--- title: Access table metadata sidebar_label: Metadata --- This guide will show you how to use methods from Deephaven's `Table` class to access the metadata for your table. A table's metadata provides basic information about its source data, such as the table type and size, whether the data is refreshing, and the data types of each column. You may want to confirm if a column is an `int` or a `double`, or check whether a column is a partitioning column or grouping column. ## `meta_table` The `meta_table` attribute creates a new table that contains the table's meta data. Specifically, this table contains information about every column in the original table. ```python syntax result = source.meta_table ``` This can be useful when you want to confirm which columns in a table are partitioning or grouping, or verify the data type of a column. Let's create a table of weather data for Miami, Florida. ```python test-set=1 from deephaven import new_table from deephaven.column import string_col, int_col, double_col miami = new_table( [ string_col( "Month", [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", ], ), int_col("Temp", [60, 62, 65, 68, 73, 76, 77, 77, 76, 74, 68, 63]), double_col( "Rain", [1.62, 2.25, 3.00, 3.14, 5.34, 9.67, 6.50, 8.88, 9.86, 6.33, 3.27, 2.04], ), ] ) ``` We can access the meta data as follows: ```python test-set=1 meta = miami.meta_table ``` Obviously, this is more useful for a table we are unfamiliar with, but as you can see, the `meta` table provides information about the column and data types. Now, let's create a table of weather data for Miami over three dates in January, then [averages](../reference/table-operations/group-and-aggregate/avgBy.md) the high and low temperatures by day. ```python test-set=2 order=miami,avg_temp from deephaven import new_table from deephaven.column import string_col, int_col miami = new_table( [ string_col("Day", ["Jan 1", "Jan 1", "Jan 2", "Jan 2", "Jan 3", "Jan 3"]), int_col("Temp", [45, 62, 48, 63, 39, 59]), ] ) avg_temp = miami.avg_by(by=["Day"]) ``` Although the `Temp` column is originally created as an [int column](../reference/table-operations/create/intCol.md), the `Temp` column in the `avg_by` table becomes a double column. We can see this by hovering over the column header in the UI, and also by accessing the table's metadata. ```python test-set=2 order=meta meta = avg_temp.meta_table ``` ## `attributes` The `attributes` method returns all of a Table's defined attributes. ```python test-set=1 order=:log print(miami.attributes()) ``` ## `has_columns` Use `has_columns` to check whether a table contains a column (or columns) with the provided column name(s). It will return `True` if all columns are in the table, or `False` if _any_ of the provided column names are not found in the table. ```python test-set=1 order=:log print(miami.has_columns("Month")) print(miami.has_columns(["Month", "NotAColumnName"])) ``` ## `to_string` Use [`to_string`](../reference/table-operations/metadata/to_string.md) return the first _n_ rows of a table as a pipe-delimited string. ```python syntax source.to_string( num_rows: int = 10, cols: Union[str, Sequence[str]] = None ) -> str ``` - `num_rows` is the number of rows from the beginning of the table to return. The default is 10. - `cols` is the column name(s) to include in the string. The default is `None`, which includes all columns. ```python test-set=1 order=:log print(miami.to_string(4)) ``` ## `table_diff` Use [`table_diff`](../reference/table-operations/metadata/table_diff.md) to compare two tables and return the differences between them as a string. ```python syntax from deephaven import empty_table from deephaven.table import table_diff ## Create some tables t1 = empty_table(10).update(["A = i", "B = i", "C = i"]) t2 = empty_table(10).update( ["A = i", "C = i % 2 == 0? i: i + 1", "C = i % 2 == 0? i + 1: i"] ) ## get the diff string d = table_diff(t1, t2, max_diffs=10).split("\n") print(d) ``` ## Attributes The following attributes do not take arguments, but will print metadata information in the console. ### `columns` For a full list of a Table's column definitions, use [`columns`](../reference/table-operations/metadata/columns.md): ```python test-set=1 order=:log print(miami.columns) ``` ### `is_blink` To see whether or not a Table is a blink table, use [`is_blink`](../reference/table-operations/metadata/is_blink.md): ```python test-set=1 order=:log print(miami.is_blink) ``` ### `is_flat` To see whether or not a Table is flat, use [`is_flat`](../reference/table-operations/metadata/is_flat.md): ```python test-set=1 order=:log print(miami.is_flat) ``` ### `is_refreshing` To see whether or not a Table is refreshing, use [`is_refreshing`](../reference/table-operations/metadata/is_refreshing.md): ```python test-set=1 order=:log print(miami.is_refreshing) ``` ### `size` To see the number of rows in a Table, use [`size`](../reference/table-operations/metadata/size.md): ```python test-set=1 order=:log print(miami.size) ``` ### `update_graph` To get the source Table's update graph, use [`update_graph`](../reference/table-operations/metadata/update_graph.md): ```python test-set=1 order=:log print(miami.update_graph) ``` ## Related documentation - [Create a new table](./new-and-empty-table.md#new_table) - [How to perform dedicated aggregations for groups](./dedicated-aggregations.md) - [`attributes`](../reference/table-operations/metadata/attributes.md) - [`columns`](../reference/table-operations/metadata/columns.md) - [`has_columns`](../reference/table-operations/metadata/has_columns.md) - [`is_blink`](../reference/table-operations/metadata/is_blink.md) - [`is_flat`](../reference/table-operations/metadata/is_flat.md) - [`is_refreshing`](../reference/table-operations/metadata/is_refreshing.md) - [`size`](../reference/table-operations/metadata/size.md) - [`meta_table`](../reference/table-operations/metadata/meta_table.md) - [`to_string`](../reference/table-operations/metadata/to_string.md) - [`update_graph`](../reference/table-operations/metadata/update_graph.md)