# Working with Iceberg tables by using PyIceberg
This section explains how you can interact with Iceberg tables by using [PyIceberg](https://py.iceberg.apache.org/). The examples provided are boilerplate code that you can run on [Amazon Linux 2023 EC2](https://aws.amazon.com/linux/amazon-linux-2023/) instances, [AWS Lambda](https://aws.amazon.com/lambda/) functions, or any [Python](https://www.python.org/) environment with properly configured [AWS credentials](https://docs.aws.amazon.com/IAM/latest/UserGuide/security-creds.html).
## Prerequisites
**Note**
These examples use [PyIceberg 1.9.1](https://github.com/apache/iceberg/releases/tag/apache-iceberg-1.9.1).
To work with PyIceberg, you need PyIceberg and AWS SDK for Python (Boto3) installed. Here's an example of how you can set up a Python virtual environment to work with PyIceberg and AWS Glue Data Catalog:
1. Download [PyIceberg](https://pypi.org/project/pyiceberg/) by using the [pip python package installer](https://pypi.org/project/pip/). You also need [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) to interact with AWS services. You can configure a local Python virtual environment to test by using these commands:
```
python3 -m venv my_env
cd my_env/bin/
source activate
pip install "pyiceberg[pyarrow,pandas,glue]"
pip install boto3
```
1. Run `python` to open the Python shell and test the commands.
## Connecting to the Data Catalog
To start working with Iceberg tables in AWS Glue, you first need to connect to the AWS Glue Data Catalog.
The `load_catalog` function initializes a connection to the Data Catalog by creating a [catalog](https://py.iceberg.apache.org/reference/pyiceberg/catalog/) object that serves as your primary interface for all Iceberg operations:
```
from pyiceberg.catalog import load_catalog
region = "us-east-1"
glue_catalog = load_catalog(
'default',
**{
'client.region': region
},
type='glue'
)
```
## Listing and creating databases
To list existing databases, use the `list_namespaces` function:
```
databases = glue_catalog.list_namespaces()
print(databases)
```
To create a new database, use the `create_namespace` function:
```
database_name="mydb"
s3_db_path=f"s3://amzn-s3-demo-bucket/{database_name}"
glue_catalog.create_namespace(database_name, properties={"location": s3_db_path})
```
## Creating and writing Iceberg tables
### Unpartitioned tables
Here's an example of creating an unpartitioned Iceberg table by using the `create_table` function:
```
from pyiceberg.schema import Schema
from pyiceberg.types import NestedField, StringType, DoubleType
database_name="mydb"
table_name="pyiceberg_table"
s3_table_path=f"s3://amzn-s3-demo-bucket/{database_name}/{table_name}"
schema = Schema(
NestedField(1, "city", StringType(), required=False),
NestedField(2, "lat", DoubleType(), required=False),
NestedField(3, "long", DoubleType(), required=False),
)
glue_catalog.create_table(f"{database_name}.{table_name}", schema=schema, location=s3_table_path)
```
You can use the `list_tables` function to check the list of tables inside a database:
```
tables = glue_catalog.list_tables(namespace=database_name)
print(tables)
```
You can use the `append` function and PyArrow to insert data inside an Iceberg table:
```
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},
],
)
table = glue_catalog.load_table(f"{database_name}.{table_name}")
table.append(df)
```
### Partitioned tables
Here's an example of creating a [partitioned](https://iceberg.apache.org/docs/1.4.0/partitioning/) Iceberg table with [hidden partitioning](https://iceberg.apache.org/docs/1.4.0/partitioning/#icebergs-hidden-partitioning) by using the `create_table` function and `PartitionSpec`:
```
from pyiceberg.schema import Schema
from pyiceberg.types import (
NestedField,
StringType,
FloatType,
DoubleType,
TimestampType,
)
# Define the schema
schema = Schema(
NestedField(field_id=1, name="datetime", field_type=TimestampType(), required=True),
NestedField(field_id=2, name="drone_id", field_type=StringType(), required=True),
NestedField(field_id=3, name="lat", field_type=DoubleType(), required=False),
NestedField(field_id=4, name="lon", field_type=DoubleType(), required=False),
NestedField(field_id=5, name="height", field_type=FloatType(), required=False),
)
from pyiceberg.partitioning import PartitionSpec, PartitionField
from pyiceberg.transforms import DayTransform
partition_spec = PartitionSpec(
PartitionField(
source_id=1, # Refers to "datetime"
field_id=1000,
transform=DayTransform(),
name="datetime_day"
)
)
database_name="mydb"
partitioned_table_name="pyiceberg_table_partitioned"
s3_table_path=f"s3://amzn-s3-demo-bucket/{database_name}/{partitioned_table_name}"
glue_catalog.create_table(
identifier=f"{database_name}.{partitioned_table_name}",
schema=schema,
location=s3_table_path,
partition_spec=partition_spec
)
```
You can insert data into a partitioned table the same way as for an unpartitioned table. The partitioning is handled automatically.
```
from datetime import datetime
arrow_schema = pa.schema([
pa.field("datetime", pa.timestamp("us"), nullable=False),
pa.field("drone_id", pa.string(), nullable=False),
pa.field("lat", pa.float64()),
pa.field("lon", pa.float64()),
pa.field("height", pa.float32()),
])
data = [
{
"datetime": datetime(2024, 6, 1, 12, 0, 0),
"drone_id": "drone_001",
"lat": 52.371807,
"lon": 4.896029,
"height": 120.5,
},
{
"datetime": datetime(2024, 6, 1, 12, 5, 0),
"drone_id": "drone_002",
"lat": 37.773972,
"lon": -122.431297,
"height": 150.0,
},
{
"datetime": datetime(2024, 6, 2, 9, 0, 0),
"drone_id": "drone_001",
"lat": 53.11254,
"lon": 6.0989,
"height": 110.2,
},
{
"datetime": datetime(2024, 6, 2, 9, 30, 0),
"drone_id": "drone_003",
"lat": 48.864716,
"lon": 2.349014,
"height": 145.7,
},
]
df = pa.Table.from_pylist(data, schema=arrow_schema)
table = glue_catalog.load_table(f"{database_name}.{partitioned_table_name}")
table.append(df)
```
## Reading data
You can use the PyIceberg `scan` function to read data from your Iceberg tables. You can filter rows, select specific columns, and limit the number of returned records.
```
table= glue_catalog.load_table(f"{database_name}.{table_name}")
scan_df = table.scan(
row_filter=(
f"city = 'Amsterdam'"
),
selected_fields=("city", "lat"),
limit=100,
).to_pandas()
print(scan_df)
```
## Deleting data
The PyIceberg `delete` function lets you remove records from your table by using a `delete_filter`:
```
table = glue_catalog.load_table(f"{database_name}.{table_name}")
table.delete(delete_filter="city == 'Paris'")
```
## Accessing metadata
PyIceberg provides several functions to access table metadata. Here's how you can view information about table snapshots:
```
#List of snapshots
table.snapshots()
#Current snapshot
table.current_snapshot()
#Take a previous snapshot
second_last_snapshot_id=table.snapshots()[-2].snapshot_id
print(f"Second last SnapshotID: {second_last_snapshot_id}")
```
For a detailed list of available metadata, see the [metadata](https://py.iceberg.apache.org/reference/pyiceberg/table/metadata/) code reference section of the PyIceberg documentation.
## Using time travel
You can use table snapshots for time travel to access previous states of your table. Here's how to view the table state before the last operation:
```
second_last_snapshot_id=table.snapshots()[-2].snapshot_id
time_travel_df = table.scan(
limit=100,
snapshot_id=second_last_snapshot_id
).to_pandas()
print(time_travel_df)
```
For a complete list of available functions, see the PyIceberg [Python API](https://py.iceberg.apache.org/api/) documentation.