{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# 数字の出現数を数える\n",
    "## プログラムの概要\n",
    "あるシステムが生成する認証コードは6桁の整数です。このコードに現れる各桁の数字(Digit)の分布がどの程度一様なのかを調べます。\n",
    "実際に生成されたコードの一覧から、各桁の数字に分解して頻度を数えます。\n",
    "まずは生成されたcodeの一覧を変数`codes`に割り当てます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!python3\n",
    "#-*- coding:utf-8 -*-\n",
    "# '#'以降は行末までがコメントになります。（プログラムとしては実行されない）\n",
    "nlen=6 # コードの桁数は6桁\n",
    "codes=(\n",
    "    # 012345 のように0から始まるcodeは, 0を省いて書くことにします。\n",
    "    227524,  463251,  702567,  601620,\n",
    "    129458,  413239,  629380,  526093,\n",
    "    261547,  666552,  853626,  513298,\n",
    "    142167,  612906,  995697,  660500,\n",
    "    954404,  651454,  566439,  730975,\n",
    "    578967,  603424,  435636,  891667,\n",
    "    757294,  325567,  131075,  757309, \n",
    "    198547,  542718,  511525,  357679,\n",
    "    245280,\n",
    "    # 2021/05/14\n",
    "     40245, 314151, 689785, 246930, 429093,\n",
    "    546479, 240262, 631261, 559379, 869925, \n",
    "    814588, 766660, 588608, 815828, 946364,\n",
    "    542718, 710428, 725610, 863406, 250107, \n",
    "    629444, 865116,  86879, \n",
    "    # 2021/07/21\n",
    "    162227, 132001, 167877, 898784, 225402,\n",
    "    732262, 403174, 483069,  73898, 990700,\n",
    "    272303, 489913, 473568, 662501, 590176, \n",
    "    631682, 633141, 714916, 944273, 211866,\n",
    "    803916,  13252, 106412, 319172, 339634,\n",
    "    752409, 570635, 265899, 944273, 211866\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "次に一つの code の各桁に現れる数字の出現回数を数えます。\n",
    "一番下の桁から順番にみていきます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def count_digits(code,nlen=6): #　関数　`count_digits`を定義します。\n",
    "    # 最大二つの引数を持ちますが、二つ目の引数`nlen`は省略可能で、\n",
    "    # 省略された場合の値(既定値)は6です。\n",
    "    acc={} #これに数字毎の出現回数を入れていきます。\n",
    "    code=code+10**nlen # 最上位が0の場合を取り扱うために、10**nlenを足しておく。\n",
    "    # code =227524 であれば　code=1227524とするということ。\n",
    "    while code>=10:\n",
    "        code, d=divmod(code, 10) #商 `code` と余り `d` に分解します。\n",
    "        # code=1227524　=> code=122752,d=4\n",
    "        acc[d]=acc.get(d,0)+1 \n",
    "        # accのkeyにdがあればその値に1を足したもの、さもなければ 0+1をacc[d]に設定する。\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "一覧表`codes`の全ての`code`に現れる数字に頻度を数えて、足していきます。\n",
    "結果の度数分布表`acc`を関数の値として返します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def count_digits_in_list(codes,nlen):\n",
    "    acc={}\n",
    "    for code in codes: #codesの中に入っている全てのcodeについて\n",
    "        count=count_digits(code,nlen) #コードの中の数字の出現表を作る。\n",
    "        for d in count: #codeでの数字の出現数をaccに足して行く。\n",
    "            acc[d]=acc.get(d,0)+count[d]\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "`codes`の中の数字の度数分表を印刷し、ヒストグラムを表示させてみます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pyplot #グラフ作成の準備のおまじない\n",
    "import numpy\n",
    "\n",
    "def main():\n",
    "    counts=count_digits_in_list(codes,nlen) #codeの一覧codesから数字の出現数の表を作成する。\n",
    "    print(\"digit:  count\")# 印刷する表のラベルを印刷する。\n",
    "    for d in sorted(counts): # 出現表にあらわれる数字を整列させた順番に、\n",
    "        print (d,\":\",counts[d]) # 数字(d)とその出現数(count[d])を印刷する。\n",
    "    # print(\"average:\",len(codes)*nlen/10) #数字の出現数の平均値を印刷する。\n",
    "    print(\"average:\", sum(counts.values())/len(counts)) # 6*len(codes)/10\n",
    "    print(\"std. dev:{:4.1f}\".format(numpy.std(list(counts.values()))))\n",
    "    pyplot.bar(counts.keys(),counts.values()) # barグラフを印刷する。\n",
    "    pyplot.draw() #グラフ作成のおまじない\n",
    "    pyplot.show() #グラフ作成の最後のおまじない. Jupyter Notebookでは不要\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digit:  count\n",
      "0 : 45\n",
      "1 : 53\n",
      "2 : 61\n",
      "3 : 44\n",
      "4 : 52\n",
      "5 : 54\n",
      "6 : 71\n",
      "7 : 48\n",
      "8 : 41\n",
      "9 : 47\n",
      "average: 51.6\n",
      "std. dev: 8.5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAANzElEQVR4nO3dXYxc9X2H8edbG0RC2vC2tVwbdS0FgVAkIFpRUqpIxVCRgrAvEAK1yEpd+SZJoY2UOLmr1AtHqpJwUUWyIOlKpbzUAdlKqjTIIaoqVTRroA3gIIhrgl2/bFIolItSJ79e7HGzWa+Z2ZeZ8X/3+UjWnHPmDOc3Ah4dn52zk6pCktSeXxn1AJKkxTHgktQoAy5JjTLgktQoAy5JjVo7zINddtllNT4+PsxDSlLzDhw48JOqGpu7fagBHx8fZ2pqapiHlKTmJXltvu1eQpGkRhlwSWqUAZekRhlwSWqUAZekRhlwSWqUAZekRhlwSWpUz4AnuTLJ87P+vJXk/iSXJHkqySvd48XDGFiSNKPnnZhV9TJwLUCSNcBR4ElgJ7C/qnYl2dmtf25wo0or3/jObw38GId33TbwY2g4FnoJZTPwo6p6DdgCTHbbJ4GtyziXJKmHhQb8buCRbnldVR3rlo8D65ZtKklST30HPMn5wB3A3819rma+WHPeL9dMsiPJVJKp6enpRQ8qSfplCzkD/zjwbFWd6NZPJFkP0D2enO9FVbW7qiaqamJs7IzfhihJWqSFBPwefnH5BGAfsK1b3gbsXa6hJEm99RXwJBcCtwBPzNq8C7glySvAzd26JGlI+vpCh6p6B7h0zrafMvOpFEnSCHgnpiQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1qq9vpU9yEfAg8GGggD8CXgYeA8aBw8BdVfXGIIZczcZ3fmvgxzi867aBH0PS8uv3DPwB4NtVdRVwDXAQ2Ansr6orgP3duiRpSHoGPMkHgY8BDwFU1btV9SawBZjsdpsEtg5mREnSfPo5A98ETANfT/JckgeTXAisq6pj3T7HgXXzvTjJjiRTSaamp6eXZ2pJUl8BXwt8BPhqVV0HvMOcyyVVVcxcGz9DVe2uqomqmhgbG1vqvJKkTj8BPwIcqapnuvU9zAT9RJL1AN3jycGMKEmaT8+AV9Vx4PUkV3abNgMvAfuAbd22bcDegUwoSZpXXx8jBD4NPJzkfOAQ8Alm4v94ku3Aa8BdgxlRkjSfvgJeVc8DE/M8tXlZp5Ek9c07MSWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUf3+Mitp1fB7SNUKz8AlqVEGXJIa5SWUPvhXaknnIs/AJalRBlySGuUlFJ2TvGwl9eYZuCQ1yoBLUqP6uoSS5DDwNvAz4FRVTSS5BHgMGAcOA3dV1RuDGVOSNNdCzsB/t6qurarT306/E9hfVVcA+7t1SdKQLOUSyhZgslueBLYueRpJUt/6DXgB30lyIMmObtu6qjrWLR8H1s33wiQ7kkwlmZqenl7iuJKk0/r9GOHvVNXRJL8OPJXkh7OfrKpKUvO9sKp2A7sBJiYm5t1HkrRwfZ2BV9XR7vEk8CRwPXAiyXqA7vHkoIaUJJ2pZ8CTXJjkV08vA78HvADsA7Z1u20D9g5qSEnSmfq5hLIOeDLJ6f3/tqq+neT7wONJtgOvAXcNbkxJ0lw9A15Vh4Br5tn+U2DzIIaSJPXm70KRBPj7Z1rkrfSS1CgDLkmN8hKKpFVv0JePBnXpyDNwSWqUAZekRhlwSWqUAZekRhlwSWpUM59C8SYDSfplnoFLUqOaOQPX8Pm3Hunc5hm4JDXKgEtSowy4JDXKgEtSowy4JDXKgEtSowy4JDXKgEtSo/q+kSfJGmAKOFpVtyfZBDwKXAocAO6tqncHM6akla7VL1UYpYWcgd8HHJy1/kXgy1X1IeANYPtyDiZJem99BTzJRuA24MFuPcBNwJ5ul0lg6wDmkySdRb9n4F8BPgv8vFu/FHizqk5160eADfO9MMmOJFNJpqanp5cyqyRplp4BT3I7cLKqDizmAFW1u6omqmpibGxsMf8ISdI8+vkh5o3AHUl+H7gA+DXgAeCiJGu7s/CNwNHBjSlJmqvnGXhVfb6qNlbVOHA38N2q+gPgaeDObrdtwN6BTSlJOsNSPgf+OeDPkrzKzDXxh5ZnJElSPxb0hQ5V9T3ge93yIeD65R9JktQP78SUpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqVM+AJ7kgyb8k+dckLyb58277piTPJHk1yWNJzh/8uJKk0/o5A/8f4Kaquga4Frg1yQ3AF4EvV9WHgDeA7QObUpJ0hp4Brxn/3a2e1/0p4CZgT7d9Etg6iAElSfPr6xp4kjVJngdOAk8BPwLerKpT3S5HgA1nee2OJFNJpqanp5dhZEkS9BnwqvpZVV0LbASuB67q9wBVtbuqJqpqYmxsbHFTSpLOsKBPoVTVm8DTwEeBi5Ks7Z7aCBxd3tEkSe+ln0+hjCW5qFt+H3ALcJCZkN/Z7bYN2DugGSVJ81jbexfWA5NJ1jAT/Mer6ptJXgIeTfIXwHPAQwOcU5I0R8+AV9W/AdfNs/0QM9fDJUkj4J2YktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjeoZ8CSXJ3k6yUtJXkxyX7f9kiRPJXmle7x48ONKkk7r5wz8FPCZqroauAH4ZJKrgZ3A/qq6AtjfrUuShqRnwKvqWFU92y2/DRwENgBbgMlut0lg64BmlCTNY0HXwJOMA9cBzwDrqupY99RxYN1ZXrMjyVSSqenp6aXMKkmape+AJ/kA8A3g/qp6a/ZzVVVAzfe6qtpdVRNVNTE2NrakYSVJv9BXwJOcx0y8H66qJ7rNJ5Ks755fD5wczIiSpPn08ymUAA8BB6vqS7Oe2gds65a3AXuXfzxJ0tms7WOfG4F7gR8keb7b9gVgF/B4ku3Aa8BdA5lQkjSvngGvqn8CcpanNy/vOJKkfnknpiQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqN6BjzJ15KcTPLCrG2XJHkqySvd48WDHVOSNFc/Z+B/Ddw6Z9tOYH9VXQHs79YlSUPUM+BV9Y/Af87ZvAWY7JYnga3LO5YkqZfFXgNfV1XHuuXjwLplmkeS1Kcl/xCzqgqosz2fZEeSqSRT09PTSz2cJKmz2ICfSLIeoHs8ebYdq2p3VU1U1cTY2NgiDydJmmuxAd8HbOuWtwF7l2ccSVK/+vkY4SPAPwNXJjmSZDuwC7glySvAzd26JGmI1vbaoaruOctTm5d5FknSAngnpiQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1yoBLUqMMuCQ1akkBT3JrkpeTvJpk53INJUnqbdEBT7IG+Cvg48DVwD1Jrl6uwSRJ720pZ+DXA69W1aGqehd4FNiyPGNJknpJVS3uhcmdwK1V9cfd+r3Ab1XVp+bstwPY0a1eCby8+HEX5DLgJ0M61rlmtb533/fqspre929W1djcjWsHfdSq2g3sHvRx5koyVVUTwz7uuWC1vnff9+qyWt/3bEu5hHIUuHzW+sZumyRpCJYS8O8DVyTZlOR84G5g3/KMJUnqZdGXUKrqVJJPAf8ArAG+VlUvLttkSzf0yzbnkNX63n3fq8tqfd//b9E/xJQkjZZ3YkpSowy4JDVqRQZ8Nd7in+TyJE8neSnJi0nuG/VMw5RkTZLnknxz1LMMS5KLkuxJ8sMkB5N8dNQzDUOSP+3+G38hySNJLhj1TKOy4gK+im/xPwV8pqquBm4APrlK3vdp9wEHRz3EkD0AfLuqrgKuYRW8/yQbgD8BJqrqw8x8gOLu0U41Oisu4KzSW/yr6lhVPdstv83M/8wbRjvVcCTZCNwGPDjqWYYlyQeBjwEPAVTVu1X15kiHGp61wPuSrAXeD/zHiOcZmZUY8A3A67PWj7BKQnZaknHgOuCZEY8yLF8BPgv8fMRzDNMmYBr4enfp6MEkF456qEGrqqPAXwI/Bo4B/1VV3xntVKOzEgO+qiX5APAN4P6qemvU8wxaktuBk1V1YNSzDNla4CPAV6vqOuAdYMX/vCfJxcz8jXoT8BvAhUn+cLRTjc5KDPiqvcU/yXnMxPvhqnpi1PMMyY3AHUkOM3O57KYkfzPakYbiCHCkqk7/LWsPM0Ff6W4G/r2qpqvqf4EngN8e8UwjsxIDvipv8U8SZq6HHqyqL416nmGpqs9X1caqGmfm3/V3q2rFn5FV1XHg9SRXdps2Ay+NcKRh+TFwQ5L3d//Nb2YV/PD2bAb+2wiHrYFb/AflRuBe4AdJnu+2faGq/n50I2nAPg083J2oHAI+MeJ5Bq6qnkmyB3iWmU9ePccqvqXeW+klqVEr8RKKJK0KBlySGmXAJalRBlySGmXAJalRBlySGmXAJalR/wemnGZkRtSfwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ファイルをコマンドとして実行するためのおまじない\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## pandas/DataFrame\n",
    "Data Analysis分野で最近よく使われるDataFrameオブジェクトを使うと、\n",
    "短いプログラムでデータのリストから度数分表を取出し、またヒストグラムを表示させることができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas # DataFrameが使えるようにpandasをインポートして置きます。\n",
    "\n",
    "def split_digits(code,nlen=6):\n",
    "    acc=[] # あらわれた数字を順番に保存する。\n",
    "    code=code+10**nlen # 最上位が0の場合を取り扱うため\n",
    "    while code>=10:\n",
    "        code,d=divmod(code,10)\n",
    "        acc.insert(0,d)\n",
    "    return acc\n",
    "\n",
    "def split_codes():\n",
    "    acc=[]\n",
    "    for code in codes:\n",
    "        acc +=split_digits(code)\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    45\n",
      "1    53\n",
      "2    61\n",
      "3    44\n",
      "4    52\n",
      "5    54\n",
      "6    71\n",
      "7    48\n",
      "8    41\n",
      "9    47\n",
      "dtype: int64\n",
      "average: 51.6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "# codesのデータを一桁の数字に分解し、リストをつくります\n",
    "\n",
    "df=pandas.DataFrame(split_codes()).value_counts(sort=False)\n",
    "print(df)\n",
    "print(\"average:\",numpy.average(df))\n",
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## matplotlib.pyplot\n",
    "\n",
    "matplotlib.pyplotを使って、ヒストグラムの表示と度数分布を求めることも可能です。\n",
    "\n",
    "まずヒストグラムを作成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UKAMuSY0y4JLUKAMuSY0y4JLUqP8H9aR8cbet2/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts, digits, chart = pyplot.hist(\n",
    "    split_codes(), \n",
    "    bins=range(0,11)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib.hist()関数は、ビン毎の計数値(count)、binの端の値(digits)そして、ヒストグラムのオブジェクトを値として返してきます。ですから、次のようなスクリプトで、各ビンのラベルと係数値を印刷します。digitsの要素数はcountsの要素数より1多いことに注意が必要です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 45.0)\n",
      "(1, 53.0)\n",
      "(2, 61.0)\n",
      "(3, 44.0)\n",
      "(4, 52.0)\n",
      "(5, 54.0)\n",
      "(6, 71.0)\n",
      "(7, 48.0)\n",
      "(8, 41.0)\n",
      "(9, 47.0)\n",
      "average: 51.6\n",
      "11 > 10\n"
     ]
    }
   ],
   "source": [
    "for p in (zip(digits,counts)):\n",
    "    print(p)\n",
    "print(\"average:\", sum(counts)/len(counts))\n",
    "print(len(digits), \">\",len(counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "次に、数字の出現回数の分布をプロットしてみます。\"5\"および\"6\"の出現回数が突出していることが\n",
    "見て取れます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "pyplot.hist(\n",
    "    counts, \n",
    "    histtype=\"stepfilled\",\n",
    "    density=False,\n",
    "    align=\"left\",\n",
    "    bins=range(34-3*8,34+3*8,2),\n",
    ")\n",
    "pyplot.gca().set_xlabel(\"count\")\n",
    "pyplot.gca().set_ylabel(u\"occurence\");"
   ]
  }
 ],
 "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": "3.9.6"
  },
  "toc-autonumbering": true,
  "toc-showcode": false,
  "toc-showmarkdowntxt": true,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}