nullsweep 0.3.0

A comprehensive Python package for managing and analyzing missing data in pandas DataFrames, starting with detection and expanding to complete handling.

NullSweep

NullSweep is a Python library designed for detecting and handling patterns of missing data in pandas DataFrames. This tool provides a simple API to identify global missing data patterns across the entire dataset, patterns related to specific features within the dataset, and to impute missing values using various strategies.

Features

• Detect global patterns of missing data in a DataFrame.
• Detect missing data patterns in specific features/columns of a DataFrame.
• Impute missing data in specific features or across the entire DataFrame using a variety of strategies.
• Utilizes a modular approach with different pattern detection and imputation strategies.

Installation

Install NullSweep using pip:

pip install nullsweep

Usage

Detect Global Patterns

To detect global missing data patterns in a pandas DataFrame:

import pandas as pd
from nullsweep import detect_global_pattern

# Sample DataFrame
data = {'A': [1, 2, None], 'B': [None, 2, 3]}
df = pd.DataFrame(data)

# Detect global missing data pattern
pattern, details = detect_global_pattern(df)
print("Detected Pattern:", pattern)
print("Details:", details)

Detect Feature-Specific Patterns

To detect missing data patterns in a specific feature of a pandas DataFrame:

import pandas as pd
from nullsweep import detect_feature_pattern

# Sample DataFrame
data = {'A': [1, None, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

# Detect feature-specific missing data pattern
feature_name = 'A'
pattern, details = detect_feature_pattern(df, feature_name)
print("Detected Pattern:", pattern)
print("Details:", details)

Impute Missing Values

To handle missing values in a pandas DataFrame, the impute_nulls function offers a unified interface for various imputation strategies. Below is a comprehensive list of supported strategies, grouped by their functionality, and details about the parameters this function accepts.

Imputation Strategies

• Deletion-Based Strategies:

  • delete_column: Removes columns that meet certain criteria for missing values (e.g., columns with any or a threshold of missing values).
  • listwise: Deletes rows with missing values based on specified thresholds.

• Flagging Strategy:

  • flag: Creates binary indicator columns to flag the presence of missing values.

• Nearest Neighbors Strategies:

  • knn: Uses K-Nearest Neighbors imputation to estimate missing values based on similarity to other data points.

• Multivariate Strategies:

  • mice: Performs multiple imputation using chained equations (MICE) to estimate missing values.
  • regression: Uses regression-based imputation where missing values are predicted using regression models fitted on non-missing data.

• Continuous Features:

  • mean: Replaces missing values with the mean of the column.
  • median: Replaces missing values with the median of the column.
  • most_frequent: Replaces missing values with the most frequent value in the column.
  • constant: Replaces missing values with a user-provided constant value.
  • interpolate: Uses interpolation (linear or polynomial) to estimate missing values.
  • forwardfill: Fills missing values with the last non-missing value in a forward direction.
  • backfill: Fills missing values with the next non-missing value in a backward direction.

• Categorical Features:

  • most_frequent: Replaces missing values with the most frequent value.
  • least_frequent: Replaces missing values with the least frequent value.
  • constant: Replaces missing values with a user-provided constant value.
  • forwardfill: Fills missing values with the last non-missing value in a forward direction.
  • backfill: Fills missing values with the next non-missing value in a backward direction.

• Date Features:

  • interpolate: Uses time-based interpolation to estimate missing values.
  • forwardfill: Fills missing values with the last non-missing value in a forward direction.
  • backfill: Fills missing values with the next non-missing value in a backward direction.

• Automatic Strategy Detection:

  • auto: Automatically determines the best strategy for each column based on its data type and characteristics.

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Parameters

• df (pd.DataFrame):
  The input pandas DataFrame containing the data to process. Must not be empty.

• column (Optional[Union[Iterable, str]]):
  The target column(s) for imputation. Can be a single column name (str), a list of column names (Iterable), or None. If None, all columns with missing values will be considered for imputation.

• strategy (str):
  The imputation strategy to use. Refer to the above list for supported strategies. Defaults to "auto".

• fill_value (Optional[Any]):
  A constant value to use for imputation when the strategy is "constant".

• strategy_params (Optional[Dict[str, Any]]):
  Additional parameters to configure the imputation strategy. Examples include:

  • For interpolate: {"method": "linear", "order": 2} to specify a polynomial interpolation.
  • For constant: {"fill_value": 0} for numeric columns or "missing" for categorical columns.

• in_place (bool):
  Whether to modify the input DataFrame in place. If True, the DataFrame is updated directly. If False, a copy of the DataFrame is returned. Defaults to True.

• ****kwargs** (Any):
  Additional arguments for handler-specific configurations or compatibility.

import pandas as pd
import nullsweep as ns

# Sample DataFrame
data = {
    'Age': [25, 30, None, 35, 40],
    'Gender': ['Male', 'Female', None, 'Female', 'Male']
}
df = pd.DataFrame(data)

# Impute missing values in 'Age' using mean
df = ns.impute_nulls(df, column='Age', strategy='mean')

# Impute missing values in 'Gender' using the most frequent value
df = ns.impute_nulls(df, column='Gender', strategy='most_frequent')

# Impute missing values in 'Age' using linear interpolation
df = ns.impute_nulls(df, column='Age', strategy='interpolate')

# Impute missing values for multiple columns
df = ns.impute_nulls(df, column=['Age', 'Gender'], strategy='interpolate')

# Impute all features with missing values using automatic strategy detection
df = ns.impute_nulls(df)

# Drop rows with missing values
df = ns.impute_nulls(df, strategy="listwise")

# Create missing flags for multiple columns in new columns
df = ns.impute_nulls(df, column=['Age', 'Gender'], strategy="flag")

Contributing

Contributions are welcome! Please feel free to submit pull requests, open issues, or suggest improvements.