teradataml-plus 0.3.1
Python Package that extends the functionality of the popular teradataml package through monkey-patching.
Requires Python: >=3.9
Keywords: teradataml-plus, teradata, database, teradataml

![Logo](https://raw.githubusercontent.com/martinhillebrand/tdmlplus/refs/heads/main/media/tdmlplus-logo.png)

# teradataml-plus

Python Package that extends the functionality of the popular [teradataml](https://pypi.org/project/teradataml/) package through [monkey-patching](https://en.wikipedia.org/wiki/Monkey_patch).
This is to use field-developed assets more naturally with the existing interface.

## Installation

* `pip install teradataml-plus`

## Quickstart

```python
#always import teradata-plus (tdmlplus) first
import tdmlplus

#then import teradataml. It will have all the additional functionality
import teradataml as tdml

# one additional function is for instance to get a correlation matrix straight from the DataFrame, just like in pandas

DF = tdml.DataFrame("some_table")
DF_corr = DF.corr() # not possible withot tdmlplus
```



# History

## v0.1.0 (2025-07-25)

* `teradataml.DataFrame`
  * `corr(method="pearson")` – correlation matrix like in pandas

* `teradataml.random`
  * `randn(n, mean=0.0, std=1.0)` – random normally distributed variables

* `teradataml.dba`
  * `get_amps_count()` – get number of AMPs


## v0.2.0 (2025-07-30)

* `teradataml.DataFrame`
  * `show_CTE_query()` – generate full lineage SQL with CTEs
  * `deploy_CTE_view(view_name, replace=False)` – create a view from the full CTE SQL
  * `easyjoin(other, on, how="left", lsuffix=None, rsuffix=None)` – simplified join using common column names with suffix handling

* `tdml.dataframe.sql._SQLColumnExpression` - aka DataFrameColumn
  * `trycast(dtype)` – apply TRYCAST SQL expression to a column
  * `hashbin(num_bins, salt=None)` – compute hash bin from a column with optional salt
  * `_power_transform_get_lambda(method="yeo-johnson")` – estimate lambda for power transform
  * `power_transform(method="yeo-johnson", lambda_val=None)` – apply power transform
  * `power_fit_transform(method="yeo-johnson")` – estimate lambda and transform in one step

* `teradataml.random`
  * `_generate_sql_for_correlated_normals(cov_matrix)` – internal SQL generator for correlated normals
  * `correlated_normals(df, mean=None, cov=None)` – generate synthetic data with correlation structure

* `tdml.widgets`
  * `tab_dfs(dfs)` – display multiple DataFrames/tables in widget tabs

* `teradataml`
  * `prettyprint_sql(query)` – pretty-print SQL with indentation and keyword formatting


## v0.3.0 (2025-08-18)

* `teradataml.DataFrame`
  * `top(n=10, percentage=None)` – efficient limiting via Teradata `TOP`/`TOP PERCENT`
  * `head(n=5, sort_index=False)` – overridden to support `sort_index`; original preserved as `_head`
  * `select_dtypes(include=None, exclude=None)` – filter columns by logical dtypes
  * `select_tdtypes(include=None, exclude=None)` – filter columns by Teradata types
  * `histogram(bins=10, exclude_index=True, target_columns=None, groupby_columns=None)` – equal-width histograms for numeric columns
  * `plot_hist(bins=10, exclude_index=True, target_columns=None, groupby_columns=None, library="plotly", absolute_values=True, percentage_values=False)` – plot histograms with Plotly or Seaborn
  * `hist(...)` – alias for `plot_hist`
  * `categorical_summary(target_columns=None, exclude_index=True, include_percentages=False)` – summaries for `CHAR`/`VARCHAR` columns
  * `column_summary(target_columns=None, exclude_index=True)` – general per-column summary
  * `fill_RowId(rowid_columnname="row_id")` – add sequential row id
  * `reset_index(...)` – alias for `fill_RowId`

* `tdml.dataframe.sql._SQLColumnExpression` - aka DataFrameColumn
  * `histogram(bins=10)` – column-level histogram with numeric type validation
  * `plot_hist(bins=10, library="plotly", absolute_values=True, percentage_values=False, **plotting_args)` – column-level plotting wrapper
  * `hist(...)` – alias for `plot_hist`
  * `map(value_map, keep_original=True, default_else_value=None, output_type=None)` – SQL `CASE` mapping with output type inference