When appending data to a partitioned table in BigQuery using Python and a DataFrame, you can specify the partition to which the data should be written. Here’s how you can do it step by step:
Prerequisites
1. Install the required libraries:
pip install google-cloud-bigquery pandas
2. Ensure your BigQuery table is partitioned (e.g., by date or integer range).
Code Example
Here’s an example of appending a DataFrame to a BigQuery partitioned table:
from google.cloud import bigquery
import pandas as pd
# Set up BigQuery client
client = bigquery.Client()
# Your project and dataset details
project_id = "your-project-id"
dataset_id = "your-dataset-id"
table_id = "your-table-id" # Replace with your table name
# Full table ID (project.dataset.table)
full_table_id = f"{project_id}.{dataset_id}.{table_id}"
# Sample DataFrame to append
data = {
"partition_column": ["2025-01-01", "2025-01-02"], # Partition column (e.g., DATE)
"data_column": [100, 200] # Other columns
}
df = pd.DataFrame(data)
# Define job configuration
job_config = bigquery.LoadJobConfig(
write_disposition=bigquery.WriteDisposition.WRITE_APPEND, # Append data
schema_update_options=[
bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION # If needed
],
time_partitioning=bigquery.TimePartitioning( # Specify time partitioning
field="partition_column" # The column used for partitioning
),
)
# Load DataFrame into BigQuery
job = client.load_table_from_dataframe(df, full_table_id, job_config=job_config)
job.result() # Wait for the job to complete
# Print success message
print(f"Data appended to table: {full_table_id}")
Key Points:
1. Partition Column: Ensure that the partition_column in your DataFrame matches the partitioning column of the BigQuery table.
2. Job Configuration:
• Use bigquery.TimePartitioning to specify the partition column if the table is time-partitioned.
• Use bigquery.RangePartitioning for range-partitioned tables.
3. Write Disposition: Set WRITE_APPEND to append data to the table.
4. Schema Update: If the schema changes (e.g., new columns), include SchemaUpdateOption.ALLOW_FIELD_ADDITION.
Partition Types:
• Time Partitioning: Based on a DATE or TIMESTAMP column.
• Range Partitioning: Based on an integer column.
If your table uses integer range partitions, adjust the configuration:
range_partitioning = bigquery.RangePartitioning(
field="partition_column", # Integer column
range_=bigquery.PartitionRange(start=0, end=1000, interval=100),
)
Common Errors and Solutions:
• Partition Not Found: Ensure the partition_column values in the DataFrame are compatible with the table’s partitioning.
• Schema Mismatch: Ensure the DataFrame columns match the table schema. Use explicit casting if needed.
Let me know if you need help with a specific part!