int16, int32, …).
• Optimizes numeric types for minimal memory usage.
• Recognizes many numeric formats, including: "0.1", "10", ".0452", "1000.00", "0,1", ",50", "1000.000,00", "1,1", "10000", "5e6", "2.5e-2", "0e1", ".3e-2"
• Categories of numeric recognition:
  • Standard integers and floats: "10", "0.1", "10000", "1000.00"
  • Floats without leading zero: ".0452", ".3e-2"
  • Scientific notation: "5e6", "2.5e-2", "0e1"
  • European-style decimals: "0,1", ",50", "1000.000,00"
  • False floats converted to integers: "1000.00", "0.0", "1.0"

Date/Time Conversion

• Parses strings representing dates in multiple formats: yyyy?mm?dd, dd?mm?yyyy, yyyymmdd, dd?mm?yy → datetime.
• Converts time strings like hhmm UTC, hh:mm:ss, hh:mm:ss.s → timedelta.

Null Standardization

• Converts various null representations to cudf.NA.

Automatic CSV Detection

• Detects the correct delimiter.
• Detects file encoding automatically.

Memory Optimization

• Provides memory usage information for DataFrames.
• Automatically converts columns to the most memory-efficient types.

Test coverage with pytest

• ✅ 100% in Numeric Normalization.
• ✅ 100% in String Normalization.

Requirements to Contribute

To contribute to this project, please ensure the following:

1. Typing
   All variables, attributes, and functions must be typed.

2. Automated Tests
   All contributions must include automated tests using pytest.

3. Project Standards
   All code must follow the project coding standards.

4. Imports Order
   All imports must be declared in alphabetical order.

Quick Introduction

from jiboia_gpu import jiboia_gpu as jb

# =========================
# DataFrame-related functions
# =========================
# jb.df -> DataFrame-related functions:
#   - Delete columns
#   - Normalize the entire DataFrame
#   - Show RAM and VRAM memory usage
# Examples:
jb.df.normalize(df)          # Normalize the entire DataFrame
jb.df.cudf_size_info(df)     # Show VRAM usage of the DataFrame

# =========================
# Number-related functions
# =========================
# jb.num -> Number-related functions
# Example:
jb.num.normalize(df, column_name)       # Normalize numeric columns

# =========================
# Date/Time normalization
# =========================
jb.datetime.normalize(df, column_name)  # Convert strings to datetime

# =========================
# Null normalization
# =========================
jb.null.normalize(df, column_name)      # Standardize null values to cudf.NA

# =========================
# String normalization
# =========================
jb.str.normalize(df, column_name)       # Normalize strings: trim, remove extra spaces, detect data pollution, creates the categorical type if there are 50% or more of the same values

# =========================
# Time normalization
# =========================
jb.time.normalize(df, column_name)      # Convert time strings to timedelta

# =========================
# Boolean normalization
# =========================
jb.bool.normalize(df, column_name)      # Convert columns to boolean values

# =========================
# CSV reading
# =========================
jb.csv.read_files(
    folder_path="my_folder/",
    start_part=1,
    end_part=10
)  # Read multiple CSV files automatically


---

## Example of use

```python
from jiboia-gpu import jiboia_gpu as jb

col_str = "col_str"
col_str_val = [
    "Surucuçu",
    "Cobra Cipó",
    "King  Cobra",
    "Jararacuçu",
    "NA",
    " Jiboia",
    " Coral Verdadeira ",
    "Jararaca  ",
    "Surucucu",
    "NA",
    None,
    "solid snake",
    "null",
    "unknown",
    "Sea Snake ",
]

col_number_str = "col_number_str"
col_number_str_val = [
    "1",
    "0.1",
    "NA",
    ".1",
    "0,1",
    ",50",
    "1000.000,00",
    "1,1",
    "5e6",
    "2.5e-2",
    "0e1",
    "unknown",
    None,
    "0e1",
    ".3e-2",
]

col_number_false_float_str = "col_number_false_float_str"
col_number_false_float_str_val = [
    "10",
    ".0",
    "1000.00",
    "0.0",
    "100,00",
    "1",
    None,
    "Na",
    "10000",
    "5e2",
    "[1,2,3,4]",
    "0e1",
    None,
    "10",
    "1"
]

col_bool = "col_bool"
col_bool_val = [
    'YES',
    'No',
    None,
    'invalid',
    'YES',
    'No',
    None,
    'YEs',
    'invalido',
    'yes',
    'NA',
    'on',
    'off',
    None,
    'on'
]

col_date = "col_date"
col_date_val = [
    '15.06.2018',
    '28/01/1990',
    None,
    'invalid',
    '1988 02 08',
    '20211212',
    '01-02-2024',
    '2023/12/20',
    '20234550',
    '2023-12-12',
    '8-2-86',
    '8-12-25',
    '12-12-12',
    '08-12-25',
    None
]


col_datetime = "col_datetime"
col_datetime_val = [
    "2025-01-01 01:10:10",
    "2026-02-02 02:20:20",
    "2027-03-03 03:30:30",
    "2028-04-04 04:40:40",
    "2029-05-05 05:50:50",
    "2030-06-06 06:00:00",
    "2031-07-07 07:10:10",
    "invalid",
    "2033-09-09 09:30:30",
    "2034-10-10 10:40:40",
    "2035-11-11 11:50:50",
    "2036-12-12 12:00:00",
    "2037-01-13 13:10:10",
    "2038-02-14 14:20:20",
    "2039-03-15 15:30:30", 
]

col_time = "col_time"
col_time_val = [
    "0000UTC",
    "0130UTC",
    "NA",
    "0540 UTC",
    "1200UTC",
    "00:21",
    "1545UTC",
    "invalid",
    "2359UTC",
    "null",
    "23",
    "20:23",
    "10:12:12",
    "07:32:12.1247",
    None
]

col_cat = "col_cat"
col_cat_val = [
    "constrictor snake",
    "sea snake",
    "poisonous snake",
    "constrictor snake",
    "invalid",
    "poisonous snake",
    "constrictor snake",
    "sea snake",
    "poisonous snake",
    "constrictor snake",
    None,
    "poisonous snake",
    "constrictor snake",
    "sea snake",
    "poisonous snake",
]

df: cudf.DataFrame = cudf.DataFrame({
    col_str: col_str_val,
    col_number_str: col_number_str_val,
    col_number_false_float_str: col_number_false_float_str_val,
    col_bool: col_bool_val,
    col_date: col_date_val,
    col_datetime: col_datetime_val,
    col_time: col_time_val,
    col_cat: col_cat_val
})

Done! all values null in column col_time converted to <NA>
Done! all duplicate and edge spaces have been removed in column col_cat
Done! all values null in column col_cat converted to <NA>
Done! column col_str converted to object
Done! column col_number_str converted to float64
Done! column col_number_false_float_str converted to int16
Done! column col_bool converted to bool
Done! column col_date converted to object
Done! column col_date converted to datetime64[s]
Done! column col_datetime converted to object
Done! column col_time converted to timedelta64[ns]
Done! column col_cat converted to object
Done! the column col_cat was converted to a category

print(df_normalized.dtypes)
# results:
col_str                                object
col_number_str                        float64
col_number_false_float_str              int16
col_bool                                 bool
col_date                        datetime64[s]
col_datetime                   datetime64[ns]
col_time                      timedelta64[ns]
col_cat                              category
dtype: object