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4 | 4 | "cell_type": "markdown", |
5 | 5 | "metadata": { |
6 | 6 | "application/vnd.databricks.v1+cell": { |
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40 | 46 | "cell_type": "markdown", |
41 | 47 | "metadata": { |
42 | 48 | "application/vnd.databricks.v1+cell": { |
43 | | - "cellMetadata": {}, |
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100 | 109 | "execution_count": 0, |
101 | 110 | "metadata": { |
102 | 111 | "application/vnd.databricks.v1+cell": { |
103 | | - "cellMetadata": {}, |
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105 | 117 | "nuid": "137536ea-9bec-4171-8dc2-4bf8b0d771b4", |
106 | 118 | "showTitle": false, |
107 | 119 | "tableResultSettingsMap": {}, |
108 | 120 | "title": "" |
109 | 121 | } |
110 | 122 | }, |
111 | | - "outputs": [], |
| 123 | + "outputs": [ |
| 124 | + { |
| 125 | + "output_type": "stream", |
| 126 | + "name": "stdout", |
| 127 | + "output_type": "stream", |
| 128 | + "text": [ |
| 129 | + "+--------+-----------+------+\n|emp_name| department|salary|\n+--------+-----------+------+\n| Kathy|Engineering| 50000|\n| Roy| Marketing| 30000|\n| Charles|Engineering| 45000|\n| Jack|Engineering| 85000|\n|Benjamin| Marketing| 34000|\n| Anthony| Marketing| 42000|\n| Edward|Engineering|102000|\n| Terry|Engineering| 44000|\n| Evelyn| Marketing| 53000|\n| Arthur|Engineering| 32000|\n+--------+-----------+------+\n\n" |
| 130 | + ] |
| 131 | + } |
| 132 | + ], |
112 | 133 | "source": [ |
113 | 134 | "salaries_data_2853 = [\n", |
114 | 135 | " (\"Kathy\", \"Engineering\", 50000),\n", |
|
127 | 148 | "salaries_df_2853 = spark.createDataFrame(salaries_data_2853, salaries_columns_2853)\n", |
128 | 149 | "salaries_df_2853.show()" |
129 | 150 | ] |
| 151 | + }, |
| 152 | + { |
| 153 | + "cell_type": "code", |
| 154 | + "execution_count": 0, |
| 155 | + "metadata": { |
| 156 | + "application/vnd.databricks.v1+cell": { |
| 157 | + "cellMetadata": { |
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| 165 | + "title": "" |
| 166 | + } |
| 167 | + }, |
| 168 | + "outputs": [], |
| 169 | + "source": [ |
| 170 | + "dept_max_2853 = salaries_df_2853\\\n", |
| 171 | + " .groupBy(\"department\")\\\n", |
| 172 | + " .agg(max(\"salary\").alias(\"max_salary\"))" |
| 173 | + ] |
| 174 | + }, |
| 175 | + { |
| 176 | + "cell_type": "code", |
| 177 | + "execution_count": 0, |
| 178 | + "metadata": { |
| 179 | + "application/vnd.databricks.v1+cell": { |
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| 187 | + "tableResultSettingsMap": {}, |
| 188 | + "title": "" |
| 189 | + } |
| 190 | + }, |
| 191 | + "outputs": [], |
| 192 | + "source": [ |
| 193 | + "pivoted_2853 = dept_max_2853\\\n", |
| 194 | + " .groupBy().pivot(\"department\").agg(first(\"max_salary\"))" |
| 195 | + ] |
| 196 | + }, |
| 197 | + { |
| 198 | + "cell_type": "code", |
| 199 | + "execution_count": 0, |
| 200 | + "metadata": { |
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| 210 | + "title": "" |
| 211 | + } |
| 212 | + }, |
| 213 | + "outputs": [ |
| 214 | + { |
| 215 | + "output_type": "display_data", |
| 216 | + "data": { |
| 217 | + "text/html": [ |
| 218 | + "<style scoped>\n", |
| 219 | + " .table-result-container {\n", |
| 220 | + " max-height: 300px;\n", |
| 221 | + " overflow: auto;\n", |
| 222 | + " }\n", |
| 223 | + " table, th, td {\n", |
| 224 | + " border: 1px solid black;\n", |
| 225 | + " border-collapse: collapse;\n", |
| 226 | + " }\n", |
| 227 | + " th, td {\n", |
| 228 | + " padding: 5px;\n", |
| 229 | + " }\n", |
| 230 | + " th {\n", |
| 231 | + " text-align: left;\n", |
| 232 | + " }\n", |
| 233 | + "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>salary_difference</th></tr></thead><tbody><tr><td>49000</td></tr></tbody></table></div>" |
| 234 | + ] |
| 235 | + }, |
| 236 | + "metadata": { |
| 237 | + "application/vnd.databricks.v1+output": { |
| 238 | + "addedWidgets": {}, |
| 239 | + "aggData": [], |
| 240 | + "aggError": "", |
| 241 | + "aggOverflow": false, |
| 242 | + "aggSchema": [], |
| 243 | + "aggSeriesLimitReached": false, |
| 244 | + "aggType": "", |
| 245 | + "arguments": {}, |
| 246 | + "columnCustomDisplayInfos": {}, |
| 247 | + "data": [ |
| 248 | + [ |
| 249 | + 49000 |
| 250 | + ] |
| 251 | + ], |
| 252 | + "datasetInfos": [], |
| 253 | + "dbfsResultPath": null, |
| 254 | + "isJsonSchema": true, |
| 255 | + "metadata": {}, |
| 256 | + "overflow": false, |
| 257 | + "plotOptions": { |
| 258 | + "customPlotOptions": {}, |
| 259 | + "displayType": "table", |
| 260 | + "pivotAggregation": null, |
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| 265 | + "removedWidgets": [], |
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| 267 | + { |
| 268 | + "metadata": "{}", |
| 269 | + "name": "salary_difference", |
| 270 | + "type": "\"long\"" |
| 271 | + } |
| 272 | + ], |
| 273 | + "type": "table" |
| 274 | + } |
| 275 | + }, |
| 276 | + "output_type": "display_data" |
| 277 | + } |
| 278 | + ], |
| 279 | + "source": [ |
| 280 | + "pivoted_2853\\\n", |
| 281 | + " .select( abs(col(\"Engineering\") - col(\"Marketing\")).alias(\"salary_difference\")).display()" |
| 282 | + ] |
130 | 283 | } |
131 | 284 | ], |
132 | 285 | "metadata": { |
133 | 286 | "application/vnd.databricks.v1+notebook": { |
134 | | - "computePreferences": null, |
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135 | 294 | "dashboards": [], |
136 | 295 | "environmentMetadata": { |
137 | 296 | "base_environment": "", |
138 | | - "environment_version": "1" |
| 297 | + "environment_version": "2" |
139 | 298 | }, |
140 | 299 | "inputWidgetPreferences": null, |
141 | 300 | "language": "python", |
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