title: Create a BigQuery table from pandas dataframe, WITHOUT specifying schema explicitly
body: <p>I have a pandas dataframe and want to create a BigQuery table from it. I understand that there are many posts asking about this question, but all the answers I can find so far require explicitly specifying the schema of every column. For example:</p>
  <pre><code>from google.cloud import bigquery as bq
  
  client = bq.Client()
  
  dataset_ref = client.dataset('my_dataset', project = 'my_project')
  table_ref = dataset_ref.table('my_table')  
  
  job_config = bq.LoadJobConfig( 
   schema=[ 
       bq.SchemaField(&quot;a&quot;, bq.enums.SqlTypeNames.STRING),
       bq.SchemaField(&quot;b&quot;, bq.enums.SqlTypeNames.INT64), 
       bq.SchemaField(&quot;c&quot;, bq.enums.SqlTypeNames.FLOAT64),         
   ]
  ) 
  
  client.load_table_from_dataframe(my_df, table_ref, job_config=job_config).result()
  </code></pre>
  <p>However, sometimes I have a dataframe of many columns (for example, 100 columns), it's really non-trival to specify all the columns. Is there a way to do it efficiently?</p>
  <p>Btw, I found this post with similar question: <a href="https://stackoverflow.com/questions/48886761/efficiently-write-a-pandas-dataframe-to-google-bigquery">Efficiently write a Pandas dataframe to Google BigQuery</a>
  But seems like <code>bq.Schema.from_dataframe</code> does not exist:</p>
  <pre><code>AttributeError: module 'google.cloud.bigquery' has no attribute 'Schema'
  </code></pre>