4_Problems/2_17_Matplotlib Histograms.ipynb

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   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/lukebarousse/Python_Data_Analytics_Course/blob/main/4_Problems/2_17_Matplotlib Histograms.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44d5e298",
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   "source": [
    "# Importing Libraries\n",
    "import ast\n",
    "import pandas as pd\n",
    "from datasets import load_dataset\n",
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "# Loading Data\n",
    "dataset = load_dataset('lukebarousse/data_jobs')\n",
    "df = dataset['train'].to_pandas()\n",
    "\n",
    "# Data Cleanup\n",
    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])\n",
    "df['job_skills'] = df['job_skills'].apply(lambda x: ast.literal_eval(x) if pd.notna(x) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8def2d55",
   "metadata": {},
   "source": [
    "## 🟩 Data Scientist Hourly Salaries (2.17.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c857a41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9161bfa6",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e80afe4",
   "metadata": {},
   "source": [
    "## 🟨 Job Postings by Number of Skills (2.17.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26d2dbf1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "16af731e",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb1085e",
   "metadata": {},
   "source": [
    "## 🟥 Data Engineer Salaries (2.17.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4571c079",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f71d3550",
   "metadata": {},
   "source": [
    "---"
   ]
  }
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