dlpy.audio.AudioTable

class dlpy.audio.AudioTable(name, **table_params)

Bases: swat.cas.table.CASTable

CAS Table for Audio

__init__(name, **table_params)

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

Methods

__init__(name, **table_params) Initialize self.
abs() Return a new CASTable with absolute values of numerics
all([axis, bool_only, skipna, level]) Return True for each column with only elements that evaluate to true
any([axis, bool_only, skipna, level]) Return True for each column with at least one true element
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 computedvars parameter
append_computedvarsprogram(*items, **kwargs) Append code to computedvarsprogram parameter
append_groupby(*items, **kwargs) Append variable names to groupby parameter
append_orderby(*items, **kwargs) Append orderby parameters
append_where(*items, **kwargs) Append code to where parameter
as_matrix([columns, n]) Represent CASTable as a Numpy array
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
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_audio_feature_table([frame_shift, …]) Extracts audio features from the audio table and create a new CASTable that contains the features.
create_audio_table(conn, data_path, …[, …]) Creates an Audio table and takes care of all the necessary steps
create_audio_table_speechrecognition(conn, …) Creates an Audio table and takes care of all the necessary steps
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 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
drop_duplicates(casout[, subset]) Remove duplicate rows from a CASTable.
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
extract_audio_features(conn, table[, …]) Extracts audio features from the audio files
fillna([value, method, axis, inplace, …]) Fill missing values using the specified method
from_audio_sashdat(conn, path[, casout]) Create an AudioTable from a sashdat file
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_records(connection, data[, casout]) Create a CASTable from records
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() Return True if the table has By group variables configured
has_param(*keys) Return True if the specified parameters exist
has_params(*keys) Return True if the specified parameters exist
head([n, columns, bygroup_as_index, casout]) Retrieve first n rows
hist([column, by]) Make a histogram from the table data
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
load_audio_files(conn[, path, casout, …]) Load audio files from path
load_audio_metadata(conn, path, audio_path) Pre-process and loads the metadata
load_audio_metadata_speechrecognition(conn, …) Pre-process and loads the metadata
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
nunique([dropna, casout]) Return number of unique elements per column in the CASTable
pop(colname) Remove a column from the CASTable and return it
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
rename(columns[, errors]) Rename columns of the CASTable.
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 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 CASTable
sort_values(by[, axis, ascending, inplace, …]) Specify sort parameters for data in a CASTable
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
to_clipboard(*args, **kwargs) Write the table data to the clipboard
to_csv(*args, **kwargs) Write table data to comma-separated values (CSV)
to_datastep_params() Create a data step table specification
to_dense(*args, **kwargs) Return dense representation of table data
to_dict(*args, **kwargs) Convert table data to a Python dictionary
to_excel(*args, **kwargs) Write table data to an Excel spreadsheet
to_frame([sample_pct, sample_seed, sample, …]) Retrieve entire table as a SASDataFrame
to_gbq(*args, **kwargs) Write table data to a Google BigQuery table
to_hdf(*args, **kwargs) Write table data to HDF
to_html(*args, **kwargs) Render the table data to an HTML table
to_input_datastep_params() Create an input data step table specification
to_json(*args, **kwargs) Convert the table data to a JSON string
to_latex(*args, **kwargs) Render the table data to a LaTeX tabular environment
to_msgpack(*args, **kwargs) Write 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 table data
to_records(*args, **kwargs) Convert table data to record array
to_sparse(*args, **kwargs) Convert table data to SparseDataFrame
to_sql(*args, **kwargs) Write table records to SQL database
to_stata(*args, **kwargs) Write table data to Stata file
to_string(*args, **kwargs) Render the 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) Represent table data as a numpy.xarray
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