--- hide: - navigation --- # Python API PyIceberg is based around catalogs to load tables. First step is to instantiate a catalog that loads tables. Let's use the following configuration to define a catalog called `prod`: ```yaml catalog: prod: uri: http://rest-catalog/ws/ credential: t-1234:secret ``` Note that multiple catalogs can be defined in the same `.pyiceberg.yaml`: ```yaml catalog: hive: uri: thrift://127.0.0.1:9083 s3.endpoint: http://127.0.0.1:9000 s3.access-key-id: admin s3.secret-access-key: password rest: uri: https://rest-server:8181/ warehouse: my-warehouse ``` and loaded in python by calling `load_catalog(name="hive")` and `load_catalog(name="rest")`. This information must be placed inside a file called `.pyiceberg.yaml` located either in the `$HOME` or `%USERPROFILE%` directory (depending on whether the operating system is Unix-based or Windows-based, respectively) or in the `$PYICEBERG_HOME` directory (if the corresponding environment variable is set). For more details on possible configurations refer to the [specific page](https://py.iceberg.apache.org/configuration/). Then load the `prod` catalog: ```python from pyiceberg.catalog import load_catalog catalog = load_catalog( "docs", **{ "uri": "http://127.0.0.1:8181", "s3.endpoint": "http://127.0.0.1:9000", "py-io-impl": "pyiceberg.io.pyarrow.PyArrowFileIO", "s3.access-key-id": "admin", "s3.secret-access-key": "password", } ) ``` Let's create a namespace: ```python catalog.create_namespace("docs_example") ``` And then list them: ```python ns = catalog.list_namespaces() assert ns == [("docs_example",)] ``` And then list tables in the namespace: ```python catalog.list_tables("docs_example") ``` ## Create a table To create a table from a catalog: ```python from pyiceberg.schema import Schema from pyiceberg.types import ( TimestampType, FloatType, DoubleType, StringType, NestedField, StructType, ) schema = Schema( NestedField(field_id=1, name="datetime", field_type=TimestampType(), required=True), NestedField(field_id=2, name="symbol", field_type=StringType(), required=True), NestedField(field_id=3, name="bid", field_type=FloatType(), required=False), NestedField(field_id=4, name="ask", field_type=DoubleType(), required=False), NestedField( field_id=5, name="details", field_type=StructType( NestedField( field_id=4, name="created_by", field_type=StringType(), required=False ), ), required=False, ), ) from pyiceberg.partitioning import PartitionSpec, PartitionField from pyiceberg.transforms import DayTransform partition_spec = PartitionSpec( PartitionField( source_id=1, field_id=1000, transform=DayTransform(), name="datetime_day" ) ) from pyiceberg.table.sorting import SortOrder, SortField from pyiceberg.transforms import IdentityTransform # Sort on the symbol sort_order = SortOrder(SortField(source_id=2, transform=IdentityTransform())) catalog.create_table( identifier="docs_example.bids", schema=schema, location="s3://pyiceberg", partition_spec=partition_spec, sort_order=sort_order, ) ``` To create a table using a pyarrow schema: ```python import pyarrow as pa schema = pa.schema( [ pa.field("foo", pa.string(), nullable=True), pa.field("bar", pa.int32(), nullable=False), pa.field("baz", pa.bool_(), nullable=True), ] ) catalog.create_table( identifier="docs_example.bids", schema=schema, ) ``` To create a table with some subsequent changes atomically in a transaction: ```python with catalog.create_table_transaction( identifier="docs_example.bids", schema=schema, location="s3://pyiceberg", partition_spec=partition_spec, sort_order=sort_order, ) as txn: with txn.update_schema() as update_schema: update_schema.add_column(path="new_column", field_type=StringType()) with txn.update_spec() as update_spec: update_spec.add_identity("symbol") txn.set_properties(test_a="test_aa", test_b="test_b", test_c="test_c") ``` ## Load a table ### Catalog table Loading the `bids` table: ```python table = catalog.load_table("docs_example.bids") # Equivalent to: table = catalog.load_table(("docs_example", "bids")) # The tuple syntax can be used if the namespace or table contains a dot. ``` This returns a `Table` that represents an Iceberg table that can be queried and altered. ### Static table To load a table directly from a metadata file (i.e., **without** using a catalog), you can use a `StaticTable` as follows: ```python from pyiceberg.table import StaticTable static_table = StaticTable.from_metadata( "s3://warehouse/wh/nyc.db/taxis/metadata/00002-6ea51ce3-62aa-4197-9cf8-43d07c3440ca.metadata.json" ) ``` The static-table is considered read-only. ## Check if a table exists To check whether the `bids` table exists: ```python catalog.table_exists("docs_example.bids") ``` Returns `True` if the table already exists. ## Write support With PyIceberg 0.6.0 write support is added through Arrow. Let's consider an Arrow Table: ```python import pyarrow as pa df = pa.Table.from_pylist( [ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029}, {"city": "San Francisco", "lat": 37.773972, "long": -122.431297}, {"city": "Drachten", "lat": 53.11254, "long": 6.0989}, {"city": "Paris", "lat": 48.864716, "long": 2.349014}, ], ) ``` Next, create a table based on the schema: ```python from pyiceberg.catalog import load_catalog catalog = load_catalog("default") from pyiceberg.schema import Schema from pyiceberg.types import NestedField, StringType, DoubleType schema = Schema( NestedField(1, "city", StringType(), required=False), NestedField(2, "lat", DoubleType(), required=False), NestedField(3, "long", DoubleType(), required=False), ) tbl = catalog.create_table("default.cities", schema=schema) ``` Now write the data to the table: !!! note inline end "Fast append" PyIceberg default to the [fast append](https://iceberg.apache.org/spec/#snapshots) to minimize the amount of data written. This enables quick writes, reducing the possibility of conflicts. The downside of the fast append is that it creates more metadata than a normal commit. [Compaction is planned](https://github.com/apache/iceberg-python/issues/270) and will automatically rewrite all the metadata when a threshold is hit, to maintain performant reads. ```python tbl.append(df) # or tbl.overwrite(df) ``` The data is written to the table, and when the table is read using `tbl.scan().to_arrow()`: ``` pyarrow.Table city: string lat: double long: double ---- city: [["Amsterdam","San Francisco","Drachten","Paris"]] lat: [[52.371807,37.773972,53.11254,48.864716]] long: [[4.896029,-122.431297,6.0989,2.349014]] ``` You both can use `append(df)` or `overwrite(df)` since there is no data yet. If we want to add more data, we can use `.append()` again: ```python df = pa.Table.from_pylist( [{"city": "Groningen", "lat": 53.21917, "long": 6.56667}], ) tbl.append(df) ``` When reading the table `tbl.scan().to_arrow()` you can see that `Groningen` is now also part of the table: ``` pyarrow.Table city: string lat: double long: double ---- city: [["Amsterdam","San Francisco","Drachten","Paris"],["Groningen"]] lat: [[52.371807,37.773972,53.11254,48.864716],[53.21917]] long: [[4.896029,-122.431297,6.0989,2.349014],[6.56667]] ``` The nested lists indicate the different Arrow buffers, where the first write results into a buffer, and the second append in a separate buffer. This is expected since it will read two parquet files. To avoid any type errors during writing, you can enforce the PyArrow table types using the Iceberg table schema: ```python from pyiceberg.catalog import load_catalog import pyarrow as pa catalog = load_catalog("default") table = catalog.load_table("default.cities") schema = table.schema().as_arrow() df = pa.Table.from_pylist( [{"city": "Groningen", "lat": 53.21917, "long": 6.56667}], schema=schema ) table.append(df) ``` !!! example "Under development" Writing using PyIceberg is still under development. Support for [partial overwrites](https://github.com/apache/iceberg-python/issues/268) and writing to [partitioned tables](https://github.com/apache/iceberg-python/issues/208) is planned and being worked on. ## Inspecting tables To explore the table metadata, tables can be inspected. !!! tip "Time Travel" To inspect a tables's metadata with the time travel feature, call the inspect table method with the `snapshot_id` argument. Time travel is supported on all metadata tables except `snapshots` and `refs`. ```python table.inspect.entries(snapshot_id=805611270568163028) ``` ### Snapshots Inspect the snapshots of the table: ```python table.inspect.snapshots() ``` ``` pyarrow.Table committed_at: timestamp[ms] not null snapshot_id: int64 not null parent_id: int64 operation: string manifest_list: string not null summary: map child 0, entries: struct not null child 0, key: string not null child 1, value: string ---- committed_at: [[2024-03-15 15:01:25.682,2024-03-15 15:01:25.730,2024-03-15 15:01:25.772]] snapshot_id: [[805611270568163028,3679426539959220963,5588071473139865870]] parent_id: [[null,805611270568163028,3679426539959220963]] operation: [["append","overwrite","append"]] manifest_list: [["s3://warehouse/default/table_metadata_snapshots/metadata/snap-805611270568163028-0-43637daf-ea4b-4ceb-b096-a60c25481eb5.avro","s3://warehouse/default/table_metadata_snapshots/metadata/snap-3679426539959220963-0-8be81019-adf1-4bb6-a127-e15217bd50b3.avro","s3://warehouse/default/table_metadata_snapshots/metadata/snap-5588071473139865870-0-1382dd7e-5fbc-4c51-9776-a832d7d0984e.avro"]] summary: [[keys:["added-files-size","added-data-files","added-records","total-data-files","total-delete-files","total-records","total-files-size","total-position-deletes","total-equality-deletes"]values:["5459","1","3","1","0","3","5459","0","0"],keys:["added-files-size","added-data-files","added-records","total-data-files","total-records",...,"total-equality-deletes","total-files-size","deleted-data-files","deleted-records","removed-files-size"]values:["5459","1","3","1","3",...,"0","5459","1","3","5459"],keys:["added-files-size","added-data-files","added-records","total-data-files","total-delete-files","total-records","total-files-size","total-position-deletes","total-equality-deletes"]values:["5459","1","3","2","0","6","10918","0","0"]]] ``` ### Partitions Inspect the partitions of the table: ```python table.inspect.partitions() ``` ``` pyarrow.Table partition: struct not null child 0, dt_month: int32 child 1, dt_day: date32[day] spec_id: int32 not null record_count: int64 not null file_count: int32 not null total_data_file_size_in_bytes: int64 not null position_delete_record_count: int64 not null position_delete_file_count: int32 not null equality_delete_record_count: int64 not null equality_delete_file_count: int32 not null last_updated_at: timestamp[ms] last_updated_snapshot_id: int64 ---- partition: [ -- is_valid: all not null -- child 0 type: int32 [null,null,612] -- child 1 type: date32[day] [null,2021-02-01,null]] spec_id: [[2,1,0]] record_count: [[1,1,2]] file_count: [[1,1,2]] total_data_file_size_in_bytes: [[641,641,1260]] position_delete_record_count: [[0,0,0]] position_delete_file_count: [[0,0,0]] equality_delete_record_count: [[0,0,0]] equality_delete_file_count: [[0,0,0]] last_updated_at: [[2024-04-13 18:59:35.981,2024-04-13 18:59:35.465,2024-04-13 18:59:35.003]] ``` ### Entries To show all the table's current manifest entries for both data and delete files. ```python table.inspect.entries() ``` ``` pyarrow.Table status: int8 not null snapshot_id: int64 not null sequence_number: int64 not null file_sequence_number: int64 not null data_file: struct not null, record_count: int64 not null, file_size_in_bytes: int64 not null, column_sizes: map, value_counts: map, null_value_counts: map, nan_value_counts: map, lower_bounds: map, upper_bounds: map, key_metadata: binary, split_offsets: list, equality_ids: list, sort_order_id: int32> not null child 0, content: int8 not null child 1, file_path: string not null child 2, file_format: string not null child 3, partition: struct<> not null child 4, record_count: int64 not null child 5, file_size_in_bytes: int64 not null child 6, column_sizes: map child 0, entries: struct not null child 0, key: int32 not null child 1, value: int64 child 7, value_counts: map child 0, entries: struct not null child 0, key: int32 not null child 1, value: int64 child 8, null_value_counts: map child 0, entries: struct not null child 0, key: int32 not null child 1, value: int64 child 9, nan_value_counts: map child 0, entries: struct not null child 0, key: int32 not null child 1, value: int64 child 10, lower_bounds: map child 0, entries: struct not null child 0, key: int32 not null child 1, value: binary child 11, upper_bounds: map child 0, entries: struct not null child 0, key: int32 not null child 1, value: binary child 12, key_metadata: binary child 13, split_offsets: list child 0, item: int64 child 14, equality_ids: list child 0, item: int32 child 15, sort_order_id: int32 readable_metrics: struct not null, lat: struct not null, long: struct not null> child 0, city: struct not null child 0, column_size: int64 child 1, value_count: int64 child 2, null_value_count: int64 child 3, nan_value_count: int64 child 4, lower_bound: string child 5, upper_bound: string child 1, lat: struct not null child 0, column_size: int64 child 1, value_count: int64 child 2, null_value_count: int64 child 3, nan_value_count: int64 child 4, lower_bound: double child 5, upper_bound: double child 2, long: struct not null child 0, column_size: int64 child 1, value_count: int64 child 2, null_value_count: int64 child 3, nan_value_count: int64 child 4, lower_bound: double child 5, upper_bound: double ---- status: [[1]] snapshot_id: [[6245626162224016531]] sequence_number: [[1]] file_sequence_number: [[1]] data_file: [ -- is_valid: all not null -- child 0 type: int8 [0] -- child 1 type: string ["s3://warehouse/default/cities/data/00000-0-80766b66-e558-4150-a5cf-85e4c609b9fe.parquet"] -- child 2 type: string ["PARQUET"] -- child 3 type: struct<> -- is_valid: all not null -- child 4 type: int64 [4] -- child 5 type: int64 [1656] -- child 6 type: map [keys:[1,2,3]values:[140,135,135]] -- child 7 type: map [keys:[1,2,3]values:[4,4,4]] -- child 8 type: map [keys:[1,2,3]values:[0,0,0]] -- child 9 type: map [keys:[]values:[]] -- child 10 type: map [keys:[1,2,3]values:[416D7374657264616D,8602B68311E34240,3A77BB5E9A9B5EC0]] -- child 11 type: map [keys:[1,2,3]values:[53616E204672616E636973636F,F5BEF1B5678E4A40,304CA60A46651840]] -- child 12 type: binary [null] -- child 13 type: list [[4]] -- child 14 type: list [null] -- child 15 type: int32 [null]] readable_metrics: [ -- is_valid: all not null -- child 0 type: struct -- is_valid: all not null -- child 0 type: int64 [140] -- child 1 type: int64 [4] -- child 2 type: int64 [0] -- child 3 type: int64 [null] -- child 4 type: string ["Amsterdam"] -- child 5 type: string ["San Francisco"] -- child 1 type: struct -- is_valid: all not null -- child 0 type: int64 [135] -- child 1 type: int64 [4] -- child 2 type: int64 [0] -- child 3 type: int64 [null] -- child 4 type: double [37.773972] -- child 5 type: double [53.11254] -- child 2 type: struct -- is_valid: all not null -- child 0 type: int64 [135] -- child 1 type: int64 [4] -- child 2 type: int64 [0] -- child 3 type: int64 [null] -- child 4 type: double [-122.431297] -- child 5 type: double [6.0989]] ``` ### References To show a table's known snapshot references: ```python table.inspect.refs() ``` ``` pyarrow.Table name: string not null type: string not null snapshot_id: int64 not null max_reference_age_in_ms: int64 min_snapshots_to_keep: int32 max_snapshot_age_in_ms: int64 ---- name: [["main","testTag"]] type: [["BRANCH","TAG"]] snapshot_id: [[2278002651076891950,2278002651076891950]] max_reference_age_in_ms: [[null,604800000]] min_snapshots_to_keep: [[null,10]] max_snapshot_age_in_ms: [[null,604800000]] ``` ### Manifests To show a table's current file manifests: ```python table.inspect.manifests() ``` ``` pyarrow.Table content: int8 not null path: string not null length: int64 not null partition_spec_id: int32 not null added_snapshot_id: int64 not null added_data_files_count: int32 not null existing_data_files_count: int32 not null deleted_data_files_count: int32 not null added_delete_files_count: int32 not null existing_delete_files_count: int32 not null deleted_delete_files_count: int32 not null partition_summaries: list> not null child 0, item: struct child 0, contains_null: bool not null child 1, contains_nan: bool child 2, lower_bound: string child 3, upper_bound: string ---- content: [[0]] path: [["s3://warehouse/default/table_metadata_manifests/metadata/3bf5b4c6-a7a4-4b43-a6ce-ca2b4887945a-m0.avro"]] length: [[6886]] partition_spec_id: [[0]] added_snapshot_id: [[3815834705531553721]] added_data_files_count: [[1]] existing_data_files_count: [[0]] deleted_data_files_count: [[0]] added_delete_files_count: [[0]] existing_delete_files_count: [[0]] deleted_delete_files_count: [[0]] partition_summaries: [[ -- is_valid: all not null -- child 0 type: bool [false] -- child 1 type: bool [false] -- child 2 type: string ["test"] -- child 3 type: string ["test"]]] ``` ## Add Files Expert Iceberg users may choose to commit existing parquet files to the Iceberg table as data files, without rewriting them. ``` # Given that these parquet files have schema consistent with the Iceberg table file_paths = [ "s3a://warehouse/default/existing-1.parquet", "s3a://warehouse/default/existing-2.parquet", ] # They can be added to the table without rewriting them tbl.add_files(file_paths=file_paths) # A new snapshot is committed to the table with manifests pointing to the existing parquet files ``` !!! note "Name Mapping" Because `add_files` uses existing files without writing new parquet files that are aware of the Iceberg's schema, it requires the Iceberg's table to have a [Name Mapping](https://iceberg.apache.org/spec/?h=name+mapping#name-mapping-serialization) (The Name mapping maps the field names within the parquet files to the Iceberg field IDs). Hence, `add_files` requires that there are no field IDs in the parquet file's metadata, and creates a new Name Mapping based on the table's current schema if the table doesn't already have one. !!! note "Partitions" `add_files` only requires the client to read the existing parquet files' metadata footer to infer the partition value of each file. This implementation also supports adding files to Iceberg tables with partition transforms like `MonthTransform`, and `TruncateTransform` which preserve the order of the values after the transformation (Any Transform that has the `preserves_order` property set to True is supported). Please note that if the column statistics of the `PartitionField`'s source column are not present in the parquet metadata, the partition value is inferred as `None`. !!! warning "Maintenance Operations" Because `add_files` commits the existing parquet files to the Iceberg Table as any other data file, destructive maintenance operations like expiring snapshots will remove them. ## Schema evolution PyIceberg supports full schema evolution through the Python API. It takes care of setting the field-IDs and makes sure that only non-breaking changes are done (can be overriden). In the examples below, the `.update_schema()` is called from the table itself. ```python with table.update_schema() as update: update.add_column("some_field", IntegerType(), "doc") ``` You can also initiate a transaction if you want to make more changes than just evolving the schema: ```python with table.transaction() as transaction: with transaction.update_schema() as update_schema: update.add_column("some_other_field", IntegerType(), "doc") # ... Update properties etc ``` ### Union by Name Using `.union_by_name()` you can merge another schema into an existing schema without having to worry about field-IDs: ```python from pyiceberg.catalog import load_catalog from pyiceberg.schema import Schema from pyiceberg.types import NestedField, StringType, DoubleType, LongType catalog = load_catalog() schema = Schema( NestedField(1, "city", StringType(), required=False), NestedField(2, "lat", DoubleType(), required=False), NestedField(3, "long", DoubleType(), required=False), ) table = catalog.create_table("default.locations", schema) new_schema = Schema( NestedField(1, "city", StringType(), required=False), NestedField(2, "lat", DoubleType(), required=False), NestedField(3, "long", DoubleType(), required=False), NestedField(10, "population", LongType(), required=False), ) with table.update_schema() as update: update.union_by_name(new_schema) ``` Now the table has the union of the two schemas `print(table.schema())`: ``` table { 1: city: optional string 2: lat: optional double 3: long: optional double 4: population: optional long } ``` ### Add column Using `add_column` you can add a column, without having to worry about the field-id: ```python with table.update_schema() as update: update.add_column("retries", IntegerType(), "Number of retries to place the bid") # In a struct update.add_column("details.confirmed_by", StringType(), "Name of the exchange") ``` ### Rename column Renaming a field in an Iceberg table is simple: ```python with table.update_schema() as update: update.rename_column("retries", "num_retries") # This will rename `confirmed_by` to `exchange` update.rename_column("properties.confirmed_by", "exchange") ``` ### Move column Move a field inside of struct: ```python with table.update_schema() as update: update.move_first("symbol") update.move_after("bid", "ask") # This will move `confirmed_by` before `exchange` update.move_before("details.created_by", "details.exchange") ``` ### Update column Update a fields' type, description or required. ```python with table.update_schema() as update: # Promote a float to a double update.update_column("bid", field_type=DoubleType()) # Make a field optional update.update_column("symbol", required=False) # Update the documentation update.update_column("symbol", doc="Name of the share on the exchange") ``` Be careful, some operations are not compatible, but can still be done at your own risk by setting `allow_incompatible_changes`: ```python with table.update_schema(allow_incompatible_changes=True) as update: # Incompatible change, cannot require an optional field update.update_column("symbol", required=True) ``` ### Delete column Delete a field, careful this is a incompatible change (readers/writers might expect this field): ```python with table.update_schema(allow_incompatible_changes=True) as update: update.delete_column("some_field") ``` ## Partition evolution PyIceberg supports partition evolution. See the [partition evolution](https://iceberg.apache.org/spec/#partition-evolution) for more details. The API to use when evolving partitions is the `update_spec` API on the table. ```python with table.update_spec() as update: update.add_field("id", BucketTransform(16), "bucketed_id") update.add_field("event_ts", DayTransform(), "day_ts") ``` Updating the partition spec can also be done as part of a transaction with other operations. ```python with table.transaction() as transaction: with transaction.update_spec() as update_spec: update_spec.add_field("id", BucketTransform(16), "bucketed_id") update_spec.add_field("event_ts", DayTransform(), "day_ts") # ... Update properties etc ``` ### Add fields New partition fields can be added via the `add_field` API which takes in the field name to partition on, the partition transform, and an optional partition name. If the partition name is not specified, one will be created. ```python with table.update_spec() as update: update.add_field("id", BucketTransform(16), "bucketed_id") update.add_field("event_ts", DayTransform(), "day_ts") # identity is a shortcut API for adding an IdentityTransform update.identity("some_field") ``` ### Remove fields Partition fields can also be removed via the `remove_field` API if it no longer makes sense to partition on those fields. ```python with table.update_spec() as update:some_partition_name # Remove the partition field with the name update.remove_field("some_partition_name") ``` ### Rename fields Partition fields can also be renamed via the `rename_field` API. ```python with table.update_spec() as update: # Rename the partition field with the name bucketed_id to sharded_id update.rename_field("bucketed_id", "sharded_id") ``` ## Table properties Set and remove properties through the `Transaction` API: ```python with table.transaction() as transaction: transaction.set_properties(abc="def") assert table.properties == {"abc": "def"} with table.transaction() as transaction: transaction.remove_properties("abc") assert table.properties == {} ``` Or, without context manager: ```python table = table.transaction().set_properties(abc="def").commit_transaction() assert table.properties == {"abc": "def"} table = table.transaction().remove_properties("abc").commit_transaction() assert table.properties == {} ``` ## Snapshot properties Optionally, Snapshot properties can be set while writing to a table using `append` or `overwrite` API: ```python tbl.append(df, snapshot_properties={"abc": "def"}) # or tbl.overwrite(df, snapshot_properties={"abc": "def"}) assert tbl.metadata.snapshots[-1].summary["abc"] == "def" ``` ## Query the data To query a table, a table scan is needed. A table scan accepts a filter, columns, optionally a limit and a snapshot ID: ```python from pyiceberg.catalog import load_catalog from pyiceberg.expressions import GreaterThanOrEqual catalog = load_catalog("default") table = catalog.load_table("nyc.taxis") scan = table.scan( row_filter=GreaterThanOrEqual("trip_distance", 10.0), selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"), limit=100, ) # Or filter using a string predicate scan = table.scan( row_filter="trip_distance > 10.0", ) [task.file.file_path for task in scan.plan_files()] ``` The low level API `plan_files` methods returns a set of tasks that provide the files that might contain matching rows: ```json [ "s3://warehouse/wh/nyc/taxis/data/00003-4-42464649-92dd-41ad-b83b-dea1a2fe4b58-00001.parquet" ] ``` In this case it is up to the engine itself to filter the file itself. Below, `to_arrow()` and `to_duckdb()` that already do this for you. ### Apache Arrow !!! note "Requirements" This requires [`pyarrow` to be installed](index.md). Using PyIceberg it is filter out data from a huge table and pull it into a PyArrow table: ```python table.scan( row_filter=GreaterThanOrEqual("trip_distance", 10.0), selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"), ).to_arrow() ``` This will return a PyArrow table: ``` pyarrow.Table VendorID: int64 tpep_pickup_datetime: timestamp[us, tz=+00:00] tpep_dropoff_datetime: timestamp[us, tz=+00:00] ---- VendorID: [[2,1,2,1,1,...,2,2,2,2,2],[2,1,1,1,2,...,1,1,2,1,2],...,[2,2,2,2,2,...,2,6,6,2,2],[2,2,2,2,2,...,2,2,2,2,2]] tpep_pickup_datetime: [[2021-04-01 00:28:05.000000,...,2021-04-30 23:44:25.000000]] tpep_dropoff_datetime: [[2021-04-01 00:47:59.000000,...,2021-05-01 00:14:47.000000]] ``` This will only pull in the files that that might contain matching rows. ### Pandas !!! note "Requirements" This requires [`pandas` to be installed](index.md). PyIceberg makes it easy to filter out data from a huge table and pull it into a Pandas dataframe locally. This will only fetch the relevant Parquet files for the query and apply the filter. This will reduce IO and therefore improve performance and reduce cost. ```python table.scan( row_filter="trip_distance >= 10.0", selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"), ).to_pandas() ``` This will return a Pandas dataframe: ``` VendorID tpep_pickup_datetime tpep_dropoff_datetime 0 2 2021-04-01 00:28:05+00:00 2021-04-01 00:47:59+00:00 1 1 2021-04-01 00:39:01+00:00 2021-04-01 00:57:39+00:00 2 2 2021-04-01 00:14:42+00:00 2021-04-01 00:42:59+00:00 3 1 2021-04-01 00:17:17+00:00 2021-04-01 00:43:38+00:00 4 1 2021-04-01 00:24:04+00:00 2021-04-01 00:56:20+00:00 ... ... ... ... 116976 2 2021-04-30 23:56:18+00:00 2021-05-01 00:29:13+00:00 116977 2 2021-04-30 23:07:41+00:00 2021-04-30 23:37:18+00:00 116978 2 2021-04-30 23:38:28+00:00 2021-05-01 00:12:04+00:00 116979 2 2021-04-30 23:33:00+00:00 2021-04-30 23:59:00+00:00 116980 2 2021-04-30 23:44:25+00:00 2021-05-01 00:14:47+00:00 [116981 rows x 3 columns] ``` It is recommended to use Pandas 2 or later, because it stores the data in an [Apache Arrow backend](https://datapythonista.me/blog/pandas-20-and-the-arrow-revolution-part-i) which avoids copies of data. ### DuckDB !!! note "Requirements" This requires [DuckDB to be installed](index.md). A table scan can also be converted into a in-memory DuckDB table: ```python con = table.scan( row_filter=GreaterThanOrEqual("trip_distance", 10.0), selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"), ).to_duckdb(table_name="distant_taxi_trips") ``` Using the cursor that we can run queries on the DuckDB table: ```python print( con.execute( "SELECT tpep_dropoff_datetime - tpep_pickup_datetime AS duration FROM distant_taxi_trips LIMIT 4" ).fetchall() ) [ (datetime.timedelta(seconds=1194),), (datetime.timedelta(seconds=1118),), (datetime.timedelta(seconds=1697),), (datetime.timedelta(seconds=1581),), ] ``` ### Ray !!! note "Requirements" This requires [Ray to be installed](index.md). A table scan can also be converted into a Ray dataset: ```python ray_dataset = table.scan( row_filter=GreaterThanOrEqual("trip_distance", 10.0), selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"), ).to_ray() ``` This will return a Ray dataset: ``` Dataset( num_blocks=1, num_rows=1168798, schema={ VendorID: int64, tpep_pickup_datetime: timestamp[us, tz=UTC], tpep_dropoff_datetime: timestamp[us, tz=UTC] } ) ``` Using [Ray Dataset API](https://docs.ray.io/en/latest/data/api/dataset.html) to interact with the dataset: ```python print(ray_dataset.take(2)) [ { "VendorID": 2, "tpep_pickup_datetime": datetime.datetime(2008, 12, 31, 23, 23, 50), "tpep_dropoff_datetime": datetime.datetime(2009, 1, 1, 0, 34, 31), }, { "VendorID": 2, "tpep_pickup_datetime": datetime.datetime(2008, 12, 31, 23, 5, 3), "tpep_dropoff_datetime": datetime.datetime(2009, 1, 1, 16, 10, 18), }, ] ``` ### Daft PyIceberg interfaces closely with Daft Dataframes (see also: [Daft integration with Iceberg](https://www.getdaft.io/projects/docs/en/latest/user_guide/integrations/iceberg.html)) which provides a full lazily optimized query engine interface on top of PyIceberg tables. !!! note "Requirements" This requires [Daft to be installed](index.md). A table can be read easily into a Daft Dataframe: ```python df = table.to_daft() # equivalent to `daft.read_iceberg(table)` df = df.where(df["trip_distance"] >= 10.0) df = df.select("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime") ``` This returns a Daft Dataframe which is lazily materialized. Printing `df` will display the schema: ``` ╭──────────┬───────────────────────────────┬───────────────────────────────╮ │ VendorID ┆ tpep_pickup_datetime ┆ tpep_dropoff_datetime │ │ --- ┆ --- ┆ --- │ │ Int64 ┆ Timestamp(Microseconds, None) ┆ Timestamp(Microseconds, None) │ ╰──────────┴───────────────────────────────┴───────────────────────────────╯ (No data to display: Dataframe not materialized) ``` We can execute the Dataframe to preview the first few rows of the query with `df.show()`. This is correctly optimized to take advantage of Iceberg features such as hidden partitioning and file-level statistics for efficient reads. ```python df.show(2) ``` ``` ╭──────────┬───────────────────────────────┬───────────────────────────────╮ │ VendorID ┆ tpep_pickup_datetime ┆ tpep_dropoff_datetime │ │ --- ┆ --- ┆ --- │ │ Int64 ┆ Timestamp(Microseconds, None) ┆ Timestamp(Microseconds, None) │ ╞══════════╪═══════════════════════════════╪═══════════════════════════════╡ │ 2 ┆ 2008-12-31T23:23:50.000000 ┆ 2009-01-01T00:34:31.000000 │ ├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤ │ 2 ┆ 2008-12-31T23:05:03.000000 ┆ 2009-01-01T16:10:18.000000 │ ╰──────────┴───────────────────────────────┴───────────────────────────────╯ (Showing first 2 rows) ```