%python
from google.cloud import bigquery
import pandas as pd
​
# Construct a BigQuery client object.
client = z.getDatasource("BigQuery_Sample")
​
# Create Query
query = """
    SELECT name, SUM(number) as total_people
    FROM `bigquery-public-data.usa_names.usa_1910_2013`
    WHERE state = 'TX'
    GROUP BY name, state
    ORDER BY total_people DESC
    LIMIT 20
"""
​
# Execute Query and return Results
query_job = client.query(query).result()
​
# Convert query result object into a pandas dataframe
df = query_job.to_dataframe()
​
z.show(df)

%python
import pandas as pd
​
# Create data frame
data = {"names": ["Edward Norton", "Nicholas Cage", "Sean Connery", "Ed Harris"],
     "Row1": [5, 6, 7, 8],
     "Row2": [9, 10, 11, 12],
     "Row3": [13, 14, 15, 16],
     "Row4": [17, 18, 19, 20],
     "Row5": [21, 22, 23, 24]}
​
# Create Pandas DataFrame
df = pd.DataFrame.from_dict(data)

%python
from google.cloud import bigquery
from google.cloud.exceptions import NotFound
​
# Create BigQuery Client Object
client = z.getDatasource("BigQuery_Sample")
​
# Get Project ID
project_id = client.project
​
# Create BigQuery Dataset Reference Name (ID)
dataset_id = "write_dataset4"
dataset_id_full = "{}.{}".format(project_id, dataset_id)
​
# Create BigQuery Dataset Object - Check if it already exists
dataset = bigquery.Dataset(dataset_id_full)
try:
    client.get_dataset(dataset_id)  # Make an API request.
    print("Dataset {} already exists".format(dataset_id))
except NotFound:
    # Create the new BigQuery dataset.
    dataset = client.create_dataset(dataset)
​
​
# Create BigQuery Table Reference Name (ID)
table_id = "write_df_to_table"
table_id_full = "{}.{}.{}".format(project_id, dataset_id, table_id)
​
# Create BigQuery Table Object
table_ref = dataset.table(table_id)
​
# Configure the query job.
job_config = bigquery.QueryJobConfig()
​
# Set the destination to write to the table object created above
job_config.destination = table_ref
​
# Create LoadJobConfig to specify the table schema and write characteristics
job_config = bigquery.LoadJobConfig(
    # Specify a (partial) schema. All columns are always written to the table. The schema is used to assist in data type definitions
    schema=[
        # Specify the type of columns whose type cannot be auto-detected. For example the "title" column uses pandas dtype "object", so its data type is ambiguous
        bigquery.SchemaField("names", bigquery.enums.SqlTypeNames.STRING)
    ],
    # Optionally, set the write disposition. BigQuery appends loaded rows to an existing table by default, but with WRITE_TRUNCATE write disposition it replaces the table with the loaded data
    write_disposition="WRITE_TRUNCATE",
)

%python
# Execute write
job = client.load_table_from_dataframe(
    df, table_id_full, job_config=job_config
)
job.result()
​
# Display results jf loaded succesfully
table = client.get_table(table_id_full)
print( "Loaded {} rows and {} columns to {}".format(table.num_rows, len(table.schema), table_id_full))