diff --git a/bigquery/datalab-migration/README.md b/bigquery/datalab-migration/README.md
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+# Datalab Migration Guide
+
+This directory contains samples used in the `datalab` to
+`google-cloud-bigquery` migration guide.
diff --git a/bigquery/datalab-migration/requirements.txt b/bigquery/datalab-migration/requirements.txt
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+google-cloud-bigquery[pandas,pyarrow]==1.8.1
+datalab==1.1.4
+ipython==7.2.0; python_version > '2.7'
+ipython==5.5; python_version == '2.7'
+google-cloud-monitoring==0.28.1
diff --git a/bigquery/datalab-migration/samples_test.py b/bigquery/datalab-migration/samples_test.py
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+++ b/bigquery/datalab-migration/samples_test.py
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+# Copyright 2018 Google Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+
+import google.auth
+import google.datalab
+import pytest
+import IPython
+from IPython.testing import tools
+from IPython.terminal import interactiveshell
+
+
+# Get default project
+_, PROJECT_ID = google.auth.default()
+# Set Datalab project ID
+context = google.datalab.Context.default()
+context.set_project_id(PROJECT_ID)
+
+
+@pytest.fixture(scope='session')
+def ipython_interactive():
+    config = tools.default_config()
+    config.TerminalInteractiveShell.simple_prompt = True
+    shell = interactiveshell.TerminalInteractiveShell.instance(config=config)
+    return shell
+
+
+@pytest.fixture
+def to_delete():
+    from google.cloud import bigquery
+    client = bigquery.Client()
+    doomed = []
+    yield doomed
+    for dataset_id in doomed:
+        dataset = client.get_dataset(dataset_id)
+        client.delete_dataset(dataset, delete_contents=True)
+
+
+def _set_up_ipython(extension):
+    ip = IPython.get_ipython()
+    ip.extension_manager.load_extension(extension)
+    return ip
+
+
+def _strip_region_tags(sample_text):
+    """Remove blank lines and region tags from sample text"""
+    magic_lines = [line for line in sample_text.split('\n')
+                   if len(line) > 0 and '# [' not in line]
+    return '\n'.join(magic_lines)
+
+
+def test_datalab_query_magic(ipython_interactive):
+    import google.datalab.bigquery as bq
+
+    ip = _set_up_ipython('google.datalab.kernel')
+
+    sample = """
+    # [START bigquery_migration_datalab_query_magic]
+    %%bq query
+    SELECT word, SUM(word_count) as count
+    FROM `bigquery-public-data.samples.shakespeare`
+    GROUP BY word
+    ORDER BY count ASC
+    LIMIT 100
+    # [END bigquery_migration_datalab_query_magic]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    results = ip.user_ns["_"]  # Last returned object in notebook session
+    assert isinstance(results, bq.QueryResultsTable)
+    df = results.to_dataframe()
+    assert len(df) == 100
+
+
+def test_client_library_query_magic(ipython_interactive):
+    import pandas
+
+    ip = _set_up_ipython('google.cloud.bigquery')
+
+    sample = """
+    # [START bigquery_migration_client_library_query_magic]
+    %%bigquery
+    SELECT word, SUM(word_count) as count
+    FROM `bigquery-public-data.samples.shakespeare`
+    GROUP BY word
+    ORDER BY count ASC
+    LIMIT 100
+    # [END bigquery_migration_client_library_query_magic]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    df = ip.user_ns["_"]  # Last returned object in notebook session
+    assert isinstance(df, pandas.DataFrame)
+    assert len(df) == 100
+
+
+def test_datalab_query_magic_results_variable(ipython_interactive):
+    ip = _set_up_ipython('google.datalab.kernel')
+
+    sample = """
+    # [START bigquery_migration_datalab_query_magic_define_query]
+    %%bq query -n my_query
+    SELECT name FROM `bigquery-public-data.usa_names.usa_1910_current`
+    WHERE state = "TX"
+    LIMIT 100
+    # [END bigquery_migration_datalab_query_magic_define_query]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    sample = """
+    # [START bigquery_migration_datalab_execute_query]
+    import google.datalab.bigquery as bq
+
+    my_variable = my_query.execute().result().to_dataframe()
+    # [END bigquery_migration_datalab_execute_query]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    variable_name = "my_variable"
+    assert variable_name in ip.user_ns  # verify that variable exists
+    my_variable = ip.user_ns[variable_name]
+    assert len(my_variable) == 100
+    ip.user_ns.pop(variable_name)  # clean up variable
+
+
+def test_client_library_query_magic_results_variable(ipython_interactive):
+    ip = _set_up_ipython('google.cloud.bigquery')
+
+    sample = """
+    # [START bigquery_migration_client_library_query_magic_results_variable]
+    %%bigquery my_variable
+    SELECT name FROM `bigquery-public-data.usa_names.usa_1910_current`
+    WHERE state = "TX"
+    LIMIT 100
+    # [END bigquery_migration_client_library_query_magic_results_variable]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    variable_name = "my_variable"
+    assert variable_name in ip.user_ns  # verify that variable exists
+    my_variable = ip.user_ns[variable_name]
+    assert len(my_variable) == 100
+    ip.user_ns.pop(variable_name)  # clean up variable
+
+
+def test_datalab_magic_parameterized_query(ipython_interactive):
+    import pandas
+
+    ip = _set_up_ipython('google.datalab.kernel')
+
+    sample = """
+    # [START bigquery_migration_datalab_magic_define_parameterized_query]
+    %%bq query -n my_query
+    SELECT word, SUM(word_count) as count
+    FROM `bigquery-public-data.samples.shakespeare`
+    WHERE corpus = @corpus_name
+    GROUP BY word
+    ORDER BY count ASC
+    LIMIT @limit
+    # [END bigquery_migration_datalab_magic_define_parameterized_query]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    sample = """
+    # [START bigquery_migration_datalab_magic_query_params]
+    corpus_name = "hamlet"
+    limit = 10
+    # [END bigquery_migration_datalab_magic_query_params]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    sample = """
+    # [START bigquery_migration_datalab_magic_execute_parameterized_query]
+    %%bq execute -q my_query --to-dataframe
+    parameters:
+    - name: corpus_name
+      type: STRING
+      value: $corpus_name
+    - name: limit
+      type: INTEGER
+      value: $limit
+    # [END bigquery_migration_datalab_magic_execute_parameterized_query]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+    df = ip.user_ns["_"]  # Retrieves last returned object in notebook session
+    assert isinstance(df, pandas.DataFrame)
+    assert len(df) == 10
+
+
+def test_client_library_magic_parameterized_query(ipython_interactive):
+    import pandas
+
+    ip = _set_up_ipython('google.cloud.bigquery')
+
+    sample = """
+    # [START bigquery_migration_client_library_magic_query_params]
+    params = {"corpus_name": "hamlet", "limit": 10}
+    # [END bigquery_migration_client_library_magic_query_params]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    sample = """
+    # [START bigquery_migration_client_library_magic_parameterized_query]
+    %%bigquery --params $params
+    SELECT word, SUM(word_count) as count
+    FROM `bigquery-public-data.samples.shakespeare`
+    WHERE corpus = @corpus_name
+    GROUP BY word
+    ORDER BY count ASC
+    LIMIT @limit
+    # [END bigquery_migration_client_library_magic_parameterized_query]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    df = ip.user_ns["_"]  # Retrieves last returned object in notebook session
+    assert isinstance(df, pandas.DataFrame)
+    assert len(df) == 10
+
+
+def test_datalab_list_tables_magic(ipython_interactive):
+    ip = _set_up_ipython('google.datalab.kernel')
+
+    sample = """
+    # [START bigquery_migration_datalab_list_tables_magic]
+    %bq tables list --dataset bigquery-public-data.samples
+    # [END bigquery_migration_datalab_list_tables_magic]
+    """
+    ip.run_cell(_strip_region_tags(sample))
+
+    # Retrieves last returned object in notebook session
+    html_element = ip.user_ns["_"]
+    assert "shakespeare" in html_element.data
+
+
+def test_datalab_query():
+    # [START bigquery_migration_datalab_query]
+    import google.datalab.bigquery as bq
+
+    sql = """
+        SELECT name FROM `bigquery-public-data.usa_names.usa_1910_current`
+        WHERE state = "TX"
+        LIMIT 100
+    """
+    df = bq.Query(sql).execute().result().to_dataframe()
+    # [END bigquery_migration_datalab_query]
+
+    assert len(df) == 100
+
+
+def test_client_library_query():
+    # [START bigquery_migration_client_library_query]
+    from google.cloud import bigquery
+
+    client = bigquery.Client()
+    sql = """
+        SELECT name FROM `bigquery-public-data.usa_names.usa_1910_current`
+        WHERE state = "TX"
+        LIMIT 100
+    """
+    df = client.query(sql).to_dataframe()
+    # [END bigquery_migration_client_library_query]
+
+    assert len(df) == 100
+
+
+def test_datalab_load_table_from_gcs_csv(to_delete):
+    # [START bigquery_migration_datalab_load_table_from_gcs_csv]
+    import google.datalab.bigquery as bq
+
+    # Create the dataset
+    dataset_id = 'import_sample'
+    # [END bigquery_migration_datalab_load_table_from_gcs_csv]
+    # Use unique dataset ID to avoid collisions when running tests
+    dataset_id = 'test_dataset_{}'.format(int(time.time() * 1000))
+    to_delete.append(dataset_id)
+    # [START bigquery_migration_datalab_load_table_from_gcs_csv]
+    bq.Dataset(dataset_id).create()
+
+    # Create the table
+    schema = [
+        {'name': 'name', 'type': 'STRING'},
+        {'name': 'post_abbr', 'type': 'STRING'},
+    ]
+    table = bq.Table(
+        '{}.us_states'.format(dataset_id)).create(schema=schema)
+    table.load(
+        'gs://cloud-samples-data/bigquery/us-states/us-states.csv',
+        mode='append',
+        source_format='csv',
+        csv_options=bq.CSVOptions(skip_leading_rows=1)
+    )  # Waits for the job to complete
+    # [END bigquery_migration_datalab_load_table_from_gcs_csv]
+
+    assert table.length == 50
+
+
+def test_client_library_load_table_from_gcs_csv(to_delete):
+    # [START bigquery_migration_client_library_load_table_from_gcs_csv]
+    from google.cloud import bigquery
+
+    client = bigquery.Client(location='US')
+
+    # Create the dataset
+    dataset_id = 'import_sample'
+    # [END bigquery_migration_client_library_load_table_from_gcs_csv]
+    # Use unique dataset ID to avoid collisions when running tests
+    dataset_id = 'test_dataset_{}'.format(int(time.time() * 1000))
+    to_delete.append(dataset_id)
+    # [START bigquery_migration_client_library_load_table_from_gcs_csv]
+    dataset = client.create_dataset(dataset_id)
+
+    # Create the table
+    job_config = bigquery.LoadJobConfig(
+        schema=[
+            bigquery.SchemaField('name', 'STRING'),
+            bigquery.SchemaField('post_abbr', 'STRING')
+        ],
+        skip_leading_rows=1,
+        # The source format defaults to CSV, so the line below is optional.
+        source_format=bigquery.SourceFormat.CSV
+    )
+    load_job = client.load_table_from_uri(
+        'gs://cloud-samples-data/bigquery/us-states/us-states.csv',
+        dataset.table('us_states'),
+        job_config=job_config
+    )
+    load_job.result()  # Waits for table load to complete.
+    # [END bigquery_migration_client_library_load_table_from_gcs_csv]
+
+    table = client.get_table(dataset.table('us_states'))
+    assert table.num_rows == 50
+
+
+def test_datalab_load_table_from_dataframe(to_delete):
+    # [START bigquery_migration_datalab_load_table_from_dataframe]
+    import google.datalab.bigquery as bq
+    import pandas
+
+    # Create the dataset
+    dataset_id = 'import_sample'
+    # [END bigquery_migration_datalab_load_table_from_dataframe]
+    # Use unique dataset ID to avoid collisions when running tests
+    dataset_id = 'test_dataset_{}'.format(int(time.time() * 1000))
+    to_delete.append(dataset_id)
+    # [START bigquery_migration_datalab_load_table_from_dataframe]
+    bq.Dataset(dataset_id).create()
+
+    # Create the table and load the data
+    dataframe = pandas.DataFrame([
+        {'title': 'The Meaning of Life', 'release_year': 1983},
+        {'title': 'Monty Python and the Holy Grail', 'release_year': 1975},
+        {'title': 'Life of Brian', 'release_year': 1979},
+        {
+            'title': 'And Now for Something Completely Different',
+            'release_year': 1971
+        },
+    ])
+    schema = bq.Schema.from_data(dataframe)
+    table = bq.Table(
+        '{}.monty_python'.format(dataset_id)).create(schema=schema)
+    table.insert(dataframe)  # Starts steaming insert of data
+    # [END bigquery_migration_datalab_load_table_from_dataframe]
+    # The Datalab library uses tabledata().insertAll() to load data from
+    # pandas DataFrames to tables. Because it can take a long time for the rows
+    # to be available in the table, this test does not assert on the number of
+    # rows in the destination table after the job is run. If errors are
+    # encountered during the insertion, this test will fail.
+    # See https://cloud.google.com/bigquery/streaming-data-into-bigquery
+
+
+def test_client_library_load_table_from_dataframe(to_delete):
+    # [START bigquery_migration_client_library_load_table_from_dataframe]
+    from google.cloud import bigquery
+    import pandas
+
+    client = bigquery.Client(location='US')
+
+    dataset_id = 'import_sample'
+    # [END bigquery_migration_client_library_load_table_from_dataframe]
+    # Use unique dataset ID to avoid collisions when running tests
+    dataset_id = 'test_dataset_{}'.format(int(time.time() * 1000))
+    to_delete.append(dataset_id)
+    # [START bigquery_migration_client_library_load_table_from_dataframe]
+    dataset = client.create_dataset(dataset_id)
+
+    # Create the table and load the data
+    dataframe = pandas.DataFrame([
+        {'title': 'The Meaning of Life', 'release_year': 1983},
+        {'title': 'Monty Python and the Holy Grail', 'release_year': 1975},
+        {'title': 'Life of Brian', 'release_year': 1979},
+        {
+            'title': 'And Now for Something Completely Different',
+            'release_year': 1971
+        },
+    ])
+    table_ref = dataset.table('monty_python')
+    load_job = client.load_table_from_dataframe(dataframe, table_ref)
+    load_job.result()  # Waits for table load to complete.
+    # [END bigquery_migration_client_library_load_table_from_dataframe]
+
+    table = client.get_table(table_ref)
+    assert table.num_rows == 4