What's New in VerticaPy v0.9.0

⚠ Many functions have deprecated (see the list below).

New features:

Utilities

[New Function] create_schema: Creates a new schema.
[New Function] create_table: Creates a new table using the input column names and data types.
[New Parameters] insert_into: New parameters have been added to simplify usage.
[New Parameters] vDataFrame.to_csv and vDataFrame.to_json: To increase flexibility, these functions now take a single argument for the file and directory name instead of two.
[New Parameters] vDataFrame: vDataFrames can now be created directly from a SQL query.
[Enhancement] set_option: The new option 'overwrite_model' automatically overwrites existing models if they share the same name.

[New Parameters] vDataFrame.agg and vDataFrame.describe: Multiple aggregations can now be computed sequentially or in parallel. This option is also available for any '.agg' sub-method (that is, .sum, .var, .mean, etc.).
[Enhancement] vDataFrame.agg: This method can now handle incompatible aggregations by sending aggregations in multiple queries. For example, if you attempt to compute the number of distinct elements and the approximate_median at the same time (incompatible aggregations), VerticaPy will send the incompatible aggregations in separate queries.

[New Parameters] vDataFrame.agg, vDataFrame.nunique, vDataFrame.quantile, vDataFrame[].nunique and vDataFrame[].quantile: You can now compute both exact and approximate statistics.
[New Method] vDataFrame.count_percent: Aggregates the vDataFrame by computing the number and percent of missing values. To complement this change, vDataFrame.count now only returns the number of missing values.
[New Parameters] vDataFrame.pivot_table: You can now fill empty values with a custom value.
[Enhancement] vDataFrame.corr, vDataFrame.corr_pvalue, vDataFrame.acf, and vDataFrame.hchart: You can now compute Spearman's rrank correlation coefficient with the DENSE_RANK function instead of the RANK function.
[New Alias] vDataFrame.asfreq: The vDataFrame.asfreq method has been renamed to (is an alias for) vDataFrame.interpolate.

[New Alias] vModel.classification_report and vModel.regression_report: These methods have a new alias: vModel.report.
[New Parameter] vModel.classification_report and classification_report: These methods have a new 'nbins' parameter to control the precision for calculating the PRC AUC, ROC AUC, and the best cutoff.
[New Method] vModel.predict_proba: Computes the model's class probabilities. Previously, this task was performed by vModel.predict. vModel.predict now only computes predictions.

[New Methods] tablesample.append, tablesample.decimal_to_float, tablesample.merge, tablesample.shape, tablesample.sort: These methods make it easier to perform operations on tablesamples.

In the past, many VerticaPy functions needed a 'cursor' to run. To simplify usage, VerticaPy now uses a single connection, and all existing functions that need to connect to the database have been changed to use this new process. For a list of the new connection functions, see Connect. For an example of how to connect to a database with VerticaPy, see the connection tutorial.

[New Functionality] SQL Magic: You can now export your results, read SQL files, use local variables (tablesamples, pandas, vDataFrames, and all standard types). Older/obsolete options have been deprecated.
[Enhancement] SQL comments: You can now use SQL comments with '--'.
[Enhancement] SQL Magic returns vDataFrames instead of tablesamples.

[New Functionality] hchart Magic: : You can now export your results, read SQL files, use local variables (tablesamples, pandas, vDataFrames, and all standard types). Older/obsolete options have been deprecated.
[Enhancement] SQL comments: You can now use SQL comments with '--'.

Functions that previously used a cursor now instead leverage a single connection by VerticaPy; you cannot pass cursors to these functions anymore. The connection functions have been renamed. For a list of the new connection functions, see Connect. For an example of how to connect to a database with VerticaPy, see the connection tutorial.
Some functions use a temporary schema to create objects. You can now change this temporary schema with set_option. As a result, the method vDataFrame.'set_schema_writing' has been deprecated and will be removed in a later release.
vModel.predict no longer computes class probabilities. This functionality has been moved to vModel.predict_proba.
The methods vModel.to_sklearn and vModel.shapExplainer have been deprecated due to integration complexity and the limitations of their respective APIs. A function to compute shap values will be added in a future release. As a substitute for vModel.to_sklearn, you can try instead converting vModels to memModels, which are machine-independent, more flexible, and can be used for in-memory or in-database scoring.
To simplify usage, the magic command vCharts has been renamed to hchart to simplify usage.

The function vHelp has been renamed to help_start.
The function vdf_from_relation has been renamed to vDataFrameSQL, and you can now create a vDataFrame directly from a query. This is the preferred usage.
The method vDataFrame.set_cursor has been deprecated.
vDataFrame.count no only returns the number of missing elements, rather than the number and the percent. To get the percent of missing elements, use vDataFrame.count_percent.
The default parameters of vDataFrame.describe have been changed. The cardinality is only computed if the parameter 'unique' is set to True.
Many parameters of SQL Magic and
[New Functionality] hchart Magic are deprecated.