test

Introduction to Pandas dataframes (Laptop-Dolly's conflicted copy 2022-01-21).ipynb

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      "id": "7c4191ef",
      "cell_type": "markdown",
      "source": "## Introduction to Pandas dataframes"
    },
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      "id": "d7d4e532",
      "cell_type": "code",
      "source": "import pandas as pd\nurl = \"https://raw.githubusercontent.com/jigsawlabs-student/pandas-free-curriculum/master/houston_claims.csv\"\nclaims_df = pd.read_csv(url, index_col = 0)\n\nclaims_df[:3]",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "  reportedCity                dateOfLoss elevatedBuildingIndicator floodZone  \\\n0      HOUSTON  2017-08-27T00:00:00.000Z                     False         X   \n1      HOUSTON  2008-09-12T00:00:00.000Z                     False         X   \n2      HOUSTON  2004-06-29T00:00:00.000Z                     False         X   \n\n   latitude  longitude  lowestFloodElevation  amountPaidOnBuildingClaim  \\\n0      29.7      -95.5                   NaN                  195857.43   \n1      29.5      -95.1                   NaN                       0.00   \n2      29.8      -95.6                   NaN                    1420.89   \n\n   amountPaidOnContentsClaim                yearofLoss  reportedZipcode  \\\n0                        0.0  2017-01-01T00:00:00.000Z            77096   \n1                        0.0  2008-01-01T00:00:00.000Z            77058   \n2                        0.0  2004-01-01T00:00:00.000Z            77042   \n\n                         id  \n0  5e398d6774cbd479fc898dea  \n1  5e398d6774cbd479fc898dfc  \n2  5e398d6774cbd479fc898e4b  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>reportedCity</th>\n      <th>dateOfLoss</th>\n      <th>elevatedBuildingIndicator</th>\n      <th>floodZone</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>lowestFloodElevation</th>\n      <th>amountPaidOnBuildingClaim</th>\n      <th>amountPaidOnContentsClaim</th>\n      <th>yearofLoss</th>\n      <th>reportedZipcode</th>\n      <th>id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>HOUSTON</td>\n      <td>2017-08-27T00:00:00.000Z</td>\n      <td>False</td>\n      <td>X</td>\n      <td>29.7</td>\n      <td>-95.5</td>\n      <td>NaN</td>\n      <td>195857.43</td>\n      <td>0.0</td>\n      <td>2017-01-01T00:00:00.000Z</td>\n      <td>77096</td>\n      <td>5e398d6774cbd479fc898dea</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>HOUSTON</td>\n      <td>2008-09-12T00:00:00.000Z</td>\n      <td>False</td>\n      <td>X</td>\n      <td>29.5</td>\n      <td>-95.1</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>2008-01-01T00:00:00.000Z</td>\n      <td>77058</td>\n      <td>5e398d6774cbd479fc898dfc</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HOUSTON</td>\n      <td>2004-06-29T00:00:00.000Z</td>\n      <td>False</td>\n      <td>X</td>\n      <td>29.8</td>\n      <td>-95.6</td>\n      <td>NaN</td>\n      <td>1420.89</td>\n      <td>0.0</td>\n      <td>2004-01-01T00:00:00.000Z</td>\n      <td>77042</td>\n      <td>5e398d6774cbd479fc898e4b</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
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      "id": "13838d76",
      "cell_type": "code",
      "source": "type(claims_df)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "pandas.core.frame.DataFrame"
          },
          "metadata": {}
        }
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    },
    {
      "metadata": {
        "trusted": true
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      "id": "8a300702",
      "cell_type": "code",
      "source": "type(loss_date_ser)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'loss_date_ser' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mtype\u001b[39m(\u001b[43mloss_date_ser\u001b[49m)\n",
            "\u001b[0;31mNameError\u001b[0m: name 'loss_date_ser' is not defined"
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    {
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        "trusted": true
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      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
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    {
      "metadata": {
        "trusted": false
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      "id": "595c4788",
      "cell_type": "code",
      "source": "loss_date_ser.to_list()[:2]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
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      "id": "44e3e5a2",
      "cell_type": "code",
      "source": "claims_df[:2]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
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      "id": "96b54e52",
      "cell_type": "code",
      "source": "claims_df.index",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "95dbb457",
      "cell_type": "markdown",
      "source": "### Working with columns"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "74235562",
      "cell_type": "code",
      "source": "import pandas as pd\nurl = \"https://raw.githubusercontent.com/jigsawlabs-student/pandas-free-curriculum/master/houston_claims.csv\"\ndf = pd.read_csv(url, index_col = 0)\ndf[:1]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "29beb86b",
      "cell_type": "code",
      "source": "df.columns",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "266cdda6",
      "cell_type": "code",
      "source": "df['amountPaidOnBuildingClaim']",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "bbc31803",
      "cell_type": "code",
      "source": "df.amountPaidOnBuildingClaim",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "25246655",
      "cell_type": "markdown",
      "source": "### Selecting mulitple columns"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "a8c28dd4",
      "cell_type": "code",
      "source": "df.columns",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "548cd9b8",
      "cell_type": "code",
      "source": "columns = ['reportedZipcode', 'amountPaidOnBuildingClaim']\nselected_df = df[columns]\nselected_df[:3]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "bb175c47",
      "cell_type": "code",
      "source": "type(df[['latitude', 'longitude']])",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "23a32258",
      "cell_type": "code",
      "source": "type(df['amountPaidOnBuildingClaim'])",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "bbed9e49",
      "cell_type": "markdown",
      "source": "### Dropping Columns"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "6eb11bf4",
      "cell_type": "code",
      "source": "import pandas as pd\nurl = \"https://raw.githubusercontent.com/jigsawlabs-student/pandas-free-curriculum/master/houston_claims.csv\"\ndf = pd.read_csv(url, index_col = 0)\ndf[:2]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "ac503932",
      "cell_type": "code",
      "source": "df.columns",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "f4345456",
      "cell_type": "code",
      "source": "df_dropped = df.drop(columns = ['reportedCity', 'dateOfLoss', 'elevatedBuildingIndicator'])\ndf_dropped[:4]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "e2b90c32",
      "cell_type": "code",
      "source": "loss_date = df['dateOfLoss']\nloss_date[:3]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "dbd6ce0f",
      "cell_type": "markdown",
      "source": "## Pandas Selecting Columns Lab"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "3dd7de50",
      "cell_type": "code",
      "source": "url = 'https://raw.githubusercontent.com/jigsawlabs-student/pandas-free-curriculum/master/imdb_movies.csv'\n\nimdb_movies = pd.read_csv(url, index_col = 0)\nimdb_movies[:2]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "6cdb0a83",
      "cell_type": "code",
      "source": "df_cols = ['title', 'genre', 'budget', 'runtime', 'year', 'month', 'revenue']\ndf_cols",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "e2a70065",
      "cell_type": "code",
      "source": "imdb_movies.columns",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "d1f765d0",
      "cell_type": "code",
      "source": "imdb_movies.dtypes",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "ce8e32f6",
      "cell_type": "code",
      "source": "X = imdb_movies['budget', 'runtime', 'year', 'month']",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "2216421f",
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
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