audit_accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/christopher/mc/lib/python3.7/site-packages/github3/session.py:3: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
      "  from collections import Callable\n",
      "/home/christopher/mc/lib/python3.7/site-packages/github3/structs.py:11: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
      "  class GitHubIterator(models.GitHubCore, collections.Iterator):\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "from conda_forge_tick.utils import loads\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/christopher/mc/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/regro/cf-graph-countyfair/master/audits/depfinder_accuracy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time'] = pd.to_datetime(df['time'].values, unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>accurate</th>\n",
       "      <th>cf_over_specified</th>\n",
       "      <th>cf_under_specified</th>\n",
       "      <th>cf_over_and_under_specified</th>\n",
       "      <th>errored</th>\n",
       "      <th>definder_version</th>\n",
       "      <th>audit_creation_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2020-12-01 09:10:19.840078592</td>\n",
       "      <td>2920</td>\n",
       "      <td>798</td>\n",
       "      <td>1854</td>\n",
       "      <td>983</td>\n",
       "      <td>276</td>\n",
       "      <td>2.7.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-12-01 10:10:06.961130142</td>\n",
       "      <td>2921</td>\n",
       "      <td>798</td>\n",
       "      <td>1854</td>\n",
       "      <td>983</td>\n",
       "      <td>276</td>\n",
       "      <td>2.7.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-12-01 11:08:15.784851551</td>\n",
       "      <td>2921</td>\n",
       "      <td>798</td>\n",
       "      <td>1854</td>\n",
       "      <td>983</td>\n",
       "      <td>276</td>\n",
       "      <td>2.7.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-12-01 12:10:21.701864958</td>\n",
       "      <td>2921</td>\n",
       "      <td>798</td>\n",
       "      <td>1854</td>\n",
       "      <td>983</td>\n",
       "      <td>276</td>\n",
       "      <td>2.7.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-12-01 13:08:23.680642605</td>\n",
       "      <td>2921</td>\n",
       "      <td>798</td>\n",
       "      <td>1854</td>\n",
       "      <td>983</td>\n",
       "      <td>276</td>\n",
       "      <td>2.7.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             time  accurate  cf_over_specified  \\\n",
       "242 2020-12-01 09:10:19.840078592      2920                798   \n",
       "243 2020-12-01 10:10:06.961130142      2921                798   \n",
       "244 2020-12-01 11:08:15.784851551      2921                798   \n",
       "245 2020-12-01 12:10:21.701864958      2921                798   \n",
       "246 2020-12-01 13:08:23.680642605      2921                798   \n",
       "\n",
       "     cf_under_specified  cf_over_and_under_specified  errored  \\\n",
       "242                1854                          983      276   \n",
       "243                1854                          983      276   \n",
       "244                1854                          983      276   \n",
       "245                1854                          983      276   \n",
       "246                1854                          983      276   \n",
       "\n",
       "    definder_version  audit_creation_version  \n",
       "242            2.7.0                       4  \n",
       "243            2.7.0                       4  \n",
       "244            2.7.0                       4  \n",
       "245            2.7.0                       4  \n",
       "246            2.7.0                       4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/christopher/mc/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "df = df.set_index('time').drop(columns=['audit_creation_version', 'definder_version'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy().div(df.sum(axis=1), axis=0) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df2.plot.area(title='overall accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/christopher/mc/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "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>accurate</th>\n",
       "      <th>cf_over_specified</th>\n",
       "      <th>cf_under_specified</th>\n",
       "      <th>cf_over_and_under_specified</th>\n",
       "      <th>errored</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-01 09:10:19.840078592</th>\n",
       "      <td>42.746304</td>\n",
       "      <td>11.682038</td>\n",
       "      <td>27.140975</td>\n",
       "      <td>14.390280</td>\n",
       "      <td>4.040404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01 10:10:06.961130142</th>\n",
       "      <td>42.754684</td>\n",
       "      <td>11.680328</td>\n",
       "      <td>27.137002</td>\n",
       "      <td>14.388173</td>\n",
       "      <td>4.039813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01 11:08:15.784851551</th>\n",
       "      <td>42.754684</td>\n",
       "      <td>11.680328</td>\n",
       "      <td>27.137002</td>\n",
       "      <td>14.388173</td>\n",
       "      <td>4.039813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01 12:10:21.701864958</th>\n",
       "      <td>42.754684</td>\n",
       "      <td>11.680328</td>\n",
       "      <td>27.137002</td>\n",
       "      <td>14.388173</td>\n",
       "      <td>4.039813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01 13:08:23.680642605</th>\n",
       "      <td>42.754684</td>\n",
       "      <td>11.680328</td>\n",
       "      <td>27.137002</td>\n",
       "      <td>14.388173</td>\n",
       "      <td>4.039813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                accurate  cf_over_specified  \\\n",
       "time                                                          \n",
       "2020-12-01 09:10:19.840078592  42.746304          11.682038   \n",
       "2020-12-01 10:10:06.961130142  42.754684          11.680328   \n",
       "2020-12-01 11:08:15.784851551  42.754684          11.680328   \n",
       "2020-12-01 12:10:21.701864958  42.754684          11.680328   \n",
       "2020-12-01 13:08:23.680642605  42.754684          11.680328   \n",
       "\n",
       "                               cf_under_specified  \\\n",
       "time                                                \n",
       "2020-12-01 09:10:19.840078592           27.140975   \n",
       "2020-12-01 10:10:06.961130142           27.137002   \n",
       "2020-12-01 11:08:15.784851551           27.137002   \n",
       "2020-12-01 12:10:21.701864958           27.137002   \n",
       "2020-12-01 13:08:23.680642605           27.137002   \n",
       "\n",
       "                               cf_over_and_under_specified   errored  \n",
       "time                                                                  \n",
       "2020-12-01 09:10:19.840078592                    14.390280  4.040404  \n",
       "2020-12-01 10:10:06.961130142                    14.388173  4.039813  \n",
       "2020-12-01 11:08:15.784851551                    14.388173  4.039813  \n",
       "2020-12-01 12:10:21.701864958                    14.388173  4.039813  \n",
       "2020-12-01 13:08:23.680642605                    14.388173  4.039813  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/christopher/mc/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "audit = loads(requests.get('https://raw.githubusercontent.com/regro/cf-graph-countyfair/master/audits/depfinder/_net_audit.json').text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6832"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(audit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_minus_cf = {k: v['df_minus_cf'] for k, v in audit.items() if v and 'df_minus_cf' in v}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7698, 2837)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_minus_cf_counts = [len(v) for v in df_minus_cf.values()]\n",
    "sum(df_minus_cf_counts), len(df_minus_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of feedstock missing N pkgs')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_minus_cf_counts, bins=range(10), align='left')\n",
    "plt.title('Number of feedstock missing N pkgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_minus_cf_pkgs = [vv for v in df_minus_cf.values() for vv in v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the most common pkgs that are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('numpy', 433),\n",
       " ('pytest', 260),\n",
       " ('six', 232),\n",
       " ('matplotlib', 230),\n",
       " ('requests', 168),\n",
       " ('scipy', 164),\n",
       " ('pandas', 162),\n",
       " ('pyyaml', 128),\n",
       " ('ipython', 118),\n",
       " ('traitlets', 117)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(df_minus_cf_pkgs).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_minus_df = {k: v['cf_minus_df'] for k, v in audit.items() if v and 'cf_minus_df' in v}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3579"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_minus_df_counts = [len(v) for v in cf_minus_df.values()]\n",
    "sum(cf_minus_df_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of feedstock with too many N pkgs')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cf_minus_df_counts, bins=range(10), align='left')\n",
    "plt.title('Number of feedstock with too many N pkgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_minus_df_pkgs = [vv for v in cf_minus_df.values() for vv in v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the most common pkgs too many"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('six', 113),\n",
       " ('future', 80),\n",
       " ('numpy', 76),\n",
       " ('scipy', 74),\n",
       " ('pytest', 57),\n",
       " ('requests', 46),\n",
       " ('matplotlib-base', 43),\n",
       " ('boost', 36),\n",
       " ('netcdf4', 35),\n",
       " ('pillow', 35)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(cf_minus_df_pkgs).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_df_minus_cf = {k: next(iter(v)) for k, v in df_minus_cf.items() if len(v) == 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the most common pkgs that are the only pkg too few"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('numpy', 111),\n",
       " ('pytest', 93),\n",
       " ('six', 52),\n",
       " ('cython', 43),\n",
       " ('matplotlib', 31),\n",
       " ('traitlets', 30),\n",
       " ('werkzeug', 25),\n",
       " ('django', 24),\n",
       " ('docutils', 23),\n",
       " ('py', 23)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(single_df_minus_cf.values()).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_cf_minus_df = {k: next(iter(v)) for k, v in cf_minus_df.items() if len(v) == 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the most common pkgs that are the only pkg too many"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('six', 71),\n",
       " ('future', 45),\n",
       " ('scipy', 32),\n",
       " ('numpy', 29),\n",
       " ('requests', 20),\n",
       " ('boost', 20),\n",
       " ('pytest', 17),\n",
       " ('matplotlib-base', 17),\n",
       " ('pyqt', 13),\n",
       " ('lxml', 13)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(single_cf_minus_df.values()).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the most common pkgs that are the only 2 pkgs too many"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "double_df_minus_cf = [vv for k, v in df_minus_cf.items() if len(v) == 2 for vv in v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('numpy', 96),\n",
       " ('pytest', 58),\n",
       " ('six', 51),\n",
       " ('matplotlib', 48),\n",
       " ('requests', 37),\n",
       " ('scipy', 31),\n",
       " ('pyyaml', 29),\n",
       " ('traitlets', 29),\n",
       " ('ipython', 29),\n",
       " ('tornado', 23)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(double_df_minus_cf).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "audits2 = {}\n",
    "for k, v in audit.items():\n",
    "    if v:\n",
    "        audits2[k] = {}\n",
    "        for kk, vv in v.items():\n",
    "            if kk in ['cf_minus_df', 'df_minus_cf']:\n",
    "                z = vv - {'matplotlib', 'matplotlib-base', 'pytest'}\n",
    "                _ = {kk: z}\n",
    "                if z:\n",
    "                    audits2[k].update(_)\n",
    "    else:\n",
    "        audits2[k] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_minus_cf = {k: v['df_minus_cf'] for k, v in audits2.items() if v and 'df_minus_cf' in v}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7208, 2712)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_minus_cf_counts = [len(v) for v in df_minus_cf.values()]\n",
    "sum(df_minus_cf_counts), len(df_minus_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of feedstock missing N pkgs')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_minus_cf_counts, bins=range(10), align='left')\n",
    "plt.title('Number of feedstock missing N pkgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}