pandas/pandas_apply_fn_on_dataframe.ipynb

{
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      "metadata": {
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        "id": "view-in-github"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ml4devs/ml4devs-notebooks/blob/master/pandas/pandas_apply_fn_on_dataframe.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSyUJQr70wl2"
      },
      "source": [
        "<h1><center>How to Efficently Apply a Function to Pandas Dataframe Rows</center></h1>\n",
        "\n",
        "<p><center>\n",
        "<address>&copy; Satish Chandra Gupta<br/>\n",
        "LinkedIn: <a href=\"https://www.linkedin.com/in/scgupta/\">scgupta</a>,\n",
        "Twitter: <a href=\"https://twitter.com/scgupta\">scgupta</a>\n",
        "</address> \n",
        "</center></p>\n",
        "\n",
        "---\n",
        "Blog post: [Best Way to Apply a Function to Each Row in Pandas DataFrame](https://www.ml4devs.com/articles/pandas-dataframe-apply-function-iterate-over-rows/)\n",
        "\n",
        "Applying a function to all rows in a Pandas DataFrame is one of the most common operations during data wrangling. Pandas DataFrame apply function is the most obvious choice for doing it. It takes a function as an argument and applies it along an axis of the DataFrame. However, it is not always the best choice.\n",
        "\n",
        "In this notebook, you will explore various alternatives. You will measure, compare, and plot their performances.\n",
        "\n",
        "## Problem\n",
        "\n",
        "Given a dataframe with each row representing a task, categorize each task using the [Eisenhower Method](https://en.wikipedia.org/wiki/Time_management#The_Eisenhower_Method) into 4 buckets based on their imporance and urgency:\n",
        "\n",
        "- Important and Urgent: **Do** right away\n",
        "- Important but not Urgent: **Schedule** for later\n",
        "- Not Important but Urgent: **Delegate** to someone else\n",
        "- Neither Important nor Urgent: **Delete** time wasters.\n",
        "\n",
        "<center>\n",
        "<img src=\"https://miro.medium.com/max/575/1*_44gqprR6NYVRn38zgo9vA.jpeg\" />\n",
        "</center>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jiwj6K9a7Zuq"
      },
      "source": [
        "---\n",
        "## Environment Setup\n",
        "\n",
        "We will try several alternatives. Install needed libs and import the modules."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8VM04L0t7dor"
      },
      "outputs": [],
      "source": [
        "!pip install \\\n",
        "    datetime pandas numpy \\\n",
        "    faker line_profiler perfplot \\\n",
        "    numba pandarallel dask[dataframe] swifter \\\n",
        "    gpustat \\\n",
        "    &> /dev/null"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M6jDUCigwMuQ"
      },
      "outputs": [],
      "source": [
        "%load_ext line_profiler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zx98I0FB0gtv"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime\n",
        "import perfplot\n",
        "\n",
        "from faker import Faker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlR_iCmAQAQx",
        "outputId": "74b5fe21-64a6-4a15-8acc-8ef6eb8294c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Architecture:        x86_64\n",
            "CPU op-mode(s):      32-bit, 64-bit\n",
            "Byte Order:          Little Endian\n",
            "CPU(s):              2\n",
            "On-line CPU(s) list: 0,1\n",
            "Thread(s) per core:  2\n",
            "Core(s) per socket:  1\n",
            "Socket(s):           1\n",
            "NUMA node(s):        1\n",
            "Vendor ID:           GenuineIntel\n",
            "CPU family:          6\n",
            "Model:               79\n",
            "Model name:          Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "Stepping:            0\n",
            "CPU MHz:             2199.998\n",
            "BogoMIPS:            4399.99\n",
            "Hypervisor vendor:   KVM\n",
            "Virtualization type: full\n",
            "L1d cache:           32K\n",
            "L1i cache:           32K\n",
            "L2 cache:            256K\n",
            "L3 cache:            56320K\n",
            "NUMA node0 CPU(s):   0,1\n",
            "Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n"
          ]
        }
      ],
      "source": [
        "!lscpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGx3VA0FP2yG",
        "outputId": "3b5ce790-bdbd-46e2-e460-d64db8acd235"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import multiprocessing\n",
        "\n",
        "CPU_COUNT = multiprocessing.cpu_count()\n",
        "\n",
        "CPU_COUNT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTH57hIAcgzm",
        "outputId": "e6880f5b-0828-4b5d-bba7-6319407717ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/bin/bash: nvidia-smi: command not found\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgfb6AnKdsPd",
        "outputId": "442163df-6a0d-4fad-ab8c-74f56557e547"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error on querying NVIDIA devices. Use --debug flag for details\n"
          ]
        }
      ],
      "source": [
        "!gpustat -cp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0IL8WfzMbui"
      },
      "source": [
        "---\n",
        "\n",
        "## Generate Test Data\n",
        "\n",
        "In real life, this data would be read from a CSV file or a data store. But for this tutorial, let's generate the test data to keep it simple."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VkMVQnxVMYoK"
      },
      "outputs": [],
      "source": [
        "def generate_test_data(size: int, days: int = 30):\n",
        "  fake = Faker()\n",
        "  Faker.seed(42)\n",
        "\n",
        "  PRIORITIES = {\n",
        "    0: 'HIGH',\n",
        "    1: 'MEDIUM',\n",
        "    2: 'LOW'\n",
        "  }\n",
        "\n",
        "  return pd.DataFrame({\n",
        "    'task_name': [\n",
        "      f'Task {i + 1}'\n",
        "      for i in range(size)\n",
        "    ],\n",
        "    'due_date': [\n",
        "      fake.date_between(start_date='today', end_date=f'+{days}d')\n",
        "      for _ in range(size)\n",
        "    ],\n",
        "    'priority': [\n",
        "      PRIORITIES[fake.pyint(min_value=0, max_value=(len(PRIORITIES) - 1))]\n",
        "      for i in range(size)\n",
        "    ]\n",
        "  })\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "UhWpIMDNIc2-",
        "outputId": "f9aa9334-4cb2-47d6-df53-f277a844f78e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10 entries, 0 to 9\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   task_name  10 non-null     object\n",
            " 1   due_date   10 non-null     object\n",
            " 2   priority   10 non-null     object\n",
            "dtypes: object(3)\n",
            "memory usage: 368.0+ bytes\n"
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Task 5</td>\n",
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              "      <td>LOW</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Task 6</td>\n",
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              "      <th>6</th>\n",
              "      <td>Task 7</td>\n",
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              "      <th>7</th>\n",
              "      <td>Task 8</td>\n",
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              "      <td>HIGH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Task 9</td>\n",
              "      <td>2022-02-23</td>\n",
              "      <td>HIGH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Task 10</td>\n",
              "      <td>2022-02-19</td>\n",
              "      <td>HIGH</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "  task_name    due_date priority\n",
              "0    Task 1  2022-02-22      LOW\n",
              "1    Task 2  2022-02-19      LOW\n",
              "2    Task 3  2022-02-19      LOW\n",
              "3    Task 4  2022-02-23     HIGH\n",
              "4    Task 5  2022-02-20      LOW\n",
              "5    Task 6  2022-02-20   MEDIUM\n",
              "6    Task 7  2022-02-20     HIGH\n",
              "7    Task 8  2022-02-19     HIGH\n",
              "8    Task 9  2022-02-23     HIGH\n",
              "9   Task 10  2022-02-19     HIGH"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Try generate_test_data\n",
        "tmp_df = generate_test_data(10, 5)\n",
        "tmp_df.info()\n",
        "tmp_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "RuQ8EIoeb9hK",
        "outputId": "30dc7490-1ca1-4de6-8a74-63fd67b8b9c0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Task 1'"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tmp_df['task_name'].loc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIlB4k22CQZl",
        "outputId": "3d37caad-d1ca-41f9-da7b-09ba4b2ab14f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "datetime.date(2022, 2, 22)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tmp_df['due_date'].loc[0]\n",
        "# pd.to_datetime(tmp_df['due_date']).dt.date.loc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Uowr-JbbcDDY",
        "outputId": "7ce6aaeb-dfb3-4256-e3f3-8af5376d145f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'LOW'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tmp_df['priority'].loc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3i4ZSQSdGUD"
      },
      "source": [
        "### Generate Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jMkJuYz33bNW"
      },
      "outputs": [],
      "source": [
        "K_MAX = 21"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jsRezhLNdWK6"
      },
      "outputs": [],
      "source": [
        "# Generate a million rows. Use sample from it to create various size data sets\n",
        "# Will take some time as Faker will be called sequencially.\n",
        "test_data_set = generate_test_data(1 + 2 ** K_MAX, 30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ApFngawExp-E",
        "outputId": "a1dec394-2f76-45ba-8dd1-e6fd1b9fde99"
      },
      "outputs": [
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              "        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>task_name</th>\n",
              "      <th>due_date</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Task 1</td>\n",
              "      <td>2022-02-24</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Task 2</td>\n",
              "      <td>2022-02-20</td>\n",
              "      <td>HIGH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Task 3</td>\n",
              "      <td>2022-03-04</td>\n",
              "      <td>MEDIUM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Task 4</td>\n",
              "      <td>2022-03-02</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Task 5</td>\n",
              "      <td>2022-03-01</td>\n",
              "      <td>HIGH</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b6132fb-1743-49d6-ae67-b04ac40771d6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6b6132fb-1743-49d6-ae67-b04ac40771d6 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-6b6132fb-1743-49d6-ae67-b04ac40771d6');\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",
              "  "
            ],
            "text/plain": [
              "  task_name    due_date priority\n",
              "0    Task 1  2022-02-24      LOW\n",
              "1    Task 2  2022-02-20     HIGH\n",
              "2    Task 3  2022-03-04   MEDIUM\n",
              "3    Task 4  2022-03-02      LOW\n",
              "4    Task 5  2022-03-01     HIGH"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data_set.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xcNRQLUJPHpC"
      },
      "outputs": [],
      "source": [
        "def test_data_sample(size: int):\n",
        "  return test_data_set.sample(n=size).copy().reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DyP1e_64eFVz",
        "outputId": "c7456917-9c26-4629-fa28-cb6387447732"
      },
      "outputs": [
        {
          "data": {
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              "      <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>task_name</th>\n",
              "      <th>due_date</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Task 1378271</td>\n",
              "      <td>2022-02-26</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Task 509777</td>\n",
              "      <td>2022-03-07</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Task 284259</td>\n",
              "      <td>2022-03-16</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Task 1441642</td>\n",
              "      <td>2022-02-28</td>\n",
              "      <td>MEDIUM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Task 1209702</td>\n",
              "      <td>2022-03-02</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-010ed07b-046e-4f33-8880-2a708defa4bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      </button>\n",
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              "\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-010ed07b-046e-4f33-8880-2a708defa4bb 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-010ed07b-046e-4f33-8880-2a708defa4bb');\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",
              "  "
            ],
            "text/plain": [
              "      task_name    due_date priority\n",
              "0  Task 1378271  2022-02-26      LOW\n",
              "1   Task 509777  2022-03-07      LOW\n",
              "2   Task 284259  2022-03-16      LOW\n",
              "3  Task 1441642  2022-02-28   MEDIUM\n",
              "4  Task 1209702  2022-03-02      LOW"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Test sample of size 10\n",
        "test_data_sample(10).head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "s8FXpGcqzUCg",
        "outputId": "a40f0a05-45b8-4bff-b297-6c18d9c8f7ce"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-adaa3277-60b6-4e71-9f30-bf1b25d4ecd0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>task_name</th>\n",
              "      <th>due_date</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Task 1</td>\n",
              "      <td>2022-02-24</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Task 2</td>\n",
              "      <td>2022-02-20</td>\n",
              "      <td>HIGH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Task 3</td>\n",
              "      <td>2022-03-04</td>\n",
              "      <td>MEDIUM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Task 4</td>\n",
              "      <td>2022-03-02</td>\n",
              "      <td>LOW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Task 5</td>\n",
              "      <td>2022-03-01</td>\n",
              "      <td>HIGH</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-adaa3277-60b6-4e71-9f30-bf1b25d4ecd0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-adaa3277-60b6-4e71-9f30-bf1b25d4ecd0 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-adaa3277-60b6-4e71-9f30-bf1b25d4ecd0');\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",
              "  "
            ],
            "text/plain": [
              "  task_name    due_date priority\n",
              "0    Task 1  2022-02-24      LOW\n",
              "1    Task 2  2022-02-20     HIGH\n",
              "2    Task 3  2022-03-04   MEDIUM\n",
              "3    Task 4  2022-03-02      LOW\n",
              "4    Task 5  2022-03-01     HIGH"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data_set.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dV30ih-zMjZh"
      },
      "source": [
        "---\n",
        "## Optimize DataFrame Storage\n",
        "\n",
        "Compacting the data not only saves space, but also speeds up the processing. Two common opportunities are:\n",
        "\n",
        "- Converting timestamp strings or datetime to [datetime64](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html)\n",
        "- Converting strings of enum types to [Categorical](https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html) data type\n",
        "\n",
        "For fair comparision, compression test data to max extent possible."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyIn47WiMrHK",
        "outputId": "11d19b66-e765-4f70-fb36-ba79f5a21172"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2097153 entries, 0 to 2097152\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Dtype \n",
            "---  ------     ----- \n",
            " 0   task_name  object\n",
            " 1   due_date   object\n",
            " 2   priority   object\n",
            "dtypes: object(3)\n",
            "memory usage: 48.0+ MB\n"
          ]
        }
      ],
      "source": [
        "test_data_set.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8JnGXQNKaa8B"
      },
      "outputs": [],
      "source": [
        "priority_dtype = pd.api.types.CategoricalDtype(\n",
        "  categories=['LOW', 'MEDIUM', 'HIGH'],\n",
        "  ordered=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08OvgJZL-s8s",
        "outputId": "02362962-120a-45c4-a6b6-b6b7ec2af8dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: 'LOW', 1: 'MEDIUM', 2: 'HIGH'}"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "priority_map = dict(enumerate(pd.Series(['']).astype(priority_dtype).cat.categories))\n",
        "priority_map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pv5F5MnHiqrA",
        "outputId": "874aeeb6-81b8-44cb-dec1-d23167471310"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2097153 entries, 0 to 2097152\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Dtype   \n",
            "---  ------     -----   \n",
            " 0   task_name  object  \n",
            " 1   due_date   object  \n",
            " 2   priority   category\n",
            "dtypes: category(1), object(2)\n",
            "memory usage: 34.0+ MB\n"
          ]
        }
      ],
      "source": [
        "test_data_set['priority'] = test_data_set['priority'].astype(priority_dtype)\n",
        "test_data_set.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "AYjdRWmYoGSx",
        "outputId": "a751e0c6-5741-4a37-ba66-7247d6e9e7b3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'LOW'"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data_set['priority'].loc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6aUQ7kNNniA3"
      },
      "source": [
        "Using category for priority reduced the memory usage by ~30%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQ0d8vSlHGAW"
      },
      "source": [
        "---\n",
        "\n",
        "## Functions for The Eisenhower Method\n",
        "\n",
        "Decide about action needed using [Eisenhower method](https://en.wikipedia.org/wiki/Time_management#The_Eisenhower_Method):\n",
        "\n",
        "| Important | Urgent    | Binary | Int | Action   |\n",
        "| --------- | --------- | :----: | :-: | -------- |\n",
        "| True (1)  | True (1)  |   11   |  3  | DO       |\n",
        "| True (1)  | False (0) |   10   |  2  | SCHEDULE |\n",
        "| False (0) | True (1)  |   01   |  1  | DELEGATE |\n",
        "| False (0) | False (0) |   00   |  0  | DELETE   |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y9HmUqc4HYmN"
      },
      "outputs": [],
      "source": [
        "action_dtype = pd.api.types.CategoricalDtype(\n",
        "  categories=['DELETE', 'DELEGATE', 'SCHEDULE', 'DO'],\n",
        "  ordered=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3yHfVqRHZGm",
        "outputId": "20cce817-046e-4c2f-9d83-1fca8804fa52"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: 'DELETE', 1: 'DELEGATE', 2: 'SCHEDULE', 3: 'DO'}"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "action_map = dict(enumerate(pd.Series(['']).astype(action_dtype).cat.categories))\n",
        "action_map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J-ckAXUV0OWB"
      },
      "outputs": [],
      "source": [
        "def eisenhower_action(is_important: bool, is_urgent: bool) -> int:\n",
        "  return 2 * is_important + is_urgent\n",
        "\n",
        "def eisenhower_action_str(is_important: bool, is_urgent: bool) -> str:\n",
        "  return action_map[eisenhower_action(is_important, is_urgent)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BzVGTCZ4XyUw"
      },
      "outputs": [],
      "source": [
        "# Let's say anything due by tomorrow is Urgent\n",
        "cutoff_date = datetime.date.today() + datetime.timedelta(days=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ZrVtS6eX4YdJ",
        "outputId": "338edcf8-924d-48f5-8580-90b9f932a8ea"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'DELETE'"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Test compute_eisenhower_action\n",
        "\n",
        "eisenhower_action_str(\n",
        "  test_data_set.loc[0].priority == 'HIGH',\n",
        "  test_data_set.loc[0].due_date <= cutoff_date\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6N6KK2ebrrl"
      },
      "source": [
        "---\n",
        "\n",
        "## Experiment with Different Alternatives\n",
        "\n",
        "In the rest of the notebook, we will evaluate various alternatives for applying `eisenhower_action` function to DataFrame rows. First, you will measure the time for a sample of 100k rows. Then, you will measure and plot the time for up to a million rows.\n",
        "\n",
        "<center>\n",
        "<img src=\"https://miro.medium.com/max/700/1*jQ6Z3Yqm84fyiSME_2P8Bw.jpeg\" />\n",
        "</center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wwy6iBF3Ui8v"
      },
      "outputs": [],
      "source": [
        "# Work with a smaller data set to try each alternative\n",
        "data_sample = test_data_sample(100000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z30TqS6PQ07E"
      },
      "source": [
        "### Method 1: Loop Over All Rows of a DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JsxOxGu2RQgX"
      },
      "outputs": [],
      "source": [
        "def loop_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "\n",
        "  result = []\n",
        "  for i in range(len(df)):\n",
        "    row = df.iloc[i]\n",
        "    result.append(\n",
        "      eisenhower_action(row.priority == 'HIGH', row.due_date <= cutoff_date)\n",
        "    )\n",
        "\n",
        "  return pd.Series(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9k4BSpRTlVu",
        "outputId": "16e6d854-2f6c-4d12-d7c7-0ac85901ecd6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 loop, best of 5: 30.1 s per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_loop'] = loop_impl(data_sample)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gfaj7lxELRNf"
      },
      "outputs": [],
      "source": [
        "%lprun -f loop_impl  loop_impl(test_data_sample(100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtX8RTnMM7uU"
      },
      "source": [
        "### Method 2: Iterate over rows with `iterrows` Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hBXHW8cGRMqV"
      },
      "outputs": [],
      "source": [
        "def iterrows_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return pd.Series(\n",
        "    eisenhower_action(row.priority == 'HIGH', row.due_date <= cutoff_date)\n",
        "    for index, row in df.iterrows()\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08rVStuRNCmh",
        "outputId": "4d7f0152-a751-4ec3-8165-0fb9179ffb22"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 loop, best of 5: 6.25 s per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_iterrow'] = iterrows_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbQdCzeyaFGu"
      },
      "source": [
        "### Method 3: Iterate over rows with `itertuples` Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zJTC1fd_aZsn"
      },
      "outputs": [],
      "source": [
        "def itertuples_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return pd.Series(\n",
        "    eisenhower_action(row.priority == 'HIGH', row.due_date <= cutoff_date)\n",
        "    for row in df.itertuples()\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6O01yB1cSc8",
        "outputId": "dbff5c3e-7567-4246-a166-a7c63839f552"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 loop, best of 5: 209 ms per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_itertuples'] = itertuples_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuIUVfqyNEVz"
      },
      "source": [
        "### Method 4: Pandas `apply` Function to Every Row"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tUYovpRjXZWC"
      },
      "outputs": [],
      "source": [
        "def apply_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return df.apply(\n",
        "    lambda row: eisenhower_action(row.priority == 'HIGH', row.due_date <= cutoff_date),\n",
        "    axis=1\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKhDaS_3ZZdN",
        "outputId": "3a373d2f-52c7-4526-e61b-90acf8dcc86c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 loop, best of 5: 2.12 s per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_impl'] = apply_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgfEOC0GhMML"
      },
      "source": [
        "### Method 5: List Comprehension"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EW0UvK5UsEJ2"
      },
      "outputs": [],
      "source": [
        "def list_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return pd.Series([\n",
        "    eisenhower_action(priority == 'HIGH', due_date <= cutoff_date)\n",
        "    for (priority, due_date) in zip(df['priority'], df['due_date'])\n",
        "  ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEAqo7hxxZ21",
        "outputId": "d58d1fd9-c3c7-4cd9-b66b-690dbe7fab19"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10 loops, best of 5: 91.8 ms per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_list'] = list_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHJ953jYmg5h"
      },
      "source": [
        "### Method 6: Python `map` Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P-Dt6Bramwr4"
      },
      "outputs": [],
      "source": [
        "def map_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return pd.Series(map(eisenhower_action, df['priority'] == 'HIGH', df['due_date'] <= cutoff_date))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbJU71OCnVDY",
        "outputId": "88c7f076-1213-4365-e414-9e92d31c296b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10 loops, best of 5: 82.1 ms per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_map'] = map_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPdjc4cery0V"
      },
      "source": [
        "### Method 7: *Vectorization*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9blk7rtir4IM"
      },
      "outputs": [],
      "source": [
        "def vec_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return (2*(df['priority'] == 'HIGH') + (df['due_date'] <= cutoff_date))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFhrzXfB5qxj",
        "outputId": "5386749e-a265-4b21-e501-c598dd36d4fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10 loops, best of 5: 18.3 ms per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_vec'] = vec_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5pnlol0XcBS"
      },
      "source": [
        "### Method 8: NumPy `vectorize` function\n",
        "\n",
        "Ref.: [From Python to NumPy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_cYUZ2Z-iutb"
      },
      "outputs": [],
      "source": [
        "def np_vec_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return np.vectorize(eisenhower_action)(df['priority'] == 'HIGH', df['due_date'] <= cutoff_date)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3mno_5Rjv1C",
        "outputId": "cc89821d-0775-45ba-bd30-96b5a427e2f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10 loops, best of 5: 36.7 ms per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_np_vec'] = np_vec_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0a_Hr0n0Gqk"
      },
      "source": [
        "### Method 9: Numba Decorators\n",
        "\n",
        "[Numba](https://numba.pydata.org) is commonly used to speed up applying mathmatical functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "04aAxGcG1Rfb"
      },
      "outputs": [],
      "source": [
        "import numba\n",
        "\n",
        "@numba.vectorize\n",
        "def numba_eisenhower_action(is_important: bool, is_urgent: bool) -> int:\n",
        "  return 2 * is_important + is_urgent\n",
        "\n",
        "def numba_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return numba_eisenhower_action(\n",
        "    (df['priority'] == 'HIGH').to_numpy(),\n",
        "    (df['due_date'] <= cutoff_date).to_numpy()\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJgIE2YX319K",
        "outputId": "3debbac8-1f2d-4ce5-ba47-4bfd5986d860"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The slowest run took 11.67 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1 loop, best of 5: 23.7 ms per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_numba'] = numba_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trRh9Sbc6Mfb"
      },
      "source": [
        "### Method 10: Multiprocessing with `pandarallel` "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVS686u86T4H",
        "outputId": "ccaa9758-9258-4bc4-eeab-b497b59d2b25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO: Pandarallel will run on 2 workers.\n",
            "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
          ]
        }
      ],
      "source": [
        "from pandarallel import pandarallel\n",
        "\n",
        "pandarallel.initialize()\n",
        "\n",
        "def pandarallel_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return df.parallel_apply(\n",
        "    lambda row: eisenhower_action(row.priority == 'HIGH', row.due_date <= cutoff_date),\n",
        "    axis=1\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qD9isuTE8xvT",
        "outputId": "2603bc49-7f04-4304-8578-796e6e790883"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 loop, best of 5: 2.52 s per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_pandarallel'] = pandarallel_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5BSckOfA5Fe"
      },
      "source": [
        "### Method 11: Parallelize with Dask\n",
        "\n",
        "[Dask](https://dask.org) is a parallel computing library that supports scaling up NumPy, Pandas, Scikit-learn and many other Python libraries.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "abwkBjCDB0AH"
      },
      "outputs": [],
      "source": [
        "import dask.dataframe as dd\n",
        "\n",
        "def dask_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return dd.from_pandas(df, npartitions=CPU_COUNT).apply(\n",
        "    lambda row: eisenhower_action(row.priority == 'HIGH', row.due_date <= cutoff_date),\n",
        "    axis=1,\n",
        "    meta=(int)\n",
        "  ).compute()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1R656fvAD6F3",
        "outputId": "01de2676-bbb2-47dd-975d-e93a274ca559"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 loop, best of 5: 2.41 s per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_dask'] = dask_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MToH2DqoEoWW"
      },
      "source": [
        "### Method 12: Opportunistic Parallelization with Swifter\n",
        "\n",
        "[Swifter](https://github.com/jmcarpenter2/swifter) automatically decides which is faster: to use dask parallel processing or a simple pandas apply."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8dF8rOshHMSa"
      },
      "outputs": [],
      "source": [
        "import swifter\n",
        "\n",
        "def swifter_impl(df):\n",
        "  cutoff_date = datetime.date.today() + datetime.timedelta(days=2)\n",
        "  return df.swifter.apply(\n",
        "    lambda row: eisenhower_action(row.priority == 'HIGH', row.due_date <= cutoff_date),\n",
        "    axis=1\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbzxhRChHp6B",
        "outputId": "baa0a0bf-34a3-44d6-c2c6-2486c5ab239a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10 loops, best of 5: 25.8 ms per loop\n"
          ]
        }
      ],
      "source": [
        "%timeit data_sample['action_swifter'] = swifter_impl(data_sample)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TZMFYH1MNCL"
      },
      "source": [
        "### Test Correctness\n",
        "\n",
        "If any of the results from following cells are having rows, then there has been a mistake."
      ]
    },
    {
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      "execution_count": null,
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        "id": "5XMIXhq-MRqZ",
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              "      <th>action_vec</th>\n",
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              "      <th>action_numba</th>\n",
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              "      <th>action_swifter</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          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",
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              "  "
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              "Empty DataFrame\n",
              "Columns: [task_name, due_date, priority, action_loop, action_iterrow, action_itertuples, action_impl, action_list, action_map, action_vec, action_np_vec, action_numba, action_pandarallel, action_dask, action_swifter]\n",
              "Index: []"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lfcfp6pu-2J6"
      },
      "source": [
        "---\n",
        "\n",
        "## Plot Performance Over DataFrame Size\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "rgPwgV5w_CFx",
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Overall <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n",
              "Kernels <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:30</span>\n",
              "</pre>\n"
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              "Kernels \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  0%\u001b[0m \u001b[36m0:00:30\u001b[0m\n"
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "dark"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "kernels = [\n",
        "  loop_impl,\n",
        "  iterrows_impl,\n",
        "  itertuples_impl,\n",
        "  apply_impl,\n",
        "  list_impl,\n",
        "  map_impl,\n",
        "  vec_impl,\n",
        "  np_vec_impl,\n",
        "  numba_impl,\n",
        "  pandarallel_impl,\n",
        "  dask_impl,\n",
        "  swifter_impl\n",
        "]\n",
        "\n",
        "labels = [str(k.__name__)[:-5] for k in kernels]\n",
        "\n",
        "perfplot.show(\n",
        "  setup=lambda n: test_data_sample(n),\n",
        "  kernels=kernels,\n",
        "  labels=labels,\n",
        "  n_range=[2**k for k in range(K_MAX)],\n",
        "  xlabel='N',\n",
        "  logx=True,\n",
        "  logy=True,\n",
        "  #equality_check=None\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tq3Ki7Q0--LT"
      },
      "source": [
        "## Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3yYtmsUmWiEu"
      },
      "source": [
        "Performance Comparision:\n",
        "\n",
        "| Method             | CPU Time (s) | GPU Time (s) |\n",
        "| ------------------ | -----------: | -----------: |\n",
        "| Python Loop        |    56.600000 |    48.300000 |\n",
        "| Pandas iterrow()   |     9.040000 |     7.550000 |\n",
        "| Pandas apply()     |     1.850000 |     1.540000 |\n",
        "| Pandas itertuples()|     0.211000 |     0.162000 |\n",
        "| List Comprehension |     0.078400 |     0.051800 |\n",
        "| Python Map         |     0.071500 |     0.053900 |\n",
        "| NumPy vectorize()  |     0.035700 |     0.019500 |\n",
        "| Vectorization      |     0.020000 |     0.007920 |\n",
        "| ------------------ | ------------ | ------------ |\n",
        "| Pandarallel        |     2.270000 |     1.870000 |\n",
        "| Dask               |     2.130000 |     1.750000 |\n",
        "| Swifter            |     0.022900 |     0.010700 |\n",
        "| Numba              |     0.018900 |     0.006960 |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCxwz1GTbrru"
      },
      "source": [
        "---\n",
        "<p>Copyright &copy 2020 - 2022 <a href=\"https://www.linkedin.com/in/scgupta\">Satish Chandra Gupta</a>.</p>\n",
        "<img src=\"https://licensebuttons.net/l/by-nc-sa/3.0/88x31.png\" align=\"left\"/> <p>&nbsp;<a href=\"https://creativecommons.org/licenses/by-nc-sa/4.0/\">CC BY-NC-SA 4.0 International</a> License.</p>"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "include_colab_link": true,
      "name": "pandas-df-apply-alternatives.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.7.12 64-bit ('kaggle')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.12"
    },
    "vscode": {
      "interpreter": {
        "hash": "180997e3444791da3f2b3061c2f2c1b4404ea4925238c499191fa40f3054d99b"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}