estimate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:48.148191Z",
     "iopub.status.busy": "2024-05-31T05:08:48.147986Z",
     "iopub.status.idle": "2024-05-31T05:08:48.157621Z",
     "shell.execute_reply": "2024-05-31T05:08:48.157006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Estimate of Public Jupyter Notebooks on GitHub\n",
       "\n",
       "- [View the daily report notebook](https://github.com/parente/nbestimate/blob/master/estimate.ipynb)\n",
       "- [See the raw count data in a CSV](https://github.com/parente/nbestimate/blob/master/ipynb_counts.csv)\n",
       "\n",
       "## Data Collection History\n",
       "\n",
       "- Late-2014 to mid-2016: I wrote a script that scrapes the GitHub web search UI for the count,\n",
       "  appends to a CSV, executes a notebook, and stores the results in a gist at\n",
       "  https://gist.github.com/parente/facb555dfbae28e817e0. I scheduled the script to run daily.\n",
       "- Mid-2106 to Late-2016: The GitHub web search UI started requiring authentication to see global\n",
       "  search results. I stopped collecting data.\n",
       "- Late-2016 to early-2019: I rewrote the process to include a human-in-the-loop who entered the hit\n",
       "  count after viewing the search results page. I moved the CSV, notebook, and scripts to this repo,\n",
       "  and sporadically ran the script.\n",
       "- Early-2019: I found out that the GitHub search API now supports global search. I automated the\n",
       "  entire collection process again and set it to run on TravisCI on a daily schedule.\n",
       "- December 2020: [GitHub changed their code search index results](https://github.blog/changelog/2020-12-17-changes-to-code-search-indexing/)\n",
       "  to exclude repositories without activity for the past year. The ipynb search result count\n",
       "  dropped from nearly 10 million to 4.5 million `ipynb` files, stayed there for a day or so, and\n",
       "  then began climbing again from that new origin.\n",
       "- June 2021: I started collecting data again but disabled the notebook showing the historical and\n",
       "  predicted counts.\n",
       "- July 2021: I revived the notebook showing the historical counts but kept prediction disabled.\n",
       "- April 2023: I noticed that after reporting an all time high of 11 million search results on\n",
       "  2023-04-19, the GitHub code search API started returning orders of magnitude fewer results in the\n",
       "  following days (1M, 100k, 1k). In contrast, the GitHub search UI reports 3.4M hits for the same\n",
       "  query, which is still a far cry from the 11M reported by the API previously.\n",
       "\n",
       "## Assumptions\n",
       "\n",
       "- That the search query hits are less than or equal to the total number of `*.ipynb` files on GitHub.\n",
       "- That the result is **not** inflated due to GitHub forks.\n",
       "  - Evidence: We do not see the tutorial notebooks from the ipython/ipython GitHub repository\n",
       "    duplicated in the search results because of the 2,000+ forks of the ipython/ipython repo.\n",
       "- That the result **is** inflated a tiny bit by manually created duplicates of notebooks.\n",
       "  - Evidence: Some people seem to download their favorite notebooks and then upload them into\n",
       "    their own git repositories for safe keeping.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown\n",
    "Markdown(filename='README.md')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:48.194425Z",
     "iopub.status.busy": "2024-05-31T05:08:48.193897Z",
     "iopub.status.idle": "2024-05-31T05:08:48.815329Z",
     "shell.execute_reply": "2024-05-31T05:08:48.814664Z"
    },
    "urth": {
     "dashboard": {}
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:48.818072Z",
     "iopub.status.busy": "2024-05-31T05:08:48.817763Z",
     "iopub.status.idle": "2024-05-31T05:08:49.102291Z",
     "shell.execute_reply": "2024-05-31T05:08:49.101659Z"
    },
    "urth": {
     "dashboard": {}
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.105141Z",
     "iopub.status.busy": "2024-05-31T05:08:49.104693Z",
     "iopub.status.idle": "2024-05-31T05:08:49.108063Z",
     "shell.execute_reply": "2024-05-31T05:08:49.107524Z"
    },
    "urth": {
     "dashboard": {}
    }
   },
   "outputs": [],
   "source": [
    "mpl.style.use('ggplot')\n",
    "figsize = (14,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.110267Z",
     "iopub.status.busy": "2024-05-31T05:08:49.110068Z",
     "iopub.status.idle": "2024-05-31T05:08:49.113530Z",
     "shell.execute_reply": "2024-05-31T05:08:49.112895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was last rendered at 2024-05-31 05:08:49.110935 UTC\n"
     ]
    }
   ],
   "source": [
    "now = datetime.datetime.utcnow()\n",
    "print(f'This notebook was last rendered at {now} UTC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "source": [
    "First, let's load the historical data into a DataFrame indexed by date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.115796Z",
     "iopub.status.busy": "2024-05-31T05:08:49.115599Z",
     "iopub.status.idle": "2024-05-31T05:08:49.125641Z",
     "shell.execute_reply": "2024-05-31T05:08:49.125028Z"
    },
    "urth": {
     "dashboard": {}
    }
   },
   "outputs": [],
   "source": [
    "hits_df = pd.read_csv('ipynb_counts.csv', index_col=0, header=0, parse_dates=True)\n",
    "hits_df.reset_index(inplace=True)\n",
    "hits_df.drop_duplicates(subset='date', inplace=True)\n",
    "hits_df.set_index('date', inplace=True)\n",
    "hits_df.sort_index(ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.128160Z",
     "iopub.status.busy": "2024-05-31T05:08:49.127807Z",
     "iopub.status.idle": "2024-05-31T05:08:49.135039Z",
     "shell.execute_reply": "2024-05-31T05:08:49.134492Z"
    },
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-05-29</th>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-30</th>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-31</th>\n",
       "      <td>936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            hits\n",
       "date            \n",
       "2024-05-29  1208\n",
       "2024-05-30   992\n",
       "2024-05-31   936"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits_df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "source": [
    "There might be missing counts for days that we failed to sample. We build up the expected date range and insert NaNs for dates we missed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.137348Z",
     "iopub.status.busy": "2024-05-31T05:08:49.137000Z",
     "iopub.status.idle": "2024-05-31T05:08:49.140431Z",
     "shell.execute_reply": "2024-05-31T05:08:49.139874Z"
    },
    "urth": {
     "dashboard": {}
    }
   },
   "outputs": [],
   "source": [
    "til_today = pd.date_range(hits_df.index[0], hits_df.index[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.142519Z",
     "iopub.status.busy": "2024-05-31T05:08:49.142321Z",
     "iopub.status.idle": "2024-05-31T05:08:49.145664Z",
     "shell.execute_reply": "2024-05-31T05:08:49.145126Z"
    },
    "urth": {
     "dashboard": {}
    }
   },
   "outputs": [],
   "source": [
    "hits_df = hits_df.reindex(til_today)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "source": [
    "Now we plot the known notebook counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.147857Z",
     "iopub.status.busy": "2024-05-31T05:08:49.147662Z",
     "iopub.status.idle": "2024-05-31T05:08:49.366091Z",
     "shell.execute_reply": "2024-05-31T05:08:49.365384Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "ax.set_title(f'GitHub search hits for {len(hits_df)} days')\n",
    "ax.plot(hits_df.hits, 'ko', markersize=1, label='hits')\n",
    "ax.legend(loc='upper left')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('# of ipynb files');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Growth appears exponential until December 2020, at which point the count dropped suddenly and resumed growth from a new origin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total change in the number of `*.ipynb` hits between the first day we have data and today is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.368977Z",
     "iopub.status.busy": "2024-05-31T05:08:49.368566Z",
     "iopub.status.idle": "2024-05-31T05:08:49.374502Z",
     "shell.execute_reply": "2024-05-31T05:08:49.373827Z"
    },
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hits   -64912.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_delta_nbs = hits_df.iloc[-1] - hits_df.iloc[0]\n",
    "total_delta_nbs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "source": [
    "The mean daily change for the entire duration is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.376886Z",
     "iopub.status.busy": "2024-05-31T05:08:49.376682Z",
     "iopub.status.idle": "2024-05-31T05:08:49.381628Z",
     "shell.execute_reply": "2024-05-31T05:08:49.380991Z"
    },
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hits   -18.414752\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_delta_nbs = total_delta_nbs / len(hits_df)\n",
    "avg_delta_nbs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "urth": {
     "dashboard": {
      "hidden": true
     }
    }
   },
   "source": [
    "The change in hit count between any two consecutive days for which we have data looks like the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.384157Z",
     "iopub.status.busy": "2024-05-31T05:08:49.383797Z",
     "iopub.status.idle": "2024-05-31T05:08:49.387159Z",
     "shell.execute_reply": "2024-05-31T05:08:49.386580Z"
    },
    "urth": {
     "dashboard": {}
    }
   },
   "outputs": [],
   "source": [
    "daily_deltas = (hits_df.hits - hits_df.hits.shift())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.389307Z",
     "iopub.status.busy": "2024-05-31T05:08:49.389109Z",
     "iopub.status.idle": "2024-05-31T05:08:49.685718Z",
     "shell.execute_reply": "2024-05-31T05:08:49.685136Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "ax.plot(daily_deltas, 'ko', markersize=2)\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('$\\Delta$ # of ipynb files')\n",
    "ax.set_title('Day-to-Day Change');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The large jumps in the data are from GitHub reporting drastically different counts from one day to the next.\n",
    "\n",
    "Let's drop outliers defined as values more than two standard deviations away from a centered 90 day rolling mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.688370Z",
     "iopub.status.busy": "2024-05-31T05:08:49.687949Z",
     "iopub.status.idle": "2024-05-31T05:08:49.694688Z",
     "shell.execute_reply": "2024-05-31T05:08:49.694034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hits\n",
       "False    3431\n",
       "True       94\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_delta_rolling = daily_deltas.rolling(window=90, min_periods=0, center=True)\n",
    "outliers = abs(daily_deltas - daily_delta_rolling.mean()) > 2*daily_delta_rolling.std()\n",
    "outliers.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.696938Z",
     "iopub.status.busy": "2024-05-31T05:08:49.696734Z",
     "iopub.status.idle": "2024-05-31T05:08:49.700700Z",
     "shell.execute_reply": "2024-05-31T05:08:49.700150Z"
    }
   },
   "outputs": [],
   "source": [
    "cleaned_hits_df = hits_df.copy()\n",
    "cleaned_hits_df[outliers] = np.NaN\n",
    "cleaned_daily_deltas = (cleaned_hits_df.hits - cleaned_hits_df.hits.shift())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T05:08:49.702787Z",
     "iopub.status.busy": "2024-05-31T05:08:49.702590Z",
     "iopub.status.idle": "2024-05-31T05:08:49.928266Z",
     "shell.execute_reply": "2024-05-31T05:08:49.927572Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "ax.plot(cleaned_daily_deltas, 'ko', markersize=2)\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('$\\Delta$ # of ipynb files')\n",
    "ax.set_title('Day-to-Day Change Sans Outliers');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  },
  "urth": {
   "dashboard": {
    "cellMargin": 10,
    "defaultCellHeight": 20,
    "layoutStrategy": "packed",
    "maxColumns": 12
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}