complexifier 0.2.1

A library to make data worse

Complexifier

This makes your pandas dataframe even worse

Dependencies

• pandas
• typo
• random

Installation

complexifier can be installed using pip

pip install complexifier

Usage

Once installed you can use complexifier to add mistakes and outliers to your data

This library has several methods available:

create_spag_error(word: str) -> str

Introduces a 10% chance of a random spelling error in a given word. This function is useful for simulating typos and spelling mistakes in text data.

introduce_spag_error(df: pd.DataFrame, columns=None) -> pd.DataFrame

Applies the create_spag_error function to each string entry in specified columns of a DataFrame, introducing random spelling errors with a 10% probability.

Parameters:

• df: The DataFrame to be altered.
• columns: Optional; specify column names to apply errors to. If not provided, it defaults to all string columns.

add_or_subtract_outliers(df: pd.DataFrame, columns=None) -> pd.DataFrame

Randomly adds or subtracts values in specified numeric columns at random indices, simulating outliers between 1% and 10% of the rows.

Parameters:

• df: DataFrame to be modified.
• columns: Optional; specify columns to adjust.

add_standard_deviations(df: pd.DataFrame, columns=None, min_std=1, max_std=5) -> pd.DataFrame

Adds between 1 to 5 standard deviations to random entries in specified numeric columns to simulate data anomalies.

Parameters:

• df: The DataFrame to manipulate.
• columns: Optional; specify columns to modify.
• min_std: Minimum number of standard deviations to add.
• max_std: Maximum number of standard deviations to add.

duplicate_rows(df: pd.DataFrame, sample_size=None) -> pd.DataFrame

Introduces duplicate rows into a DataFrame. This function is useful for testing deduplication processes.

Parameters:

• df: DataFrame where duplicates will be introduced.
• sample_size: Optional; number of rows to duplicate. A random percentage between 1% and 10% if not specified.

add_nulls(df: pd.DataFrame, columns=None, min_percent=1, max_percent=10) -> pd.DataFrame

Inserts null values into specified DataFrame columns. This simulates missing data conditions.

Parameters:

• df: The DataFrame to modify.
• columns: Optional; specific columns to add nulls to.
• min_percent: Minimum percentage of null values to insert.
• max_percent: Maximum percentage of null values to insert.