09_ds05_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6fc38144",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#빈도-분석\" data-toc-modified-id=\"빈도-분석-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>빈도 분석</a></span><ul class=\"toc-item\"><li><span><a href=\"#crosstab-(교차표)\" data-toc-modified-id=\"crosstab-(교차표)-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>crosstab (교차표)</a></span></li><li><span><a href=\"#freqltems()\" data-toc-modified-id=\"freqltems()-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>freqltems()</a></span></li><li><span><a href=\"#Chi-Square\" data-toc-modified-id=\"Chi-Square-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Chi Square</a></span><ul class=\"toc-item\"><li><span><a href=\"#데이터\" data-toc-modified-id=\"데이터-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>데이터</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ef5afb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SparkSession 생성\n",
    "import pyspark\n",
    "import os\n",
    "\n",
    "myConf=pyspark.SparkConf()\n",
    "spark = pyspark.sql.SparkSession\\\n",
    "    .builder\\\n",
    "    .master(\"local\")\\\n",
    "    .appName(\"myApp\")\\\n",
    "    .config(conf=myConf)\\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3559fbb8",
   "metadata": {},
   "source": [
    "## 빈도 분석\n",
    "명목변수 : 이름이 붙어진 변수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ab16546",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"kim\",\"lee\",\"park\",\"lim\"]\n",
    "items = [\"espresso\",\"latte\",\"americano\",\"affocato\",\"long black\",\"macciato\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59ec03f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "coffeeDf = spark.createDataFrame([(names[i%4], items[i%6]) for i in range(500)],\\\n",
    "                           [\"name\",\"coffee\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51a2008e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----+--------+\n",
      "|summary|name|  coffee|\n",
      "+-------+----+--------+\n",
      "|  count| 500|     500|\n",
      "|   mean|null|    null|\n",
      "| stddev|null|    null|\n",
      "|    min| kim|affocato|\n",
      "|    max|park|macciato|\n",
      "+-------+----+--------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "coffeeDf.describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d1ff160",
   "metadata": {
    "scrolled": 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>name</th>\n",
       "      <th>coffee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>kim</td>\n",
       "      <td>espresso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>125</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name    coffee\n",
       "count   500       500\n",
       "unique    4         6\n",
       "top     kim  espresso\n",
       "freq    125        84"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffeeDf.toPandas().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c17c4a",
   "metadata": {},
   "source": [
    "### crosstab (교차표)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e3af98b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+--------+---------+--------+-----+----------+--------+\n",
      "|name|affocato|americano|espresso|latte|long black|macciato|\n",
      "+----+--------+---------+--------+-----+----------+--------+\n",
      "|park|    null|       42|      42| null|        41|    null|\n",
      "| lim|      42|     null|    null|   42|      null|      41|\n",
      "| kim|    null|       41|      42| null|        42|    null|\n",
      "| lee|      41|     null|    null|   42|      null|      42|\n",
      "+----+--------+---------+--------+-----+----------+--------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "coffeeDf.groupBy('name').pivot('coffee').count().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8262f69",
   "metadata": {},
   "source": [
    "null값이 0으로"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "81412be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+--------+---------+--------+-----+----------+--------+\n",
      "|name_coffee|affocato|americano|espresso|latte|long black|macciato|\n",
      "+-----------+--------+---------+--------+-----+----------+--------+\n",
      "|        lim|      42|        0|       0|   42|         0|      41|\n",
      "|        lee|      41|        0|       0|   42|         0|      42|\n",
      "|       park|       0|       42|      42|    0|        41|       0|\n",
      "|        kim|       0|       41|      42|    0|        42|       0|\n",
      "+-----------+--------+---------+--------+-----+----------+--------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "coffeeDf.stat.crosstab(\"name\", \"coffee\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1930d7e",
   "metadata": {},
   "source": [
    "### freqltems()\n",
    "임계치 이하의 최빈값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90312b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+-----------------+\n",
      "|name_freqItems| coffee_freqItems|\n",
      "+--------------+-----------------+\n",
      "|   [lim, park]|[latte, espresso]|\n",
      "+--------------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "freq = coffeeDf.stat.freqItems([\"name\",\"coffee\"], 0.5)\n",
    "freq.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "624a863e",
   "metadata": {},
   "source": [
    "### Chi Square\n",
    "빈도 분석\n",
    "- features : 속성\n",
    "- label : 맞추려는 값"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61af3e0",
   "metadata": {},
   "source": [
    "#### 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3dd60a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- coffee: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "coffeeDf.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ae20e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import StringIndexer\n",
    "\n",
    "labelIndexer = StringIndexer(inputCol=\"name\", outputCol=\"label\")\n",
    "model=labelIndexer.fit(coffeeDf)\n",
    "_coffeeDf=model.transform(coffeeDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2c81856",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- coffee: string (nullable = true)\n",
      " |-- label: double (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_coffeeDf.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f987d98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+---------+-----+\n",
      "|name|   coffee|label|\n",
      "+----+---------+-----+\n",
      "| kim| espresso|  0.0|\n",
      "| lee|    latte|  1.0|\n",
      "|park|americano|  3.0|\n",
      "| lim| affocato|  2.0|\n",
      "+----+---------+-----+\n",
      "only showing top 4 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_coffeeDf.show(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d419a3de",
   "metadata": {},
   "source": [
    "#### features를 벡터로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8f4f69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "featureIndexer = StringIndexer(inputCol=\"coffee\", outputCol=\"_features\")\n",
    "model=featureIndexer.fit(_coffeeDf)\n",
    "_coffeeDf=model.transform(_coffeeDf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8504103a",
   "metadata": {},
   "source": [
    "벡터라 `\"\"`가 아니라 `[\"\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c6d6e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "va = VectorAssembler(inputCols=[\"_features\"], outputCol=\"features\")\n",
    "_coffeeDf = va.transform(_coffeeDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3f42edaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- coffee: string (nullable = true)\n",
      " |-- label: double (nullable = false)\n",
      " |-- _features: double (nullable = false)\n",
      " |-- features: vector (nullable = true)\n",
      "\n",
      "+----+----------+-----+---------+--------+\n",
      "|name|    coffee|label|_features|features|\n",
      "+----+----------+-----+---------+--------+\n",
      "| kim|  espresso|  0.0|      0.0|   [0.0]|\n",
      "| lee|     latte|  1.0|      1.0|   [1.0]|\n",
      "|park| americano|  3.0|      3.0|   [3.0]|\n",
      "| lim|  affocato|  2.0|      2.0|   [2.0]|\n",
      "| kim|long black|  0.0|      4.0|   [4.0]|\n",
      "| lee|  macciato|  1.0|      5.0|   [5.0]|\n",
      "|park|  espresso|  3.0|      0.0|   [0.0]|\n",
      "+----+----------+-----+---------+--------+\n",
      "only showing top 7 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_coffeeDf.printSchema()\n",
    "_coffeeDf.show(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ecc474",
   "metadata": {},
   "source": [
    "#### ChiSquareTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba6cc969",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.stat import ChiSquareTest\n",
    "r = ChiSquareTest.test(_coffeeDf, \"features\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186d21be",
   "metadata": {},
   "source": [
    "pValues가 0.05보다 작으면 귀무가설 기각 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07cecd7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------------+-------------------+\n",
      "|pValues|degreesOfFreedom|         statistics|\n",
      "+-------+----------------+-------------------+\n",
      "|  [0.0]|            [15]|[500.0963855421687]|\n",
      "+-------+----------------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec0ce532",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.stat import ChiSquareTest\n",
    "r = r.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3927fdf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pValues: [0.0]\n",
      "degreesOfFreedom: [15]\n",
      "statistics: [500.0963855421687]\n"
     ]
    }
   ],
   "source": [
    "print(\"pValues: \" + str(r.pValues))\n",
    "print(\"degreesOfFreedom: \" + str(r.degreesOfFreedom))\n",
    "print(\"statistics: \" + str(r.statistics))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cecba563",
   "metadata": {},
   "source": [
    "## 상관관계\n",
    "- 연속변수에서 옳다\n",
    "- 명목변수에는 의미 없다 -> 빈도분석이 나음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9360d2f8",
   "metadata": {},
   "source": [
    "### 상관관계 방향과 강도\n",
    "- `+` : 서로 같은 반향으로 영향\n",
    "- `0` : 서로 영향 없음\n",
    "- `-` : 서로 다른 방향으로 영향\n",
    "\n",
    "상관 관계 : 공분산(covariance)을 표준편차의 곱으로 나눈 값"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298c116a",
   "metadata": {},
   "source": [
    "### 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d68dcffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=[10,7,12,12,9,16,12,18,8,12,14,16]\n",
    "Y=[6,4,7,8,10,7,10,15,5,6,11,13]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff2fb25",
   "metadata": {},
   "source": [
    "### 그래프"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed873e0",
   "metadata": {},
   "source": [
    "#### 단순 Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e149d83d",
   "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": [
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.title('correlation')\n",
    "plt.plot(X, Y, 'go')\n",
    "plt.ylabel('Y')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df64597d",
   "metadata": {},
   "source": [
    "#### Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d7cd1fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nunu\\anaconda3\\envs\\py3.8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Nunu\\anaconda3\\envs\\py3.8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 44288 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Nunu\\anaconda3\\envs\\py3.8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 44228 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Nunu\\anaconda3\\envs\\py3.8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 49345 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Nunu\\anaconda3\\envs\\py3.8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 44288 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Nunu\\anaconda3\\envs\\py3.8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 44228 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "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": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "plt.rc('font', family='NanumMyeongjo')\n",
    "ax.title.set_text('상관관계')\n",
    "ax.scatter(X, Y)\n",
    "ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator(1))\n",
    "ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator(3))\n",
    "ax.grid(b=True, which='major', color='y', linewidth=0.5)\n",
    "ax.grid(b=True, which='minor', color='b', linewidth=0.5)\n",
    "#fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8085e667",
   "metadata": {},
   "source": [
    "### numpy 공분산 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "73404c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "xbar=np.mean(X)\n",
    "ybar=np.mean(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7cd4b11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simple cov= 8.454545454545455\n"
     ]
    }
   ],
   "source": [
    "covxy=sum( [ (x-xbar)*(y-ybar) for x,y in zip(X,Y) ]) / (len(X)-1)\n",
    "print (\"simple cov=\", covxy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef465df",
   "metadata": {},
   "source": [
    "#### numpy 공분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f3bd5d48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "np cov=\n",
      " [[11.42424242  8.45454545]\n",
      " [ 8.45454545 11.18181818]]\n"
     ]
    }
   ],
   "source": [
    "print (\"np cov=\\n\",np.cov(X,Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9762b00f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.424242424242424\n",
      "11.181818181818182\n"
     ]
    }
   ],
   "source": [
    "print (np.sum( (X-np.mean(X))**2) / (len(X)-1))\n",
    "print (np.sum( (Y-np.mean(Y))**2) / (len(Y)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "03cf5528",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.424242424242424\n"
     ]
    }
   ],
   "source": [
    "print(np.var(X, ddof=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5cb202cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "npcov of X 11.424242424242426\n",
      "npcov of Y 11.181818181818182\n"
     ]
    }
   ],
   "source": [
    "print (\"npcov of X\", np.cov(X)) #sum( [ (x-xbar)**2 for x in X ])/(len(X)-1)\n",
    "print (\"npcov of Y\", np.cov(Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be554d7",
   "metadata": {},
   "source": [
    "### 상관관계"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ae7bef",
   "metadata": {},
   "source": [
    "#### numpy 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bb7aff9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "xbar=np.mean(X)\n",
    "ybar=np.mean(Y)\n",
    "sx=np.sqrt(np.var(X,ddof=1))\n",
    "sy=np.sqrt(np.var(Y,ddof=1))\n",
    "covxy=sum( [ (x-xbar)*(y-ybar) for x,y in zip(X,Y) ]) / (len(X)-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2607c1ac",
   "metadata": {},
   "source": [
    "정의 관계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "433c17a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "corr:  0.7480322896763185\n"
     ]
    }
   ],
   "source": [
    "rxy=covxy/(sx*sy)\n",
    "print (\"corr: \",rxy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b10ffa",
   "metadata": {},
   "source": [
    "#### 깔끔한 상관관계 계산식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "31a514fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7480322896763182\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "numer = sum ( [ x*y for x,y in zip(X,Y) ] ) - len(X) * (xbar * ybar)\n",
    "denom = math.sqrt( (sum( [ x*x for x in X ]) - len(X) * xbar**2) * ( sum( [ y * y for y in Y ]) -len(X) * ybar**2))\n",
    "print(numer/denom)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbe1704",
   "metadata": {},
   "source": [
    "#### zscore를 이용한 상관관계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "202dc55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "분자: 8.97638747611582\n"
     ]
    }
   ],
   "source": [
    "numer =  sum ( [ ( (x-np.mean(X))/np.std(X) ) * ( (y-np.mean(Y))/np.std(Y) ) for x,y in zip(X,Y) ] )\n",
    "\n",
    "print(f\"분자: {numer}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f5fd90dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.97638747611582\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "print(sum (stats.zscore(X) * stats.zscore(Y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fedb1cb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7480322896763183\n"
     ]
    }
   ],
   "source": [
    "print(sum (stats.zscore(X) * stats.zscore(Y))/len(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37b7b74",
   "metadata": {},
   "source": [
    "#### 함수로 상관관계 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fc772ac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pearson(X,Y):\n",
    "    numer=np.sum(np.array(X)*np.array(Y)) - len(X)*np.mean(X)*np.mean(Y)\n",
    "    denom1=np.sum(np.array(X)*np.array(X)) - len(X)*np.power(np.mean(X),2)\n",
    "    denom2=np.sum(np.array(Y)*np.array(Y)) - len(Y)*np.power(np.mean(Y),2)\n",
    "    denom=np.sqrt(denom1*denom2)\n",
    "    #print(numer,denom,numer/denom)\n",
    "    r=1 if denom==0 else numer/denom\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c45f3846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7480322896763182"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearson(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a137fc2",
   "metadata": {},
   "source": [
    "#### scipy로 상관관계\n",
    "- p-value도 계산해줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "637d6cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7480322896763182, 0.005144376414013758)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.pearsonr(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7039c2db",
   "metadata": {},
   "source": [
    "#### 무작위 데이터로 상관관계 구하기\n",
    "평균 = 0, 표준편차 = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bddde8f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "xx=np.random.normal(0,1,100)\n",
    "yy=np.random.normal(0,1,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff612ca",
   "metadata": {},
   "source": [
    "상관관계, 관계 거의 미약, 거의 독립적"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6c4be500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.028825817687361335, 0.7758791858261523)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(xx,yy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6644aa0c",
   "metadata": {},
   "source": [
    "비모수추정 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4d557b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.04783678367836783, pvalue=0.6364812481381432)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.spearmanr(xx,yy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6f99bc",
   "metadata": {},
   "source": [
    "비모수 추정방법 : 순위에 따른 상관관계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e28ccea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KendalltauResult(correlation=0.03191919191919193, pvalue=0.6379675114791763)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kendalltau(xx,yy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02cd58cd",
   "metadata": {},
   "source": [
    "#### spark로 상관관계\n",
    "\n",
    "10개의 무작위 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eb2de0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import rand\n",
    "\n",
    "df = spark.range(0, 10).withColumn('rand1', rand(seed=10)).withColumn('rand2', rand(seed=27))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "34180ee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+------------------+------------------+\n",
      "| id|             rand1|             rand2|\n",
      "+---+------------------+------------------+\n",
      "|  0|0.1709497137955568|0.8894415403143504|\n",
      "|  1|0.8051143958005459|0.9658033539477212|\n",
      "+---+------------------+------------------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6b561093",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13506684062243296\n"
     ]
    }
   ],
   "source": [
    "print (df.stat.corr('rand1', 'rand2'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39cf18f1",
   "metadata": {},
   "source": [
    "#### 상관관계 표\n",
    "여러개의 변수로 구할 때"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "594cc34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "a=np.array([0.1, .32, .2, 0.4, 0.8])\n",
    "b=np.array([.23, .18, .56, .61, .12])\n",
    "c=np.array([.9, .3, .6, .5, .3])\n",
    "d=np.array([.34, .75, .91, .19, .21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d2b00a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.35153114, -0.74736506, -0.48917666],\n",
       "       [-0.35153114,  1.        ,  0.23810227,  0.15958285],\n",
       "       [-0.74736506,  0.23810227,  1.        , -0.03960706],\n",
       "       [-0.48917666,  0.15958285, -0.03960706,  1.        ]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef([a,b,c,d])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7826216c",
   "metadata": {},
   "source": [
    "## Iris\n",
    "- sepal width, sepal length, petal_length, petal_weight (꽃받침 너비, 길이, 꽃잎 길이, 너비)\n",
    "- 꽃 종류 : setosa, virginica, versicolor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aeefe6c",
   "metadata": {},
   "source": [
    "### pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "01c66d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "iris1 = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aff05f5b",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6758b72c",
   "metadata": {},
   "source": [
    " kind =‘scatter',‘kde',‘hist',‘reg'\n",
    " \n",
    " hue = species = 서로 다른 색을 쓰겠다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6e918dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 798.25x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.pairplot(iris1, kind=\"scatter\", hue=\"species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22b1255",
   "metadata": {},
   "source": [
    "### spark로 상관관계 (correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb798c32",
   "metadata": {},
   "source": [
    "##### 데이터\n",
    "웹사이트에 있는 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7799da5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_name = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "iris = pd.read_csv(file_name, names=[\"sepal_length\",\"sepal_width\",\"petal_length\", \"petal_width\",\"species\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "54651ab1",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e172ca3",
   "metadata": {},
   "source": [
    "#### 박스 (boxplot)\n",
    "IQR (Interquartile Rnage) 결측값이 있는지  확인, 동그라미는 outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "33cca1b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j+wci4st1Z+l3EfFmRDwp6SVJT9Wdp1+tL5i9HBGv1Z1luyhpbIttS3pWFEqpIuJ5SZ26c/SxcUkd209JetD2U7b7cjiu0Ev/gVt4UtILEfFt282eIRDcpvWZMgx33KaI+MSV720/EBHP1JlnO4aupG3vl3RI0prtvxiE/ztUF9uHJD0u6Qu2xyXdKeloraH6mO22Ls/qeFHSDyQ9V28iZMBj4QCQGGPSAJAYJQ0AiVHSAJAYJQ0AiVHSAJAYJQ0AiVHSAJDY/wMK8x5kR5p4pQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "iris4 = [ iris.sepal_length, iris.sepal_width, iris.petal_length, iris.petal_width ]\n",
    "plt.boxplot(iris4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf91cd0",
   "metadata": {},
   "source": [
    "#### DataFrame 생성\n",
    "pandas -> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0971f28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sepal_length: double (nullable = true)\n",
      " |-- sepal_width: double (nullable = true)\n",
      " |-- petal_length: double (nullable = true)\n",
      " |-- petal_width: double (nullable = true)\n",
      " |-- species: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "irisDf = spark.createDataFrame(iris)\n",
    "irisDf.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f994ae3e",
   "metadata": {},
   "source": [
    "#### 상관관계\n",
    "변수가 4개면 spark라면 features를 만들어야함 또한 각각을 벡터로 만들어야함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ea40abd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "assembler = VectorAssembler(inputCols=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],\n",
    "                                       outputCol=\"features\")\n",
    "df_vector = assembler.transform(irisDf).select(\"features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae662fa",
   "metadata": {},
   "source": [
    "상관관계 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3eb25116",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.stat import Correlation\n",
    "\n",
    "# get correlation matrix\n",
    "matrix = Correlation.corr(df_vector, \"features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d3b8182",
   "metadata": {},
   "source": [
    "#### 상관관계표"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c392994d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(pearson(features)=DenseMatrix(4, 4, [1.0, -0.1094, 0.8718, 0.818, -0.1094, 1.0, -0.4205, -0.3565, 0.8718, -0.4205, 1.0, 0.9628, 0.818, -0.3565, 0.9628, 1.0], False))]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0f2cb66b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.10936925,  0.87175416,  0.81795363],\n",
       "       [-0.10936925,  1.        , -0.4205161 , -0.35654409],\n",
       "       [ 0.87175416, -0.4205161 ,  1.        ,  0.9627571 ],\n",
       "       [ 0.81795363, -0.35654409,  0.9627571 ,  1.        ]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.collect()[0][0].toArray()"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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   "file_extension": ".py",
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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 "nbformat": 4,
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