Pandas + SQL - Exercise.ipynb

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    "import pandas as pd\n",
    "from pandasql import sqldf\n",
    "import numpy as np"
   ]
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    "#### Load the AWProduct.csv file into a Pandas Dataframe. Name the Dataframe df"
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    "#### Using SQL: Display the first 10 rows of the Dataframe. Only show the following columns: ProductKey, ProductAlternateKey, ProductSubcategoryKey, ProductName"
   ]
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   "source": [
    "#### Using Pandas: Display the first 10 rows of the Dataframe. Only show the following columns: ProductKey, ProductAlternateKey, ProductSubcategoryKey, ProductName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
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   "source": [
    "#### Using SQL: Display ProductName, DealerPrice only for those rows where the DealerPrice > 10. Limit your output to only 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#### Using Pandas: Display ProductName, DealerPrice only for those rows where the DealerPrice > 10. Limit your output to only 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
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   "source": [
    "#### Using SQL: Display ProductName, DealerPrice, Color only for those rows where the DealerPrice > 10 AND Color = Silver. Limit your output to only 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Pandas: Display ProductName, DealerPrice, Color only for those rows where the DealerPrice > 10 AND Color = Silver. Limit your output to only 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Using SQL: Group by color. Find the ListPrice AVERAGE per color. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Pandas: Group by color. Find the ListPrice AVERAGE per color. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Load the AWProductSubcategory.csv file into a Pandas Dataframe. Name the Dataframe df_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Using SQL: Join the df and df_s Dataframes by the ProductSubcategoryKey column. Display ProductAlternateKey, ProductName, ProductSubcategoryKey, ProductCategory, ProductSubcategoryName. Limit your output to only 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Pandas: Join the df and df_s Dataframes by the ProductSubcategoryKey column. Display ProductAlternateKey, ProductName, ProductSubcategoryKey, ProductCategory, ProductSubcategoryName. Limit your output to only 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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