productivity-as-function-of-coffee.ipynb

productivity-as-function-of-coffee.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "productivity-as-function-of-coffee.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNl5ALjypxSjhVj5EWuSq1p",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/jonkrohn/afbe11cea35efe03301d7c26fab5ec5c/productivity-as-function-of-coffee.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVT5OvWpXLU7"
      },
      "source": [
        "# Daily Productivity as a Function of Coffee Intake"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AFDGaxCRPNW"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import scipy.stats as st"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMorJbhEXv6I"
      },
      "source": [
        "The data are from my daily habit-tracking spreadsheet. Each integer represents the number of \"pomodoros\" (25-minute blocks of uninterrupted, deeply focused work) completed on a given workday. (See [here](https://www.superdatascience.com/podcast/the-pomodoro-technique) for my introduction to the pomodoro productivity technique.)\n",
        "\n",
        "The `coffee` data are from the final two workweeks (Mon Aug 2 to Fri Aug 20) before I began weaning myself off of my two-cup-per-day coffee habit.\n",
        "\n",
        "The `no_coffee` data are from the first two workweeks since I weaned myself of coffee (Mon Sep 13 to Fri Sep 24). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhH-26y-SAgA"
      },
      "source": [
        "coffee = np.array([14, 14, 12, 11, 4, 12, 8, 12, 9, 13])\n",
        "no_coffee = np.array([14, 15, 19, 15, 12, 13, 16, 17, 19, 14])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNz49rgfSrWd",
        "outputId": "ee33d3bf-2dc8-4aa1-c680-9034c6a50693"
      },
      "source": [
        "coffee.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.9"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpymNXZBSt-O",
        "outputId": "d470eaaf-312a-4d75-98c3-27392c615995"
      },
      "source": [
        "no_coffee.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.4"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNV8jTy6Ter3"
      },
      "source": [
        "coffee_df = pd.DataFrame(list(zip(coffee, np.repeat('Coffee', len(coffee)))), columns=['Pomodoros', 'Condition'])\n",
        "no_coffee_df = pd.DataFrame(list(zip(no_coffee, np.repeat('No Coffee', len(no_coffee)))), columns=['Pomodoros', 'Condition'])\n",
        "combined_df = pd.concat([coffee_df, no_coffee_df])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "U45MD987WnMu",
        "outputId": "10d2a530-d5fc-41c4-e01b-a9089c776587"
      },
      "source": [
        "_ = sns.boxplot(x='Condition', y='Pomodoros', data=combined_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGf89s376dM888k+3bt3ddiqSOdBb8SZYkuTrJpV3V0EcbN25k8+bNnH/++V2XIqkjXfb41wFf6fD4vbN9+3Yuu+wyqorLLrvMXr/UU50Ef5Jjgd8E3tnF8ftq48aN3HPPPQDs3LnTXr/UU131+N8KvBy4Z6YVkqxJMplkcmpqanSVLWIf//jH2bFjBwA7duzg8ssv77giSV0YefAn+S3g5qratLf1quqcqpqoqomxsbERVbe4nXzyySxduhSApUuX8vSnP73jiiR1oYse/y8Bz0hyA/A+4NeSvKeDOnpn9erVHHTQ4J98yZIlPP/5z++4IkldGHnwV9WfV9WxVbUSeC7wyap63qjr6KOjjz6aVatWkYRVq1Zx9NFHd12SpA4s7boAjdbq1au54YYb7O1LPZaq6rqGWU1MTNTk5GTXZUjSgpJkU1VN7N7ulbuS1DMGvyT1jMEvST1j8EtSzyyIL3eTTAHf6rqORWQ58P2ui5D2wM/mgXV8Vf3MFbALIvh1YCWZ3NM3/VLX/GyOhqd6JKlnDH5J6hmDv5/O6boAaQZ+NkfAc/yS1DP2+CWpZwx+SeoZg3+BSvKAJO9L8o0km5J8OMnD9rL+m5JsaX6OJfl8c7P7Xxll3Vr4klSSt0xb/rMkfznHffxGc4e9Lzefw7fMsv69PrNJfi/JV5J8ah/fRq85LfMClCTAB4GNVfXcpu1E4BjgazNstgY4qqp2JnkusLmq/mgkBWuxuRM4Ncnrq2rOF1slOQF4G/CbVXVdkiUMPp978zSmfWaTXAb8cVX961yPL3v8C9VTgbur6u92NVTVNcC/Nj36a5NsTvIcgCSXAIcDm5K8Angj8MwkX0yyLMkpST6b5KokFyY5vNnu8Uk+3fxF8dEkDxz9W9U8tIPB6JuX7v5CkpVJPpnkS0k+keS4PWz/cuCvq+o6gKraWVVvn2n7JI/l3p/Zs4FfBt7VfN6XND//vdnuT6bVc9a09tce+F/FAlVVPhbYAzgT+Ns9tP8ucDmwhEHv/0bggc1rt01b7wXA25rny4ErgcOa5VcArwHuA3wGGGvanwOc2/V799H9A7gNOAK4ATgS+DPgL5vXPgSsbp6fAfzTHra/Cjhxhn3vcfvpn9lm+Qpgonm+Bnh18/wQYBJ4EHAKg/+gwqCTeynwlK5/f/Ph4amexeWXgQuqaifwvSSfBp4AXLKXbZ4IPBL4t8EZJA4GPgs8HDgBuLxpXwJ8p73StZBU1a1JzmfQCblj2ktPAk5tnv8Dg576XOzL9qcAj0ny7Gb5SOChTfspwNVN++FN+5VzrGnRMfgXpi3As2ddazgBLq+q0+7VmDwa2FJVTzpAx9Hi81YGvfd3z3G7LcDjgWsOUB0B1lbVR+/VmPw68Pqq+vsDdJxFw3P8C9MngUOS/PQLsSSPAX4APKc55zkGPAX4wiz7+hzwS0nGm/0c1owO+iowluRJTft9kjyqhfeiBaqqbgHeD7xwWvNngOc2z/8A+Jc9bPom4FW7RqElOSjJi+aw/e4+Crw4yX2a/T0syWFN+xnTvrNakeTnhn1/i5k9/gWoqirJ7wBvbb6s/QmD860vYfDn7DVAAS+vqu/Osq+pJC8ALkhySNP86qr6WvOn8/9OciSDz8pbGfTWpF3eAvzPactrgXcnOQuYAk7ffYOq+lKSlzD4zB3K4LN66bDb78E7gZXAVc2ItyngWVX1sSS/AHy2OV15G/A84OY5v8tFxikbJKlnPNUjST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/CrF+Y6m+mQ+zwpyaXN82ckeWXz/FlJHjltvb9KcvL+vQPpwHEcvxa9fZzNdE6q6hL+c2qMZzEYl/7l5rXXHIhjSAeKPX71wVxnMz0pyRVJLkpyXZL3Nv95kGRV03YV/zmnDElekORtSZ4MPAN4UzOT5EOSnLdrHpkkT8tgTvnNSc7dddFckhuSvLaZIXVzkkeM7Lej3jH41QcnAJv20H4q8FjgROBkBmG9a+rpX2RwJfQjgQczmNbivsA7gN9mMNfMA3bfYVV9hkHP/6yqemxVfWPXa8325wHPqapHM/iL+8XTNv9+VT0OeDuDGS+lVhj86rOfzmZaVd8Dds1mCvCFqrqpqu4BvshgSoBHAN+sqq/X4JL398zxeA9vtt91emkjg/mUdrm4+bmpOZ7UCoNffbBrNsi5uHPa852M5vuwXccc1fHUUwa/+uBAzWZ6HbAyyUOa5dNmWO9HwP320P7VZvvxZvkPGfyVIY2Uwa9Frzkt8zvAyc1wzi3A64F/BL7EYDbTTzLLbKZV9RMGd3v6v82XuzPN8vg+4KzmS9yH7Lb96cCFSTYD9wB/N8M+pNY4O6ck9Yw9fknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ75/yVuUF9WS94iAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSD8RgiiSvi2",
        "outputId": "68eb9c95-90e4-4eaa-e475-ccbf5217ca84"
      },
      "source": [
        "st.ttest_ind(coffee, no_coffee, equal_var=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-3.6433299160125303, pvalue=0.002041972256638596)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REFMSwFjaL4x"
      },
      "source": [
        "Over this period, I was significantly more productive on days where I didn't drink coffee ($\\mu_n =$ 15.4 pomodoros = 6.4 hours of deeply focused work) than on days where I did ($\\mu_c =$ 10.9 pomodoros = 4.5 hours of deeply focused work), $t = 3.6, p < .01$."
      ]
    }
  ]
}