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bitoollearner
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Pyspark Leetcode solution
1 parent bca1030 commit 7780899

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lines changed

‎Solved/1378. Replace Employee ID With The Unique Identifier (Easy)-(Solved).ipynb

Lines changed: 66 additions & 7 deletions
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@@ -40,7 +46,10 @@
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@@ -118,15 +127,27 @@
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"text": [
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"+---+--------+\n| id| name|\n+---+--------+\n| 1| Alice|\n| 7| Bob|\n| 11| Meir|\n| 90| Winston|\n| 3|Jonathan|\n+---+--------+\n\n+---+---------+\n| id|unique_id|\n+---+---------+\n| 3| 1|\n| 11| 2|\n| 90| 3|\n+---+---------+\n\n"
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}
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],
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"source": [
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"employees_data_1378 = [\n",
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" (1, \"Alice\"),\n",
@@ -150,15 +171,53 @@
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"employee_uni_df_1378 = spark.createDataFrame(employee_uni_data_1378, employee_uni_columns_1378)\n",
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"employee_uni_df_1378.show()"
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]
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},
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"+---------+--------+\n|unique_id| name|\n+---------+--------+\n| NULL| Alice|\n| NULL| Bob|\n| 2| Meir|\n| 3| Winston|\n| 1|Jonathan|\n+---------+--------+\n\n"
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]
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}
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],
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"source": [
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"employees_df_1378\\\n",
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" .join(employee_uni_df_1378, on=\"id\", how=\"left\") \\\n",
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" .select(\"unique_id\", \"name\").show()"
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]
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}
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‎Solved/1384. Total Sales Amount by Year (Hard)-(Solved).ipynb

Lines changed: 139 additions & 8 deletions
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"name": "stdout",
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"text": [
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"+----------+------------+\n|product_id|product_name|\n+----------+------------+\n| 1| LC Phone|\n| 2| LC T-Shirt|\n| 3| LC Keychain|\n+----------+------------+\n\n+----------+------------+----------+-------------------+------------------+\n|product_id|period_start|period_end|average_daily_sales| year_list|\n+----------+------------+----------+-------------------+------------------+\n| 1| 2019-01-25|2019-02-28| 100| [2019]|\n| 2| 2018-12-01|2020-01-01| 10|[2018, 2019, 2020]|\n| 3| 2019-12-01|2020-01-31| 1| [2019, 2020]|\n+----------+------------+----------+-------------------+------------------+\n\n"
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]
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}
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],
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"source": [
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"product_data_1384 = [\n",
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" (1, \"LC Phone\"),\n",
@@ -146,18 +167,128 @@
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" (3, \"2019-12-01\", \"2020-01-31\", 1)\n",
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"]\n",
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"sales_columns_1384 = [\"product_id\", \"period_start\", \"period_end\", \"average_daily_sales\"]\n",
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"sales_df_1384 = spark.createDataFrame(sales_data_1384, sales_columns_1384)\n",
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"sales_df_1384 = spark.createDataFrame(sales_data_1384, sales_columns_1384)\\\n",
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" .withColumn(\"period_start\", col(\"period_start\").cast(DateType())) \\\n",
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" .withColumn(\"period_end\", col(\"period_end\").cast(DateType()))\\\n",
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" .withColumn(\"year_list\",sequence(year(col(\"period_start\")), year(col(\"period_end\"))))\n",
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"\n",
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"sales_df_1384.show()"
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]
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},
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},
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"outputs": [],
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"source": [
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"sales_exploded_df_1384 = sales_df_1384.withColumn(\"report_year\", explode(col(\"year_list\")))"
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]
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},
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"outputs": [],
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"source": [
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"sales_split_df_1384 = sales_exploded_df_1384\\\n",
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" .withColumn(\"start_date\",greatest(col(\"period_start\"), col(\"report_year\").cast(\"string\").cast(DateType())))\\\n",
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" .withColumn(\"end_date\",least(col(\"period_end\"),(col(\"report_year\") + lit(1)).cast(\"string\").cast(DateType()) - expr(\"INTERVAL 1 DAY\")))\n"
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]
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"source": [
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"sales_split_df_1384 = sales_split_df_1384\\\n",
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" .withColumn(\"days\",datediff(col(\"end_date\"), col(\"start_date\")) + lit(1))\\\n",
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" .withColumn(\"total_amount\",col(\"days\") * col(\"average_daily_sales\"))"
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]
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},
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"name": "stdout",
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"text": [
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"+----------+------------+-----------+------------+\n|product_id|product_name|report_year|total_amount|\n+----------+------------+-----------+------------+\n| 1| LC Phone| 2019| 3500|\n| 2| LC T-Shirt| 2018| 310|\n| 2| LC T-Shirt| 2019| 3650|\n| 2| LC T-Shirt| 2020| 10|\n| 3| LC Keychain| 2019| 31|\n| 3| LC Keychain| 2020| 31|\n+----------+------------+-----------+------------+\n\n"
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]
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}
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],
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"source": [
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"sales_split_df_1384\\\n",
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" .join(product_df_1384, on=\"product_id\", how=\"left\") \\\n",
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" .select(\"product_id\", \"product_name\", \"report_year\", \"total_amount\") \\\n",
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" .orderBy(\"product_id\", \"report_year\").show()"
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]
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