dsfns 1.4

Data Preprocessing fns

Installation

pip install dsfns

FNS Package

Function Descriptions

1. outl_iqr(df, columns) Identifies and handles outliers in the specified columns using the Interquartile Range (IQR) method. Parameters: o df: DataFrame — The input data in which outliers will be detected. o columns: list — List of column names in which outliers need to be identified.

2. outl_winsor(df, column, capping_method='iqr') Applies Winsorization to cap outliers in the specified column using either IQR or other capping methods. Parameters: o df: DataFrame — The input data to apply Winsorization. o column: str — The name of the column to apply the Winsorization to. o capping_method: str, default 'iqr' — Method used to define the outlier thresholds (options: 'iqr' , std, 'quantiles' or 'mad').

3. outl_clip(df, columns) Clips extreme values to a predefined threshold in the specified columns, effectively handling outliers. Parameters: o df: DataFrame — The input data to clip outliers from. o columns: list — List of columns in which to clip the outliers.

4. miss_repl(df, columns, type='mean') Replaces missing values in the specified columns using a chosen method. Parameters: o df: DataFrame — The input data in which missing values will be replaced. o columns: list — List of column names where missing values need to be replaced. o type: str, default 'mean' — The method used for replacement ('mean', 'median', or mode)

5. miss_all(df) Identifies and returns all rows in the DataFrame that contain missing values with mean for numeric columns and mode (with index[0]) for object. Parameters: o df: DataFrame — The input data to check for missing values.

6. norm(df) Normalizes a given value (or a set of values) to specific scale [0, 1]. Parameters: o df: data to be normalized.

7. outlierColumns(df) Returns a list of columns that contain outliers based on IQR. Parameters: o df: DataFrame — The input data to check for outliers.

VERSION 1.3

8. outlierCount(df, columns) Counts the number of outliers in the specified columns. Parameters: o df: DataFrame — The input data to count outliers in. o columns: list — List of columns to check for outliers.

9. highFrequency(df, perc=0.5) Identifies and returns columns where more than the given percentage (default 70%) of values are identical, typically used to detect low-variance or high-frequency columns. Parameters: o df: DataFrame — The input data to identify high-frequency columns. o perc: float, default 0.7 — The percentage threshold for identifying high-frequency columns.

10. miss_impute(df, columns, strategy='mean') The miss_impute function is designed to handle missing values in a pandas DataFrame by applying various imputation strategies. It utilizes the SimpleImputer from scikit-learn to efficiently fill missing values in specified columns. Parameters: o df: DataFrame — The input data to identify high-frequency columns. o columns (list): A list of column names in the DataFrame where missing values need to be imputed. o strategy (str, default='mean'):The imputation strategy to be applied. Options include:

    'mean': Replace missing values with the mean of the column. 'median': Replace missing values with the median of the column. 'mode' or 'most_frequent': Replace missing values with the most frequently occurring value in the column.

11. Encoding_Label(df) Encodes categorical columns into numeric labels for compatibility with machine learning algorithms.

    Parameters: df: A Pandas DataFrame containing the dataset. Functionality: Identifies columns with data types object or category. Uses LabelEncoder from scikit-learn to transform these categorical columns into numerical labels.

12. Scaler(df, method='minmax') Purpose: Scales numerical data for better performance during machine learning model training.

    Parameters: df: A Pandas DataFrame containing numeric data. method: Specifies the scaling technique to use. Options are: 'minmax' (default): Rescales data to a range of 0 to 1. 'standard': Standardizes data to have a mean of 0 and a standard deviation of 1. 'robust': Scales data using the median and interquartile range, making it robust to outliers.