challenge_7_python_pandas.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/abiflynn/python_pandas/blob/main/challenge_7_python_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6S891YTOFwFw"
      },
      "source": [
        "#Challenge 7: Python Pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "KJANm_M4FwF2"
      },
      "outputs": [],
      "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",
        "authors = pd.read_csv(gd_path(files_id[\"authors\"]), sep=\";\")\n",
        "titles = pd.read_csv(gd_path(files_id[\"titles\"]), sep=\";\")\n",
        "sales = pd.read_csv(gd_path(files_id[\"sales\"]), sep=\";\")\n",
        "employee = pd.read_csv(gd_path(files_id[\"employee\"]), sep=\";\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3x-iXp2GFwF4"
      },
      "source": [
        "## 1. Find the total amount of authors for each state"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "SiPaXMEpFwF5",
        "outputId": "09a79512-9116-4c62-fa2c-2d192350948a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "state\n",
              "CA    15\n",
              "IN     1\n",
              "KS     1\n",
              "MD     1\n",
              "MI     1\n",
              "OR     1\n",
              "TN     1\n",
              "UT     2\n",
              "Name: au_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "(\n",
        "authors\n",
        "    .groupby(by='state')\n",
        "        ['au_id'].count()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hYOsg693FwF6"
      },
      "source": [
        "## 2. Find the total amount of authors by each state and order them in descending order"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "mcebsMxuFwF7",
        "outputId": "9761dc16-1ae8-43f4-b49f-d51f9ce14781",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "state\n",
              "CA    15\n",
              "UT     2\n",
              "IN     1\n",
              "KS     1\n",
              "MD     1\n",
              "MI     1\n",
              "OR     1\n",
              "TN     1\n",
              "Name: au_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "(\n",
        "authors\n",
        "    .groupby(by='state')\n",
        "        ['au_id'].count()\n",
        "    .sort_values(ascending=False)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uh-uSzIdFwF8"
      },
      "source": [
        "## 3. What's the price of the most expensive title from each publisher?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "H_EWOlpoFwF9",
        "outputId": "4d222428-e144-4c6d-ffec-eb8552543699",
        "colab": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pub_id\n",
              "736     19.99\n",
              "877     21.59\n",
              "1389    22.95\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "(\n",
        "titles\n",
        "    .groupby(by = 'pub_id')\n",
        "        ['price'].max()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtGDBpJzFwF9"
      },
      "source": [
        "## 4. Find out the top 3 stores with the most sales"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "DB6K_7-nFwF-",
        "outputId": "1c8a7499-cca4-43f1-c2c1-e7b38c13f7e4",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "stor_id\n",
              "7131    130\n",
              "7066    125\n",
              "7067     90\n",
              "Name: qty, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "(\n",
        "sales\n",
        "    .groupby(by='stor_id')\n",
        "        ['qty'].sum()\n",
        "        .sort_values(ascending = False)\n",
        "    .head(n = 3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNkILJG8FwF-"
      },
      "source": [
        "## 5. Find the average job level for each job_id from the employees table.\n",
        "    Order the jobs in ascending order by its average job level."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "GS4fwkTBFwF_",
        "outputId": "043bd997-e6e1-4e05-a189-dd0348381dfb",
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        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "job_id\n",
              "4     227.000000\n",
              "2     215.000000\n",
              "3     200.000000\n",
              "5     187.285714\n",
              "6     176.000000\n",
              "7     142.500000\n",
              "8     125.250000\n",
              "9     123.250000\n",
              "11    121.750000\n",
              "10    101.750000\n",
              "14     74.666667\n",
              "13     66.333333\n",
              "12     55.666667\n",
              "Name: job_lvl, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "(\n",
        "employee\n",
        "    .groupby(by='job_id')\n",
        "        ['job_lvl'].mean()\n",
        "        .sort_values(ascending=False)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaZDJ9MjFwF_"
      },
      "source": [
        "## 6. For each type (business, psychology…), find out how many books each publisher has"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "bzPb9L4mFwGA",
        "outputId": "ec9302e2-e6d7-49f4-8407-95ced55d415f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type          pub_id\n",
              "UNDECIDED     877       1\n",
              "business      736       1\n",
              "              1389      3\n",
              "mod_cook      877       2\n",
              "popular_comp  1389      3\n",
              "psychology    736       4\n",
              "              877       1\n",
              "trad_cook     877       3\n",
              "Name: title_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "(\n",
        "titles\n",
        "    .groupby(by=['type', 'pub_id'])\n",
        "        ['title_id'].count()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9PkN4vgFwGA"
      },
      "source": [
        "## 7. Add the average price of each publisher - book type combination from your previous query"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     title_id      price\n",
              "type         pub_id                     \n",
              "UNDECIDED    877            1   0.000000\n",
              "business     736            1   2.990000\n",
              "             1389           3  17.310000\n",
              "mod_cook     877            2  11.490000\n",
              "popular_comp 1389           3  14.316667\n",
              "psychology   736            4  11.482500\n",
              "             877            1  21.590000\n",
              "trad_cook    877            3  15.963333"
            ],
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          },
          "metadata": {},
          "execution_count": 8
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      ],
      "source": [
        "(\n",
        "titles\n",
        "    .groupby(by=['type', 'pub_id'])\n",
        "    .aggregate({'title_id': 'count',\n",
        "                'price': 'mean'})\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiXAvhfDFwGB"
      },
      "source": [
        "## 8. From your previous query, keep only the combinations of publisher - book type with an average price higher than 12"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     title_id      price\n",
              "type         pub_id                     \n",
              "business     1389           3  17.310000\n",
              "popular_comp 1389           3  14.316667\n",
              "psychology   877            1  21.590000\n",
              "trad_cook    877            3  15.963333"
            ],
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              "      <th>psychology</th>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ccec466-cc03-402a-8623-703dbef5e892 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ccec466-cc03-402a-8623-703dbef5e892');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "pub_type=(\n",
        "titles\n",
        "    .groupby(by=['type', 'pub_id'])\n",
        "    .aggregate({'title_id': 'count',\n",
        "                'price': 'mean'})\n",
        ")\n",
        "\n",
        "(\n",
        "pub_type\n",
        "    .loc[pub_type['price']>12]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69qs12yVFwGC"
      },
      "source": [
        "## 9. Order the results of your previous query by these two criteria:\n",
        "1. Count of books, descendingly\n",
        "2. Average price, descendingly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
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        "id": "w7GphOtSFwGC",
        "outputId": "3b20f61c-c9a5-4f4c-83e9-6562afa96a72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     title_id      price\n",
              "type         pub_id                     \n",
              "business     1389           3  17.310000\n",
              "trad_cook    877            3  15.963333\n",
              "popular_comp 1389           3  14.316667\n",
              "psychology   877            1  21.590000"
            ],
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              "\n",
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              "      <th>business</th>\n",
              "      <th>1389</th>\n",
              "      <td>3</td>\n",
              "      <td>17.310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trad_cook</th>\n",
              "      <th>877</th>\n",
              "      <td>3</td>\n",
              "      <td>15.963333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popular_comp</th>\n",
              "      <th>1389</th>\n",
              "      <td>3</td>\n",
              "      <td>14.316667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>psychology</th>\n",
              "      <th>877</th>\n",
              "      <td>1</td>\n",
              "      <td>21.590000</td>\n",
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              "  </tbody>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "(\n",
        "pub_type\n",
        "    .loc[pub_type['price']>12]\n",
        "    .sort_values(by=['title_id', 'price'], ascending = False)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zPp2XERFwGD"
      },
      "source": [
        "## 10. Some authors have a contract, while others don't - it's indicated in the \"contract\" column of the authors table.\n",
        "     \n",
        "    Select all the states and cities where there are 2 or more contracts overall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      contract\n",
              "state city                    \n",
              "CA    Berkeley               2\n",
              "      Oakland                4\n",
              "      Palo Alto              2\n",
              "UT    Salt Lake City         2"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>contract</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">CA</th>\n",
              "      <th>Berkeley</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oakland</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Palo Alto</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UT</th>\n",
              "      <th>Salt Lake City</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "contracts_state_city = (\n",
        "authors\n",
        "    .groupby(by=['state', 'city'])\n",
        "    .aggregate({'contract': 'sum'})\n",
        ")\n",
        "\n",
        "(\n",
        "contracts_state_city\n",
        "    .loc[contracts_state_city['contract']>=2]\n",
        ")"
      ]
    }
  ],
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