vDataFrame

• 

  vDataFrame

  Object Introduction
• 

  Descriptive Statistics

  Data Exploration
• 

  Correlation & Dependancy

  Statistical Relationships
• 

  Plotting & Graphics

  Data Visualization
• 

  Preprocessing

  Data Cleaning
• 

  Features Engineering

  Create new Features
• 

  Join, sort, and Transform

  Data Enrichment
• 

  Filter and Sample

  Data Filtering
• 

  Utilities

  Serialization and Management

API Reference

Analytic Functions

Method	Definition
vDataFrame.analytic	Adds a new vcolumn to the vDataFrame by using an advanced analytical function on one or two specific vcolumns.
vDataFrame.asfreq	Computes a regular time interval vDataFrame by interpolating the missing values using different techniques.
vDataFrame.sessionize	Adds a new vcolumn to the vDataFrame which will correspond to sessions.

Binary Operator Functions

Method	Definition
vDataFrame[].add	Adds the input element to the vcolumn.
vDataFrame[].div	Divides the vcolumn by the input element.
vDataFrame[].mul	Multiplies the vcolumn by the input element.
vDataFrame[].sub	Substracts the input element to the vcolumn.

Copy

Method	Definition
vDataFrame[].add_copy	Adds a copy vcolumn to the parent vDataFrame.
vDataFrame.copy	Returns a copy of the vDataFrame.

Correlation & Dependancy

Method	Definition
vDataFrame.acf	Computes the correlations of the input vcolumn and its lags.
vDataFrame.chaid	Returns a CHAID (Chi-square Automatic Interaction Detector) tree.
vDataFrame.corr	Computes the Correlation Matrix of the vDataFrame.
vDataFrame.corr_pvalue	Computes the Correlation Coefficient of the two input vcolumns and its pvalue.
vDataFrame.cov	Computes the Covariance Matrix of the vDataFrame.
vDataFrame.iv_woe	Computes the Information Value (IV) Table.
vDataFrame[].iv_woe	Computes the Information Value (IV) / Weight Of Evidence (WOE) Table.
vDataFrame.pacf	Computes the partial correlations of the input vcolumn and its lags.
vDataFrame.pivot_table_chi2	Returns the chi-squared term using the pivot table of the response vColumn against the input vcolumns.
vDataFrame.regr	Computes the Regression Matrix of the vDataFrame.

Customized Features Creation

Method	Definition
vDataFrame.case_when	Creates a new feature by evaluating the specified conditions.
vDataFrame.eval	Evaluates a customized expression.

Data Types Conversion

Method	Definition
vDataFrame.astype	Converts the vColumns to the input types.
vDataFrame[].astype	Converts the vColumn to the input type.
vDataFrame.bool_to_int	Converts all the booleans vColumns to integers.

Dealing with Missing Values

Method	Definition
vDataFrame.dropna	Filters the vDataFrame where the input vColumns are missing.
vDataFrame[].dropna	Filters the vDataFrame where the vColumn is missing.
vDataFrame.fillna	Fills the vColumns missing elements using specific rules.
vDataFrame[].fillna	Fills the vColumn missing elements using specific rules.

Descriptive Statistics

Method	Definition
vDataFrame.aad	Aggregates the vDataFrame using 'aad' (Average Absolute Deviation).
vDataFrame[].aad	Aggregates the vcolumn using 'aad' (Average Absolute Deviation).
vDataFrame.agg / aggregate	Aggregates the vDataFrame using the input functions.
vDataFrame[].agg / aggregate	Aggregates the vcolumn using the input functions.
vDataFrame.all	Aggregates the vDataFrame using 'bool_and'.
vDataFrame.any	Aggregates the vDataFrame using 'bool_or'.
vDataFrame.avg / mean	Aggregates the vDataFrame using 'avg' (Average).
vDataFrame[].avg / mean	Aggregates the vcolumn using 'avg' (Average).
vDataFrame.count	Aggregates the vDataFrame using a list of 'count' (Number of missing values).
vDataFrame[].count	Aggregates the vcolumn using 'count' (Number of Missing elements).
vDataFrame.describe	Aggregates the vDataFrame using multiple statistical aggregations.
vDataFrame[].describe	Aggregates the vcolumn using multiple statistical aggregations.
vDataFrame[].distinct	Returns the vcolumn distinct categories.
vDataFrame.duplicated	Returns the duplicated values.
vDataFrame.groupby	Aggregates the vDataFrame by grouping the elements.
vDataFrame.kurt / kurtosis	Aggregates the vDataFrame using 'kurtosis'.
vDataFrame[].kurt / kurtosis	Aggregates the vcolumn using 'kurtosis'.
vDataFrame.mad	Aggregates the vDataFrame using 'mad' (Median Absolute Deviation).
vDataFrame[].mad	Aggregates the vcolumn using 'mad' (Median Absolute Deviation).
vDataFrame.max	Aggregates the vDataFrame using 'max' (Maximum).
vDataFrame[].max	Aggregates the vcolumn using 'max' (Maximum).
vDataFrame.median	Aggregates the vDataFrame using 'median'.
vDataFrame[].median	Aggregates the vcolumn using 'median'.
vDataFrame.min	Aggregates the vDataFrame using 'min' (Minimum).
vDataFrame[].min	Aggregates the vcolumn using 'min' (Minimum).
vDataFrame[].mode	Returns the nth most occurent element.
vDataFrame[].nlargest	Returns the n largest vcolumn elements.
vDataFrame[].nsmallest	Returns the n smallest vcolumn elements.
vDataFrame.nunique	Aggregates the vDataFrame using 'unique' (cardinality).
vDataFrame[].numh	Computes the optimal vcolumn bar width.
vDataFrame[].nunique	Aggregates the vcolumn using 'unique' (cardinality).
vDataFrame.prod /product	Aggregates the vDataFrame using 'product'.
vDataFrame[].prod /product	Aggregates the vcolumn using 'product'.
vDataFrame.quantile	Aggregates the vDataFrame using a list of 'quantiles'.
vDataFrame[].quantile	Aggregates the vcolumn using an input 'quantile'.
vDataFrame.score	Computes the score using the input columns and the input method.
vDataFrame.sem	Aggregates the vDataFrame using 'sem' (Standard Error of the Mean).
vDataFrame[].sem	Aggregates the vcolumn using 'sem' (Standard Error of the Mean).
vDataFrame.shape	Returns the number of rows and columns of the vDataFrame.
vDataFrame.skew / skewness	Aggregates the vDataFrame using 'skewness'.
vDataFrame[].skew / skewness	Aggregates the vcolumn using 'skewness'.
vDataFrame.std	Aggregates the vDataFrame using 'std' (Standard Deviation).
vDataFrame[].std	Aggregates the vcolumn using 'std' (Standard Deviation).
vDataFrame.sum	Aggregates the vDataFrame using 'sum'.
vDataFrame[].sum	Aggregates the vcolumn using 'sum'.
vDataFrame[].topk	Returns the top-k most occurent elements and their percentages of the distribution.
vDataFrame[].value_counts	Returns the top-k most frequent elements and how often they appear.
vDataFrame.var	Aggregates the vDataFrame using 'variance'.
vDataFrame[].var	Aggregates the vcolumn using 'variance'.

Encoding

Method	Definition
vDataFrame[].cut	Discretizes the vColumn using the input list.
vDataFrame[].decode	Encodes the vColumn using a user-defined encoding.
vDataFrame[].discretize	Discretizes the vColumn using the input method.
vDataFrame.get_dummies	Encodes the vColumn using the One-Hot Encoding algorithm.
vDataFrame[].get_dummies	Encodes the vColumn using the One-Hot Encoding algorithm.
vDataFrame[].label_encode	Encodes the vColumn using a bijection from the different categories to [0, n - 1]
vDataFrame[].mean_encode	Encode the vColumn using the average of the response partitioned by the different vcolumn categories.

Features Transformations

Method	Definition
vDataFrame.abs	Applies the absolute value function to the input vcolumns.
vDataFrame[].abs	Applies the absolute value function to the input vcolumn.
vDataFrame.apply	Applies each function of the dictionary to the input vcolumns.
vDataFrame[].apply	Applies a function to the vcolumn.
vDataFrame[].apply_fun	Applies a default function to the vcolumn.
vDataFrame.applymap	Applies a function to all the vcolumns.
vDataFrame[].date_part	Extracts a specific TS field from the vcolumn.
vDataFrame[].round	Rounds the vcolumn by keeping only the input number of digits after comma.
vDataFrame[].slice	Slices the vcolumn using a TS rule. The vcolumn will be transformed.

Filter Columns

Method	Definition
vDataFrame.drop	Drops the input vcolumns from the vDataFrame.
vDataFrame[].drop	Drops the vcolumn from the vDataFrame.
vDataFrame.drop_duplicates	Filters the duplicated using a partition by the input vcolumns.
vDataFrame[].drop_outliers	Drops the vcolumns outliers.
vDataFrame.search	Searches for elements that match the input conditions.
vDataFrame.select	Returns a copy of the vDataFrame with only the selected vcolumns.

Filter Records

Method	Definition
vDataFrame.at_time	Filters the vDataFrame by only keeping the records at the input time.
vDataFrame.between_time	Filters the vDataFrame by only keeping the records between two input times.
vDataFrame.filter	Filters the vDataFrame using the input expressions.
vDataFrame.first	Filters the vDataFrame by only keeping the first records.
vDataFrame.isin	Looks if some specific records are in the vDataFrame.
vDataFrame[].isin	Looks if some specific records are in the vcolumn.
vDataFrame.last	Filters the vDataFrame by only keeping the last records.

Information

Method	Definition
vDataFrame.catcol	Returns the vDataFrame categorical vcolumns based on a cardinality threshold.
vDataFrame[].category	Returns the vcolumn category.
vDataFrame[].ctype	Returns the vcolumn DB type.
vDataFrame.current_relation	Returns the current vDataFrame relation.
vDataFrame.datecol	Returns all the vDataFrame vcolumns of type date.
vDataFrame.dtypes	Returns the different vcolumns types.
vDataFrame[].dtype	Displays and Returns the vcolumn Data type.
vDataFrame.empty	Returns True if the vDataFrame is empty.
vDataFrame.expected_store_usage	Returns the vDataFrame expected store usage.
vDataFrame.explain	Provides information on how Vertica is computing the current vDataFrame relation.
vDataFrame.get_columns	Returns the vDataFrame vcolumns.
vDataFrame.head	Returns the vDataFrame head.
vDataFrame[].head	Returns the vcolumn head.
vDataFrame.iloc	Returns a part of the vDataFrame (delimited by an offset and a limit).
vDataFrame[].iloc	Returns a part of the vcolumn (delimited by an offset and a limit).
vDataFrame.info	Displays information about the different vDataFrame transformations.
vDataFrame[].isdate	Returns True if the vcolumn category is date, False otherwise.
vDataFrame[].isnum	Returns True if the vcolumn is numerical, False otherwise.
vDataFrame.memory_usage	Returns the vDataFrame memory usage.
vDataFrame[].memory_usage	Returns the vcolumn memory usage.
vDataFrame.numcol	Returns the vDataFrame numerical vcolumns.
vDataFrame.tail	Returns the vDataFrame tail.
vDataFrame[].tail	Returns the vcolumn tail.
vDataFrame[].store_usage	Returns the vcolumn expected store usage (unit: b).
vDataFrame.swap	Swap the two input vcolumns.
vDataFrame.version	Returns the Vertica version.

Join, Sort, and Transform

Method	Definition
vDataFrame.append	Merges the vDataFrame with another vDataFrame or an input relation.
vDataFrame.cdt	Returns the complete disjunctive table of the vDataFrame.
vDataFrame.groupby	Aggregates the vDataFrame by grouping its elements.
vDataFrame.join	Joins the vDataFrame with another vDataFrame or an input relation.
vDataFrame.narrow	Returns the narrow table of the vDataFrame using the input vcolumns.
vDataFrame.pivot	Returns the pivot of the vDataFrame using the input aggregation.
vDataFrame.polynomial_comb	Returns a vDataFrame containing the product combination of different input columns. This function is ideal for bivariate analysis.
vDataFrame.recommend	Recommends items based on the collaborative filtering (CF) technique.
vDataFrame.sort	Sorts the vDataFrame using the input vcolumns.

Management

Method	Definition
vDataFrame.del_catalog	Delete the current vDataFrame catalog.
vDataFrame.load	Loads a previous structure of the vDataFrame.
vDataFrame.save	Saves the current structure of the vDataFrame.
vDataFrame.set_cursor	Sets a new DB cursor. Very usefull if the connection to the DB is lost.
vDataFrame.set_schema_writing	Sets a new writing schema, this schema will be to use to create temporary table if it is necessary.

Moving Windows

Method	Definition
vDataFrame.cummax	Adds a new vcolumn to the vDataFrame by computing the cumulative maximum of the input vcolumn.
vDataFrame.cummin	Adds a new vcolumn to the vDataFrame by computing the cumulative minimum of the input vcolumn.
vDataFrame.cumprod	Adds a new vcolumn to the vDataFrame by computing the cumulative product of the input vcolumn.
vDataFrame.cumsum	Adds a new vcolumn to the vDataFrame by computing the cumulative sum of the input vcolumn.
vDataFrame.rolling	Adds a new vcolumn to the vDataFrame by using an advanced analytical window function on one or two specific vcolumns.

Normalization and Global Outliers

Method	Definition
vDataFrame[].clip	Clips the vColumn.
vDataFrame[].fill_outliers	Fills the vColumns outliers using the input method.
vDataFrame.normalize	Normalizes the input vColumns using the input method.
vDataFrame[].normalize	Normalizes the input vColumns using the input method.
vDataFrame.outliers	Adds a new vColumns labeled with 0 and 1. 1 means that the record is a global outlier.

Plotting & Graphics

Method	Definition
vDataFrame.animated	Draws the animated chart.
vDataFrame.bar	Draws the bar chart of the input vColumns based on an aggregation.
vDataFrame[].bar	Draws the bar chart of the vColumn based on an aggregation.
vDataFrame.boxplot	Draws the box plot of the input vColumns.
vDataFrame[].boxplot	Draws the box plot of the vColumn.
vDataFrame.bubble	Draws the bubble plot of the input vColumns.
vDataFrame.contour	Draws the contour plot of the input function using the 2 input vColumns.
vDataFrame.density	Draws the density plot of the vColumns.
vDataFrame[].density	Draws the density plot of the vColumns.
vDataFrame.hchart	Draws responsive charts using the Highchart API.
vDataFrame[].geo_plot	Draws a geospatial object.
vDataFrame.heatmap	Draws the heatmap of two input vColumns.
vDataFrame.hexbin	Draws the hexbin of the input vColumns based on an aggregation.
vDataFrame.hist	Draws the histogram of the input vColumns based on an aggregation.
vDataFrame[].hist	Draws the histogram of the vColumn based on an aggregation.
vDataFrame.outliers_plot	Draws the global outliers plot one or two columns based on their ZSCORE.
vDataFrame.pie	Draws the nested density pie chart of the input vColumns.
vDataFrame[].pie	Draws the pie chart of the vColumn based on an aggregation.
vDataFrame.pivot_table	Draws the pivot table of one or two columns based on an aggregation.
vDataFrame.plot	Draws a time series plot.
vDataFrame[].plot	Draws the time series of the vColumn.
vDataFrame[].range_plot	Draws the range plot of the vColumn.
vDataFrame.scatter	Draws the scatter plot of the input vColumns.
vDataFrame.scatter_matrix	Draws the scatter matrix of the vDataFrame.
vDataFrame[].spider	Draws the spider plot of the input vColumn based on an aggregation.
vDataFrame.stacked_area	Draws a time series stacked area chart.

Renaming

Method	Definition
vDataFrame[].rename	Renames the vColumn.

Sample

Method	Definition
vDataFrame.balance	Balances the dataset using the input method.
vDataFrame.sample	Downsamples the vDataFrame by filtering using a random vcolumn.

Serialization

Method	Definition
vDataFrame.to_csv	Creates a CSV file of the current vDataFrame relation.
vDataFrame.to_db	Saves the vDataFrame current relation to the Vertica database.
vDataFrame.to_geopandas	Converts the vDataFrame to a Geopandas DataFrame.
vDataFrame.to_json	Creates a JSON file of the current vDataFrame relation.
vDataFrame.to_list	Converts the vDataFrame to a Python list.
vDataFrame.to_numpy	Converts the vDataFrame to a Numpy array.
vDataFrame.to_pandas	Converts the vDataFrame to a pandas DataFrame.
vDataFrame.to_pickle	Saves the vDataFrame to a Python pickle file.
vDataFrame.to_shp	Creates a SHP file of the current vDataFrame relation.

Splitting into Train/Test

Method	Definition
vDataFrame.train_test_split	Creates 2 vDataFrame (train/test) which can be to use to evaluate a model.

Working with Text

Method	Definition
vDataFrame.regexp	Computes a new vcolumn based on regular expressions.
vDataFrame[].str_contains	Verifies if the regular expression is in each of the vcolumn records. The vcolumn will be transformed.
vDataFrame[].str_count	Computes the regular expression count match in each record of the vcolumn. The vcolumn will be transformed.
vDataFrame[].str_extract	Extracts the regular expression in each record of the vcolumn. The vcolumn will be transformed.
vDataFrame[].str_replace	Replaces the regular expression matches in each of the vcolumn record by an input value. The vcolumn will be transformed.
vDataFrame[].str_slice	Slices the vcolumn. The vcolumn will be transformed.

Working with weights

Method	Definition
vDataFrame.add_duplicates	Duplicates the vDataFrame using the input weight.