dlpy.timeseries.TimeseriesTable

class dlpy.timeseries.TimeseriesTable(name, timeid=None, groupby_var=None, sequence_opt=None, inputs_target=None, target=None, autoregressive_sequence=None, acc_interval=None, **table_params)

Table for preprocessing timeseries

It creates an instance of TimeseriesTable by loading from files on the server side, or files on the client side, or in memory CASTable, pandas.DataFrame or :class:`pandas.Series. It then performs inplace timeseries formatting, timeseries accumulation, timeseries subsequence generation, and timeseries partitioning to prepare the timeseries into a format that can be followed by subsequent deep learning models.

Parameters

namestring, optional

Name of the CAS table

timeidstring, optional

Specifies the column name for the timeid. Default: None

groupby_varstring or list-of-strings, optional

The groupby variables. Default: None.

sequence_optdict, optional

Dictionary with keys: ‘input_length’, ‘target_length’ and ‘token_size’. It will be created by the prepare_subsequences method. Default: None

inputs_targetdict, optional

Dictionary with keys: ‘inputs’, ‘target’. It will be created by the prepare_subsequences method. Default: None

Returns

TimeseriesTable

Attributes

timeid_typestring

Specifies whether the table uses ‘date’ or ‘datetime’ format

__init__(name, timeid=None, groupby_var=None, sequence_opt=None, inputs_target=None, target=None, autoregressive_sequence=None, acc_interval=None, **table_params)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(name[, timeid, groupby_var, …])	Initialize self.
abs()	Return a new CASTable with absolute values of numerics
all([axis, bool_only, skipna, level])	Return whether all elements in the column are True
any([axis, bool_only, skipna, level])	Return whether any elements in the column are True
append(other[, ignore_index, …])	Append rows of other to self
append_columns(*items, **kwargs)	Append variable names to action inputs parameter
append_computed_columns(names, code[, inplace])	Append computed columns as specified
append_computedvars(*items, **kwargs)	Append variable names to tbl.computedvars parameter
append_computedvarsprogram(*items, **kwargs)	Append code to tbl.computedvarsprogram parameter
append_groupby(*items, **kwargs)	Append variable names to tbl.groupby parameter
append_orderby(*items, **kwargs)	Append orderby parameters
append_where(*items, **kwargs)	Append code to where parameter
as_matrix([columns, n])	Convert the CASTable to its Numpy-array representation
boxplot([column, by])	Make a boxplot from the table data
clip([lower, upper, axis])	Clip values at thresholds
clip_lower(threshold[, axis])	Clip values at lower threshold
clip_upper(threshold[, axis])	Clip values at upper threshold
convert_to_sas_time_format(python_time, …)
copy([deep, exclude])	Make a copy of the CASTable object
corr([method, min_periods])	Compute pairwise correlation of columns
count([axis, level, numeric_only])	Return total number of non-missing values in each column
create_lags(varname, nlags, byvar)
css([casout])	Return the corrected sum of squares of the values of each column
cv([casout])	Return the coefficient of variation of the values of each column
datastep(code[, casout])	Execute Data step code against the CAS table
del_action_params(*names)	Delete parameters for specified action names
del_param(*keys)	Delete parameters
del_params(*keys)	Delete parameters
describe([percentiles, include, exclude, stats])	Get descriptive statistics
drop(labels[, axis, level, inplace, errors])	Return a new CASTable object with the specified columns removed
dropna([axis, how, thresh, subset, inplace])	Drop rows that contain missing values
eval(expr[, inplace, kwargs])	Evaluate a CAS table expression
exists()	Return True if table exists in the server
fillna([value, method, axis, inplace, …])	Fill missing values using the specified method
find_file_caslib(conn, path)	Check whether the specified path is in the caslibs of the current session
from_csv(connection, path[, casout])	Create a CASTable from a CSV file
from_dict(connection, data[, casout])	Create a CASTable from a dictionary
from_items(connection, items[, casout])	Create a CASTable from a (key, value) pairs
from_localfile(conn, path[, columns, …])	Create an TimeseriesTable from a file on the client side.
from_pandas(conn, pandas_df[, casout])	Create an TimeseriesTable from a pandas DataFrame or Series
from_records(connection, data[, casout])	Create a CASTable from records
from_serverfile(conn, path[, columns, …])	Create an TimeseriesTable from a file on the server side
from_table(tbl[, columns, casout])	Create an TimeseriesTable from a CASTable
generate_splitting_code(timeid, start, end, …)
get(key[, default])	Get item from object for given key (ex: DataFrame column)
get_action_names()	Return a list of available CAS actions
get_action_params(name, *default)	Return parameters for specified action name
get_actionset_names()	Return a list of available actionsets
get_connection()	Get the registered connection object
get_dtype_counts()	Retrieve the frequency of CAS table column data types
get_fetch_params()	Return options to be used during the table.fetch action
get_ftype_counts()	Retrieve the frequency of CAS table column data types
get_groupby_vars()	Return a list of By group variable names
get_inputs_param()	Return the column names for the inputs= action parameter
get_param(key, *default)	Return the value of a parameter
get_params(*keys)	Return the values of one or more parameters
get_value(index, col, **kwargs)	Retrieve a single scalar value
groupby(by[, axis, level, as_index, sort, …])	Specify grouping variables for the table
has_groupby_vars()	Does the table have By group variables configured?
has_param(*keys)	Return a boolean indicating whether or not the parameters exist
has_params(*keys)	Return a boolean indicating whether or not the parameters exist
head([n, columns, bygroup_as_index, casout])	Retrieve first n rows
hist([column, by])	Make a histogram from the table data
identify_coltype(col, tbl_colinfo)
info([verbose, buf, max_cols, memory_usage, …])	Print summary of CASTable information
invoke(_name_, **kwargs)	Invoke an action on the registered connection
iteritems()	Iterate over column names and CASColumn objects
iterrows([chunksize])	Iterate over the rows of a CAS table as (index, pandas.Series) pairs
itertuples([index, chunksize])	Iterate over rows as tuples
kurt([axis, skipna, level, numeric_only, casout])	Return the kurtosis of the values of each column
kurtosis([axis, skipna, level, …])	Return the kurtosis of the values of each column
lookup(row_labels, col_labels)	Retrieve values indicated by row_labels, col_labels positions
max([axis, skipna, level, numeric_only, casout])	Return the maximum value of each column
mean([axis, skipna, level, numeric_only, casout])	Return the mean value of each column
median([axis, skipna, level, numeric_only, …])	Return the median value of each numeric column
merge(right[, how, on, left_on, right_on, …])	Merge CASTable objects using a database-style join on a column
min([axis, skipna, level, numeric_only, casout])	Return the minimum value of each column
mode([axis, numeric_only, max_tie, skipna])	Return the mode of each column
next()	Return next item in the iteration
nlargest(n, columns[, keep, casout])	Return the n largest values ordered by columns
nmiss([axis, level, numeric_only, casout])	Return total number of missing values in each column
nsmallest(n, columns[, keep, casout])	Return the n smallest values ordered by columns
nth(n[, dropna, bygroup_as_index, casout])	Return the nth row
pop(colname)	Remove a column from the CASTable and return it
prepare_subsequences(seq_len, target[, …])	Prepare the subsequences that will be pass into RNN
probt([casout])	Return the p-value of the T-statistics of the values of each column
quantile([q, axis, numeric_only, …])	Return values at the given quantile
query(expr[, inplace, engine])	Query the table with a boolean expression
replace([to_replace, value, inplace, limit, …])	Replace values in the data set
reset_index([level, drop, inplace, …])	Reset the CASTable index
retrieve(_name_, **kwargs)	Invoke an action on the registered connection and retrieve results
sample([n, frac, replace, weights, …])	Returns a random sample of the CAS table rows
select_dtypes([include, exclude, inplace])	Return a subset CASTable including/excluding columns based on data type
set_action_params(name, **kwargs)	Set parameters for specified action name
set_connection(connection)	Set the connection to use for action calls
set_param(*args, **kwargs)	Set paramaters according to key/value pairs
set_params(*args, **kwargs)	Set paramaters according to key/value pairs
skew([axis, skipna, level, numeric_only, casout])	Return the skewness of the values of each column
skewness([axis, skipna, level, …])	Return the skewness of the values of each column
slice([start, stop, columns, …])	Retrieve the specified rows
sort(by[, axis, ascending, inplace, kind, …])	Specify sort parameters for data in a CAS table
sort_values(by[, axis, ascending, inplace, …])	Specify sort parameters for data in a CAS table
std([axis, skipna, level, ddof, …])	Return the standard deviation of the values of each column
stderr([casout])	Return the standard error of the values of each column
sum([axis, skipna, level, numeric_only, casout])	Return the sum of the values of each column
tail([n, columns, bygroup_as_index, casout])	Retrieve last n rows
timeseries_accumlation([acc_interval, …])	Accumulate the TimeseriesTable into regular consecutive intervals
timeseries_formatting(timeid, timeseries[, …])	Format the TimeseriesTable
timeseries_partition([training_start, …])	Split the dataset into training, validation and testing set
to_clipboard(*args, **kwargs)	Write the CAS table data to the clipboard
to_csv(*args, **kwargs)	Write CAS table data to comma separated values (CSV)
to_datastep_params()	Create a data step table specification
to_dense(*args, **kwargs)	Return dense representation of CAS table data
to_dict(*args, **kwargs)	Convert CAS table data to a Python dictionary
to_excel(*args, **kwargs)	Write CAS table data to an Excel spreadsheet
to_frame([sample_pct, sample_seed, sample, …])	Retrieve entire table as a SASDataFrame
to_gbq(*args, **kwargs)	Write CAS table data to a Google BigQuery table
to_hdf(*args, **kwargs)	Write CAS table data to HDF
to_html(*args, **kwargs)	Render the CAS table data to an HTML table
to_input_datastep_params()	Create an input data step table specification
to_json(*args, **kwargs)	Convert the CAS table data to a JSON string
to_latex(*args, **kwargs)	Render the CAS table data to a LaTeX tabular environment
to_msgpack(*args, **kwargs)	Write CAS table data to msgpack object
to_outtable()	Create a copy of the CASTable object with only output table paramaters
to_outtable_params()	Create a copy of the CASTable parameters using only the output table parameters
to_params()	Return parameters of CASTable object
to_pickle(*args, **kwargs)	Pickle (serialize) the CAS table data
to_records(*args, **kwargs)	Convert CAS table data to record array
to_sparse(*args, **kwargs)	Convert CAS table data to SparseDataFrame
to_sql(*args, **kwargs)	Write CAS table records to SQL database
to_stata(*args, **kwargs)	Write CAS table data to Stata file
to_string(*args, **kwargs)	Render the CAS table to a console-friendly tabular output
to_table()	Create a copy of the CASTable object with only input table paramaters
to_table_name()	Return the name of the table
to_table_params()	Create a copy of the table parameters containing only input table parameters
to_view(*args, **kwargs)	Create a view using the current CASTable parameters
to_xarray(*args, **kwargs)	Return an numpy.xarray() from the CAS table
tvalue([casout])	Return the T-statistics for hypothesis testing of the values of each column
uss([casout])	Return the uncorrected sum of squares of the values of each column
var([axis, skipna, level, ddof, …])	Return the variance of the values of each column
with_params(**kwargs)	Create copy of table with kwargs inserted as parameters
xs(key[, axis, level, copy, drop_level])	Return a cross-section from the CASTable

Attributes

all_params
at
axes	List of the row axis labels and column axis labels
columns	The visible columns in the table
created_date	Return the created date of the table in the server
dtypes	Series of the data types in the table
ftypes	Series of the ftypes (indication of sparse/dense and dtype) in the table
getdoc
iat
iloc	Integer location based indexing for selection by position
index	The table index
ix	Label-based indexer with integer position fallback
last_accessed_date	Return the last access date of the table in the server
last_modified_date	Return the last modified date of the table in the server
loc	Label-based indexer
ndim	Number of axes dimensions
outtable_params
param_names
plot	Make plots of the data in the CAS table
running_caslib
shape	Return a tuple representing the dimensionality of the table
size	Number of elements in the table
table_params
timeid_type
values	Numpy representation of the table