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bitoollearner
committed
LeetCode Pyspark Solution
1 parent 45e1b58 commit 6e994f6

10 files changed

+1259
-70
lines changed

‎Solved/1853. Convert Date Format (Easy)-(Solved).ipynb

Lines changed: 133 additions & 7 deletions
Original file line numberDiff line numberDiff line change
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@@ -21,7 +24,10 @@
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"showTitle": false,
@@ -40,7 +46,10 @@
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"cell_type": "markdown",
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"metadata": {
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@@ -92,15 +101,27 @@
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"application/vnd.databricks.v1+cell": {
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+----------+\n| day|\n+----------+\n|2022-04-12|\n|2021-08-09|\n|2020-06-26|\n+----------+\n\n"
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]
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}
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],
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"source": [
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"days_data_1853 = [\n",
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" (\"2022-04-12\",),\n",
@@ -112,15 +133,120 @@
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"days_df_1853 = spark.createDataFrame(days_data_1853, days_columns_1853)\n",
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"days_df_1853.show()"
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]
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},
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{
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"cell_type": "code",
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"0": {
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"dataGridStateBlob": "{\"version\":1,\"tableState\":{\"columnPinning\":{\"left\":[\"#row_number#\"],\"right\":[]},\"columnSizing\":{\"day\":178},\"columnVisibility\":{}},\"settings\":{\"columns\":{}},\"syncTimestamp\":1756317812080}",
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}
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},
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"title": ""
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}
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},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<style scoped>\n",
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" .table-result-container {\n",
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" max-height: 300px;\n",
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" overflow: auto;\n",
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" }\n",
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" table, th, td {\n",
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" border: 1px solid black;\n",
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" border-collapse: collapse;\n",
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" }\n",
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" th, td {\n",
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" padding: 5px;\n",
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" }\n",
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" th {\n",
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" text-align: left;\n",
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" }\n",
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"</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>day</th></tr></thead><tbody><tr><td>Tuesday, April 12, 2022</td></tr><tr><td>Monday, August 9, 2021</td></tr><tr><td>Friday, June 26, 2020</td></tr></tbody></table></div>"
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]
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},
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"arguments": {},
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"data": [
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[
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"Tuesday, April 12, 2022"
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],
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[
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"Monday, August 9, 2021"
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],
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[
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"Friday, June 26, 2020"
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]
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],
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"datasetInfos": [],
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"dbfsResultPath": null,
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"isJsonSchema": true,
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"metadata": {},
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"overflow": false,
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"plotOptions": {
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"displayType": "table",
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},
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"schema": [
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{
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"metadata": "{}",
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"name": "day",
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"type": "\"string\""
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}
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],
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"type": "table"
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}
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"display(days_df_1853.withColumn( \"day\", date_format(\"day\", \"EEEE, MMMM d, yyyy\")))"
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]
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}
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],
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"metadata": {
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}
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},
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"dashboards": [],
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"language": "python",

‎Solved/1867. Orders With Maximum Quantity Above Average (Medium)-(Solved).ipynb

Lines changed: 116 additions & 7 deletions
Original file line numberDiff line numberDiff line change
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"cell_type": "markdown",
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"metadata": {
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},
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"showTitle": false,
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"execution_count": 0,
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"cell_type": "markdown",
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@@ -126,15 +135,27 @@
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"execution_count": 0,
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"metadata": {
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"application/vnd.databricks.v1+cell": {
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"title": ""
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}
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+--------+----------+--------+\n|order_id|product_id|quantity|\n+--------+----------+--------+\n| 1| 1| 12|\n| 1| 2| 10|\n| 1| 3| 15|\n| 2| 1| 8|\n| 2| 4| 4|\n| 2| 5| 6|\n| 3| 3| 5|\n| 3| 4| 18|\n| 4| 5| 2|\n| 4| 6| 8|\n| 5| 7| 9|\n| 5| 8| 9|\n| 3| 9| 20|\n| 2| 9| 4|\n+--------+----------+--------+\n\n"
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]
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}
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],
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"source": [
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"orders_data_1867 = [\n",
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" (1, 1, 12),\n",
@@ -157,15 +178,103 @@
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"orders_df_1867 = spark.createDataFrame(orders_data_1867, orders_columns_1867)\n",
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"orders_df_1867.show()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"application/vnd.databricks.v1+cell": {
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},
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"nuid": "45bc923e-bde3-439c-b726-b9581c8e8132",
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"showTitle": false,
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"order_stats_df_1867 = orders_df_1867\\\n",
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" .groupBy(\"order_id\")\\\n",
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" .agg(\n",
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" (sum(\"quantity\") / count(\"product_id\")).alias(\"avg_qty\"),\n",
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" max(\"quantity\").alias(\"max_qty\")\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"application/vnd.databricks.v1+cell": {
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},
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"nuid": "1190269f-e9e6-44b5-9867-fd069b4205a4",
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"global_avg_max_df_1867 = order_stats_df_1867\\\n",
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" .agg(\n",
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" max(\"avg_qty\").alias(\"global_max_avg\")\n",
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" ).collect()[0][\"global_max_avg\"]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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}
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+--------+\n|order_id|\n+--------+\n| 1|\n| 3|\n+--------+\n\n"
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]
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}
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],
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"source": [
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"order_stats_df_1867\\\n",
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" .filter(col(\"max_qty\") > global_avg_max_df_1867)\\\n",
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" .select(\"order_id\").show()"
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]
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}
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],
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},
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"language": "python",

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