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API Reference

This document provides a detailed reference for all the classes and functions available in the suraj_datalab package.

Modules

Analyze Module

categorical_feature(df, feature, target)

Analyze the distribution of a categorical feature with respect to a target variable.

Parameters:

  • df (pandas.DataFrame): The input DataFrame.
  • feature (str): The name of the categorical feature to analyze.
  • target (str): The name of the target variable.

Returns:

  • pandas.DataFrame: A DataFrame containing the distribution of the feature with respect to the target.

numerical_feature(df, feature, target=None, figsize=(15, 6), bins="sturges")

Analyze the distribution of a numerical feature, with optional grouping by a target variable.

Parameters:

  • df (pandas.DataFrame): The input DataFrame.
  • feature (str): The name of the numerical feature to analyze.
  • target (str, optional): The name of the target column for grouping the analysis. Default is None.
  • figsize (tuple, optional): The size of the figure. Default is (15, 6).
  • bins (int or str, optional): The number of bins or the method to calculate them. Default is "sturges".

Returns:

  • pandas.DataFrame: A DataFrame containing outlier percentages and summary statistics.

missing_values(dataframe)

Generate a summary of missing values in the DataFrame.

Parameters:

  • dataframe (pandas.DataFrame): The input DataFrame.

Returns:

  • pandas.DataFrame: A DataFrame containing missing values count, percentage, and data types for columns with missing values.

Clean Module

RareCategoryReplacer(columns, proportion_threshold=0.02, replacement_value="Others")

Class for replacing rare categories in specified columns of a DataFrame.

Parameters:

  • columns (list): List of column names to apply the rare category replacement.
  • proportion_threshold (float, optional): Threshold below which a category is considered rare. Default is 0.02.
  • replacement_value (str, optional): Value to replace rare categories with. Default is "Others".

Attributes:

  • rare_categories_ (dict): Dictionary containing the rare categories for each specified column.
  • important_categories_ (dict): Dictionary containing the important categories for each specified column.

Methods:

  • fit(X, y=None): Fit the transformer by calculating rare categories.
  • transform(X): Transform the data by replacing rare categories.
  • fit_transform(X, y=None): Fit and transform the data in a single step.

Fold Creator Module

create_kfolds(file_path, n_splits=5, shuffle=True, random_state=42, save_path=None)

Create K-Fold indices for a dataset loaded from a CSV file.

Parameters:

  • file_path (str): Path to the input CSV file.
  • n_splits (int, optional): Number of folds. Default is 5.
  • shuffle (bool, optional): Whether to shuffle the data. Default is True.
  • random_state (int, optional): Seed for the random number generator. Default is 42.
  • save_path (str, optional): Path to save the CSV file. If None, the file is not saved.

Returns:

  • pandas.DataFrame: DataFrame with an additional kfold column.

create_classification_kfolds(file_path, target_column, n_splits=5, random_state=42, save_path=None)

Create stratified K-Fold indices for classification tasks from a CSV file.

Parameters:

  • file_path (str): Path to the input CSV file.
  • target_column (str): The name of the target column.
  • n_splits (int, optional): Number of folds. Default is 5.
  • random_state (int, optional): Seed for the random number generator. Default is 42.
  • save_path (str, optional): Path to save the CSV file. If None, the file is not saved.

Returns:

  • pandas.DataFrame: DataFrame with an additional kfold column.

create_regression_kfolds(file_path, target_column, n_splits=5, binning_method="sturges", custom_bins=None, random_state=42, save_path=None)

Create stratified K-Fold indices for regression tasks using various binning methods from a CSV file.

Parameters:

  • file_path (str): Path to the input CSV file.
  • target_column (str): The name of the target column.
  • n_splits (int, optional): Number of folds. Default is 5.
  • binning_method (str, optional): Method for binning the target variable. Options: 'sturges', 'quantile', 'kmeans', 'custom'. Default is 'sturges'.
  • custom_bins (list, optional): List of bin edges for custom binning. Required if binning_method is 'custom'.
  • random_state (int, optional): Seed for the random number generator. Default is 42.
  • save_path (str, optional): Path to save the CSV file. If None, the file is not saved.

Returns:

  • pandas.DataFrame: DataFrame with an additional kfold column.

Learn More

For detailed usage instructions, please visit the Usage Guide.

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