challenge_8_python_pandas.ipynb

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        "<a href=\"https://colab.research.google.com/github/abiflynn/python_pandas/blob/main/challenge_8_python_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8O2kuHipF9tS"
      },
      "source": [
        "#Challenge 8: Python Pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "IuhkaK6hF9tV"
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      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "\n",
        "# load data\n",
        "# This code is made to load our data stored on Google Drive\n",
        "def gd_path(file_id):\n",
        "    \"\"\"Generate a shareable link from Google Drive file id.\"\"\"\n",
        "    return f\"https://drive.google.com/uc?export=download&id={file_id}\"\n",
        "\n",
        "# Google Drive file ids\n",
        "files_id = {\n",
        "    \"titleauthor\": \"1F1JOiYXStWacOBca6coNVfyVtoST7ZgD\",\n",
        "    \"titles\": \"1PLdn50N9GRa53ZbuVWo0l47F_IXdvlEm\",\n",
        "    \"stores\": \"1f-GCgip7O93CpbAkYvOsc21eKnSOSHsQ\", \n",
        "    \"sales\": \"1fzFc9rwYmVIPaGOFmhLVxCi3kg19vNU2\", \n",
        "    \"roysched\": \"1zPRZPoFPEMKyrNR5VSENeYFHGCBZmxbs\", \n",
        "    \"publishers\": \"1s9E8_AVOziTrowb3wyh2jg3PV763VOyq\",\n",
        "    \"pub_info\": \"1OEgogcGKy--EpuVj0kqq7lyBZNGW6YSv\", \n",
        "    \"jobs\": \"1V1Za8hUdXD-vJOyRdX4aQV5wanIff2eM\", \n",
        "    \"employee\": \"1h9mUjsVqpP74b1w0x7KOw37n_n9Ulkt5\", \n",
        "    \"discounts\": \"111dvSxMcCsTgOuV1wDSKFJxO1Xcxd9VS\", \n",
        "    \"authors\": \"1fEF89Nhe61EebAljKlwFwfEuokK0o6aJ\"\n",
        "}\n",
        "\n",
        "# Read data from Google Drive\n",
        "sales = pd.read_csv(gd_path(files_id[\"sales\"]), sep=\";\")\n",
        "authors = pd.read_csv(gd_path(files_id[\"authors\"]), sep=\";\")"
      ]
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      "source": [
        "## 1. Select the top 5 sales with most quantity sold between 1993-03-11 and 1994-09-13 from the table sales"
      ]
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              "    stor_id   ord_num             ord_date  qty    payterms title_id\n",
              "3      7066  QA7442.3  1994-09-13 00:00:00   75  ON invoice   PS2091\n",
              "2      7066     A2976  1993-05-24 00:00:00   50      Net 30   PC8888\n",
              "20     8042   QA879.1  1993-05-22 00:00:00   30      Net 30   PC1035\n",
              "11     7131    P3087a  1993-05-29 00:00:00   25      Net 60   PS2106\n",
              "13     7131    P3087a  1993-05-29 00:00:00   25      Net 60   PS7777"
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              "      <th>3</th>\n",
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              "      <td>1994-09-13 00:00:00</td>\n",
              "      <td>75</td>\n",
              "      <td>ON invoice</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>A2976</td>\n",
              "      <td>1993-05-24 00:00:00</td>\n",
              "      <td>50</td>\n",
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              "      <th>20</th>\n",
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              "      <td>30</td>\n",
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              "      <th>11</th>\n",
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              "      <td>25</td>\n",
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      "source": [
        "(\n",
        "sales\n",
        "    .loc[(sales['ord_date'] >= '1993-03-11 00:00:00') \n",
        "        & (sales['ord_date'] <= '1994-09-13 00:00:00')]\n",
        "    .sort_values(by = 'qty', ascending = False)\n",
        "    .head(5)\n",
        ")"
      ]
    },
    {
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        "id": "PoaQiRgFF9tZ"
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      "source": [
        "## 2. How many authors have an \"i\" in their first name, are from Utah, Maryland, or Kansas?"
      ]
    },
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      "cell_type": "code",
      "execution_count": 3,
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          "data": {
            "text/plain": [
              "1"
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          "metadata": {},
          "execution_count": 3
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      ],
      "source": [
        "(\n",
        "authors\n",
        "    .loc[authors['au_fname'].str.contains('i', case = False)\n",
        "        & authors['state'].isin(['UT', 'MD', 'KS'])]\n",
        "    ['au_id']\n",
        "    .count()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ErwaER7bF9ta"
      },
      "source": [
        "## 3. In California, how many authors are there in cities that contain an \"o\" in the name?\n",
        "   - Show only results for cities with more than 1 author.\n",
        "   - Sort the cities ascendingly by author count."
      ]
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            "text/plain": [
              "           au_id\n",
              "city            \n",
              "Palo Alto      2\n",
              "Oakland        5"
            ],
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              "      <th>Palo Alto</th>\n",
              "      <td>2</td>\n",
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      "source": [
        "city_authors = (\n",
        "authors\n",
        "    .loc[(authors['state']=='CA')\n",
        "        & authors['city'].str.contains('o', case = False)]\n",
        "    .groupby(by='city')\n",
        "    .aggregate({\n",
        "        'au_id': 'count',\n",
        "    })\n",
        ")\n",
        "\n",
        "(\n",
        "city_authors\n",
        "    .loc[city_authors['au_id']>1]\n",
        "    .sort_values(by = 'au_id', ascending = True)\n",
        ")"
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