Merge pull request #46 from ruixinxu/update-sample-with-display

update python 09

Author: ruixinxu

Notebooks/PySpark/09 Creating a managed Spark Table.ipynb

@@ -21,11 +21,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "execute_result",
-          "execution_count": 3,
+          "execution_count": 4,
           "data": {
             "text/plain": "DataFrame[]"
           },
@@ -50,11 +50,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "execute_result",
-          "execution_count": 4,
+          "execution_count": 5,
           "data": {
             "text/plain": "DataFrame[]"
           },
@@ -78,41 +78,68 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "execute_result",
-          "execution_count": 5,
+          "execution_count": 6,
           "data": {
             "application/json": {
-              "rows": [
-                [
-                  "Bangalore",
-                  10540000
-                ],
-                [
-                  "Beijing",
-                  21540000
-                ],
-                [
-                  "San Francisco",
-                  881549
+              "table": {
+                "rows": [
+                  {
+                    "name": "Bangalore",
+                    "population": 10540000
+                  },
+                  {
+                    "name": "Beijing",
+                    "population": 21540000
+                  },
+                  {
+                    "name": "San Francisco",
+                    "population": 881549
+                  },
+                  {
+                    "name": "Seattle",
+                    "population": 730400
+                  }
                 ],
-                [
-                  "Seattle",
-                  730400
-                ]
-              ],
-              "schema": [
-                "name",
-                "population"
-              ]
+                "schema": {
+                  "name": "string",
+                  "population": "int"
+                }
+              },
+              "isSummary": false,
+              "language": "scala"
             }
           },
           "metadata": {}
         }
       ],
-      "metadata": {},
+      "metadata": {
+        "diagram": {
+          "activateDiagramType": 1,
+          "chartConfig": {
+            "category": "bar",
+            "keys": [
+              "name"
+            ],
+            "values": [
+              "population"
+            ],
+            "yLabel": "population",
+            "xLabel": "name",
+            "aggregation": "SUM",
+            "aggByBackend": false
+          },
+          "aggData": "{\"population\":{\"Bangalore\":10540000,\"Beijing\":21540000,\"San Francisco\":881549,\"Seattle\":730400}}",
+          "isSummary": false,
+          "previewData": {
+            "filter": null
+          },
+          "isSql": false
+        }
+      },
       "source": [
         "display(spark.sql(\"SELECT * FROM cities ORDER BY name\"))"
       ],
@@ -129,11 +156,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "execute_result",
-          "execution_count": 6,
+          "execution_count": 7,
           "data": {
             "text/plain": "DataFrame[]"
           },