01_FeatureExtractionPart01-TemplateV4.0.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Initial Setup"
   ]
  },
  {
   "cell_type": "code",
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   "id": "04ed2518",
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       "VBox()"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Spark application\n"
     ]
    },
    {
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       "<table>\n",
       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>1</td><td>application_1663739913995_0002</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-69-208.ec2.internal:20888/proxy/application_1663739913995_0002/\" class=\"emr-proxy-link\" emr-resource=\"j-S0GY1HMGYB7U\n",
       "\" application-id=\"application_1663739913995_0002\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-65-214.ec2.internal:8042/node/containerlogs/container_1663739913995_0002_01_000001/livy\" >Link</a></td><td>✔</td></tr></table>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SparkSession available as 'spark'.\n"
     ]
    },
    {
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    }
   ],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "494d9d27",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "9236d4093f464aee93cbde511eac9f7f",
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       "VBox()"
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    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName(\"Basics\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a3d6550",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "9676e73fd4e14e7694bd686ba861ae7f",
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       "VBox()"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyspark.sql.session.SparkSession object at 0x7fcb35300210>"
     ]
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d956d7bf",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "05e515ff2e68461cbb25a67facb3d1ca",
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   ],
   "source": [
    "from pyspark.sql import functions as F\n",
    "\n",
    "# Import for typecasting columns\n",
    "from pyspark.sql.types import IntegerType,BooleanType,DateType,FloatType,StringType\n",
    "from pyspark.sql.types import ArrayType"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652d9b65",
   "metadata": {},
   "source": [
    " ## <font color='red'>Task 01: Read the data </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45cb47b",
   "metadata": {},
   "source": [
    "<font color='red'> Ensure you read the data so that all columns are read with the right data type.\n",
    "The \"right\" datatype at this stage are shown in the expected output cell below. </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ef807f",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "    \n",
    "**Sample input:** \n",
    "This task does not have an input. \n",
    "\n",
    "**Sample output:** \n",
    "Dataframe stored in the variable ```raw_recipes_df```.  </font>"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "2a2f1588",
   "metadata": {},
   "source": [
    "![task1.png](attachment:task1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b0c21a",
   "metadata": {},
   "source": [
    "<font color='red'> We have included some test cases given below. You can use them to check if you have completed the task correctly.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad8a30e9",
   "metadata": {},
   "source": [
    "### <font color='blue'>Solution to Task 1 </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fe11a3",
   "metadata": {},
   "source": [
    "<font color='blue'>complete the code in the following cell </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e20dae66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "984b172341df46c9963184066db782b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 01 Cell 1 out of 1\n",
    "\n",
    "raw_recipes_df = (spark.read.csv(\"s3a://raw-recipes-clean-upgrad/RAW_recipes_cleaned.csv\", \n",
    "                   # argument 1, Add an argument to communicate to the compiler that there is a header in the raw data.\n",
    "                   # argument 2, Add an argument to ask the complier to estimate the data types for all columns. \n",
    "                               ))\n",
    "\n",
    "# Please forward the exact name of data frames and columns as suggested in the code. \n",
    "# It will ensure that the assert commands function correctly.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62407b85",
   "metadata": {},
   "source": [
    "<font color='blue'>Test cases for Task 01</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5b8d8b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f8663e917cd41eab34fae416c35128d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_recipes_df.count() == 231637, \"There is a mistake in reading the data.\"\n",
    "assert len(raw_recipes_df.columns) == 12, \"There is a mistake in reading the data.\"\n",
    "assert raw_recipes_df.schema[\"minutes\"].dataType == IntegerType(), \"The data types have not been read correctly.\"\n",
    "assert raw_recipes_df.schema[\"tags\"].dataType == StringType(), \"The data types have not been read correctly.\"\n",
    "assert raw_recipes_df.schema[\"n_ingredients\"].dataType == IntegerType(), \"The data types have not been read correctly.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abbd80be",
   "metadata": {},
   "source": [
    " #### <font color='red'>If all test cases pass task 01 ends </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d240566",
   "metadata": {},
   "source": [
    "## Extract ```nutrition``` values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e60b6807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb842e560a4c48e1b55109aa5daf898c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of nutrition columns\n",
    "\n",
    "nutrition_column_names = ['calories',\n",
    "                          'total_fat_PDV',\n",
    "                          'sugar_PDV',\n",
    "                          'sodium_PDV',\n",
    "                          'protein_PDV',\n",
    "                          'saturated_fat_PDV',\n",
    "                          'carbohydrates_PDV']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383e4450",
   "metadata": {},
   "source": [
    " ## <font color='red'>Task 02: Extract individual features from the nutrition column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a836d795",
   "metadata": {},
   "source": [
    "<font color='red'> \n",
    "As read by the spark compiler, the nutrition column is a string column when it should be an array of float values. Each row in the nutrition column contains seven values. Each value represents nutrition information. \n",
    "    \n",
    "    \n",
    "**Your task is to separate the array into seven individual columns.**\n",
    "    \n",
    "Write a code that takes in the nutrition column from ```raw_recipes_df``` dataframe, and extracts individual values into seven different columns named calories, total fat (PDV), sugar (PDV), sodium (PDV), protein (PDV), saturated fat (PDV), and carbohydrates (PDV).\n",
    "\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d47c797",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "    \n",
    "### **Sample input:** \n",
    "    \n",
    "The image below shows a subset of columns from the ```raw_recipes_df``` dataset. The datatype of the nutrition column is a string.\n",
    " </font>"
   ]
  },
  {
   "attachments": {
    "task2.01.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b3c1e1cb",
   "metadata": {},
   "source": [
    "![task2.01.png](attachment:task2.01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df30c694",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "    \n",
    "### **Sample Output:** \n",
    "    \n",
    "The image below shows a subset of columns from the ```raw_recipes_df``` dataset after the extraction of nutrition values is completed. The datatype of the individual nutrition column is has to be float.\n",
    " </font>"
   ]
  },
  {
   "attachments": {
    "task2.03.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "f684f24f",
   "metadata": {},
   "source": [
    "![task2.03.png](attachment:task2.03.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5240146f",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "This task is further divided into two sub tasks \n",
    "    \n",
    "### Task 2.1 \n",
    "    \n",
    "Use string operations to remove the square brackets from the nutrition column. \n",
    "\n",
    "Sample input: nutrition column \n",
    "    </font>"
   ]
  },
  {
   "attachments": {
    "task2.01.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "19ac3c26",
   "metadata": {},
   "source": [
    "![task2.01.png](attachment:task2.01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f221977",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "\n",
    "**Sample output:** \n",
    "Nutrition column without the brackets. \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task2.02.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "7af331b0",
   "metadata": {},
   "source": [
    "![task2.02.png](attachment:task2.02.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bdc7a5",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "### Task 2.2 \n",
    "    \n",
    "Task 2.2 Split the nutrition column into seven individual columns and cast the new columns to float values. \n",
    "\n",
    "First split the column on using the comma delimiter. Then you can use a for loop to iterate over the column names declared in the variable ```nutrition_column_names```, inside each iteration write a code to extract the value at a specific index of the nutrition array  \n",
    "\n",
    "**Sample input:**\n",
    "Nutrition column without the brackets.\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task2.02.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "31435271",
   "metadata": {},
   "source": [
    "![task2.02.png](attachment:task2.02.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4655f67c",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample output:**\n",
    "Nutrition column split into multiple \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task2.03.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "884e23e8",
   "metadata": {},
   "source": [
    "![task2.03.png](attachment:task2.03.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1c05c0",
   "metadata": {},
   "source": [
    "<font color='red'> We have included some test cases given below. You can use them to check if you have completed the task correctly.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da588328",
   "metadata": {},
   "source": [
    "### <font color='blue'>Solution to Task 2 </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51ea51c",
   "metadata": {},
   "source": [
    "<font color='blue'>complete the code in the following cell </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1083715f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d40e7ee607244b3aafded001151dac7b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 02 Cell 1 out of 2\n",
    "# 2.1 - string operations to remove square brakets\n",
    "\n",
    "raw_recipes_df = (raw_recipes_df\n",
    "                  .withColumn('nutrition', \n",
    "                             # add code to remove square brackets\n",
    "                             # pyspark function to replace string characters \n",
    "                             ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c2a3cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47481ef89b904402bfd4c69f3709614d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 02 Cell 2 out of 3\n",
    "# STEP 2.2 - split the neutrition string into seven individial values. \n",
    "# Create an object to split the nutrition column\n",
    "\n",
    "nutrition_cols_split = # pyspark function to split values based on a delimiter.  \n",
    "\n",
    "# Write a loop to extract individual values from the nutrition column\n",
    "\n",
    "for col_index, col_name in enumerate(nutrition_column_names):\n",
    "    # col_index holds the index number of each column, e.g., calories will be 0\n",
    "    # col_name holds the name of each column \n",
    "    \n",
    "    raw_recipes_df = (raw_recipes_df.withColumn(col_name,\n",
    "                                        # pyspark function to extract individual values from the nutrition_cols_split object\n",
    "                                        # You can also cast the extracted value to floats in the same code. \n",
    "                                               ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056898fa",
   "metadata": {},
   "source": [
    "Hint: [Visit this page to learn more about splitting columns](https://sparkbyexamples.com/pyspark/pyspark-split-dataframe-column-into-multiple-columns/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4763390d",
   "metadata": {},
   "source": [
    "**Test cases for task 02**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7323e7ff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d357669ae3ef42e6a64027bf8798b1a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_recipes_df.schema[\"carbohydrates_PDV\"].dataType == FloatType(), \"Recheck your typecasting\"\n",
    "assert raw_recipes_df.collect()[123432][14] == 62.0, \"The columns have not been split correctly.\"\n",
    "assert raw_recipes_df.collect()[10000][12] == 60.400001525878906, \"The columns have not been split correctly.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c7c87c",
   "metadata": {},
   "source": [
    " #### <font color='red'>If all test cases pass task 02 ends </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb0277c",
   "metadata": {},
   "source": [
    "## Make nutrition-per-100 calorie columns\n",
    "\n",
    "By converting the nutrition values from absolute to relative terms, we ensure that portion size is not a factor in the analysis. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac526a5",
   "metadata": {},
   "source": [
    "Naming convention: Original column name ```total fat (PDV)```, column name after column ```total_fat_per_100_cal```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55662bc0",
   "metadata": {},
   "source": [
    "## <font color='red'>Task 03: Standardize the nutrition values </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28dd54ed",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "The current values for nutrition columns are not on the same scale. \n",
    "Your task is to standardize the nutrition columns using calories as the base of standardization. \n",
    "\n",
    "Convert the nutrition from absolute values to per 100 calorie values. \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ca70f8",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "    \n",
    "We will use the  ```sugar (PDV)``` column to demonstrate the calculations for standardization.  \n",
    "\n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task3.01.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "892a8590",
   "metadata": {},
   "source": [
    "![task3.01.png](attachment:task3.01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22596e49",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "    \n",
    "**Sample Calculation**\n",
    "\n",
    "Before transformation: ```sugar (PDV)``` for recipe id 137739 = 13.0\n",
    "\n",
    "Calories in the recipe recipe id 137739                       = 51.5\n",
    "\n",
    "Calculation:  \n",
    "sugar_per_100_cal = 13.0 * 100 / 51.5 \n",
    "\n",
    "After transformation ```sugar_per_100_cal``` = 25.24\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3347a9de",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample Input:** \n",
    "\n",
    "All nutrition columns except calories\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task3.02.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "92ffc6fe",
   "metadata": {},
   "source": [
    "![task3.02.png](attachment:task3.02.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fff4ea7",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample Output:** \n",
    "\n",
    "All nutrition columns standardized to per 100 calories \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task3.03.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "d3306ed5",
   "metadata": {},
   "source": [
    "![task3.03.png](attachment:task3.03.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b542d1c1",
   "metadata": {},
   "source": [
    "<font color='red'> We have included some test cases given below. You can use them to check if you have completed the task correctly.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3affae2f",
   "metadata": {},
   "source": [
    "### <font color='blue'>Solution to Task 3 </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ee388a",
   "metadata": {},
   "source": [
    "<font color='blue'>Complete the code in the following cell</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "237faab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97788c16fc3543cf903095e6acb4de41",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 03 Cell 1 out of 1\n",
    "\n",
    "for # loop over each of the newly created nutrition columns \n",
    "    if # the calories column should not be a part of the transformation exercise\n",
    "        # following code will name the new columns \n",
    "        nutrition_per_100_cal_col = (nutrition_col\n",
    "                                 .replace('_PDV','')\n",
    "                                 +'_per_100_cal')\n",
    "        raw_recipes_df = raw_recipes_df.withColumn(nutrition_per_100_cal_col,\n",
    "                                               # pyspark code to recreate the intended transformation \n",
    "                                                  )\n",
    "        \n",
    "        # You might end up adding nulls to the data because of our intended transformation. \n",
    "        # Perform a fill na operation to fill all the nulls with 0s. \n",
    "        # You must limit the scope of the fill na to the current column only. \n",
    "        \n",
    "        raw_recipes_df = raw_recipes_df # pyspark code to fill nulls with 0 in only the current nutrition_per_100_cal_col \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2656a22",
   "metadata": {},
   "source": [
    "**Test cases for Task 03**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8a97ad4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc63069e14b7410d8b11f0ee8cca7421",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total fat check for id 28881\n",
    "assert raw_recipes_df.filter(\"id == 28881\").select('total_fat_per_100_cal').first()[0] == 0, \"total_fat_per_100_cal for recipe 28881 should be 0\"\n",
    "\n",
    "# total fat check for id 112140\n",
    "assert round(raw_recipes_df.filter(\"id == 112140\").select('total_fat_per_100_cal').first()[0]) == 8, \"total_fat_per_100_cal for recipe 112140 should be 8\"\n",
    "\n",
    "# checking for nulls\n",
    "for c in ['total_fat_per_100_cal','sugar_per_100_cal','sodium_per_100_cal','protein_per_100_cal',\n",
    "                          'saturated_fat_per_100_cal','carbohydrates_per_100_cal']:\n",
    "    assert raw_recipes_df.select(F.count(F.when(F.isnan(c) | F.col(c).isNull(), c)).alias(c)).collect()[0][0] == 0, \"There are Nulls in the data\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b2b456",
   "metadata": {},
   "source": [
    " #### <font color='red'>If all test cases pass task 03 ends </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd6ac1d",
   "metadata": {},
   "source": [
    "## <font color='red'>Task 04: Convert the tags column from a string to an array of strings </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41905c76",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "    \n",
    "Currently, the tags column is a string column but holds an array of strings. \n",
    "\n",
    "Your task is to convert the tags columns from a string to an array of strings. \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08470494",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "Remove ```[``` ```]``` ```'``` punctuation marks from the tags column. \n",
    "Split the tags column based on the comma delimiter. \n",
    "    \n",
    "\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b724846",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample input**\n",
    "    \n",
    "Tags column in string datatype. \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task4.01.png": {
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7mvk7C8Evacsbi1ctzY4DLTdUQ7QB9YRlw5lPebL5bYH6lOyr9MRBFldpr6yAoPUVcPvaxE5gulpLLSUnK3Y1ki6U8xkZ1aT9xg1HUtrxzC9jVzvcCjbcTeQ14HmRpOIVr2j/8JrHBoJPZdDx5IkqlFtKLRtLuZHQVu+KBhTJzTgavjLmSLX4vj3uypcJi3Fv0u2aH7IYHZ62Z2Jm0/bwaZwBi/Hh8In4hJZyuKlOnWm1n8i/Ynv9eCXPo+GZ3jAWo6fD9pR/8GkcnR5ub4tNSx/w77TOUITXky4B7bXb8q8Avm07C7zl4NOmt8FtZCM8kawkaBXXwtXsVBUyklCG1c0n1NHsPETIfIe6GnAEv6RvAq5kyOB7PLCdwBo9hsxnx8m9yYk8orobduTbFWQdsQdN45Db14PToEhib6esgPW17cOfkPW15QahQd3E7ejwNBLn/93dXfX35NvbJAfWZgP5urBaNpbKV8dT0syXf8tXt6OL9s3d8Jl8mXA/vbgczEZH8iVZffhj3j1/Ibvrgxfj7uRCjKvSl7vjn8Qnrf7Pg+jyotCQdmVP2okzRbXWd229kqvZxaQ7fnHAX+z1X55Ekzeew+DC+IDg2Rr7LFBI+ztfDZfXbX8r/yoEVexVMpLZQlZxPfzNTmmhIsn7qtSJ8xXtaua3+Er2a0iXLTY7XgdnVCuiFyuowiTZdjDzdxaCX1LoZqfb/kb+FUDA4DdEfa1x0zP/7+U8GvxQS38bMo0DbV9Bd128gi9lByNZ1S7u7VQQvMP1CllfWz78CVlfW07jLXYTAXddVn8to+7Rs1IDoPVlfgWb31i+en1+/f3zd/+TL1LYp9+//lO+UP599/z773/5LF9J9Obz3//9+j/2/2v1Gb1pTv75Fznhv7+zb7/7V7wrZuhYkBubCS0ogZabsSJZdKnitRBonlSuxKra+Dr+rr75JwvY93rVaDUleybsa+w74suMHQq7AGKlqCKS4iqQASTxrDi9CCbxkYdRZivOIuz6U0cVeNh1rRgFs+pdxlywgkbVpBRfeghZwfdUMd+sfufTPX/3WXynSH25mCuuxMvy1ZdHZsHM4BsFMzYQWZi4yoxJ6q0UxZMwnrDYDRoloZqE5sP+1nVt1qZRX+bkjC+NNy6r2WHMnDEKFmeyXVl8bn/Gq1lkXTyZz9//5XP8aZz2TFwAQRajYPATG4vZuBlljte9YLPD3mTf1B+pqcwUUip2Meswq1LVGsUkrkGKniqYsTmkKtGIpPmpdxJWHWy2ulLsdXdGsh6J+jKyReReXDYz94zVNJI/mfnWdmFMQkuU72YuxQiLft+sQUWVOau+zI0luY6e7asWrnWUzIhZW7EzjYmnpfLOytgqrfkzxkfmIjLTePMy6yurilMJZn5ZkR+JBuGzDlnBTdIVfL5FFOpZjPhTZbGXuoTxR/H3jZkonhbJej+ur8xIbh6FQgWE5NYXZ6Sx1ba4gy++r1ZZTGvFjSLsqFxzY7G+74qw+WUlUbaN4uF6Jwrx+k+RM3EtexqEOPntovJ0LbnrwphLYROpCvMthZiTxJ8mssKR5BvFCmw2btbqx1uKvTpsRdhL/an+iFZQpa5Y2YLBDMaZxiy2z9+94+XRTVxcMGOSIutoft+xdvHWbRSAMd+Pt+5E8OMcrouzneRvmk0QFd65fRkNOA+ya0SiNsa6JJbuDj5jrr6uTaoOMxMoB+pcHUowFW2RbGrpxvZoRF4wM98ufwbnqlXKfHfwaSbeDtcKfqoYVAvmUvhMzLVOfqEepUdC9dbiqpLUlsaIDYn9QeGOV5jmo2MkaoJPKEKs3xQ9WcHqFzUtX2g0n5ItkWOzp2KI6pE1lz9bkRBslXmDyGaVnoQWZKe+WLRcrrnp2nFIzsqZPVZ1pAJeLiZ2vpoFUykhZl5mzjTPOMIcrb6anGalV8q5gsQxk7p5y0aSVUxfpgLztaPaib/gr69M9E01lZZRXx6ZBftFl8SalV50srSpustbemhWAWL+sNDf8ffNWhMBUZ+a0XZGnnjTuAaZzc6fr3VJ7FAIdiNDxNxk+V2T+CUzX0yuImaFxcx8uwze4JuT2OlnBj9RrRwVI7Fl+eqL3tdzTqaxYz41Sm5xUjp6OhSCIyDeNJbSk/DUUnP2Bt+q4lpY9WVliMw9mZDGGlmFNKuYT64/spLK2x56l8L+1hFOhSUz/uZMhPQS5VYmly5mtZ3gGyFii/evsuZJY5qb/r65jq6A2KgY6WbHmlxFj/7WZfaWZGP89ZVXxfJlIhTOyFhpbIfCzx18MSv2N48bixWV2RE9ayl8vXQB0qlOX06G3U4ew7+/v1YJll7idjNfF8YqvCxYur781e3NfB3txAqKpVvyW6p0RcT0gmonwsVWjYpHZaBgyjLXsutiJhUPbO5S+JyTCcxZWWFXcR34Rqrq0Vx9OxSprNBlNtJAZ4sVn/p40phHmP3N10iljaMRKLSOCn1ZZ5pEC3JuEb5u3Qp+7UGzmiCr8HZKx+GyC2llyJbT2FoXO5LefSpzvUz2puqq6I3SyWaXNmbXDrG+6Wh8UmSjZzGjx4gI6MLE0gHxBl9scfKjVCH1JHbAhfRSrHdck9Sh9NXx/Axe8nJ56L1LWmwx6k8GPzsufukM+T1AxQ1Uz6KjE/m+QLdge7p8qU7Wrfvcb36eMPmZr2R8bT7ds/aQl4ufw9xu551+rO8AQEEoekePwUxeLtRhYYlvQVDW/Yfr+WCmbhjU+WncnV9/iNdlfv2x+MX99t0Mos6L8+78jw/x9N3xjSgzreN8+Y94t7TF+0n3/Fd5VwtxC4L38Q3UzL8t1no9AGqL4HeB0HfByKmvUvLqy8NdsKjVH+pNOJ2TS7ZR9L6oBOBWH6/nJzN1pn3RpQe0+G1oFECrGBZmMJVtEd3/5csynuTyOp2U2WlcA2+zc3CmY9L671E3Wi4LJFj3/EZMVXwSL9ZQiAzfe0bz+ksE8u/lPL4jyrMfu4lGzxF8/8ZiBb+YzPoK1BqHJe4hM4q3uHW50jjbYCpvP9T5YaDjsvXMN+qL31rH3FTZR7LjpjwSuZfdUunM5zfw0f1adnvoWAr72pm+c5+V+eXdXk+iwUt1hWar93IQTa713SHd21cdWNVHJy/VdifuaySTytMaZ6ZxZktVJqly2nxnGtfHWV/ZVexOsCylG3B/8D09y+LaqMdUgf29p9Pt26HRtptavTOVYKn62l7mc+Ganew++uNonz+uQH9HHa3Q/ddYnDl9Y7jMlqp8m18HfQsyakyMO/fVsetipXEJ3h31Le886MaNb5Ji9Ssfyjn28+uSkcaqneRpQxsYl3MA4l1Hp9X0jW+XMqtb31rPQvvGeneabTi06yIP9g+OjJaZZ7v8WnYDvr00zjooztyncjVu1AjoyXkne6Rb0dqkG3A/O42L4HebEY9gYUnOOwPrntGlMj97RMLZ4dqTFENJpfboeOv04zORh3WqfJ/QQiM1dG9Nb0XKOiNXz6Ivxk0VLnvGaCPd/mZrzEaE7X/3D5c/q8T6xyyX8dDG3blpPZVw0pOF2mebX1zgvf4Vf+iT+MQqsPGEL+OhY1QL5ni0eQdVEt9co8P2y3SN86ehKfkPhVgtv0RzVSqmdyk/4LOdDS7l2pgP/2r1rujBVk/FJ9t75uD6MuqrdCQz68tdxVmM59CZlcJdDllB1RCqRItXlcUcnta8DVMiuW7SkpfGXvFtSuipcWpfsDO8m52oKot/FspI460zWio7wSows6LAr2IZ2B7sfPibyOrF29O5XXeu4Hs3Fgp+SbtcXx60+vm/wxXkSeMKdi+S5tYdZxXte4jbvZVqqfSudlZ76FoKYzTgh+tsePwOUObmUXQ4bMNSTS79kCaPvZMfaVlp7GupvHsCHpXb/K3JqWJPglXgb8BLdhP3y2U0V/tgTG8iPxDcvadPavUN5q5L317ITlmr2ckI/nx4OhGDPgX5WqpwbX5tNr/rQmlc2sM5ABEDH3ntoedQzrmfX5usDtclp1v3raNbMmKmUN16SMle1XymCw0Ky1Gn2+uJvpVkTgO+PUUPiq3gexs3+m1ejufw3+z17wG1KdeAU+Z5Ge1h8UGnUpmfGXw3mqQ0GksUI9d8IFX/Blyn6iOhXPbBGI3vxu0RxZGHNT2Ow3/EOPovrT//7c74QYM20W2M3Bt4I0hnKpkZTLtrJ0eqlJRVWl7DWhc6FBmoZ0kK+rdiPhgq3psOJn1jOEy/T/T3k4dbWXurBn0GMcn/oYCa7O65/CVDiqdSQU49uJ92rzlq/h7GXp1LZn2VjGRmfbmr2M96Dh0LsnxbOpmxGln2rbaYFq8fYyrkHf8EZT/PLVYxjTOoeuFnuMuWLTuNt8h+vOyn8ZqP5NPbHVm/fmVPQSXMP7zPbtzK2dn68ouHmcJwpXEFuxXJIlt34ZbKOBzKbg9d2IFc71Kn6806G15qIC/rOK1GqSY3Ph7ztcYZaZzdUnn3BFzCt/mbVlsVexrw8t0EnWDS1VuEUHk32D9UvRg9HUa6eZmWfghtPdZrdrKDT0GePRkeFtzLzWypArX59djpXZcHcABCx9HiB4mq7SELfmo/vyblO9zC3XoRyYhpAbv1kJK9qvVLJA2w8FGnxXvjat2gDXhI/v387OD7GrfODwN+dvDi+nIrI24lG/Asmx90CnmQlYnOKqVzkFcfr6MtDE+TyiOhq+nZcB4PBToZZ30y1HvxsCYOje6nx7R/oy+pY0GZD89EohRZygatZq+GczkOy8+sHr5SD7h/dTqv6UGNknk4QXuE9tA7NdbJS8Z4evVyd3Eo3XPxS1ZP38p5idXfwM+D1E6dvso5S5FabTeKQlHiF5UgP+G6gl9BwfpKocUnrwUIWV+0q6R/87gdOX6S3etf0Xh6vJPEu9tehX0m8dvvcf6ZPjnoghRHATLCYuzusBap9PkmtHlqhdLYRfzIubF9TTqiVrtEvGnlf22fuKRCdBNMkbEz78bCq1heCcW28UI/ble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    }
   },
   "cell_type": "markdown",
   "id": "61c53092",
   "metadata": {},
   "source": [
    "![task4.01.png](attachment:task4.01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d04cf2",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample outout**\n",
    "    \n",
    "Tags column in array of ArrayType(StringType())  \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task4.02.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "22614b82",
   "metadata": {},
   "source": [
    "![task4.02.png](attachment:task4.02.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fe1ba7",
   "metadata": {},
   "source": [
    "<font color='red'> We have included some test cases given below. You can use them to check if you have completed the task correctly.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7580844",
   "metadata": {},
   "source": [
    "### <font color='blue'>Solution to Task 4 </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98593e1f",
   "metadata": {},
   "source": [
    "<font color='blue'>Complete the code in the following cell</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c749ab84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7029eaf018d4f95abae818f79db2458",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 04 Cell 1 out of 1\n",
    "\n",
    "raw_recipes_df = (raw_recipes_df\n",
    "                  .withColumn('tags', \n",
    "                             # pyspark function to remove symbols like '[' ']' \"'\" from the strings in the tags column.\n",
    "                             )\n",
    "                  .withColumn('tags', \n",
    "                             # pyspark function to split the column using the comma delimiter.\n",
    "                             ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abea8362",
   "metadata": {},
   "source": [
    "**Test cases for Task 04**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "493f28b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c34a35202784eccbceb5355d1f3ca1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_recipes_df.schema[\"tags\"].dataType == ArrayType(StringType(), True), \"You have not split the string into an array.\"\n",
    "assert raw_recipes_df.collect()[2][5] == ['time-to-make','course', 'preparation', 'main-dish', 'chili', 'crock-pot-slow-cooker', 'dietary', 'equipment', '4-hours-or-less'], \"Recheck your string cleaning and splitting operations.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923e5db1",
   "metadata": {},
   "source": [
    "#### <font color='red'>If all test cases pass task 04 ends </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e3185b",
   "metadata": {},
   "source": [
    "## Join Recipe Data to Review Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "63fba4f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f148fbc02964f2188b5cb3d1f519b71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reading the second data set. \n",
    "# keep this cell unedited\n",
    "\n",
    "raw_ratings_df = (spark.read.csv(\"s3a://raw-interactions-upgrad/RAW_interactions_cleaned.csv\", \n",
    "                                 header=True, \n",
    "                                 inferSchema= True)\n",
    "                  .withColumn(\"review_date\",  F.col(\"date\"))\n",
    "                  .drop(F.col(\"date\"))\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "31b18096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8574ddead0d4cd5abc43adbf97d6caa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- user_id: integer (nullable = true)\n",
      " |-- recipe_id: integer (nullable = true)\n",
      " |-- rating: integer (nullable = true)\n",
      " |-- review: string (nullable = true)\n",
      " |-- review_date: string (nullable = true)"
     ]
    }
   ],
   "source": [
    "raw_ratings_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4e8cac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "252a75529f214168bd7a9afc58c02fef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_ratings_df.count() == 1132367, \"There is a mistake in reading the data.\"\n",
    "assert len(raw_ratings_df.columns) == 5, \"There is a mistake in reading the data.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "17084afb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2214f0c8a8564d15935db30d5ab72c1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------+------+--------------------+-----------+\n",
      "|user_id|recipe_id|rating|              review|review_date|\n",
      "+-------+---------+------+--------------------+-----------+\n",
      "|  38094|    40893|     4|Great with a sala...| 2003-02-17|\n",
      "|1293707|    40893|     5|So simple  so del...| 2011-12-21|\n",
      "|   8937|    44394|     4|This worked very ...| 2002-12-01|\n",
      "| 126440|    85009|     5|I made the Mexica...| 2010-02-27|\n",
      "|  57222|    85009|     5|Made the cheddar ...| 2011-10-01|\n",
      "+-------+---------+------+--------------------+-----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "raw_ratings_df.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6335b6",
   "metadata": {},
   "source": [
    "## <font color='red'>Task 05: Read the second data file </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb38527e",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "Along with raw recipes data, we also have raw ratings data. \n",
    "\n",
    "The code to read the data is already written above. Your task is to join the raw ratings and raw recipes data. \n",
    "\n",
    "The resulting dataframe must have the same number of rows as in the raw ratings data. \n",
    "    \n",
    "Join both the dataframes using the recipie IDs.\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe730a0",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "    \n",
    "**Sample Input**\n",
    "    \n",
    "```raw recipes_df``` and ```raw_ratings_df```"
   ]
  },
  {
   "attachments": {
    "task5.01.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "874180c3",
   "metadata": {},
   "source": [
    "![task5.01.png](attachment:task5.01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5408d705",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample Output**\n",
    "\n",
    "Combined dataframe with 30 columns and 1132367 rows \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task5.02.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "4f83c600",
   "metadata": {},
   "source": [
    "![task5.02.png](attachment:task5.02.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f4fe5a",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Calculation explanation** \n",
    "\n",
    "There are 25 columns in the ```raw_recipes_df``` and five in the ```raw_ratings_df```. So total columns in the combined dataframe 25 + 5 = 30\n",
    "\n",
    "The number of rows in the combined dataframe must be the same as the rows in the ```raw_ratings_df```. So total rows in combined dataframe 1132367\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7c934d",
   "metadata": {},
   "source": [
    "<font color='red'> We have included some test cases given below. You can use them to check if you have completed the task correctly.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f87f2f0",
   "metadata": {},
   "source": [
    "### <font color='blue'>Solution to Task 5 </font>\n",
    "\n",
    "<font color='blue'>Complete the code in the following cell</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "800f2ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "689b3ef9c0054f40aed2e654ae071da0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 05 Cell 1 out of 1\n",
    "\n",
    "interaction_level_df = raw_ratings_df.join(raw_recipes_df,\n",
    "                                           # add the key on which the join should happen\n",
    "                                           # mention the type of join expected. \n",
    "                                           )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a20faf3",
   "metadata": {},
   "source": [
    "**Test cases for Task 05**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7a59d3cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb763ed5c79d4f2ca34d5b70de4c1952",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert (interaction_level_df.count() ,len(interaction_level_df.columns)) == (1132367, 30), \"The type of join is incorrect\"\n",
    "\n",
    "list1 = raw_ratings_df.select('recipe_id').collect()\n",
    "list2 = raw_recipes_df.select('id').collect()\n",
    "exclusive_set = set(list1)-set(list2)\n",
    "\n",
    "assert len(exclusive_set) == 0, \"There is a mistake in reading one of the two data files.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b84f908",
   "metadata": {},
   "source": [
    "#### <font color='red'>If all test cases pass task 05 ends </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470302a4",
   "metadata": {},
   "source": [
    "## <font color='red'>Task 06:  Create time-based features</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41a69cd3",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "Currently, both the date columns, the submitted date, and the review date are in string forms. \n",
    "    \n",
    "First convert the ```submitted``` and ```review_date``` to DateType()\n",
    "\n",
    "Use review date and submission date to derive new features:\n",
    "1. ```days_since_submission_on_review_date``` Number of days between the recipe submission and the current review.  \n",
    "2. ```months_since_submission_on_review_date``` Number of months between the recipe submission and the current review. \n",
    "3. ```years_since_submission_on_review_date```Number of years between the recipe submission and the current review. \n",
    "\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72e2573",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample input**\n",
    "\n",
    "The following columns need to be used to calculate the time based features. \n",
    "    \n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task6.01.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "538e9195",
   "metadata": {},
   "source": [
    "![task6.01.png](attachment:task6.01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34178367",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample Output:**\n",
    "\n",
    "New date based features have been added to the interactions dataframe\n",
    "\n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "task6.02.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "82adcc3c",
   "metadata": {},
   "source": [
    "![task6.02.png](attachment:task6.02.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d410639",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "\n",
    "**Sample Calculation**\n",
    "\n",
    "Recipe 40893 was submitted on 2002-09-21\n",
    "User 38094 reviewed recipe 40893 on 2003-02-17\n",
    "\n",
    "```days_since_submission_on_review_date``` number of calender days between 2002-09-21 and 2003-02-17 that is 149\n",
    "\n",
    "```months_since_submission_on_review_date``` number of calender months between 2002-09-21 and 2003-02-17 that is 4.87 (calculated by a pyspark function)\n",
    "\n",
    "```years_since_submission_on_review_date``` number of calender months divided by 12 that is 0.40\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc67d3ac",
   "metadata": {},
   "source": [
    "### <font color='blue'>Solution to Task 6 </font>\n",
    "\n",
    "<font color='blue'>Complete the code in the following cell</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4325e82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebe33cef21674841a3f385c763cbf463",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 06 Cell 1 out of 2\n",
    "\n",
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn('submitted', # pyspark function to cast a column to DateType()\n",
    "                                   )\n",
    "                        .withColumn('review_date', # pyspark function to cast a column to DateType()\n",
    "                                   )\n",
    "                                             \n",
    "                       )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc2f5452",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn('days_since_submission_on_review_date',\n",
    "                                     # Pyspark function to find the number of days between two dates              \n",
    "                                   )\n",
    "                        .withColumn('months_since_submission_on_review_date',\n",
    "                                     # Pyspark function to find the number of months between two dates          \n",
    "                                   )\n",
    "                        .withColumn('years_since_submission_on_review_date',\n",
    "                                     # Pyspark function to find the number of months between two dates / 12          \n",
    "                                   )\n",
    "                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32301322",
   "metadata": {},
   "source": [
    "**Test cases for Task 06**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "416eb59e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c21efea15f5424cbac030309efc578e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert interaction_level_df.schema[\"days_since_submission_on_review_date\"].dataType == IntegerType()\n",
    "\n",
    "assert (interaction_level_df.filter((interaction_level_df.user_id == 428885) & (interaction_level_df.recipe_id == 335241))\n",
    "                            .select('days_since_submission_on_review_date').collect()[0][0]) == 77\n",
    "assert (interaction_level_df.filter((interaction_level_df.user_id == 2025676) & (interaction_level_df.recipe_id == 94265))\n",
    "                            .select('months_since_submission_on_review_date').collect()[0][0]) == 153.22580645\n",
    "assert (interaction_level_df.filter((interaction_level_df.user_id == 338588) & (interaction_level_df.recipe_id == 21859))\n",
    "                            .select('years_since_submission_on_review_date').collect()[0][0]) == 4.564516129166667"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a764c83",
   "metadata": {},
   "source": [
    "#### <font color='red'>If all test cases pass task 06 ends</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794e0e3e",
   "metadata": {},
   "source": [
    "## Save the data we have created so far in a parquet file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "24f6017a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "model_id": "f41fd04ef08b4d4194a143930264af52",
       "version_major": 2,
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      "text/plain": [
       "VBox()"
      ]
     },
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    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- user_id: integer (nullable = true)\n",
      " |-- recipe_id: integer (nullable = true)\n",
      " |-- rating: integer (nullable = true)\n",
      " |-- review: string (nullable = true)\n",
      " |-- review_date: date (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- id: integer (nullable = true)\n",
      " |-- minutes: integer (nullable = true)\n",
      " |-- contributor_id: integer (nullable = true)\n",
      " |-- submitted: date (nullable = true)\n",
      " |-- tags: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- nutrition: string (nullable = true)\n",
      " |-- n_steps: integer (nullable = true)\n",
      " |-- steps: string (nullable = true)\n",
      " |-- description: string (nullable = true)\n",
      " |-- ingredients: string (nullable = true)\n",
      " |-- n_ingredients: integer (nullable = true)\n",
      " |-- calories: float (nullable = true)\n",
      " |-- total_fat_PDV: float (nullable = true)\n",
      " |-- sugar_PDV: float (nullable = true)\n",
      " |-- sodium_PDV: float (nullable = true)\n",
      " |-- protein_PDV: float (nullable = true)\n",
      " |-- saturated_fat_PDV: float (nullable = true)\n",
      " |-- carbohydrates_PDV: float (nullable = true)\n",
      " |-- total_fat_per_100_cal: double (nullable = true)\n",
      " |-- sugar_per_100_cal: double (nullable = true)\n",
      " |-- sodium_per_100_cal: double (nullable = true)\n",
      " |-- protein_per_100_cal: double (nullable = true)\n",
      " |-- saturated_fat_per_100_cal: double (nullable = true)\n",
      " |-- carbohydrates_per_100_cal: double (nullable = true)\n",
      " |-- days_since_submission_on_review_date: integer (nullable = true)\n",
      " |-- months_since_submission_on_review_date: double (nullable = true)\n",
      " |-- years_since_submission_on_review_date: double (nullable = true)"
     ]
    }
   ],
   "source": [
    "interaction_level_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "864a70a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c13b7a62d48744da844cdb4423b694c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
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    },
    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "assert (interaction_level_df.count() ,len(interaction_level_df.columns) ) == (1132367, 33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f66d1048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "813533e7334742f2bcea0365f20888c9",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
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    },
    {
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     },
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   ],
   "source": [
    "## Write the raw_recipes_df\n",
    "## create a folder named data in you current directry before running this. \n",
    "\n",
    "interaction_level_df.write\n",
    "                    .mode('overwrite')\n",
    "                    .parquet('s3a://upgradfoodrecsysdir/interaction_level_df_processed.parquet') # Modify the path as you need"
   ]
  }
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