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.

Table of Contents

• Requirements
• Key Features
• Usage
  • DataFrame Normalization
  • Numeric Normalization
  • Date and Time Normalization
  • Null Normalization
  • String Normalization
  • Time Normalization
  • Boolean Normalization
  • Automatic CSV Reading
• Full Example
• Contribution Guidelines

---

Usage

DataFrame Normalization

from jiboia_gpu import jiboia_gpu as jb

jb.df.normalize(df)          # Normalize the entire DataFrame
jb.df.cudf_size_info(df)     # Show RAM and VRAM memory usage

Numeric Normalization

jb.num.normalize(df, column_name)  # Normalize numeric columns

Date and Time Normalization

jb.dt.normalize(df, column_name)   # Convert strings to datetime
jb.time.normalize(df, column_name) # Convert strings to timedelta

Null Normalization

jb.null.normalize(df, column_name) # Convert null values to cudf.NA

String Normalization

jb.str.normalize(df, column_name)  # Remove extra spaces and create categories when appropriate

Time Normalization

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

Boolean Normalization

jb.bool.normalize(df, column_name) # Convert values to boolean

Automatic CSV Reading

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

---

Full Example

Below is a detailed example of creating a DataFrame with messy data and performing full normalization.

import jiboia_gpu as jb
import cudf

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

# ---- Creating a DataFrame with Mixed Dirty Data --- #

# Strings with multiple spaces, at the beginning and end
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 ",
]

# Numbers in string with varied shapes
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",
]

# Integers in string with varied shapes
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"
]

# Boolean data in string of varying form
col_bool = "col_bool"
col_bool_val = [
    'YES',
    'No',
    None,
    'invalid',
    'YES',
    'No',
    None,
    'YEs',
    'invalido',
    'yes',
    'NA',
    'on',
    'off',
    None,
    'on'
]

# Date data in string and in various formats
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
]

# Datetime in string
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", 
]

# Time data in string and various forms
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
]

# Strings that can be reduced into categories
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",
]

# Normalizing the dataframe
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

---

Contribution Guidelines

1. Typing is mandatory for all variables, attributes, and functions.
2. Automated tests with pytest are required for all contributions.
3. Project standards must be followed.
4. Imports must be declared in alphabetical order.