fabricatio_plot.toolboxes.dataframe_curd

CRUD Data Operations Toolbox Module.

This module provides focused tools for core data operations following CRUD principles: - Create: Generate new data structures/columns - Read: Extract and inspect data - Update: Transform and modify existing data - Delete: Remove data elements

Designed for clear separation of concerns with minimal dependencies.

Attributes

data_crud_toolbox

Functions

create_empty_dataframe(→ pandas.DataFrame)

Create an empty DataFrame with specified columns and optional data types.

add_computed_column(→ pandas.DataFrame)

Create a new column by evaluating an expression on existing columns.

get_row_by_index(→ pandas.Series)

Retrieve a single row by its index value.

fill_missing_values(→ pandas.DataFrame)

Update missing values in a single column using specified strategy.

transform_column(→ pandas.DataFrame)

Apply mathematical transformation to a numeric column.

drop_columns(→ pandas.DataFrame)

Delete specified columns from DataFrame.

drop_rows_by_condition(→ pandas.DataFrame)

Delete rows that meet a specified condition.

rename_column(→ pandas.DataFrame)

Atomically rename a single column.

set_index_from_column(→ pandas.DataFrame)

Set DataFrame index from an existing column.

Module Contents

fabricatio_plot.toolboxes.dataframe_curd.data_crud_toolbox
fabricatio_plot.toolboxes.dataframe_curd.create_empty_dataframe(columns: List[str], dtypes: List[str] | None = None) → pandas.DataFrame

Create an empty DataFrame with specified columns and optional data types.

Parameters:

  • columns – List of column names

  • dtypes – Optional list of data types corresponding to columns (e.g., [‘int’, ‘float’, ‘str’])

Returns:

Empty DataFrame with schema defined

fabricatio_plot.toolboxes.dataframe_curd.add_computed_column(df: pandas.DataFrame, new_column: str, expression: str, dtype: str | None = None) → pandas.DataFrame

Create a new column by evaluating an expression on existing columns.

Parameters:

  • df – Input DataFrame

  • new_column – Name for the new column

  • expression – Python expression using existing columns (e.g., “price * quantity”)

  • dtype – Optional data type for the new column

Returns:

DataFrame with new computed column added

fabricatio_plot.toolboxes.dataframe_curd.get_row_by_index(df: pandas.DataFrame, index_value: Any) → pandas.Series

Retrieve a single row by its index value.

Parameters:

  • df – Input DataFrame

  • index_value – Value of the index to retrieve

Returns:

Row as a pandas Series

Raises:

KeyError – If index_value not found in index

fabricatio_plot.toolboxes.dataframe_curd.fill_missing_values(df: pandas.DataFrame, column: str, strategy: Literal['mean', 'median', 'mode', 'constant'] = 'mean', constant_value: Any | None = None) → pandas.DataFrame

Update missing values in a single column using specified strategy.

Parameters:

  • df – Input DataFrame

  • column – Column name to process

  • strategy – Filling strategy (‘mean’, ‘median’, ‘mode’, ‘constant’)

  • constant_value – Value to use when strategy=’constant’

Returns:

DataFrame with missing values filled in specified column

fabricatio_plot.toolboxes.dataframe_curd.transform_column(df: pandas.DataFrame, column: str, transformation: Literal['log', 'sqrt', 'square', 'normalize']) → pandas.DataFrame

Apply mathematical transformation to a numeric column.

Parameters:

  • df – Input DataFrame

  • column – Column name to transform

  • transformation – Type of transformation (‘log’, ‘sqrt’, ‘square’, ‘normalize’)

Returns:

DataFrame with transformed column

fabricatio_plot.toolboxes.dataframe_curd.drop_columns(df: pandas.DataFrame, columns: List[str]) → pandas.DataFrame

Delete specified columns from DataFrame.

Parameters:

  • df – Input DataFrame

  • columns – List of column names to drop

Returns:

DataFrame with columns removed

Raises:

KeyError – If any column in ‘columns’ doesn’t exist in DataFrame

fabricatio_plot.toolboxes.dataframe_curd.drop_rows_by_condition(df: pandas.DataFrame, condition: str) → pandas.DataFrame

Delete rows that meet a specified condition.

Parameters:

  • df – Input DataFrame

  • condition – Query string for rows to drop (e.g., “price < 0”, “category.isna()”)

Returns:

DataFrame with matching rows removed

fabricatio_plot.toolboxes.dataframe_curd.rename_column(df: pandas.DataFrame, old_name: str, new_name: str) → pandas.DataFrame

Atomically rename a single column.

Parameters:

  • df – Input DataFrame

  • old_name – Current column name

  • new_name – New column name

Returns:

DataFrame with column renamed

Raises:

KeyError – If old_name not found in columns

fabricatio_plot.toolboxes.dataframe_curd.set_index_from_column(df: pandas.DataFrame, column: str, drop: bool = True) → pandas.DataFrame

Set DataFrame index from an existing column.

Parameters:

  • df – Input DataFrame

  • column – Column name to use as index

  • drop – Whether to drop the column after setting index (default: True)

Returns:

DataFrame with new index set

Raises:

KeyError – If column not found in DataFrame