dsfns 1.5

Data Preprocessing fns

Installation

pip install dsfns

FNS Package

FUNCTION DESCRIPTIONS

1. Outlier_IQR(df, columns) Identifies and handles outliers in the specified columns using the Interquartile Range (IQR) method. Parameters:

   • df: DataFrame — The input data in which outliers will be detected.
   • columns: list — List of column names in which outliers need to be identified.

2. Outlier_Winsorizer(df, column, capping_method='iqr') Applies Winsorization to cap outliers in the specified column using either IQR or other capping methods. Parameters:

   • df: DataFrame — The input data to apply Winsorization.
   • column: str — The name of the column to apply the Winsorization to.
   • capping_method: str, default 'iqr' — Method used to define the outlier thresholds (options: 'iqr' , std, 'quantiles' or 'mad').

3. Outlier_Clip(df, columns) Clips extreme values to a predefined threshold in the specified columns, effectively handling outliers. Parameters:

   • df: DataFrame — The input data to clip outliers from.
   • columns: list — List of columns in which to clip the outliers.

4. MissingVal_Repl(df, columns, type='mean') Replaces missing values in the specified columns using a chosen method. Parameters:

   • df: DataFrame — The input data in which missing values will be replaced.
   • columns: list — List of column names where missing values need to be replaced.
   • type: str, default 'mean' — The method used for replacement ('mean', 'median', or mode)

5. MissingVal_Imputer(df,columns,strategy='mean') The MissingVal_Imputer function is designed to handle missing values in specified columns of a pandas DataFrame using different imputation strategies. It replaces missing values (NaN) with appropriate values based on the chosen strategy. Parameters:

   • df (pandas.DataFrame): The input DataFrame where missing values need to be imputed.
   • columns (list): A list of column names where missing value imputation is to be applied.
   • strategy (str, default='mean'):The strategy for imputing missing values. Supported values: 'mean': Replaces missing values with the mean of the column. 'median': Replaces missing values with the median of the column. 'mode': Replaces missing values with the most frequent value in the column (converted to 'most_frequent' internally).

6. MissingVal_Fillna(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:

   • df: DataFrame — The input data to check for missing values.

VERSION 1.3

7. outlierColumns(df) Returns a list of columns that contain outliers based on IQR. Parameters:

   • df: DataFrame — The input data to check for outliers.

8. outlierCount(df, columns) Counts the number of outliers in the specified columns. Parameters:

   • df: DataFrame — The input data to count outliers in.
   • 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:

   • df: DataFrame — The input data to identify high-frequency columns.
   • perc: float, default 0.5 — The percentage threshold for identifying high-frequency columns.

VERSION 1.5

10. Encoding(df, method='label') Encodes categorical columns into numeric labels for compatibility with machine learning algorithms. Parameters:

    • df: A Pandas DataFrame containing the dataset.
    • method: 'label' for label encoding OR 'onehot' for OneHotEncoding

11. Scaling(df, method='minmax') 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.