diff --git a/Quickstart.ipynb b/Quickstart.ipynb
index 905bf18..26251ec 100644
--- a/Quickstart.ipynb
+++ b/Quickstart.ipynb
@@ -2,56 +2,68 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# TileDB Quickstart Notebook"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "This notebook will walk through usage of TileDB Cloud including User Defined Functions and Serverless SQL\n",
     "\n",
     "We will start off using the quickstart example arrays and then move to a real work dataset based on transit data from the Boston metro area."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
    "source": [
     "import tiledb, tiledb.cloud\n",
     "import pandas\n",
     "import numpy"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
    "source": [
     "# In TileDB Cloud hosted notebooks you are automatically signed in to the tiledb cloud python client\n",
     "# Uncomment the login command if running outside of TileDB Cloud\n",
     "#tiledb.cloud.login(username=\"xxx\", password=\"yyy\")"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "First let's access the quickstart sparse array through the cloud service."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 4,
+   "source": [
+    "with tiledb.open(\"tiledb://TileDB-Inc/quickstart_sparse\", ctx=tiledb.cloud.Ctx()) as A:\n",
+    "    df = pandas.DataFrame(A[:])\n",
+    "\n",
+    "df"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
+      "text/plain": [
+       "   a  rows  cols\n",
+       "0  1     1     1\n",
+       "1  3     2     3\n",
+       "2  2     2     4"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -98,49 +110,24 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   a  rows  cols\n",
-       "0  1     1     1\n",
-       "1  3     2     3\n",
-       "2  2     2     4"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
-   "source": [
-    "with tiledb.open(\"tiledb://TileDB-Inc/quickstart_sparse\", ctx=tiledb.cloud.Ctx()) as A:\n",
-    "    df = pandas.DataFrame(A[:])\n",
-    "\n",
-    "df"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Next let's run User Defined Function. This will compute the mean of the \"a\" attribute."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 5,
    "source": [
     "def mean(data):\n",
     "    import numpy\n",
@@ -151,22 +138,48 @@
     "    results = A.apply(mean, [(1,4), (1,4)])\n",
     "\n",
     "results"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TileDBCloudError",
+     "evalue": "Docker logs: Traceback (most recent call last):\r\n  File \"/opt/array_udf_entry.py\", line 918, in <module>\r\n    main()\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 1137, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 1062, in main\r\n    rv = self.invoke(ctx)\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 1404, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 763, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"/opt/array_udf_entry.py\", line 720, in main\r\n    udf_function = cloudpickle.loads(pickled_udf)\r\nTypeError: code() takes at most 15 arguments (16 given)\r\n\nError(s): received an error from the container: docker container exited with non-zero code: 1. - Code: 5000",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTileDBCloudError\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_11116/1372410501.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Run UDF on entire dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtiledb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tiledb://TileDB-Inc/quickstart_sparse\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtiledb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcloud\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCtx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/tiledb/cloud/cloudarray.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;36m2.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \"\"\"\n\u001b[0;32m---> 45\u001b[0;31m         return self.apply_async(\n\u001b[0m\u001b[1;32m     46\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/tiledb/cloud/array.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mUDFResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTaskResult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0;31m# Set last udf task id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/tiledb/cloud/array.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mGenApiException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeoutError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0m_maybe_set_last_udf_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mtiledb_cloud_error\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetheader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTASK_ID_HEADER\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTileDBCloudError\u001b[0m: Docker logs: Traceback (most recent call last):\r\n  File \"/opt/array_udf_entry.py\", line 918, in <module>\r\n    main()\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 1137, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 1062, in main\r\n    rv = self.invoke(ctx)\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 1404, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"/opt/conda/lib/python3.7/site-packages/click/core.py\", line 763, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"/opt/array_udf_entry.py\", line 720, in main\r\n    udf_function = cloudpickle.loads(pickled_udf)\r\nTypeError: code() takes at most 15 arguments (16 given)\r\n\nError(s): received an error from the container: docker container exited with non-zero code: 1. - Code: 5000"
+     ]
+    }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "A serverless SQL query can also be run to to compute the mean. Serverless SQL returns a pandas dataframe of the results."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": null,
+   "source": [
+    "# Run sql\n",
+    "results = tiledb.cloud.sql.exec(\"select AVG(`a`) as avg from `tiledb://TileDB-Inc/quickstart_sparse` WHERE `rows` between 1 and 4 and `cols` between 1 and 4\")\n",
+    "results"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
+      "text/plain": [
+       "   avg\n",
+       "0    2"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -197,33 +210,23 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   avg\n",
-       "0    2"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     }
    ],
-   "source": [
-    "# Run sql\n",
-    "results = tiledb.cloud.sql.exec(\"select AVG(`a`) as avg from `tiledb://TileDB-Inc/quickstart_sparse` WHERE `rows` between 1 and 4 and `cols` between 1 and 4\")\n",
-    "results"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# Realworld Example"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Using ridership data from the MBTA[1] (Boston-area Transit Authority) we will run a sql query to extract ridership data for all services exclusing buses and then create a line graph.\n",
     "\n",
@@ -231,22 +234,53 @@
     "\n",
     "\n",
     "[1] Data taken from MBTA data dashboard https://mbtabackontrack.com/performance/"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "### Serverless SQL To Filter and Fetch Data"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": null,
+   "source": [
+    "# Run a serverless sql query to extract the total monthly ridership exclusing any buses\n",
+    "# SQL here allows us to convert the full datetime to YEAR_MONTH format, since the granularity is only monthly\n",
+    "data = tiledb.cloud.sql.exec(\"\"\"\n",
+    "SELECT\n",
+    "    EXTRACT(YEAR_MONTH FROM service_date) as service_date,\n",
+    "    route_or_line,\n",
+    "    total_monthly_ridership\n",
+    "FROM `tiledb://TileDB-Inc/MBTA_Average_Monthly_Ridership_by_Mode`\n",
+    "WHERE route_or_line != 'Bus'\n",
+    "ORDER BY service_date ASC\n",
+    "\"\"\")\n",
+    "data"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
+      "text/plain": [
+       "     route_or_line  service_date  total_monthly_ridership\n",
+       "0      Orange Line        201601             4.094142e+06\n",
+       "1        Blue Line        201601             1.145383e+06\n",
+       "2          Boat-F4        201601             8.172000e+03\n",
+       "3       Green Line        201601             3.417417e+06\n",
+       "4         Red Line        201601             4.696898e+06\n",
+       "..             ...           ...                      ...\n",
+       "592      Blue Line        202012             4.461333e+05\n",
+       "593       Red Line        202012             1.057386e+06\n",
+       "594    Silver Line        202012             2.921290e+05\n",
+       "595    Orange Line        202012             1.027770e+06\n",
+       "596  Commuter Rail        202012             5.362710e+05\n",
+       "\n",
+       "[597 rows x 3 columns]"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -342,116 +376,131 @@
        "</table>\n",
        "<p>597 rows × 3 columns</p>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "     route_or_line  service_date  total_monthly_ridership\n",
-       "0      Orange Line        201601             4.094142e+06\n",
-       "1        Blue Line        201601             1.145383e+06\n",
-       "2          Boat-F4        201601             8.172000e+03\n",
-       "3       Green Line        201601             3.417417e+06\n",
-       "4         Red Line        201601             4.696898e+06\n",
-       "..             ...           ...                      ...\n",
-       "592      Blue Line        202012             4.461333e+05\n",
-       "593       Red Line        202012             1.057386e+06\n",
-       "594    Silver Line        202012             2.921290e+05\n",
-       "595    Orange Line        202012             1.027770e+06\n",
-       "596  Commuter Rail        202012             5.362710e+05\n",
-       "\n",
-       "[597 rows x 3 columns]"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "# Run a serverless sql query to extract the total monthly ridership exclusing any buses\n",
-    "# SQL here allows us to convert the full datetime to YEAR_MONTH format, since the granularity is only monthly\n",
-    "data = tiledb.cloud.sql.exec(\"\"\"\n",
-    "SELECT\n",
-    "    EXTRACT(YEAR_MONTH FROM service_date) as service_date,\n",
-    "    route_or_line,\n",
-    "    total_monthly_ridership\n",
-    "FROM `tiledb://TileDB-Inc/MBTA_Average_Monthly_Ridership_by_Mode`\n",
-    "WHERE route_or_line != 'Bus'\n",
-    "ORDER BY service_date ASC\n",
-    "\"\"\")\n",
-    "data"
-   ]
+    "### `Array.query()` to Filter and Fetch Data"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "Next we will use Pandas to plot the ridership by route and month"
-   ]
+    "Alternatively, the data may be filtered by attributes and fetched by using the query options."
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 36,
+   "source": [
+    "with tiledb.open(\"tiledb://TileDB-Inc/MBTA_Average_Monthly_Ridership_by_Mode\", ctx=tiledb.cloud.Ctx()) as A:\n",
+    "    qc = tiledb.QueryCondition(\"route_or_line != 'Bus'\")\n",
+    "    attrs = [\"route_or_line\", \"service_date\", \"total_monthly_ridership\"]\n",
+    "    # Note the usage of `use_arrow=False` here is required for querying variable-length attributes\n",
+    "    data = A.query(use_arrow=False, attr_cond=qc, attrs=attrs).df[:]\n",
+    "data"
+   ],
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "route_or_line\n",
-       "Blue Line        AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Boat-F1          AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Boat-F3          AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Boat-F4          AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Commuter Rail    AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Green Line       AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Orange Line      AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Red Line         AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Silver Line      AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "The RIDE         AxesSubplot(0.125,0.125;0.775x0.755)\n",
-       "Name: total_monthly_ridership, dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1080x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "output_type": "error",
+     "ename": "TileDBError",
+     "evalue": "[TileDB::REST] Error: Error submitting query to REST; server returned no data. Curl error: Error in libcurl POST operation: libcurl error message 'CURLE_OK'; HTTP code 400; server response data '{\"code\":2213,\"message\":\"Error submitting query: [TileDB::QueryCondition] Error: Clause attribute may only be var-sized for ASCII strings: route_or_line\",\"request_id\":\"43720f29-9cf2-4a9d-930c-f55fde077be8\"}'.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTileDBError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_11116/3397852200.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mqc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtiledb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mQueryCondition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"route_or_line != 'Bus'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mattrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"route_or_line\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"service_date\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"total_monthly_ridership\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr_cond\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;31m# data = pandas.DataFrame(data, columns=attrs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/tiledb/multirange_indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m    293\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreload_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpyarrow\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpyarrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/tiledb/multirange_indexing.py\u001b[0m in \u001b[0;36m_run_query\u001b[0;34m(self, query, preload_metadata)\u001b[0m\n\u001b[1;32m    196\u001b[0m             )\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0mschema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTileDBError\u001b[0m: [TileDB::REST] Error: Error submitting query to REST; server returned no data. Curl error: Error in libcurl POST operation: libcurl error message 'CURLE_OK'; HTTP code 400; server response data '{\"code\":2213,\"message\":\"Error submitting query: [TileDB::QueryCondition] Error: Clause attribute may only be var-sized for ASCII strings: route_or_line\",\"request_id\":\"43720f29-9cf2-4a9d-930c-f55fde077be8\"}'."
+     ]
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Next we will use Pandas to plot the ridership by route and month"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "source": [
     "# First we tell pandas to index the dataframe by the data and route line\n",
     "data2 = data.set_index(['service_date', 'route_or_line'])\n",
     "data2.groupby('route_or_line')['total_monthly_ridership'].plot(legend=True, figsize=(15,8))"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ImportError",
+     "evalue": "matplotlib is required for plotting when the default backend \"matplotlib\" is selected.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_11116/909482865.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# First we tell pandas to index the dataframe by the data and route line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdata2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'service_date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'route_or_line'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'route_or_line'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_monthly_ridership'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"plot\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_groupby\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    219\u001b[0m     )\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_agg_template\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_agg_examples_doc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Series\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    892\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.chained_assignment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_python_apply_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selected_obj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m                 \u001b[0;31m# gh-20949\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_python_apply_general\u001b[0;34m(self, f, data)\u001b[0m\n\u001b[1;32m    926\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mapplying\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    927\u001b[0m         \"\"\"\n\u001b[0;32m--> 928\u001b[0;31m         \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmutated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m         return self._wrap_applied_output(\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, data, axis)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;31m# group might be modified\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0mgroup_axes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_is_indexed_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mmutated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"plot\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    873\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 875\u001b[0;31m         \u001b[0mplot_backend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_plot_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"backend\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    876\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    877\u001b[0m         x, y, kind, kwargs = self._get_call_args(\n",
+      "\u001b[0;32m~/anaconda3/envs/tiledb-3.8/lib/python3.8/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_get_plot_backend\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m   1784\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1785\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1786\u001b[0;31m             raise ImportError(\n\u001b[0m\u001b[1;32m   1787\u001b[0m                 \u001b[0;34m\"matplotlib is required for plotting when the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1788\u001b[0m                 \u001b[0;34m'default backend \"matplotlib\" is selected.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: matplotlib is required for plotting when the default backend \"matplotlib\" is selected."
+     ]
+    }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "### User Defined Function for Median Computation"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Next we will use a User Define Function to compute the median monthly rideship. MariaDB does not have a Median function so this is an easy way to compute this median without loading the entire dataset into the notebooks memory"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": null,
+   "source": [
+    "# median_by_route_or_line takes the numpy ordered dictionary and performs a pandas group by and apply of numpy median\n",
+    "def median_by_month(data):\n",
+    "    import pandas\n",
+    "    import numpy\n",
+    "    res = pandas.DataFrame(data)\n",
+    "    res['month'] = res[\"service_date\"].apply(lambda d: d.month)\n",
+    "    # Get the median rideship by month\n",
+    "    median_results = res.groupby('month')['total_monthly_ridership'].apply(numpy.median)\n",
+    "    return median_results\n",
+    "\n",
+    "# Run UDF on entire dataset but only fetch service_date and total_monthly_ridership columns\n",
+    "with tiledb.open(\"tiledb://TileDB-Inc/MBTA_Average_Monthly_Ridership_by_Mode\", ctx=tiledb.cloud.Ctx()) as A:\n",
+    "    ned = A.nonempty_domain()\n",
+    "    median_rides_by_month = A.apply(median_by_month, ned, attrs=[\"service_date\", \"total_monthly_ridership\"])\n",
+    "median_rides_by_month"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "month\n",
@@ -470,75 +519,50 @@
        "Name: total_monthly_ridership, dtype: float64"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
-   "source": [
-    "# median_by_route_or_line takes the numpy ordered dictionary and performs a pandas group by and apply of numpy median\n",
-    "def median_by_month(data):\n",
-    "    import pandas\n",
-    "    import numpy\n",
-    "    res = pandas.DataFrame(data)\n",
-    "    res['month'] = res[\"service_date\"].apply(lambda d: d.month)\n",
-    "    # Get the median rideship by month\n",
-    "    median_results = res.groupby('month')['total_monthly_ridership'].apply(numpy.median)\n",
-    "    return median_results\n",
-    "\n",
-    "# Run UDF on entire dataset but only fetch service_date and total_monthly_ridership columns\n",
-    "with tiledb.open(\"tiledb://TileDB-Inc/MBTA_Average_Monthly_Ridership_by_Mode\", ctx=tiledb.cloud.Ctx()) as A:\n",
-    "    ned = A.nonempty_domain()\n",
-    "    median_rides_by_month = A.apply(median_by_month, ned, attrs=[\"service_date\", \"total_monthly_ridership\"])\n",
-    "median_rides_by_month"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": null,
+   "source": [
+    "# Let's plot the months on a bar graph\n",
+    "median_rides_by_month.plot.bar(figsize=(15,8))"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f21416068d0>"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4cAAAHjCAYAAACQF2rzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAcu0lEQVR4nO3de7DndX3f8dc7bGRMVOSyEgOYpUouYA1WutIx0yFiAKX10spktRO3KSmtIZdO06mYdAarMoXpJIxOqzNUV5ExIpqkkqDFLV4ySVVYlQiIdDdIZYMKZFFJjCTAu3/8PgfPHn57Owv73cN5PGbOnN/5fC/nc77DLvs838uvujsAAACsbj8w9QQAAACYnjgEAABAHAIAACAOAQAAiDgEAAAg4hAAAIAka6aewIF01FFH9bp166aeBgAAwCQ+//nP39vda+ctW1VxuG7dumzZsmXqaQAAAEyiqv7frpa5rBQAAABxCAAAgDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAgyZqpJwAAAAfSuguumXoKj7jj4rOnngI8wplDAAAAxCEAAADiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAkqyZegIAJOsuuGbqKTzijovPnnoKj3BcAODAceYQAAAAcQgAAIA4BAAAIO45BIAVx72YADwenDkEAABAHAIAACAOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAgyZqpJwBPZN6oGgCAlcKZQwAAAJw5BAAAXPGEM4cAAABkL+Kwqo6rqk9W1a1VdUtV/foYP6KqNlfV1vH58EXbvLGqtlXVbVV15qLxF1TVTWPZ26uqxvihVfXBMf65qlq3aJuN43tsraqNi8aPH+tuHds+6bE5JAAAAKvP3pw5fDDJb3T3TyU5Ncn5VXVikguSXNfdJyS5bnydsWxDkpOSnJXkHVV1yNjXO5Ocl+SE8XHWGD83yX3d/Zwklya5ZOzriCQXJnlhkvVJLlwUoZckuXR8//vGPgAAAFiGPcZhd3+9u78wXt+f5NYkxyR5RZLLx2qXJ3nleP2KJFd29wPd/dUk25Ksr6pnJnlad3+muzvJ+5Zss7CvDyc5fZxVPDPJ5u7e0d33Jdmc5Kyx7MVj3aXfHwAAgH20T/ccjss9n5/kc0mO7u6vJ7OATPKMsdoxSe5ctNn2MXbMeL10fKdtuvvBJN9OcuRu9nVkkm+NdZfuCwAAgH2013FYVU9J8ntJ/l13f2d3q84Z692ML2eb3e1r58lUnVdVW6pqyz333DNvFQAAgFVvr+Kwqn4wszB8f3f//hj+5rhUNOPz3WN8e5LjFm1+bJK7xvixc8Z32qaq1iQ5LMmO3ezr3iRPH+su3ddOuvuy7j6lu09Zu3bt3vy4AAAAq87ePK20krw7ya3d/TuLFl2dZOHpoRuTfGTR+IbxBNLjM3vwzPXj0tP7q+rUsc/XLdlmYV+vTvKJcV/itUnOqKrDx4Nozkhy7Vj2ybHu0u8PAADAPlqz51XyoiS/kOSmqrpxjP1mkouTXFVV5yb5WpJzkqS7b6mqq5J8ObMnnZ7f3Q+N7V6f5L1JnpzkY+MjmcXnFVW1LbMzhhvGvnZU1VuS3DDWe3N37xiv35Dkyqp6a5Ivjn0AAACwDHuMw+7+k8y/xy9JTt/FNhcluWjO+JYkz50z/r2MuJyzbFOSTXPGb8/s7S0AAADYT/v0tFIAAACemMQhAAAA4hAAAABxCAAAQMQhAAAAEYcAAABk797nkEXWXXDN1FN4xB0Xnz31FAAAgCcIcQgA8ATll9rAvnBZKQAAAOIQAAAAcQgAAEDEIQAAABGHAAAAxNNKAQAAdulgeervgXjirzOHAAAAiEMAAADEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAACQZM3UE+CJYd0F10w9hUfccfHZU08BAABWHGcOAQAAEIcAAACIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAAJKsmXoCAACPhXUXXDP1FJIkd1x89tRTAFgWZw4BAAAQhwAAAIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDiEAAAgIhDAAAAIg4BAACIOAQAACDJmqknAKw+6y64ZuopJEnuuPjsqacAAHDQcOYQAAAAcQgAAIA4BAAAIOIQAACAiEMAAAAiDgEAAIg4BAAAIOIQAACAiEMAAAAiDgEAAIg4BAAAIOIQAACAiEMAAACyF3FYVZuq6u6qunnR2Juq6i+q6sbx8bJFy95YVduq6raqOnPR+Auq6qax7O1VVWP80Kr64Bj/XFWtW7TNxqraOj42Lho/fqy7dWz7pP0/FAAAAKvX3pw5fG+Ss+aMX9rdJ4+PjyZJVZ2YZEOSk8Y276iqQ8b670xyXpITxsfCPs9Ncl93PyfJpUkuGfs6IsmFSV6YZH2SC6vq8LHNJeP7n5DkvrEPAAAAlmmPcdjdf5xkx17u7xVJruzuB7r7q0m2JVlfVc9M8rTu/kx3d5L3JXnlom0uH68/nOT0cVbxzCSbu3tHd9+XZHOSs8ayF491M7Zd2BcAAADLsD/3HP5KVX1pXHa6cEbvmCR3Llpn+xg7ZrxeOr7TNt39YJJvJzlyN/s6Msm3xrpL9wUAAMAyLDcO35nk2UlOTvL1JL89xmvOur2b8eVss7t9PUpVnVdVW6pqyz333LOr1QAAAFa1ZcVhd3+zux/q7oeT/I/M7glMZmfxjlu06rFJ7hrjx84Z32mbqlqT5LDMLmPd1b7uTfL0se7Sfc2b62XdfUp3n7J27dp9/VEBAABWhWXF4biHcMGrkiw8yfTqJBvGE0iPz+zBM9d399eT3F9Vp457Bl+X5COLtll4Eumrk3xi3Jd4bZIzqurwcdnqGUmuHcs+OdbN2HZhXwAAACzDmj2tUFUfSHJakqOqantmTxA9rapOzuxyzjuS/Jsk6e5bquqqJF9O8mCS87v7obGr12f25NMnJ/nY+EiSdye5oqq2ZXbGcMPY146qekuSG8Z6b+7uhQfjvCHJlVX11iRfHPsAAABgmfYYh939mjnDu4yx7r4oyUVzxrckee6c8e8lOWcX+9qUZNOc8dvz/UtZAQAA2E/787RSAAAAniDEIQAAAOIQAAAAcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAACQvYjDqtpUVXdX1c2Lxo6oqs1VtXV8PnzRsjdW1baquq2qzlw0/oKqumkse3tV1Rg/tKo+OMY/V1XrFm2zcXyPrVW1cdH48WPdrWPbJ+3/oQAAAFi99ubM4XuTnLVk7IIk13X3CUmuG1+nqk5MsiHJSWObd1TVIWObdyY5L8kJ42Nhn+cmua+7n5Pk0iSXjH0dkeTCJC9Msj7JhYsi9JIkl47vf9/YBwAAAMu0xzjs7j9OsmPJ8CuSXD5eX57klYvGr+zuB7r7q0m2JVlfVc9M8rTu/kx3d5L3LdlmYV8fTnL6OKt4ZpLN3b2ju+9LsjnJWWPZi8e6S78/AAAAy7Dcew6P7u6vJ8n4/IwxfkySOxett32MHTNeLx3faZvufjDJt5McuZt9HZnkW2PdpfsCAABgGR7rB9LUnLHezfhyttndvh49oarzqmpLVW255557drUaAADAqrbcOPzmuFQ04/PdY3x7kuMWrXdskrvG+LFzxnfapqrWJDkss8tYd7Wve5M8fay7dF+P0t2Xdfcp3X3K2rVr9/HHBAAAWB2WG4dXJ1l4eujGJB9ZNL5hPIH0+MwePHP9uPT0/qo6ddwz+Lol2yzs69VJPjHuS7w2yRlVdfh4EM0ZSa4dyz451l36/QEAAFiGNXtaoao+kOS0JEdV1fbMniB6cZKrqurcJF9Lck6SdPctVXVVki8neTDJ+d390NjV6zN78umTk3xsfCTJu5NcUVXbMjtjuGHsa0dVvSXJDWO9N3f3woNx3pDkyqp6a5Ivjn0AAACwTHuMw+5+zS4Wnb6L9S9KctGc8S1Jnjtn/HsZcTln2aYkm+aM357Z21sAAADwGHisH0gDAADACiQOAQAAEIcAAACIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAICIQwAAACIOAQAAiDgEAAAg4hAAAIDsZxxW1R1VdVNV3VhVW8bYEVW1uaq2js+HL1r/jVW1rapuq6ozF42/YOxnW1W9vapqjB9aVR8c45+rqnWLttk4vsfWqtq4Pz8HAADAavdYnDn82e4+ubtPGV9fkOS67j4hyXXj61TViUk2JDkpyVlJ3lFVh4xt3pnkvCQnjI+zxvi5Se7r7uckuTTJJWNfRyS5MMkLk6xPcuHiCAUAAGDfPB6Xlb4iyeXj9eVJXrlo/MrufqC7v5pkW5L1VfXMJE/r7s90dyd535JtFvb14SSnj7OKZybZ3N07uvu+JJvz/aAEAABgH+1vHHaSj1fV56vqvDF2dHd/PUnG52eM8WOS3Llo2+1j7Jjxeun4Ttt094NJvp3kyN3sCwAAgGVYs5/bv6i776qqZyTZXFVf2c26NWesdzO+3G12/qazaD0vSZ71rGftZnoAAACr136dOezuu8bnu5P8QWb3/31zXCqa8fnusfr2JMct2vzYJHeN8WPnjO+0TVWtSXJYkh272de8OV7W3ad09ylr165d3g8KAADwBLfsOKyqH66qpy68TnJGkpuTXJ1k4emhG5N8ZLy+OsmG8QTS4zN78Mz149LT+6vq1HE/4euWbLOwr1cn+cS4L/HaJGdU1eHjQTRnjDEAAACWYX8uKz06yR+Md51Yk+R3u/t/VdUNSa6qqnOTfC3JOUnS3bdU1VVJvpzkwSTnd/dDY1+vT/LeJE9O8rHxkSTvTnJFVW3L7IzhhrGvHVX1liQ3jPXe3N079uNnAQAAWNWWHYfdfXuSn54z/pdJTt/FNhcluWjO+JYkz50z/r2MuJyzbFOSTfs2awAAAOZ5PN7KAgAAgBVGHAIAACAOAQAAEIcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAEYcAAABEHAIAABBxCAAAQMQhAAAAWeFxWFVnVdVtVbWtqi6Yej4AAAAr1YqNw6o6JMl/T/LSJCcmeU1VnTjtrAAAAFamFRuHSdYn2dbdt3f33ya5MskrJp4TAADAirSS4/CYJHcu+nr7GAMAAGAfVXdPPYdlqapzkpzZ3b80vv6FJOu7+1eXrHdekvPGlz+R5LYDOtFdOyrJvVNP4iDjmMznuMznuMznuDyaYzKf4zKf4zKf4/Jojsl8jst8B9Nx+bHuXjtvwZoDPZPH0PYkxy36+tgkdy1dqbsvS3LZgZrU3qqqLd19ytTzOJg4JvM5LvM5LvM5Lo/mmMznuMznuMznuDyaYzKf4zLfSjkuK/my0huSnFBVx1fVk5JsSHL1xHMCAABYkVbsmcPufrCqfiXJtUkOSbKpu2+ZeFoAAAAr0oqNwyTp7o8m+ejU81img+5S14OAYzKf4zKf4zKf4/Jojsl8jst8jst8jsujOSbzOS7zrYjjsmIfSAMAAMBjZyXfcwgAAMBjRBwCAAAgDplOVf1kVZ1eVU9ZMn7WVHM6GFTV+qr6h+P1iVX176vqZVPP62BSVe+beg4Ho6r6mfHfyxlTz2UqVfXCqnraeP3kqvrPVfWHVXVJVR029fymUlW/VlXH7XnN1aWqnlRVr6uql4yvX1tV/62qzq+qH5x6flOpqmdX1X+oqrdV1W9X1b9dzX9+YDVxz+HEquoXu/s9U8/jQKuqX0tyfpJbk5yc5Ne7+yNj2Re6+x9MOb+pVNWFSV6a2cOiNid5YZJPJXlJkmu7+6LpZjeNqlr6FjWV5GeTfCJJuvvlB3xSB4mqur6714/X/zqzP1N/kOSMJH/Y3RdPOb8pVNUtSX56PNH6siTfTfLhJKeP8X826QQnUlXfTvLXSf48yQeSfKi775l2VtOrqvdn9vftDyX5VpKnJPn9zP57qe7eOOH0JjH+//xPk3w6ycuS3JjkviSvSvLL3f2p6WYHPN7E4cSq6mvd/ayp53GgVdVNSf5Rd/9VVa3L7B9vV3T326rqi939/EknOJFxXE5OcmiSbyQ5tru/U1VPTvK57n7epBOcQFV9IcmXk7wrSWcWhx/I7L1N092fnm5201r8Z6Wqbkjysu6+p6p+OMlnu/vvTzvDA6+qbu3unxqvd/pFU1Xd2N0nTze76VTVF5O8ILNfNP18kpcn+Xxmf5Z+v7vvn3B6k6mqL3X386pqTZK/SPKj3f1QVVWSP1ulf+felOTkcRx+KMlHu/u0qnpWko+s1v8/w2rhstIDoKq+tIuPm5IcPfX8JnJId/9VknT3HUlOS/LSqvqdzP7xv1o92N0Pdfd3k/x5d38nSbr7b5I8PO3UJnNKZv+I/a0k3x6/tf6b7v70ag7D4Qeq6vCqOjKzX/bdkyTd/ddJHpx2apO5uap+cbz+s6o6JUmq6seT/N1005pcd/fD3f3x7j43yY8meUeSs5LcPu3UJvUDVfWkJE/N7OzhwqWThyZZtZeV5vtvdXZoZscm3f21rO5jkqo6rKourqqvVNVfjo9bx9jTp57fwaaqPjb1HKZSVU+rqv9SVVdU1WuXLHvHVPPaGyv6fQ5XkKOTnJnZZRmLVZL/c+Cnc1D4RlWd3N03Jsk4g/hPkmxKsurOdizyt1X1QyMOX7AwOO71WJVx2N0PJ7m0qj40Pn8z/u5acFhm4VxJuqp+pLu/Me7jXa2/ZPmlJG+rqv+U5N4kn6mqO5PcOZatVjv999Ddf5fk6iRXjysTVqt3J/lKkkMy+wXUh6rq9iSnJrlyyolN6F1Jbqiqzyb5x0kuSZKqWptkx5QTOwhcldktDad19zeSpKp+JMnGJB9K8nMTzm0SVbWr24AqsyuhVqv3JNma5PeS/Kuq+udJXtvdD2T298tBy2WlB0BVvTvJe7r7T+Ys+93ufu2czZ7QqurYzM6SfWPOshd1959OMK3JVdWh4y+OpeNHJXlmd980wbQOKlV1dpIXdfdvTj2Xg9W4FOzo7v7q1HOZSlU9Ncnfy+wXCdu7+5sTT2lSVfXj3f1/p57HwaiqfjRJuvuucfbnJUm+1t3XTzuz6VTVSUl+KsnN3f2VqedzsKiq27r7J/Z12RNZVT2U2f2p834heWp3r8pfPi29jaGqfiuze3hfnmTzwfxsDXEIAAB7UFUfT/K/k1y+8Aunqjo6yb9M8nPd/ZIJpzeJqro5yau6e+ucZXd296p8SnJV3ZrkpHH108LYxiT/MclTuvvHJpvcHrjnEAAA9uznkxyZ5NNVtaOqdmT2RPEjkpwz5cQm9Kbsuid+9QDO42Dzh0levHiguy9P8htJ/naSGe0lZw4BAGA/rNa3Jtsdx2S+g/24iEMAANgPq/WtyXbHMZnvYD8unvgHAAB7UFVf2tWirNK3JnNM5lvJx0UcAgDAnnlrskdzTOZbscdFHAIAwJ79UWZPmrxx6YKq+tSBn85BwTGZb8UeF/ccAgAA4K0sAAAAEIcAAABEHALA5Krq6VX1y4u+Pq2q/mjKOQGw+ohDAJje05P88h7XAoDHkTgEgH1QVeuq6itV9a6qurmq3l9VL6mqP62qrVW1vqqOqKr/WVVfqqrPVtXzxrZvqqpNVfWpqrq9qn5t7PbiJM+uqhur6r+OsadU1YfH93p/VdUkPzAAq4a3sgCAffecJOckOS/JDUlem+Rnkrw8yW8muTPJF7v7lVX14iTvS3Ly2PYnk/xskqcmua2q3pnkgiTP7e6Tk9llpUmen+SkJHcl+dMkL0ryJwfihwNgdXLmEAD23Ve7+6bufjjJLUmu69l7Q92UZF1moXhFknT3J5IcWVWHjW2v6e4HuvveJHdn9mbJ81zf3dvH97hx7BcAHjfiEAD23QOLXj+86OuHM7sqZ94loAtvLLx424ey66t49nY9AHhMiEMAeOz9cZJ/kTxyiei93f2d3ax/f2aXmQLAZPwWEgAee29K8p6q+lKS7ybZuLuVu/svxwNtbk7ysSTXPP5TBICd1ewWCQAAAFYzl5UCAAAgDgEAABCHAAAARBwCAAAQcQgAAEDEIQAAABGHAAAARBwCAACQ5P8DDEmu4Tp0fLcAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 1080x576 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
-   "source": [
-    "# Let's plot the months on a bar graph\n",
-    "median_rides_by_month.plot.bar(figsize=(15,8))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "metadata": {}
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.10 64-bit ('tiledb-3.8': conda)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -550,9 +574,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.10"
+  },
+  "interpreter": {
+   "hash": "6e50e62de7a80982627a334ae6f273a427f0275f96ea91ad7267b1c54dbcdf70"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file