Noleli/days between chanukahs.ipynb

## days between chanukahs.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib 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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dates(year):\n",
    "    hebcal_str = subprocess.run('../hebcal-master/./hebcal ' + str(year), shell=True, stdout=subprocess.PIPE, encoding='utf8').stdout\n",
    "    date_search = re.search(r'(([0-9]{1,2})/([0-9]{1,2})/[0-9]{4}) ' + 'Chanukah: 1 Candle', hebcal_str)\n",
    "    month = int(date_search.group(2))\n",
    "    day = int(date_search.group(3))\n",
    "    return datetime.date(year, month, day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([{'year': y, 'this_year': get_dates(y)} for y in range(1583, 2400)]).set_index('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['last_year'] = df.set_index(df.index + 1)['this_year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diff'] = df['this_year'] - df['last_year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diff'] = df['diff'].apply(lambda td: td.days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "this_year    2019-12-22\n",
       "last_year    2018-12-02\n",
       "diff                385\n",
       "Name: 2019, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_groups = df['diff'].groupby(df['diff']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diff\n",
       "353.0    0.052696\n",
       "354.0    0.348039\n",
       "355.0    0.230392\n",
       "383.0    0.078431\n",
       "384.0    0.194853\n",
       "385.0    0.095588\n",
       "Name: diff, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_groups/diff_groups.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Fraction of years')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5qpYkSeqRLo+srQL2GJjfHbhx+kpJDgbeChxWVfdOtVfVje2/1wJfBJ48vW9VnVpVi6pq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107303e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (diff_groups/diff_groups.sum()).plot.bar(title='Days since the last Chanukah', figsize=(10, 6))\n",
    "ax.set_xlabel('Days')\n",
    "ax.set_ylabel('Fraction of years')"
   ]
  }
 ],
 "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
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"%matplotlib 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": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"def get_dates(year):\n",
	" hebcal_str = subprocess.run('../hebcal-master/./hebcal ' + str(year), shell=True, stdout=subprocess.PIPE, encoding='utf8').stdout\n",
	" date_search = re.search(r'(([0-9]{1,2})/([0-9]{1,2})/[0-9]{4}) ' + 'Chanukah: 1 Candle', hebcal_str)\n",
	" month = int(date_search.group(2))\n",
	" day = int(date_search.group(3))\n",
	" return datetime.date(year, month, day)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [],
	"source": [
	"df = pd.DataFrame([{'year': y, 'this_year': get_dates(y)} for y in range(1583, 2400)]).set_index('year')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [],
	"source": [
	"df['last_year'] = df.set_index(df.index + 1)['this_year']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [],
	"source": [
	"df['diff'] = df['this_year'] - df['last_year']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [],
	"source": [
	"df['diff'] = df['diff'].apply(lambda td: td.days)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"this_year 2019-12-22\n",
	"last_year 2018-12-02\n",
	"diff 385\n",
	"Name: 2019, dtype: object"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"df.loc[2019]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [],
	"source": [
	"diff_groups = df['diff'].groupby(df['diff']).size()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"diff\n",
	"353.0 0.052696\n",
	"354.0 0.348039\n",
	"355.0 0.230392\n",
	"383.0 0.078431\n",
	"384.0 0.194853\n",
	"385.0 0.095588\n",
	"Name: diff, dtype: float64"
	]
	},
	"execution_count": 10,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"diff_groups/diff_groups.sum()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"Text(0,0.5,'Fraction of years')"
	]
	},
	"execution_count": 11,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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am+Q44DxgDnB6VV2eZDGwoqqWAqcBH0uyEriFJtABPBNYnGQtsA44tqpu6apW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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x107303e48>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"ax = (diff_groups/diff_groups.sum()).plot.bar(title='Days since the last Chanukah', figsize=(10, 6))\n",
	"ax.set_xlabel('Days')\n",
	"ax.set_ylabel('Fraction of years')"
	]
	}
	],
	"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
	}