ETL_Deliverable1_starter_code.ipynb

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    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import re\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "import psycopg2\n",
    "\n",
    "# from config import db_password\n",
    "\n",
    "import time\n",
    "\n",
    "# 1. Create a function that takes in three arguments;\n",
    "# Wikipedia data, Kaggle metadata, and MovieLens rating data (from Kaggle)\n",
    "\n",
    "def function_name():\n",
    "    # 2. Read in the kaggle metadata and MovieLens ratings CSV files as Pandas DataFrames.\n",
    "    \n",
    "\n",
    "    # 3. Open the read the Wikipedia data JSON file.\n",
    "    \n",
    "    # 4. Read in the raw wiki movie data as a Pandas DataFrame.\n",
    "    \n",
    "    # 5. Return the three DataFrames\n",
    "    return wiki_movies_df, kaggle_metadata, ratings\n",
    "\n",
    "# 6 Create the path to your file directory and variables for the three files. \n",
    "file_dir = \n",
    "# Wikipedia data\n",
    "wiki_file = f'{file_dir}/wikipedia.movies.json'\n",
    "# Kaggle metadata\n",
    "kaggle_file = f'{file_dir}/movies_metadata.csv'\n",
    "# MovieLens rating data.\n",
    "ratings_file = f'{file_dir}/ratings.csv'\n",
    "\n",
    "# 7. Set the three variables in Step 6 equal to the function created in Step 1.\n",
    "wiki_file, kaggle_file, ratings_file = extract_transform_load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. Set the DataFrames from the return statement equal to the file names in Step 6. \n",
    "wiki_movies_df = wiki_file\n",
    "kaggle_metadata = kaggle_file\n",
    "ratings = ratings_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9. Check the wiki_movies_df DataFrame.\n",
    "wiki_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10. Check the kaggle_metadata DataFrame.\n",
    "kaggle_metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 11. Check the ratings DataFrame.\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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