diff --git a/examples/campaign-finance.ipynb b/examples/campaign-finance.ipynb
index d111c96..082ad5a 100644
--- a/examples/campaign-finance.ipynb
+++ b/examples/campaign-finance.ipynb
@@ -242,7 +242,7 @@
     "        \"E\": \"recount\",\n",
     "    }\n",
     "    first_letter = pgi[0]\n",
-    "    return first_letter.substitute(election_types, else_=ibis.NA)\n",
+    "    return first_letter.substitute(election_types, else_=ibis.null())\n",
     "\n",
     "\n",
     "cleaned = cleaned.mutate(election_type=get_election_type(_.TRANSACTION_PGI)).drop(\n",
diff --git a/examples/imdb.ipynb b/examples/imdb.ipynb
index 5428449..8af82ce 100644
--- a/examples/imdb.ipynb
+++ b/examples/imdb.ipynb
@@ -93,7 +93,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To ensure column names are Pythonic, we can relabel as `snake_case`."
+    "To ensure column names are Pythonic, we can rename as `snake_case`."
    ]
   },
   {
@@ -102,7 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "name_basics.relabel(\"snake_case\")"
+    "name_basics.rename(\"snake_case\")"
    ]
   },
   {
@@ -110,7 +110,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's grab all of the relevant IMDB tables and relabel columns."
+    "Let's grab all of the relevant IMDB tables and rename columns."
    ]
   },
   {
@@ -119,13 +119,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "name_basics = ex.imdb_name_basics.fetch().relabel(\"snake_case\")\n",
-    "title_akas = ex.imdb_title_akas.fetch().relabel(\"snake_case\")\n",
-    "title_basics = ex.imdb_title_basics.fetch().relabel(\"snake_case\")\n",
-    "title_crew = ex.imdb_title_crew.fetch().relabel(\"snake_case\")\n",
-    "title_episode = ex.imdb_title_episode.fetch().relabel(\"snake_case\")\n",
-    "title_principals = ex.imdb_title_principals.fetch().relabel(\"snake_case\")\n",
-    "title_ratings = ex.imdb_title_ratings.fetch().relabel(\"snake_case\")"
+    "name_basics = ex.imdb_name_basics.fetch().rename(\"snake_case\")\n",
+    "title_akas = ex.imdb_title_akas.fetch().rename(\"snake_case\")\n",
+    "title_basics = ex.imdb_title_basics.fetch().rename(\"snake_case\")\n",
+    "title_crew = ex.imdb_title_crew.fetch().rename(\"snake_case\")\n",
+    "title_episode = ex.imdb_title_episode.fetch().rename(\"snake_case\")\n",
+    "title_principals = ex.imdb_title_principals.fetch().rename(\"snake_case\")\n",
+    "title_ratings = ex.imdb_title_ratings.fetch().rename(\"snake_case\")"
    ]
   },
   {
@@ -420,7 +420,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ibis.show_sql(name_basics)"
+    "ibis.to_sql(name_basics)"
    ]
   },
   {
@@ -437,8 +437,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "title_akas = title_akas.mutate(title_id=tconst_to_int(_.title_id)).relabel(\n",
-    "    {\"title_id\": \"tconst\"}\n",
+    "title_akas = title_akas.mutate(title_id=tconst_to_int(_.title_id)).rename(\n",
+    "    {\"tconst\": \"title_id\"}\n",
     ")\n",
     "title_basics = title_basics.mutate(tconst=tconst_to_int(_.tconst))\n",
     "title_crew = title_crew.mutate(\n",
diff --git a/requirements.txt b/requirements.txt
index 4f3a0b3..1a0daba 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,5 +3,5 @@ jupyterlab == 3.4.8
 ipywidgets
 altair
 pandas < 2.1
-ibis-framework[sqlite,duckdb,clickhouse]
+ibis-framework[sqlite,duckdb,clickhouse,examples]
 ibis-substrait < 3.1
diff --git a/scripts/prepare_campaign_finance_data.py b/scripts/prepare_campaign_finance_data.py
index 8130a91..f966fc6 100644
--- a/scripts/prepare_campaign_finance_data.py
+++ b/scripts/prepare_campaign_finance_data.py
@@ -34,33 +34,34 @@
 if not parquet_path.exists():
     print("Generating itcont.parquet...")
     # Read in the CSV
-    t = ibis.read_csv(csv_path)
-
     # The CSV doesn't have a header, we need to manually add titles
-    header = [
-        "CMTE_ID",
-        "AMNDT_IND",
-        "RPT_TP",
-        "TRANSACTION_PGI",
-        "IMAGE_NUM",
-        "TRANSACTION_TP",
-        "ENTITY_TP",
-        "NAME",
-        "CITY",
-        "STATE",
-        "ZIP_CODE",
-        "EMPLOYER",
-        "OCCUPATION",
-        "TRANSACTION_DT",
-        "TRANSACTION_AMT",
-        "OTHER_ID",
-        "TRAN_ID",
-        "FILE_NUM",
-        "MEMO_CD",
-        "MEMO_TEXT",
-        "SUB_ID",
-    ]
-    t = t.relabel(dict(zip(t.columns, header)))
+    t = ibis.read_csv(
+        csv_path,
+        header=False,
+        names=[
+            "CMTE_ID",
+            "AMNDT_IND",
+            "RPT_TP",
+            "TRANSACTION_PGI",
+            "IMAGE_NUM",
+            "TRANSACTION_TP",
+            "ENTITY_TP",
+            "NAME",
+            "CITY",
+            "STATE",
+            "ZIP_CODE",
+            "EMPLOYER",
+            "OCCUPATION",
+            "TRANSACTION_DT",
+            "TRANSACTION_AMT",
+            "OTHER_ID",
+            "TRAN_ID",
+            "FILE_NUM",
+            "MEMO_CD",
+            "MEMO_TEXT",
+            "SUB_ID",
+        ],
+    )
 
     # For the analysis, we're only going to use a few of the columns. To save
     # bandwidth, lets select out only the columns we'll be using.