diff --git a/notebooks/colab-github-demo.ipynb b/notebooks/colab-github-demo.ipynb new file mode 100644 index 0000000..c2d4fe3 --- /dev/null +++ b/notebooks/colab-github-demo.ipynb @@ -0,0 +1,2005 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-pVhOfzLx9us" + }, + "source": [ + "# Using Google Colab with GitHub\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wKJ4bd5rt1wy" + }, + "source": [ + "\n", + "[Google Colaboratory](http://colab.research.google.com) is designed to integrate cleanly with GitHub, allowing both loading notebooks from github and saving notebooks to github." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K-NVg7RjyeTk" + }, + "source": [ + "## Loading Public Notebooks Directly from GitHub\n", + "\n", + "Colab can load public github notebooks directly, with no required authorization step.\n", + "\n", + "For example, consider the notebook at this address: https://github.com/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb.\n", + "\n", + "The direct colab link to this notebook is: https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb.\n", + "\n", + "To generate such links in one click, you can use the [Open in Colab](https://chrome.google.com/webstore/detail/open-in-colab/iogfkhleblhcpcekbiedikdehleodpjo) Chrome extension." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WzIRIt9d2huC" + }, + "source": [ + "## Browsing GitHub Repositories from Colab\n", + "\n", + "Colab also supports special URLs that link directly to a GitHub browser for any user/organization, repository, or branch. For example:\n", + "\n", + "- http://colab.research.google.com/github will give you a general github browser, where you can search for any github organization or username.\n", + "- http://colab.research.google.com/github/googlecolab/ will open the repository browser for the ``googlecolab`` organization. Replace ``googlecolab`` with any other github org or user to see their repositories.\n", + "- http://colab.research.google.com/github/googlecolab/colabtools/ will let you browse the main branch of the ``colabtools`` repository within the ``googlecolab`` organization. Substitute any user/org and repository to see its contents.\n", + "- http://colab.research.google.com/github/googlecolab/colabtools/blob/master will let you browse ``master`` branch of the ``colabtools`` repository within the ``googlecolab`` organization. (don't forget the ``blob`` here!) You can specify any valid branch for any valid repository." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rmai0dD30XzL" + }, + "source": [ + "## Loading Private Notebooks\n", + "\n", + "Loading a notebook from a private GitHub repository is possible, but requires an additional step to allow Colab to access your files.\n", + "Do the following:\n", + "\n", + "1. Navigate to http://colab.research.google.com/github.\n", + "2. Click the \"Include Private Repos\" checkbox.\n", + "3. In the popup window, sign-in to your Github account and authorize Colab to read the private files.\n", + "4. Your private repositories and notebooks will now be available via the github navigation pane." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8J3NBxtZpPcK" + }, + "source": [ + "## Saving Notebooks To GitHub or Drive\n", + "\n", + "Any time you open a GitHub hosted notebook in Colab, it opens a new editable view of the notebook. You can run and modify the notebook without worrying about overwriting the source.\n", + "\n", + "If you would like to save your changes from within Colab, you can use the File menu to save the modified notebook either to Google Drive or back to GitHub. Choose **File→Save a copy in Drive** or **File→Save a copy to GitHub** and follow the resulting prompts. To save a Colab notebook to GitHub requires giving Colab permission to push the commit to your repository." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8QAWNjizy_3O" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3VQqVi-3ScBC" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "source": [ + "# Import necessary libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "pd.read_csv(r'/content/sample_data/california_housing_train.csv')\n", + "df = pd.DataFrame()\n" + ], + "metadata": { + "id": "mkIWc21yz2pk" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.head()\n" + ], + "metadata": { + "id": "GsZsc1Is13WL", + "outputId": "6bed69ff-a659-4b79-9fec-628d62c87ddc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {}, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "plt.figure(figsize=(20,8))\n", + "df = df.rename(columns={'median_house_value': 'price'})\n", + "plt.subplot(1,2,1)\n", + "plt.title(\"median_house_value\")\n", + "sns.distplot(df.price)" + ], + "metadata": { + "id": "Dlh-frU713o4", + "outputId": "09bba2fc-1c46-4626-f2a7-4edeefa35ca7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 612 + } + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 38 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(df.price.describe(percentiles = [0.25,0.50,0.75,0.85,0.90,1]))\n" + ], + "metadata": { + "id": "UXz3x3Bq61rY", + "outputId": "a1013725-1336-46b6-e6cd-cb5880df6e67", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "count 3000.00000\n", + "mean 205846.27500\n", + "std 113119.68747\n", + "min 22500.00000\n", + "25% 121200.00000\n", + "50% 177650.00000\n", + "75% 263975.00000\n", + "85% 326175.00000\n", + "90% 372000.00000\n", + "100% 500001.00000\n", + "max 500001.00000\n", + "Name: price, dtype: float64\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "categorical_list = [x for x in df.columns if df[x].dtype !='object']\n" + ], + "metadata": { + "id": "ttDs8i_213rr" + }, + "execution_count": 59, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for x in df.columns:\n", + " print(f\"Checking column: {x}\")\n", + " print(f\"Data type: {df[x].dtype}\")\n", + " if df[x].dtype == 'object' or (df[x].dtype in ['int64', 'float64'] and df[x].nunique() < 10):\n", + " print(f\"Adding column to list: {x}\")\n", + " categorical_list.append(x)\n", + " else:\n", + " print(f\"Skipping column: {x}\")\n", + "\n", + "print(categorical_list)" + ], + "metadata": { + "id": "T7Ppxx_W13vK", + "outputId": "2e1ff9e9-015e-4438-d929-78d634b8325c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 58, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Checking column: longitude\n", + "Data type: float64\n", + "Skipping column: longitude\n", + "Checking column: latitude\n", + "Data type: float64\n", + "Skipping column: latitude\n", + "Checking column: housing_median_age\n", + "Data type: float64\n", + "Skipping column: housing_median_age\n", + "Checking column: total_rooms\n", + "Data type: float64\n", + "Skipping column: total_rooms\n", + "Checking column: total_bedrooms\n", + "Data type: float64\n", + "Skipping column: total_bedrooms\n", + "Checking column: population\n", + "Data type: float64\n", + "Skipping column: population\n", + "Checking column: households\n", + "Data type: float64\n", + "Skipping column: households\n", + "Checking column: median_income\n", + "Data type: float64\n", + "Skipping column: median_income\n", + "Checking column: price\n", + "Data type: float64\n", + "Skipping column: price\n", + "[]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "gCbYcU3Z9Tr8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "\n", + "plt.subplot(1,3,1)\n", + "plt1 = df['mainroad'].value_counts().plot(kind='bar')\n", + "plt.title('mainroad Histogram')\n", + "plt1.set(xlabel = 'mainroad', ylabel='Frequency of mainroad')\n", + "\n", + "plt.subplot(1,3,2)\n", + "plt1 = df['guestroom'].value_counts().plot(kind='bar')\n", + "plt.title('guestroom Histogram')\n", + "plt1.set(xlabel = 'guestroom', ylabel='Frequency of guestroom')\n", + "\n", + "plt.subplot(1,3,3)\n", + "plt1 = df['basement'].value_counts().plot(kind='bar')\n", + "plt.title('basement Histogram')\n", + "plt1.set(xlabel = 'basement', ylabel='Frequency of basement')\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "N_9YX5ui9Tu5", + "outputId": "621caf29-189e-4cca-acea-b6018ffa011f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "execution_count": 60, + "outputs": [ + { + "output_type": "error", + "ename": "KeyError", + "evalue": "'mainroad'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3806\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'mainroad'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mainroad'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mainroad Histogram'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mplt1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mainroad'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Frequency of mainroad'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4100\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4101\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4103\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3813\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3814\u001b[0m \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'mainroad'" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "\n", + "# Check if the columns exist before plotting\n", + "if 'mainroad' in df.columns:\n", + " plt.subplot(1, 3, 1)\n", + " plt1 = df['mainroad'].value_counts().plot(kind='bar')\n", + " plt.title('mainroad Histogram')\n", + " plt1.set(xlabel='mainroad', ylabel='Frequency of mainroad')\n", + "else:\n", + " print(\"Column 'mainroad' not found in DataFrame\")\n", + "\n", + "if 'guestroom' in df.columns:\n", + " plt.subplot(1, 3, 2)\n", + " plt1 = df['guestroom'].value_counts().plot(kind='bar')\n", + " plt.title('guestroom Histogram')\n", + " plt1.set(xlabel='guestroom', ylabel='Frequency of guestroom')\n", + "else:\n", + " print(\"Column 'guestroom' not found in DataFrame\")\n", + "\n", + "if 'basement' in df.columns:\n", + " plt.subplot(1, 3, 3)\n", + " plt1 = df['basement'].value_counts().plot(kind='bar')\n", + " plt.title('basement Histogram')\n", + " plt1.set(xlabel='basement', ylabel='Frequency of basement')\n", + "else:\n", + " print(\"Column 'basement' not found in DataFrame\")\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "YZP6fuMN9Txo", + "outputId": "017d71f2-0b32-4fdd-8a5a-251f7c3f8390", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + } + }, + "execution_count": 61, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Column 'mainroad' not found in DataFrame\n", + "Column 'guestroom' not found in DataFrame\n", + "Column 'basement' not found in DataFrame\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Print the column names in your DataFrame\n", + "print(df.columns)\n", + "\n", + "# Assuming 'housing_median_age', 'total_rooms', and 'population' are present in your DataFrame:\n", + "plt.figure(figsize=(15, 6))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "plt1 = df['housing_median_age'].value_counts().plot(kind='bar') # Replace with an actual column\n", + "plt.title('Housing Median Age Histogram')\n", + "plt1.set(xlabel='Housing Median Age', ylabel='Frequency')\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "plt1 = df['total_rooms'].value_counts().plot(kind='bar') # Replace with an actual column\n", + "plt.title('Total Rooms Histogram')\n", + "plt1.set(xlabel='Total Rooms', ylabel='Frequency')\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt1 = df['population'].value_counts().plot(kind='bar') # Replace with an actual column\n", + "plt.title('Population Histogram')\n", + "plt1.set(xlabel='Population', ylabel='Frequency')\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "BcxnWdgL9T0d", + "outputId": "c781049e-8af6-4681-9d7a-8f48669b623f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } + }, + "execution_count": 62, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n", + " 'total_bedrooms', 'population', 'households', 'median_income', 'price'],\n", + " dtype='object')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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