Running Parameterized Queries

Quite new to this google bigquery sql thing so please bear with me. I'm trying to build a google standardSQL parameterized query. The following sample was used and ran successfully

Solution 1:

So ... the problem was with this line of code that didn't work as expected. Not sure why though, as it worked with queries that didn't have parameterized vars.

results = query_job.result()
df = results().to_dataframe()

And the actual code... Remember to replace with your own login credentials for this to work.

import datetime, time
from google.cloud import bigquery
from google.oauth2 import service_account
import pandas as pd

#login
credentials = service_account.Credentials.from_service_account_file('your.json')
project_id = 'your-named-project'
client = bigquery.Client(credentials= credentials,project=project_id)

#The query
q_input = """
#standardSQL
        WITH time AS 
            (
                SELECT TIMESTAMP_MILLIS(timestamp) AS trans_time,
                    inputs.input_pubkey_base58 AS input_key,
                    outputs.output_pubkey_base58 AS output_key,
                    outputs.output_satoshis AS satoshis,
                    transaction_id AS trans_id
                FROM `bigquery-public-data.bitcoin_blockchain.transactions`
                    JOIN UNNEST (inputs) AS inputs
                    JOIN UNNEST (outputs) AS outputs
                    WHERE inputs.input_pubkey_base58 = @pubkey
                    OR outputs.output_pubkey_base58 = @pubkey
            )
        SELECT input_key, output_key, satoshis, trans_id,
            EXTRACT(DATE FROM trans_time) AS date
        FROM time
          WHERE trans_time >= @mdate AND trans_time <= @tdate AND satoshis >= @satoshis
        --ORDER BY date
"""#The desired purposedefrunQueryTransaction(varInitDate,varEndDate,pubkey,satoshis):
    global df
    query_params = [
        bigquery.ScalarQueryParameter('mdate', 'STRING', varInitDate),
        bigquery.ScalarQueryParameter('tdate', 'STRING', varEndDate),
        bigquery.ScalarQueryParameter('pubkey', 'STRING', pubkey),
        bigquery.ScalarQueryParameter('satoshis', 'INT64', satoshis),
    ]
    job_config = bigquery.QueryJobConfig()
    job_config.query_parameters = query_params
    query_job = client.query(q_input,job_config=job_config)  # API request - starts the query
    results = query_job.result()  # Waits for job to complete.
    df=pd.DataFrame(columns=['input_key', 'output_key', 'satoshis', 'trans_id', 'date'])
    for row in results:
        df.loc[len(df)] = [row.input_key, row.output_key, row.satoshis, row.trans_id, row.date]
        #print("{} : {} : {} : {} : {}".format(row.input_key, row.output_key, row.satoshis, row.trans_id, row.date))return df

#runQueryTransaction(InitialDate,EndDate,WalletPublicKey,Satoshis)
runQueryTransaction('2010-05-21','2010-05-23','1XPTgDRhN8RFnzniWCddobD9iKZatrvH4',1000000000000)

Cheers