swat.cas.table.CASColumn

class swat.cas.table.CASColumn(name, **table_params)

Bases: swat.cas.table.CASTable

Special subclass of CASTable for holding single columns

__init__(name, **table_params)

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

Methods

__init__(name, **table_params)	Initialize self.
abs()	Return absolute values element-wise
add(other[, level, fill_value, axis])	Addition of CASColumn with other, element-wise
all([axis, bool_only, skipna, level])	Return whether all elements are True
any([axis, bool_only, skipna, level])	Return whether any elements 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
between(left, right[, inclusive])	Return boolean CASColumn equivalent to left <= value <= right
boxplot([column, by])	Make a boxplot from the table data
clip([lower, upper, out, axis])	Trim values at input threshold(s)
clip_lower(threshold[, axis])	Trim values below given threshold
clip_upper(threshold[, axis])	Trim values above given threshold
copy([deep, exclude])	Make a copy of the CASTable object
corr(other[, method, min_periods])	Compute correlation with other column
count([level])	Return the number of non-NA/null observations in the CASColumn
css([casout])	Return corrected sum of squares of the values
cv([casout])	Return coefficient of variation of the values
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])	Generate various summary statistics
div(other[, level, fill_value, axis])	Floating division of CASColumn and other, element-wise
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
eq(other[, axis])	Equal-to comparison of CASColumn and other, element-wise
eval(expr[, inplace, kwargs])	Evaluate a CAS table expression
fillna([value, method, axis, inplace, …])	Fill missing values using the specified method
floordiv(other[, level, fill_value, axis])	Integer division of CASColumn and other, element-wise
from_csv(connection, path[, header, sep, …])	Create a CASColumn 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
ge(other[, axis])	Greater-than-or-equal-to comparison of CASColumn and other, element-wise
get(key[, default])	Get item from CASColumn for the given key
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
gt(other[, axis])	Greater-than comparison of CASColumn and other, element-wise
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, bygroup_as_index, casout])	Return first n rows of the column in a Series
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
isin(values)	Return a boolean CASColumn indicating if the value is in the given values
isnull()	Return a boolean CASColumn indicating if the values are null
iteritems([chunksize])	Lazily iterate over (index, value) tuples
iterrows([chunksize])	Iterate over the rows of a CAS table as (index, pandas.Series) pairs
itertuples([index, chunksize])	Iterate over rows as tuples
kurt([casout])	Return kurtosis
kurtosis([casout])	Return kurtosis
le(other[, axis])	Less-than-or-equal-to comparison of CASColumn and other, element-wise
lookup(row_labels, col_labels)	Retrieve values indicated by row_labels, col_labels positions
lt(other[, axis])	Less-than comparison of CASColumn and other, element-wise
max([axis, skipna, level, casout])	Return the maximum value
mean([axis, skipna, level, casout])	Return the mean value
median([q, axis, interpolation, casout])	Return the median value
merge(right[, how, on, left_on, right_on, …])	Merge CASTable objects using a database-style join on a column
min([axis, skipna, level, casout])	Return the minimum value
mod(other[, level, fill_value, axis])	Modulo of CASColumn and other, element-wise
mode([axis, max_tie])	Return the mode values
mul(other[, level, fill_value, axis])	Multiplication of CASColumn with other, element-wise
ne(other[, axis])	Not-equal-to comparison of CASColumn and other, element-wise
next()	Return next item in the iteration
nlargest([n, keep, casout])	Return the n largest values
nmiss([casout])	Return number of missing values
notnull()	Return a boolean CASColumn indicating if the values are not null
nsmallest([n, keep, casout])	Return the n smallest values
nth(n[, dropna, bygroup_as_index, casout])	Return the nth row
nunique([dropna, casout])	Return number of unique elements in the CASColumn
pop(colname)	Remove a column from the CASTable and return it
pow(other[, level, fill_value, axis])	Exponential power of CASColumn and other, element-wise
probt([casout])	Return p-value of the T-statistic
quantile([q, axis, interpolation, casout])	Return the value at the given quantile
query(expr[, inplace, engine])	Query the table with a boolean expression
radd(other[, level, fill_value, axis])	Addition of CASColumn and other, element-wise
rdiv(other[, level, fill_value, axis])	Floating division of CASColumn and other, element-wise
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
rfloordiv(other[, level, fill_value, axis])	Integer division of CASColumn and other, element-wise
rmod(other[, level, fill_value, axis])	Modulo of CASColumn and other, element-wise
rmul(other[, level, fill_value, axis])	Multiplication of CASColumn and other, element-wise
round([decimals, out])	Round each value of the CASColumn to the given number of decimals
rpow(other[, level, fill_value, axis])	Exponential power of CASColumn and other, element-wise
rsub(other[, level, fill_value, axis])	Subtraction of CASColumn and other, element-wise
rtruediv(other[, level, fill_value, axis])	Floating division of CASColumn and other, element-wise
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([casout])	Return skewness
skewness([casout])	Return skewness
slice([start, stop, bygroup_as_index, casout])	Return from rows from start to stop in a Series
sort([axis, ascending, inplace, kind, …])	Apply sort order parameters to fetches of the data in this column
sort_values([axis, ascending, inplace, …])	Apply sort order parameters to fetches of the data in this column
std([axis, skipna, level, ddof, casout])	Return the standard deviation of the values
stderr([casout])	Return standard error of the values
sub(other[, level, fill_value, axis])	Subtraction of CASColumn with other, element-wise
sum([axis, skipna, level, casout])	Return the sum of the values
tail([n, bygroup_as_index, casout])	Return last n rows of the column in a Series
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(*args, **kwargs)	Convert CASColumn to a pandas.DataFrame
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_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 the parameters as a dictionary
to_pickle(*args, **kwargs)	Pickle (serialize) the CAS table data
to_records(*args, **kwargs)	Convert CAS table data to record array
to_series(*args, **kwargs)	Retrieve all elements into a Series
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 xarray object from the CASColumn
tolist()	Return a list of the column values
truediv(other[, level, fill_value, axis])	Floating division of CASColumn and other, element-wise
tvalue([casout])	Return value of T-statistic for hypothetical testing
unique([casout])	Return array of unique values in the CASColumn
uss([casout])	Return uncorrected sum of squares of the values
value_counts([normalize, sort, ascending, …])	Return object containing counts of unique values
var([axis, skipna, level, ddof, casout])	Return the unbiased variance of the values
xs(*args, **kwargs)	Only exists for CASTable