diff --git a/Untitled5.ipynb b/Untitled5.ipynb
new file mode 100644
index 0000000..551b699
--- /dev/null
+++ b/Untitled5.ipynb
@@ -0,0 +1,250 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Untitled5.ipynb",
+      "provenance": [],
+      "private_outputs": true,
+      "authorship_tag": "ABX9TyMYW2UzBK+yTQ+AAqYrs5Rm",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/pannhapat/Python_Tutorials/blob/master/Untitled5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UiCKVToPg2YS",
+        "colab_type": "text"
+      },
+      "source": [
+        "Install\n",
+        "https://www.bualabs.com/archives/2804/what-is-proximity-analysis-air-quality-pollution-monitoring-toxic-chemical-pm-2-5-geospatial-ep-7/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FyTZ2En-gs1l",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        " ! pip install geopandas"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cgyYmpengt2r",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "! pip install git+https://github.com/python-visualization/folium\n",
+        "! pip install kaggle --upgrade"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xLXad9Rwg537",
+        "colab_type": "text"
+      },
+      "source": [
+        "Import Library"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "20UudRJcg_B7",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import geopandas as gpd\n",
+        "\n",
+        "import folium\n",
+        "from folium import *\n",
+        "from folium.plugins import *\n",
+        "\n",
+        "import os\n",
+        "from pathlib import Path"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5pkSPw_NhD1N",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from IPython.display import IFrame, HTML\n",
+        "\n",
+        "def embed_map(m, file_name):    \n",
+        "    m.save(file_name)\n",
+        "\n",
+        "    # # VM\n",
+        "    # return IFrame(src=file_name, width='100%', height='500px')\n",
+        "\n",
+        "    # Colab\n",
+        "    return m"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3tL_AjuOhGwg",
+        "colab_type": "text"
+      },
+      "source": [
+        "2. เตรียม Path สำหรับดาวน์โหลดข้อมูล"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7sLcqnIkhScX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "dataset = 'alexisbcook/geospatial-learn-course-data'\n",
+        "\n",
+        "# Google Colab\n",
+        "config_path = Path('/content/drive')\n",
+        "data_path = Path('/content/datasets/')/dataset\n",
+        "from google.colab import drive\n",
+        "drive.mount(str(config_path))\n",
+        "os.environ['KAGGLE_CONFIG_DIR'] = f\"{config_path}/My Drive/.kaggle\"\n",
+        "\n",
+        "## VM\n",
+        "# config_path = Path(os.getenv(\"HOME\"))\n",
+        "# data_path = config_path/\"datasets\"/dataset\n",
+        "# data_path.mkdir(parents=True, exist_ok=True)\n",
+        "# os.environ['KAGGLE_CONFIG_DIR'] = f\"{config_path}/.kaggle\""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qDsobcF9h_XQ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!kaggle datasets download {dataset} -p \"{data_path}\" --unzip"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7R3xheAghfnh",
+        "colab_type": "text"
+      },
+      "source": [
+        "4.1 การปล่อยสารพิษ\n",
+        "โหลดข้อมูลการปล่อยสารพิษ ในเมืองฟิลาเดลเฟีย จากหน่วยงาน US Environmental Protection Agency (EPA) ขึ้นมา"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QIXIp5wwhgzs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "releases = gpd.read_file(data_path/\"toxic_release_pennsylvania/toxic_release_pennsylvania/toxic_release_pennsylvania.shp\") \n",
+        "releases.head()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rdXFVXxPhkT4",
+        "colab_type": "text"
+      },
+      "source": [
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "T4wpQRVYiY5W",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "releases.shape"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "E2PjuOsPiZNU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "m_1 = folium.Map(location=[39.9526,-75.1652], zoom_start=11)\n",
+        "\n",
+        "# Add a heatmap to the base map\n",
+        "HeatMap(data=releases[['LATITUDE', 'LONGITUDE']], radius=20).add_to(m_1)\n",
+        "\n",
+        "# Display the map\n",
+        "embed_map(m_1, '25g-m_1.html')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6ki9Q1KEie-e",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "stations = gpd.read_file(data_path/\"PhillyHealth_Air_Monitoring_Stations/PhillyHealth_Air_Monitoring_Stations/PhillyHealth_Air_Monitoring_Stations.shp\")\n",
+        "stations.head()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/nl_kbtg_covid19_sheets_df.ipynb b/nl_kbtg_covid19_sheets_df.ipynb
new file mode 100644
index 0000000..c699936
--- /dev/null
+++ b/nl_kbtg_covid19_sheets_df.ipynb
@@ -0,0 +1,194 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "nl-kbtg-covid19-sheets-df.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/pannhapat/Python_Tutorials/blob/master/nl_kbtg_covid19_sheets_df.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0KUbBz_PQ4A0",
+        "colab_type": "text"
+      },
+      "source": [
+        "authen ก่อน จะได้เปิด sheets ได้"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hmpN7pzEN2hD",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from google.colab import auth\n",
+        "auth.authenticate_user()\n",
+        "import gspread\n",
+        "from oauth2client.client import GoogleCredentials\n",
+        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YtVIjLg8QtcH",
+        "colab_type": "text"
+      },
+      "source": [
+        "นำเข้าข้อมูลจาก Google Sheets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "v5sSZaESORsS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Load from sheet\n",
+        "wb = gc.open_by_url('https://docs.google.com/spreadsheets/d/1o0HqNGX5AsR9JawvL2CnW-fWQ9QUkHRn72reJeJPlNw/edit#gid=1354638512')\n",
+        "sheet = wb.sheet1\n",
+        "\n",
+        "# Skip header\n",
+        "data = sheet.get_all_values()[1:]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HPxV-DRDRQUr",
+        "colab_type": "text"
+      },
+      "source": [
+        "เอาเฉพาะ column **Confirmed**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "P9j4IK4nQbER",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "df = pd.DataFrame(data)\n",
+        "confirmed = pd.to_numeric(df[5])\n",
+        "\n",
+        "# Date range\n",
+        "start = 20\n",
+        "Y = confirmed.tolist()[start:]\n",
+        "X = [i+1 for i in range(len(Y))]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CoRUh7I_RlfW",
+        "colab_type": "text"
+      },
+      "source": [
+        "ลองใช้กราฟทำนายเลข **Confirmed** วันถัดไป"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AWdiBUrzd2ZT",
+        "colab_type": "code",
+        "outputId": "0eb5092b-c22e-4f7d-9859-1c3f9a9e1dd9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 329
+        }
+      },
+      "source": [
+        "\"\"\"\n",
+        "Created on Mon Mar 30 12:10:59 2020\n",
+        "author: yuen\n",
+        "ref : https://www.facebook.com/photo.php?fbid=10219226773277783&set=pcb.10219226774877823&type=3&theater\n",
+        "\n",
+        "publisher : NL x KBTG\n",
+        "forked : katopz, ying @ NoobLearning\n",
+        "ref : \n",
+        "\"\"\"\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Nonlinear Regression Analysis (Polynomial)\n",
+        "degree = 12\n",
+        "fit = len(Y)\n",
+        "poly_fit = np.poly1d(np.polyfit(X, Y, degree))\n",
+        "\n",
+        "# Plot data\n",
+        "xx = np.linspace(0, fit, 100)\n",
+        "plt.plot(xx, poly_fit(xx), c='r', linestyle='-')\n",
+        "plt.title('Thailand Prediction Curve : Non-Linear Polynomial fitting')\n",
+        "plt.xlabel('days')\n",
+        "plt.ylabel('Confirm cases')\n",
+        "plt.axis([0, 100, 0, 3000])\n",
+        "plt.grid(True)\n",
+        "plt.scatter(X, Y)\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Predict data\n",
+        "predictedConfirmed = poly_fit(fit)\n",
+        "print('Tomorrow will be ', predictedConfirmed)\n",
+        "print('Increase ', predictedConfirmed - confirmed[-1:].tolist()[0])\n"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Tomorrow will be  2860.597563204043\n",
+            "Increase  6.5975632040431265\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file