Verticapy

This release encompasses all the features introduced in ``1.0.0`` and introduces additional functionalities along with more precise docstrings.

Options
---------

``set_option`` function has more options:
- ``max_cellwidth``: Maximum width of any VerticaPy table's cell.
- ``max_tableheight``: Maximum height of VerticaPy tables.
- ``theme``: Theme used to display the VerticaPy objects (``light``, ``dark`` or ``sphinx``).
- ``verbosity``: This will set the amount of information to display while executing VerticaPy functions.

There are four levels of ``verbosity``:
- 0 (silent)
- 1 (warning)
- 2 (info)
- 3 (debug)

Machine Learning
-------------------

PLS Regression

Partial Least Squares (``PLS``) regression has been added to VerticaPy.

This integration will enhance VerticaPy's analytics capabilities, particularly benefiting Vertica customers who have expressed interest in utilizing PLS regression (``PLSRegression``).

Example use:

python

from verticapy.machine_learning.vertica import PLSRegression

model = PLSRegression()
model.fit(
train_data,
[
"col1",
"col2",
"col3",
],
"Y",
test_data,
)


Note: All the ML models that were introduced in version ``1.0.x`` are also supported in version ``1.1.x`` .

Performance
------------

``QueryProfiler`` has been greatly supplemented in this release:

QueryProfilerInterface

Now we have added the functionality of a ``session_control_param`` parameter.
This allows users to enter the alter session SQL before profiling the queries.

QueryProfilerInterface

- Added many more metrics for the profiled queries such as:

- ``rows_filtered_sip``
- ``container_rows_filtered_sip``
- ``container_rows_pruned_sip``

and more...

- There is a new tab which helps you select particular tooltips from any select path id.
- A new tab also highlights if there are any non-default ``SESSION PARAMETERS`` .
- Improved the efficiency of plotting the tree by chaching results.

QueryProfilerComparison

- ``QueryProfilerComparison`` class offers an extended set of functionalities, enabling the creation of complex trees with multiple metrics.

python

from verticapy.performance.vertica import QueryProfilerInterface

qprof_interface_1 = QueryProfilerInterface(
key_id = "key_1",
target_schema = "schema_1",
)

qprof_interface_2 = QueryProfilerInterface(
key_id = "key_2",
target_schema = "schema_1",
)

from verticapy.performance.vertica import QueryProfilerComparison

qprof_compare = QueryProfilerComparison(qprof_interface_1, qprof_interface_2)

qprof_compare.get_qplan_tree()



Deprecations
-------------

⚠️ Because of the re-structuring of VerticaPy, some previous imports will not work.

⚠️ Please look at the latest doc for the up-to-date imports.

Release Notes

**Changelogs**

- Full Changelog: https://github.com/vertica/VerticaPy/compare/1.0.4...1.0.5

**Installation**

The release will be on available on the defaults and can be installed using:


python3 -m pip install verticapy


If you want to install extra features, use:


python3 -m pip install verticapy[all]


Please report any issues on our [GitHub page](https://github.com/vertica/VerticaPy)

**Contributors**

We would like to extend our thanks to all the contributors who made this release possible:

- Badr Ouali oualib
- Umar Farooq Ghumman mail4umar

If you would like to contribute then please visit our updated [contributing guidelines](https://www.vertica.com/python/documentation/1.1.x/html/contribution_guidelines.html).

This minor release has some significant feature additions with other changes. Some salient ones are listed below:

QueryProfilerStats
------------------


Added a new class which calculates some of the statistics for queries that can help diagnose query performance issues.

You can call the main function to perform all the tests:


qprof = QueryProfilerStats((109090909, 1))
qprof.main_tests()


It is also inherited by the QueryProfilerInterface class, so it can be used from that class as well. For example:


qprof = QueryProfilerInterface((109090909, 1))
qprof.main_tests()


QueryProfiler
--------------

- Added the following new tables to the profile information:

- dc_scan_events

QueryProfilerInterface
-----------------------

- Added option to toggle tooltip information. There are three new check boxes which control which information will be displayed:

- Aggregate
- Opeartors
- Descriptors

Unit tests
-----------------

- Fixed some broken unit tests with a focus on QueryProfiler. This makes VerticaPy more robust against bugs.



Bugfixes
------------

- Regression metrics were corrected which were previously giving erroneous results (AIC_SCORE, BIC_SCORE, R2_SCORE).
- In certain scenarios, the vDataFrame creation was taking too much time (Issue 1235). This was resolved.
- QueryProfiler import bug fix.




Release Notes

**Changelogs**

- Full Changelog: https://github.com/vertica/VerticaPy/compare/1.0.4...1.0.5

**Installation**

The release will be on available on the defaults and can be installed using:


python3 -m pip install verticapy


If you want to install extra features, use:


python3 -m pip install verticapy[all]


Please report any issues on our [GitHub page](https://github.com/vertica/VerticaPy)

**Contributors**

We would like to extend our thanks to all the contributors who made this release possible:

- Badr Ouali oualib
- Umar Farooq Ghumman mail4umar

If you would like to contribute then please visit our updated [contributing guidelines](https://www.vertica.com/python/documentation/1.0.x/html/contribution_guidelines.html).

This minor release has some significant feature additions with other changes. Some salient ones are listed below:

Query Profiler

- Now there is a new parameter ``use_temp_relation`` which allows users to display the temporary relations separately in the ``get_qplan_tree``.
- Query start and stop time are also included in the get_queries table.
- New queries can be added to the same schema and key using the insert function.


qprof = QueryProfiler((109090909, 1))
qprof.insert(transactions = (41823718, 2))


QueryProfilerInterface

- Made the metric selecting widgets in the QueryProfilerInterface to be more intuitive.
- A new widget allows to directly jump to the specific query without having to press Next button multiple times.
- A success flag is added to the display to confirm if the query was run successfully.
- Added unit for the query execution time.

Machine Learning

- Added **Vector Auto Regression** (VAR) to the list of Vertica algortihms.

vDataFrame

- The Up and Down arrow of the ``vDataFrame.idisplay()`` are now larger in size.

Bugfixes

- Bugfix for auto-token refresh using OAuth.
- Empty vDataFrame can now be created.


import pandas as pd
from verticapy import read_pandas

df = pd.DataFrame()
df = df.reindex(columns = ["col1", "col2"])
read_pandas(df)



Release Notes

**Changelogs**

- Full Changelog: https://github.com/vertica/VerticaPy/compare/1.0.3...1.0.4

**Installation**

The release will be on available on the defaults and can be installed using:


python3 -m pip install verticapy


If you want to install extra features, use:


python3 -m pip install verticapy[all]


Please report any issues on our [GitHub page](https://github.com/vertica/VerticaPy)

**Contributors**

We would like to extend our thanks to all the contributors who made this release possible:

- Badr Ouali oualib
- Umar Farooq Ghumman mail4umar
- Daniel Mickens DMickens
- Tyler Consigny tconsigny

If you would like to contribute then please visit our updated [contributing guidelines](https://www.vertica.com/python/documentation/1.0.x/html/contribution_guidelines.html).

This minor release has some chagnes that focus on improving the QueryProfiler and QueryProfilerInterface.

Some main changes are:

- Improving the speed of the function
- Enhancing the UI
- Adding limitations to prevent overwriting of data when importing a file


Release Notes

**Changelogs**

- Full Changelog: https://github.com/vertica/VerticaPy/compare/1.0.2...1.0.3

**Installation**

The release will be on available on the defaults and can be installed using:


python3 -m pip install verticapy


If you want to install extra features, use:


python3 -m pip install verticapy[all]


Please report any issues on our [GitHub page](https://github.com/vertica/VerticaPy)

**Contributors**

We would like to extend our thanks to all the contributors who made this release possible:

- Badr Ouali oualib
- Umar Farooq Ghumman mail4umar
- Daniel Mickens DMickens


If you would like to contribute then please visit our updated [contributing guidelines](https://www.vertica.com/python/documentation/1.0.x/html/contribution_guidelines.html).

This minor release has some significant feature additions with other changes. Some salient ones are listed below:

:warning: Please note that this list may be incomplete, and for a comprehensive overview, including additional features, refer to the changelogs.

Pipelines (Beta)

- ``Pipelines`` is a YAML-based configuration for defining machine learning workflows, simplifying the process of setting up and managing machine learning pipelines.
- For beginners, it provides an easy-to-learn alternative to Python and SQL reducing the initial barriers to entry for creating models.
- For more experienced users, it offers templating features to enhance modularity, minimize errors, and promote efficient code reuse in machine learning projects.

Performance

- We have enhanced the `QueryProfiler` to improve its robustness.
- Introducing a completely new **Query Profiler Interface**, enabling users to navigate through various queries and access them without the need to re-enter all the code. All of this can be accomplished using only your mouse within Jupyter Notebook environments.

These updates significantly enhance the accessibility, debugging, and enhancement capabilities of your queries.

OAuth Refresh Tokens (Beta)

- We have updated the connector to accept OAuth refresh tokens.
- Additioanlly we have added a ``prompt`` option for `verticapy.connection.new_connection`. This allows the user to enter the secrets discretly with a masked display.


Multi-TimeSeries (Beta)

We added a new Time Series class: ``TimeSeriesByCategory``. This allows the users to build multiple models based off on a category. The number of models created
are equal to the categories. This saves users time to create multiple models separately. For more information please see `verticapy.machine_learning.vertica.tsa.ensemble.TimeSeriesByCategory`.


Plots

- Two new plots have been added for plotly that were previously missing:

- `verticapy.machine_learning.vertica.decomposition.plot_scree`
- `verticapy.machine_learning.vertica.decomposition.plot_var`


Unit Tests

- We continue to shift our old tests to the new more robust format.

Examples

- Most of the `examples <https://github.com/vertica/VerticaPy/tree/master/examples>`_ have been updated with the latest verticapy format.

Release Notes

**Changelogs**

- Full Changelog: https://github.com/vertica/VerticaPy/compare/1.0.0...1.0.1

**Installation**

The release will be on available on the defaults and can be installed using:


python3 -m pip install verticapy


If you want to install extra features, use:


python3 -m pip install verticapy[all]


Please report any issues on our [GitHub page](https://github.com/vertica/VerticaPy)

**Contributors**

We would like to extend our thanks to all the contributors who made this release possible:

- Badr Ouali oualib
- Umar Farooq Ghumman mail4umar
- Vikash Singh vikash018
- Zachary Nowak zacandcheese
- Arash Fard afard
- Abhishek Sharma abhsharma2
- Tyler Consigny tconsigny
- Jason Slaunwhite jslaunwhite-microfocus


If you would like to contribute then please visit our updated [contributing guidelines](https://www.vertica.com/python/documentation/1.0.x/html/contribution_guidelines.html).

This release includes numerous modifications to the previous code aimed at enhancing the user experience.

:warning: Please note that this list may be incomplete, and for a comprehensive overview, including additional features, refer to the changelogs.

New Options

The ``verticapy.set_option()`` function now allows you to set the following options:

- ``max_cellwidth``: Maximum width of VerticaPy table cells.
- ``max_tableheight``: Maximum height of VerticaPy tables.
- ``theme``: Set the display theme for VerticaPy objects to 'light' or 'dark'.

QueryProfiler

The ``verticapy.performance.vertica.qprof.QueryProfiler`` class offers an extended set of functionalities, enabling the creation of complex trees with multiple metrics. This can help in finding ways to improve the performance of slow-running queries.

Website

A new website is now available. It includes all the important information about the different changes and a totally new documentation generated by Sphinx. Check it out [here](https://www.vertica.com/python/documentation/1.0.x/html/)

Docstrings

Docstrings have been further enriched with relevant examples for more functions.

Release Notes

**Changelogs**

- Full Changelog: https://github.com/vertica/VerticaPy/compare/1.0.0...1.0.1

**Installation**

The release will be on available on the defaults and can be installed using:


python3 -m pip install verticapy


If you want to install extra features, use:


python3 -m pip install verticapy[all]


Please report any issues on our [GitHub page](https://github.com/vertica/VerticaPy)

**Contributors**

We would like to extend our thanks to all the contributors who made this release possible:

- Badr Ouali oualib
- Umar Farooq Ghumman mail4umar
- Arash Fard afard
- Abhishek Sharma abhsharma2
- Vikash Singh vikash018
- Tyler Consigny tconsigny
- Jason Slaunwhite jslaunwhite-microfocus


If you would like to contribute then please visit our updated [contributing guidelines](https://www.vertica.com/python/documentation/1.0.x/html/contribution_guidelines.html).