deephaven.table_factory¶
This module provides various ways to make a Deephaven table.
- class DynamicTableWriter(col_defs)[source]¶
Bases:
JObjectWrapperThe DynamicTableWriter creates a new in-memory table and supports writing data to it.
This class implements the context manager protocol and thus can be used in with statements.
Initializes the writer and creates a new in-memory table.
- Parameters:
col_defs (Dict[str, DTypes]) – a map of column names and types of the new table
- Raises:
DHError –
- j_object_type¶
alias of
DynamicTableWriter
- write_row(*values)[source]¶
Writes a row to the newly created table.
The type of a value must be convertible (safely or unsafely, e.g. lose precision, overflow, etc.) to the type of the corresponding column.
- Parameters:
*values (Any) – the values of the new row, the data types of these values must match the column definitions of the table
- Raises:
DHError –
- Return type:
None
- class InputTable(j_table)[source]¶
Bases:
TableInputTable is a subclass of Table that allows the users to dynamically add/delete/modify data in it.
Users should always create InputTables through factory methods rather than directly from the constructor.
- abs_sum_by(by=None)¶
The abs_sum_by method creates a new table containing the absolute sum for each group.
- add(table)[source]¶
Synchronously writes rows from the provided table to this input table. If this is a keyed input table, added rows with keys that match existing rows will replace those rows.
- agg_all_by(agg, by=None)¶
The agg_all_by method creates a new table containing grouping columns and grouped data. The resulting grouped data is defined by the aggregation specified.
Note, because agg_all_by applies the aggregation to all the columns of the table, it will ignore any column names specified for the aggregation.
- Parameters:
agg (Aggregation) – the aggregation
by (Union[str, Sequence[str]], optional) – the group-by column name(s), default is None
- Return type:
- Returns:
a new table
- Raises:
DHError –
- agg_by(aggs, by=None, preserve_empty=False, initial_groups=None)¶
The agg_by method creates a new table containing grouping columns and grouped data. The resulting grouped data is defined by the aggregations specified.
- Parameters:
aggs (Union[Aggregation, Sequence[Aggregation]]) – the aggregation(s)
by (Union[str, Sequence[str]]) – the group-by column name(s), if not provided, all rows from this table are grouped into a single group of rows before the aggregations are applied to the result, default is None.
preserve_empty (bool) – whether to keep result rows for groups that are initially empty or become empty as a result of updates. Each aggregation operator defines its own value for empty groups. Default is False.
initial_groups (Table) – a table whose distinct combinations of values for the group-by column(s) should be used to create an initial set of aggregation groups. All other columns are ignored. This is useful in combination with preserve_empty=True to ensure that particular groups appear in the result table, or with preserve_empty=False to control the encounter order for a collection of groups and thus their relative order in the result. Changes to this table are not expected or handled; if this table is a refreshing table, only its contents at instantiation time will be used. Default is None, the result will be the same as if a table is provided but no rows were supplied. When it is provided, the ‘by’ argument must be provided to explicitly specify the grouping columns.
- Return type:
- Returns:
a new table
- Raises:
DHError –
- aj(table, on, joins=None)¶
The aj (as-of join) method creates a new table containing all the rows and columns of the left table, plus additional columns containing data from the right table. For columns appended to the left table (joins), row values equal the row values from the right table where the keys from the left table most closely match the keys from the right table without going over. If there is no matching key in the right table, appended row values are NULL.
- Parameters:
table (Table) – the right-table of the join
on (Union[str, Sequence[str]]) – the column(s) to match, can be a common name or a match condition of two columns, e.g. ‘col_a = col_b’. The first ‘N-1’ matches are exact matches. The final match is an inexact match. The inexact match can use either ‘>’ or ‘>=’. If a common name is used for the inexact match, ‘>=’ is used for the comparison.
joins (Union[str, Sequence[str]], optional) – the column(s) to be added from the right table to the result table, can be renaming expressions, i.e. “new_col = col”; default is None
- Return type:
- Returns:
a new table
- Raises:
DHError –
- attributes()¶
Returns all the attributes defined on the table.
- Return type:
Dict[str,Any]
- avg_by(by=None)¶
The avg_by method creates a new table containing the average for each group.
- await_update(timeout=None)¶
Waits until either this refreshing Table is updated or the timeout elapses if provided.
- Parameters:
timeout (int) – the maximum time to wait in milliseconds, default is None, meaning no timeout
- Return type:
bool- Returns:
True when the table is updated or False when the timeout has been reached.
- Raises:
DHError –
- property column_names¶
The column names of the table.
- property columns¶
The column definitions of the table.
- count_by(col, by=None)¶
The count_by method creates a new table containing the number of rows for each group.
- property definition¶
The table definition.
- delete(table)[source]¶
Synchronously deletes the keys contained in the provided table from this keyed input table. If this method is called on an append-only input table, an error will be raised.
- drop_columns(cols)¶
The drop_columns method creates a new table with the same size as this table but omits any of specified columns.
- exact_join(table, on, joins=None)¶
The exact_join method creates a new table containing all the rows and columns of this table plus additional columns containing data from the right table. For columns appended to the left table (joins), row values equal the row values from the right table where the key values in the left and right tables are equal.
- Parameters:
table (Table) – the right-table of the join
on (Union[str, Sequence[str]]) – the column(s) to match, can be a common name or an equal expression, i.e. “col_a = col_b” for different column names
joins (Union[str, Sequence[str]], optional) – the column(s) to be added from the right table to the result table, can be renaming expressions, i.e. “new_col = col”; default is None
- Return type:
- Returns:
a new table
- Raises:
DHError –
- first_by(by=None)¶
The first_by method creates a new table containing the first row for each group.
- flatten()¶
Returns a new version of this table with a flat row set, i.e. from 0 to number of rows - 1.
- Return type:
- format_column_where(col, cond, formula)¶
Applies color formatting to a column of the table conditionally.
- Parameters:
col (str) – the column name
cond (str) – the condition expression
formula (str) – the formatting string in the form of assignment expression “column=color expression” where color_expression can be a color name or a Java ternary expression that results in a color.
- Return type:
- Returns:
a new table
- Raises:
DHError –
- format_columns(formulas)¶
Applies color formatting to the columns of the table.
- format_row_where(cond, formula)¶
Applies color formatting to rows of the table conditionally.
- Parameters:
cond (str) – the condition expression
formula (str) – the formatting string in the form of assignment expression “column=color expression” where color_expression can be a color name or a Java ternary expression that results in a color.
- Return type:
- Returns:
a new table
- Raises:
DHError –
- group_by(by=None)¶
The group_by method creates a new table containing grouping columns and grouped data, column content is grouped into vectors.
- has_columns(cols)¶
Whether this table contains a column for each of the provided names, return False if any of the columns is not in the table.
- Parameters:
cols (Union[str, Sequence[str]]) – the column name(s)
- Returns:
bool
- head(num_rows)¶
The head method creates a new table with a specific number of rows from the beginning of the table.
- head_by(num_rows, by=None)¶
The head_by method creates a new table containing the first number of rows for each group.
- head_pct(pct)¶
The head_pct method creates a new table with a specific percentage of rows from the beginning of the table.
- property is_blink¶
Whether this table is a blink table.
- property is_flat¶
Whether this table is guaranteed to be flat, i.e. its row set will be from 0 to number of rows - 1.
- property is_refreshing¶
Whether this table is refreshing.
- iter_chunk_dict(cols=None, chunk_size=2048)¶
Returns a generator that reads one chunk of rows at a time from the table into a dictionary. The dictionary is a map of column names to numpy arrays of the column data type.
If the table is refreshing and no update graph locks are currently being held, the generator will try to acquire the shared lock of the update graph before reading the table data. This provides a consistent view of the data. The side effect of this is that the table will not be able to refresh while the table is being iterated on. Additionally, the generator internally maintains a fill context. The auto acquired shared lock and the fill context will be released after the generator is destroyed. That can happen implicitly when the generator is used in a for-loop. When the generator is not used in a for-loop, to prevent resource leaks, it must be closed after use by either (1) setting it to None, (2) using the del statement, or (3) calling the close() method on it.
- Parameters:
cols (Optional[Union[str, Sequence[str]]]) – The columns to read. If None, all columns are read.
chunk_size (int) – The number of rows to read at a time. Default is 2048.
- Return type:
Generator[Dict[str,ndarray],None,None]- Returns:
A generator that yields a dictionary of column names to numpy arrays.
- Raises
ValueError
- iter_chunk_tuple(cols=None, tuple_name='Deephaven', chunk_size=2048)¶
Returns a generator that reads one chunk of rows at a time from the table into a named tuple. The named tuple is made up of fields with their names being the column names and their values being numpy arrays of the column data types.
If the table is refreshing and no update graph locks are currently being held, the generator will try to acquire the shared lock of the update graph before reading the table data. This provides a consistent view of the data. The side effect of this is that the table will not be able to refresh while the table is being iterated on. Additionally, the generator internally maintains a fill context. The auto acquired shared lock and the fill context will be released after the generator is destroyed. That can happen implicitly when the generator is used in a for-loop. When the generator is not used in a for-loop, to prevent resource leaks, it must be closed after use by either (1) setting it to None, (2) using the del statement, or (3) calling the close() method on it.
- Parameters:
cols (Optional[Union[str, Sequence[str]]]) – The columns to read. If None, all columns are read.
tuple_name (str) – The name of the named tuple. Default is ‘Deephaven’.
chunk_size (int) – The number of rows to read at a time. Default is 2048.
- Return type:
Generator[Tuple[ndarray,...],None,None]- Returns:
A generator that yields a named tuple for each row in the table.
- Raises:
ValueError –
- iter_dict(cols=None, *, chunk_size=2048)¶
Returns a generator that reads one row at a time from the table into a dictionary. The dictionary is a map of column names to scalar values of the column data type.
If the table is refreshing and no update graph locks are currently being held, the generator will try to acquire the shared lock of the update graph before reading the table data. This provides a consistent view of the data. The side effect of this is that the table will not be able to refresh while the table is being iterated on. Additionally, the generator internally maintains a fill context. The auto acquired shared lock and the fill context will be released after the generator is destroyed. That can happen implicitly when the generator is used in a for-loop. When the generator is not used in a for-loop, to prevent resource leaks, it must be closed after use by either (1) setting it to None, (2) using the del statement, or (3) calling the close() method on it.
- Parameters:
cols (Optional[Union[str, Sequence[str]]]) – The columns to read. If None, all columns are read.
chunk_size (int) – The number of rows to read at a time internally to reduce the number of Java/Python boundary crossings. Default is 2048.
- Return type:
Generator[Dict[str,Any],None,None]- Returns:
A generator that yields a dictionary of column names to scalar values.
- Raises:
ValueError –
- iter_tuple(cols=None, *, tuple_name='Deephaven', chunk_size=2048)¶
Returns a generator that reads one row at a time from the table into a named tuple. The named tuple is made up of fields with their names being the column names and their values being of the column data types.
If the table is refreshing and no update graph locks are currently being held, the generator will try to acquire the shared lock of the update graph before reading the table data. This provides a consistent view of the data. The side effect of this is that the table will not be able to refresh while the table is being iterated on. Additionally, the generator internally maintains a fill context. The auto acquired shared lock and the fill context will be released after the generator is destroyed. That can happen implicitly when the generator is used in a for-loop. When the generator is not used in a for-loop, to prevent resource leaks, it must be closed after use by either (1) setting it to None, (2) using the del statement, or (3) calling the close() method on it.
- Parameters:
cols (Optional[Union[str, Sequence[str]]]) – The columns to read. If None, all columns are read. Default is None.
tuple_name (str) – The name of the named tuple. Default is ‘Deephaven’.
chunk_size (int) – The number of rows to read at a time internally to reduce the number of Java/Python boundary crossings. Default is 2048.
- Return type:
Generator[Tuple[Any,...],None,None]- Returns:
A generator that yields a named tuple for each row in the table
- Raises:
ValueError –
- j_object_type¶
alias of
Table
- join(table, on=None, joins=None)¶
The join method creates a new table containing rows that have matching values in both tables. Rows that do not have matching criteria will not be included in the result. If there are multiple matches between a row from the left table and rows from the right table, all matching combinations will be included. If no columns to match (on) are specified, every combination of left and right table rows is included.
- Parameters:
table (Table) – the right-table of the join
on (Union[str, Sequence[str]]) – the column(s) to match, can be a common name or an equal expression, i.e. “col_a = col_b” for different column names; default is None
joins (Union[str, Sequence[str]], optional) – the column(s) to be added from the right table to the result table, can be renaming expressions, i.e. “new_col = col”; default is None
- Return type:
- Returns:
a new table
- Raises:
DHError –
- property key_names¶
The names of the key columns of the InputTable.
- last_by(by=None)¶
The last_by method creates a new table containing the last row for each group.
- layout_hints(front=None, back=None, freeze=None, hide=None, column_groups=None, search_display_mode=None)¶
Sets layout hints on the Table
- Parameters:
front (Union[str, List[str]]) – the columns to show at the front.
back (Union[str, List[str]]) – the columns to show at the back.
freeze (Union[str, List[str]]) – the columns to freeze to the front. These will not be affected by horizontal scrolling.
hide (Union[str, List[str]]) – the columns to hide.
column_groups (List[Dict]) –
A list of dicts specifying which columns should be grouped in the UI. The dicts can specify the following:
name (str): The group name
children (List[str]): The column names in the group
color (Optional[str]): The hex color string or Deephaven color name
search_display_mode (SearchDisplayMode) – set the search bar to explicitly be accessible or inaccessible, or use the system default.
SearchDisplayMode.SHOWwill show the search bar,SearchDisplayMode.HIDEwill hide the search bar, andSearchDisplayMode.DEFAULTwill use the default value configured by the user and system settings.
- Return type:
- Returns:
a new table with the layout hints set
- Raises:
DHError –
- lazy_update(formulas)¶
The lazy_update method creates a new table containing a new, cached, formula column for each formula.
- max_by(by=None)¶
The max_by method creates a new table containing the maximum value for each group.
- median_by(by=None)¶
The median_by method creates a new table containing the median for each group.
- property meta_table¶
The column definitions of the table in a Table form.
- min_by(by=None)¶
The min_by method creates a new table containing the minimum value for each group.
- move_columns(idx, cols)¶
The move_columns method creates a new table with specified columns moved to a specific column index value. Columns may be renamed with the same semantics as rename_columns. The renames are simultaneous and unordered, enabling direct swaps between column names. Specifying a source or destination more than once is prohibited.
- move_columns_down(cols)¶
The move_columns_down method creates a new table with specified columns appearing last in order, to the far right. Columns may be renamed with the same semantics as rename_columns. The renames are simultaneous and unordered, enabling direct swaps between column names. Specifying a source or destination more than once is prohibited.
- move_columns_up(cols)¶
The move_columns_up method creates a new table with specified columns appearing first in order, to the far left. Columns may be renamed with the same semantics as rename_columns. The renames are simultaneous and unordered, enabling direct swaps between column names. Specifying a source or destination more than once is prohibited.
- natural_join(table, on, joins=None)¶
The natural_join method creates a new table containing all the rows and columns of this table, plus additional columns containing data from the right table. For columns appended to the left table (joins), row values equal the row values from the right table where the key values in the left and right tables are equal. If there is no matching key in the right table, appended row values are NULL.
- Parameters:
table (Table) – the right-table of the join
on (Union[str, Sequence[str]]) – the column(s) to match, can be a common name or an equal expression, i.e. “col_a = col_b” for different column names
joins (Union[str, Sequence[str]], optional) – the column(s) to be added from the right table to the result table, can be renaming expressions, i.e. “new_col = col”; default is None
- Return type:
- Returns:
a new table
- Raises:
DHError –
- partition_by(by, drop_keys=False)¶
Creates a PartitionedTable from this table, partitioned according to the specified key columns.
- Parameters:
by (Union[str, Sequence[str]]) – the column(s) by which to group data
drop_keys (bool) – whether to drop key columns in the constituent tables, default is False
- Return type:
- Returns:
A PartitionedTable containing a sub-table for each group
- Raises:
DHError –
- partitioned_agg_by(aggs, by=None, preserve_empty=False, initial_groups=None)¶
The partitioned_agg_by method is a convenience method that performs an agg_by operation on this table and wraps the result in a PartitionedTable. If the argument ‘aggs’ does not include a partition aggregation created by calling
agg.partition(), one will be added automatically with the default constituent column name __CONSTITUENT__.- Parameters:
aggs (Union[Aggregation, Sequence[Aggregation]]) – the aggregation(s)
by (Union[str, Sequence[str]]) – the group-by column name(s), default is None
preserve_empty (bool) – whether to keep result rows for groups that are initially empty or become empty as a result of updates. Each aggregation operator defines its own value for empty groups. Default is False.
initial_groups (Table) – a table whose distinct combinations of values for the group-by column(s) should be used to create an initial set of aggregation groups. All other columns are ignored. This is useful in combination with preserve_empty=True to ensure that particular groups appear in the result table, or with preserve_empty=False to control the encounter order for a collection of groups and thus their relative order in the result. Changes to this table are not expected or handled; if this table is a refreshing table, only its contents at instantiation time will be used. Default is None, the result will be the same as if a table is provided but no rows were supplied. When it is provided, the ‘by’ argument must be provided to explicitly specify the grouping columns.
- Return type:
- Returns:
a PartitionedTable
- Raises:
DHError –
- raj(table, on, joins=None)¶
The reverse-as-of join method creates a new table containing all the rows and columns of the left table, plus additional columns containing data from the right table. For columns appended to the left table (joins), row values equal the row values from the right table where the keys from the left table most closely match the keys from the right table without going under. If there is no matching key in the right table, appended row values are NULL.
- Parameters:
table (Table) – the right-table of the join
on (Union[str, Sequence[str]]) – the column(s) to match, can be a common name or a match condition of two columns, e.g. ‘col_a = col_b’. The first ‘N-1’ matches are exact matches. The final match is an inexact match. The inexact match can use either ‘<’ or ‘<=’. If a common name is used for the inexact match, ‘<=’ is used for the comparison.
joins (Union[str, Sequence[str]], optional) – the column(s) to be added from the right table to the result table, can be renaming expressions, i.e. “new_col = col”; default is None
- Return type:
- Returns:
a new table
- Raises:
DHError –
- range_join(table, on, aggs)¶
The range_join method creates a new table containing all the rows and columns of the left table, plus additional columns containing aggregated data from the right table. For columns appended to the left table (joins), cell values equal aggregations over vectors of values from the right table. These vectors are formed from all values in the right table where the right table keys fall within the ranges of keys defined by the left table (responsive ranges).
range_join is a join plus aggregation that (1) joins arrays of data from the right table onto the left table, and then (2) aggregates over the joined data. Oftentimes this is used to join data for a particular time range from the right table onto the left table.
Rows from the right table with null or NaN key values are discarded; that is, they are never included in the vectors used for aggregation. For all rows that are not discarded, the right table must be sorted according to the right range column for all rows within a group.
Join key ranges, specified by the ‘on’ argument, are defined by zero-or-more exact join matches and a single range join match. The range join match must be the last match in the list.
The exact match expressions are parsed as in other join operations. That is, they are either a column name common to both tables or a column name from the left table followed by an equals sign followed by a column name from the right table. .. rubric:: Examples
- Match on the same column name in both tables:
“common_column”
- Match on different column names in each table:
“left_column = right_column” or “left_column == right_column”
The range match expression is expressed as a ternary logical expression, expressing the relationship between the left start column, the right range column, and the left end column. Each column name pair is separated by a logical operator, either < or <=. Additionally, the entire expression may be preceded by a left arrow <- and/or followed by a right arrow ->. The arrows indicate that range match can ‘allow preceding’ or ‘allow following’ to match values outside the explicit range. ‘Allow preceding’ means that if no matching right range column value is equal to the left start column value, the immediately preceding matching right row should be included in the aggregation if such a row exists. ‘Allow following’ means that if no matching right range column value is equal to the left end column value, the immediately following matching right row should be included in the aggregation if such a row exists. .. rubric:: Examples
- For less than paired with greater than:
“left_start_column < right_range_column < left_end_column”
- For less than or equal paired with greater than or equal:
“left_start_column <= right_range_column <= left_end_column”
- For less than or equal (allow preceding) paired with greater than or equal (allow following):
“<- left_start_column <= right_range_column <= left_end_column ->”
- Special Cases
In order to produce aggregated output, range match expressions must define a range of values to aggregate over. There are a few noteworthy special cases of ranges.
Empty Range An empty range occurs for any left row with no matching right rows. That is, no non-null, non-NaN right rows were found using the exact join matches, or none were in range according to the range join match.
Single-value Ranges A single-value range is a range where the left row’s values for the left start column and left end column are equal and both relative matches are inclusive (<= and >=, respectively). For a single-value range, only rows within the bucket where the right range column matches the single value are included in the output aggregations.
Invalid Ranges An invalid range occurs in two scenarios:
When the range is inverted, i.e., when the value of the left start column is greater than the value of the left end column.
When either relative-match is exclusive (< or >) and the value in the left start column is equal to the value in the left end column.
For invalid ranges, the result row will be null for all aggregation output columns.
Undefined Ranges An undefined range occurs when either the left start column or the left end column is NaN. For rows with an undefined range, the corresponding output values will be null (as with invalid ranges).
Unbounded Ranges A partially or fully unbounded range occurs when either the left start column or the left end column is null. If the left start column value is null and the left end column value is non-null, the range is unbounded at the beginning, and only the left end column subexpression will be used for the match. If the left start column value is non-null and the left end column value is null, the range is unbounded at the end, and only the left start column subexpression will be used for the match. If the left start column and left end column values are null, the range is unbounded, and all rows will be included.
Note: At this time, implementations only support static tables. This operation remains under active development.
- Parameters:
table (Table) – the right table of the join
on (Union[str, List[str]]) – the match expression(s) that must include zero-or-more exact match expression, and exactly one range match expression as described above
aggs (Union[Aggregation, List[Aggregation]]) – the aggregation(s) to perform over the responsive ranges from the right table for each row from this Table
- Return type:
- Returns:
a new table
- Raises:
DHError –
- rename_columns(cols)¶
The rename_columns method creates a new table with the specified columns renamed. The renames are simultaneous and unordered, enabling direct swaps between column names. Specifying a source or
destination more than once is prohibited.
- restrict_sort_to(cols)¶
The restrict_sort_to method adjusts the input table to produce an output table that only allows sorting on specified table columns. This can be useful to prevent users from accidentally performing expensive sort operations as they interact with tables in the UI.
- Parameters:
cols (Union[str, Sequence[str]]) – the column name(s)
- Returns:
a new table
- Raises:
DHError –
- reverse()¶
The reverse method creates a new table with all of the rows from this table in reverse order.
- rollup(aggs, by=None, include_constituents=False)¶
Creates a rollup table.
A rollup table aggregates by the specified columns, and then creates a hierarchical table which re-aggregates using one less by column on each level. The column that is no longer part of the aggregation key is replaced with null on each level.
Note some aggregations can not be used in creating a rollup tables, these include: group, partition, median, pct, weighted_avg
- Parameters:
aggs (Union[Aggregation, Sequence[Aggregation]]) – the aggregation(s)
by (Union[str, Sequence[str]]) – the group-by column name(s), default is None
include_constituents (bool) – whether to include the constituent rows at the leaf level, default is False
- Return type:
- Returns:
a new RollupTable
- Raises:
DHError –
- select(formulas=None)¶
The select method creates a new in-memory table that includes one column for each formula. If no formula is specified, all columns will be included.
- select_distinct(formulas=None)¶
The select_distinct method creates a new table containing all the unique values for a set of key columns. When the selectDistinct method is used on multiple columns, it looks for distinct sets of values in the selected columns.
- property size¶
The current number of rows in the table.
- slice(start, stop)¶
Extracts a subset of a table by row positions into a new Table.
If both the start and the stop are positive, then both are counted from the beginning of the table. The start is inclusive, and the stop is exclusive. slice(0, N) is equivalent to
head()(N) The start must be less than or equal to the stop.If the start is positive and the stop is negative, then the start is counted from the beginning of the table, inclusively. The stop is counted from the end of the table. For example, slice(1, -1) includes all rows but the first and last. If the stop is before the start, the result is an empty table.
If the start is negative, and the stop is zero, then the start is counted from the end of the table, and the end of the slice is the size of the table. slice(-N, 0) is equivalent to
tail()(N).If the start is negative and the stop is negative, they are both counted from the end of the table. For example, slice(-2, -1) returns the second to last row of the table.
- slice_pct(start_pct, end_pct)¶
Extracts a subset of a table by row percentages.
Returns a subset of table in the range [floor(start_pct * size_of_table), floor(end_pct * size_of_table)). For example, for a table of size 10, slice_pct(0.1, 0.7) will return a subset from the second row to the seventh row. Similarly, slice_pct(0, 1) would return the entire table (because row positions run from 0 to size - 1). The percentage arguments must be in range [0, 1], otherwise the function returns an error.
- snapshot()¶
Returns a static snapshot table.
- snapshot_when(trigger_table, stamp_cols=None, initial=False, incremental=False, history=False)¶
Returns a table that captures a snapshot of this table whenever trigger_table updates.
When trigger_table updates, a snapshot of this table and the “stamp key” from trigger_table form the resulting table. The “stamp key” is the last row of the trigger_table, limited by the stamp_cols. If trigger_table is empty, the “stamp key” will be represented by NULL values.
Note: the trigger_table must be append-only when the history flag is set to True. If the trigger_table is not append-only and has modified or removed rows in its updates, the result snapshot table will be put in a failure state and become unusable.
- Parameters:
trigger_table (Table) – the trigger table
stamp_cols (Union[str, Sequence[str]) – The columns from trigger_table that form the “stamp key”, may be renames. None, or empty, means that all columns from trigger_table form the “stamp key”.
initial (bool) – Whether to take an initial snapshot upon construction, default is False. When False, the resulting table will remain empty until trigger_table first updates.
incremental (bool) – Whether the resulting table should be incremental, default is False. When False, all rows of this table will have the latest “stamp key”. When True, only the rows of this table that have been added or updated will have the latest “stamp key”.
history (bool) – Whether the resulting table should keep history, default is False. A history table appends a full snapshot of this table and the “stamp key” as opposed to updating existing rows. The history flag is currently incompatible with initial and incremental: when history is True, incremental and initial must be False.
- Return type:
- Returns:
a new table
- Raises:
DHError –
- sort(order_by, order=None)¶
The sort method creates a new table where the rows are ordered based on values in a specified set of columns.
- Parameters:
order_by (Union[str, Sequence[str]]) – the column(s) to be sorted on
order (Union[SortDirection, Sequence[SortDirection], optional) – the corresponding sort directions for each sort column, default is None, meaning ascending order for all the sort columns.
- Return type:
- Returns:
a new table
- Raises:
DHError –
- sort_descending(order_by)¶
The sort_descending method creates a new table where rows in a table are sorted in descending order based on the order_by column(s).
- std_by(by=None)¶
The std_by method creates a new table containing the sample standard deviation for each group.
Sample standard deviation is computed using Bessel’s correction, which ensures that the sample variance will be an unbiased estimator of population variance.
- sum_by(by=None)¶
The sum_by method creates a new table containing the sum for each group.
- tail(num_rows)¶
The tail method creates a new table with a specific number of rows from the end of the table.
- tail_by(num_rows, by=None)¶
The tail_by method creates a new table containing the last number of rows for each group.
- tail_pct(pct)¶
The tail_pct method creates a new table with a specific percentage of rows from the end of the table.
- to_string(num_rows=10, cols=None)¶
Returns the first few rows of a table as a pipe-delimited string.
- Parameters:
num_rows (int) – the number of rows at the beginning of the table
cols (Union[str, Sequence[str]]) – the column name(s), default is None
- Return type:
str- Returns:
string
- Raises:
DHError –
- tree(id_col, parent_col, promote_orphans=False)¶
Creates a hierarchical tree table.
The structure of the table is encoded by an “id” and a “parent” column. The id column should represent a unique identifier for a given row, and the parent column indicates which row is the parent for a given row. Rows that have a None parent are part of the “root” table.
It is possible for rows to be “orphaned” if their parent is non-None and does not exist in the table. These rows will not be present in the resulting tree. If this is not desirable, they could be promoted to become children of the root table by setting ‘promote_orphans’ argument to True.
- Parameters:
id_col (str) – the name of a column containing a unique identifier for a particular row in the table
parent_col (str) – the name of a column containing the parent’s identifier, {@code null} for rows that are part of the root table
promote_orphans (bool) – whether to promote node tables whose parents don’t exist to be children of the root node, default is False
- Return type:
- Returns:
a new TreeTable organized according to the parent-child relationships expressed by id_col and parent_col
- Raises:
DHError –
- ungroup(cols=None)¶
The ungroup method creates a new table in which array columns from the source table are unwrapped into separate rows.
- update(formulas)¶
The update method creates a new table containing a new, in-memory column for each formula.
- update_by(ops, by=None)¶
Creates a table with additional columns calculated from window-based aggregations of columns in this table. The aggregations are defined by the provided operations, which support incremental aggregations over the corresponding rows in the table. The aggregations will apply position or time-based windowing and compute the results over the entire table or each row group as identified by the provided key columns.
- Parameters:
ops (Union[UpdateByOperation, List[UpdateByOperation]]) – the update-by operation definition(s)
by (Union[str, List[str]]) – the key column name(s) to group the rows of the table
- Return type:
- Returns:
a new Table
- Raises:
DHError –
- property update_graph¶
The update graph of the table.
- update_view(formulas)¶
The update_view method creates a new table containing a new, formula column for each formula.
- property value_names¶
The names of the value columns. By default, any column not marked as a key column is a value column.
- var_by(by=None)¶
The var_by method creates a new table containing the sample variance for each group.
Sample variance is computed using Bessel’s correction, which ensures that the sample variance will be an unbiased estimator of population variance.
- view(formulas)¶
The view method creates a new formula table that includes one column for each formula.
- weighted_avg_by(wcol, by=None)¶
The weighted_avg_by method creates a new table containing the weighted average for each group.
- weighted_sum_by(wcol, by=None)¶
The weighted_sum_by method creates a new table containing the weighted sum for each group.
- where(filters=None)¶
The where method creates a new table with only the rows meeting the filter criteria in the column(s) of the table.
- where_in(filter_table, cols)¶
The where_in method creates a new table containing rows from the source table, where the rows match values in the filter table. The filter is updated whenever either table changes.
- where_not_in(filter_table, cols)¶
The where_not_in method creates a new table containing rows from the source table, where the rows do not match values in the filter table.
- where_one_of(filters=None)¶
The where_one_of method creates a new table containing rows from the source table, where the rows match at least one filter.
- with_attributes(attrs)¶
Returns a new Table that has the provided attributes defined on it and shares the underlying data and schema with this table.
Note, the table attributes are immutable once defined, and are mostly used internally by the Deephaven engine. For advanced users, certain predefined plug-in attributes provide a way to extend Deephaven with custom-built plug-ins.
- function_generated_table(table_generator, source_tables=None, refresh_interval_ms=None, exec_ctx=None, args=(), kwargs={})[source]¶
Creates an abstract table that is generated by running the table_generator() function. The function will first be run to generate the table when this method is called, then subsequently either (a) whenever one of the ‘source_tables’ ticks or (b) after refresh_interval_ms have elapsed. Either ‘refresh_interval_ms’ or ‘source_tables’ must be set (but not both).
Function-generated tables can be used to produce dynamic tables from sources outside Deephaven. For example, function-generated tables can create tables that are produced by arbitrary Python logic (including using Pandas or numpy). They can also be used to retrieve data from external sources (such as files or websites).
The table definition must not change between invocations of the ‘table_generator’ function, or an exception will be raised.
Note that the ‘table_generator’ may access data in the sourceTables but should not perform further table operations on them without careful handling. Table operations may be memoized, and it is possible that a table operation will return a table created by a previous invocation of the same operation. Since that result will not have been included in the ‘source_table’, it’s not automatically treated as a dependency for purposes of determining when it’s safe to invoke ‘table_generator’, allowing races to exist between accessing the operation result and that result’s own update processing. It’s best to include all dependencies directly in ‘source_table’, or only compute on-demand inputs under a LivenessScope.
- Parameters:
table_generator (Callable[..., Table]) – The table generator function. This function must return a Table.
source_tables (Union[Table, List[Table]]) – Source tables used by the ‘table_generator’ function. The ‘table_generator’ is rerun when any of these tables tick.
refresh_interval_ms (int) – Interval (in milliseconds) at which the ‘table_generator’ function is rerun.
exec_ctx (ExecutionContext) – A custom execution context. If ‘None’, the current execution context is used. If there is no current execution context, a ValueError is raised.
args (Tuple) – Optional tuple of positional arguments to pass to table_generator. Defaults to ()
kwargs (Dict) – Optional dictionary of keyword arguments to pass to table_generator. Defaults to {}
- Return type:
- Returns:
a new table
- Raises:
DHError –
- input_table(col_defs=None, init_table=None, key_cols=None)[source]¶
Creates an in-memory InputTable from either column definitions or an initial table. When key columns are provided, the InputTable will be keyed, otherwise it will be append-only.
There are two types of in-memory InputTable - append-only and keyed.
The append-only input table is not keyed, all rows are added to the end of the table, and deletions and edits are not permitted.
The keyed input table has keys for each row and supports addition/deletion/modification of rows by the keys.
- merge(tables)[source]¶
Combines two or more tables into one aggregate table. This essentially appends the tables one on top of the other. Null tables are ignored.
- merge_sorted(tables, order_by)[source]¶
Combines two or more tables into one sorted, aggregate table. This essentially stacks the tables one on top of the other and sorts the result. Null tables are ignored. mergeSorted is more efficient than using merge followed by sort.
- new_table(cols)[source]¶
Creates an in-memory table from a list of input columns or a Dict (mapping) of column names and column data. Each column must have an equal number of elements.
When the input is a mapping, an intermediary Pandas DataFrame is created from the mapping, which then is converted to an in-memory table. In this case, as opposed to when the input is a list of InputColumns, the column types are determined by Pandas’ type inference logic.
- Parameters:
cols (Union[List[InputColumn], Mapping[str, Sequence]]) – a list of InputColumns or a mapping of columns names and column data.
- Return type:
- Returns:
a Table
- Raises:
DHError –
- ring_table(parent, capacity, initialize=True)[source]¶
Creates a ring table that retains the latest ‘capacity’ number of rows from the parent table. Latest rows are determined solely by the new rows added to the parent table, deleted rows are ignored, and updated rows are not expected and will raise an exception.
Ring table is mostly used with blink tables which do not retain their own data for more than an update cycle.
- time_table(period, start_time=None, blink_table=False)[source]¶
Creates a table that adds a new row on a regular interval.
- Parameters:
period (Union[dtypes.Duration, int, str, datetime.timedelta, np.timedelta64, pd.Timedelta]) – time interval between new row additions, can be expressed as an integer in nanoseconds, a time interval string, e.g. “PT00:00:00.001” or “PT1s”, or other time duration types.
start_time (Union[None, Instant, int, str, datetime.datetime, np.datetime64, pd.Timestamp], optional) – start time for adding new rows, defaults to None which means use the current time as the start time.
blink_table (bool, optional) – if the time table should be a blink table, defaults to False
- Return type:
- Returns:
a Table
- Raises:
DHError –