Rosh Hashana occurences by day of the week

rosh_hashana_weekdays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import subprocess\n",
    "import re\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this you need [Hebcal](https://www.hebcal.com/) installed. I didn’t bother putting it on my `$PATH`, so need to specify the path to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hebcal_path = '../hebcal-master/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_date(s):\n",
    "    return datetime.datetime.strptime(s, '%m/%d/%Y').date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(y, looking_for):\n",
    "    hebcal_str = subprocess.run(hebcal_path + './hebcal ' + str(y), shell=True, stdout=subprocess.PIPE, encoding='utf8').stdout\n",
    "    \n",
    "    looking_for = looking_for\n",
    "    found_dates = re.findall(r'([0-9]{1,2}/[0-9]{1,2}/[0-9]{4}) ' + looking_for, hebcal_str)\n",
    "    found_date = to_date(found_dates[0])\n",
    "    return found_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_year = 1584\n",
    "end_year = 2400\n",
    "num_years = end_year - start_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "for y in range(start_year, end_year):\n",
    "    data.append(get_data(y, r'Rosh Hashana \\d+'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data, columns=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekday'] = df['date'].apply(lambda d: d.strftime('%A'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>date</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1584-09-06</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1585-09-24</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1586-09-13</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1587-10-03</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1588-09-22</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   weekday\n",
       "0  1584-09-06  Thursday\n",
       "1  1585-09-24   Tuesday\n",
       "2  1586-09-13  Saturday\n",
       "3  1587-10-03  Saturday\n",
       "4  1588-09-22  Thursday"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_weekday = df.groupby(['weekday']).size()\n",
    "by_weekday = by_weekday.reindex(['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_weekday = pd.DataFrame(by_weekday, columns=['count'])\n",
    "by_weekday['freq'] = by_weekday['count']/num_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>count</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>229.0</td>\n",
       "      <td>0.280637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0.115196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>260.0</td>\n",
       "      <td>0.318627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>233.0</td>\n",
       "      <td>0.285539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count      freq\n",
       "weekday                   \n",
       "Sunday       NaN       NaN\n",
       "Monday     229.0  0.280637\n",
       "Tuesday     94.0  0.115196\n",
       "Wednesday    NaN       NaN\n",
       "Thursday   260.0  0.318627\n",
       "Friday       NaN       NaN\n",
       "Saturday   233.0  0.285539"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sJE2QdJek2ZImN5l/oqTbJd0q6TeSNi/Me1bSLfk1tco4zcystcoefiRpEDAF2B+YB8yQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bde1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = by_weekday['freq'].plot.bar(title='Rosh Hashana occurences by day of the week')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2855392156862745"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_weekday.loc['Saturday', 'freq']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 28.6%"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}