数据预处理.ipynb

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   "id": "fc046f30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:16:50.403935Z",
     "start_time": "2023-04-09T03:16:49.068394Z"
    }
   },
   "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>class</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\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\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 读取数据集\n",
    "#  Pandas 库中的 read_csv 函数从指定的 URL 地址中读取 CSV 文件，并将其存储在名为 df 的 Pandas DataFrame 对象中。\n",
    "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\",\n",
    "                 names=['sepal length', 'sepal width', 'petal length', 'petal width', 'class'])\n",
    "\n",
    "df_14 = df\n",
    "df_14"
   ]
  },
  {
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   "id": "dbfba717",
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     "end_time": "2023-04-09T03:01:00.038936Z",
     "start_time": "2023-04-09T03:01:00.011769Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据预处理\n",
    "\n",
    "X= df.iloc[:,:-1].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
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     "start_time": "2023-04-09T03:01:00.041832Z"
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    "collapsed": true
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   "outputs": [
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       "       [-4.16009689e-01, -1.51337539e+00, -3.33612130e-02,\n",
       "        -2.61192967e-01],\n",
       "       [-5.25060772e-02, -8.19166497e-01,  8.03701950e-02,\n",
       "         1.75297293e-03],\n",
       "       [ 1.89829664e-01, -8.19166497e-01,  7.62758643e-01,\n",
       "         5.27644853e-01],\n",
       "       [-5.37177559e-01, -1.24957601e-01,  4.21564419e-01,\n",
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       "       [-4.16009689e-01, -1.28197243e+00,  1.37235899e-01,\n",
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       "       [-4.16009689e-01, -1.05056946e+00,  3.64698715e-01,\n",
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       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
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       "       [ 1.52267624e+00, -1.24957601e-01,  1.21768427e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 5.53333275e-01, -3.56360566e-01,  1.04708716e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 7.95669016e-01, -1.24957601e-01,  1.16081857e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 2.12851559e+00, -1.24957601e-01,  1.61574420e+00,\n",
       "         1.18500970e+00],\n",
       "       [-1.14301691e+00, -1.28197243e+00,  4.21564419e-01,\n",
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       "       [ 1.76501198e+00, -3.56360566e-01,  1.44514709e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 1.03800476e+00, -1.28197243e+00,  1.16081857e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 1.64384411e+00,  1.26346019e+00,  1.33141568e+00,\n",
       "         1.71090158e+00],\n",
       "       [ 7.95669016e-01,  3.37848329e-01,  7.62758643e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 6.74501145e-01, -8.19166497e-01,  8.76490051e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 1.15917263e+00, -1.24957601e-01,  9.90221459e-01,\n",
       "         1.18500970e+00],\n",
       "       [-1.73673948e-01, -1.28197243e+00,  7.05892939e-01,\n",
       "         1.05353673e+00],\n",
       "       [-5.25060772e-02, -5.87763531e-01,  7.62758643e-01,\n",
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       "       [ 6.74501145e-01,  3.37848329e-01,  8.76490051e-01,\n",
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       "       [ 7.95669016e-01, -1.24957601e-01,  9.90221459e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 2.24968346e+00,  1.72626612e+00,  1.67260991e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 2.24968346e+00, -1.05056946e+00,  1.78634131e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 1.89829664e-01, -1.97618132e+00,  7.05892939e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 1.28034050e+00,  3.37848329e-01,  1.10395287e+00,\n",
       "         1.44795564e+00],\n",
       "       [-2.94841818e-01, -5.87763531e-01,  6.49027235e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 2.24968346e+00, -5.87763531e-01,  1.67260991e+00,\n",
       "         1.05353673e+00],\n",
       "       [ 5.53333275e-01, -8.19166497e-01,  6.49027235e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 1.64384411e+00,  3.37848329e-01,  1.27454998e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 4.32165405e-01, -5.87763531e-01,  5.92161531e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 3.10997534e-01, -1.24957601e-01,  6.49027235e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 1.64384411e+00, -1.24957601e-01,  1.16081857e+00,\n",
       "         5.27644853e-01],\n",
       "       [ 1.88617985e+00, -5.87763531e-01,  1.33141568e+00,\n",
       "         9.22063763e-01],\n",
       "       [ 2.49201920e+00,  1.72626612e+00,  1.50201279e+00,\n",
       "         1.05353673e+00],\n",
       "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 5.53333275e-01, -5.87763531e-01,  7.62758643e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 3.10997534e-01, -1.05056946e+00,  1.04708716e+00,\n",
       "         2.64698913e-01],\n",
       "       [ 2.24968346e+00, -1.24957601e-01,  1.33141568e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 5.53333275e-01,  8.00654259e-01,  1.04708716e+00,\n",
       "         1.57942861e+00],\n",
       "       [ 6.74501145e-01,  1.06445364e-01,  9.90221459e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.89829664e-01, -1.24957601e-01,  5.92161531e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.28034050e+00,  1.06445364e-01,  9.33355755e-01,\n",
       "         1.18500970e+00],\n",
       "       [ 1.03800476e+00,  1.06445364e-01,  1.04708716e+00,\n",
       "         1.57942861e+00],\n",
       "       [ 1.28034050e+00,  1.06445364e-01,  7.62758643e-01,\n",
       "         1.44795564e+00],\n",
       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 1.15917263e+00,  3.37848329e-01,  1.21768427e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
       "         1.71090158e+00],\n",
       "       [ 1.03800476e+00, -1.24957601e-01,  8.19624347e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 5.53333275e-01, -1.28197243e+00,  7.05892939e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 7.95669016e-01, -1.24957601e-01,  8.19624347e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 4.32165405e-01,  8.00654259e-01,  9.33355755e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 6.86617933e-02, -1.24957601e-01,  7.62758643e-01,\n",
       "         7.90590793e-01]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_std = sc.fit_transform(X)\n",
    "X_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b74c0353",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:00.649885Z",
     "start_time": "2023-04-09T03:01:00.620851Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>class</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\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\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 处理缺失值\n",
    "df = df.dropna()\n",
    "df\n",
    "# 如果没有指定任何参数，则会删除所有包含缺失值的行。如果你希望只删除包含特定列中缺失值的行，可以指定 subset 参数，例如：\n",
    "# df = df.dropna(subset=['column1', 'column2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ff373b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:00.665394Z",
     "start_time": "2023-04-09T03:01:00.651397Z"
    }
   },
   "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>class</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\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\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 处理异常值\n",
    "# df = df[(df['sepal length'] > 0) & (df['sepal width'] > 0) & (df['petal length'] > 0 ) & (df['petal width'] > 0)]\n",
    "# df = df[(df['sepal length'] > 7) & (df['sepal width'] > 2.1) & (df['petal length'] > 5 ) & (df['petal width'] > 2)]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a63c7ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:00.681394Z",
     "start_time": "2023-04-09T03:01:00.666394Z"
    }
   },
   "outputs": [],
   "source": [
    "# 数据可视化\n",
    "# fig 是一个 Figure 对象，表示整个图形，而 axs 是一个包含 2 行 2 列的 axis 对象的 2 维数组，表示图形网格中的每个子图\n",
    "# fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "# axs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83ca076a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:00.983847Z",
     "start_time": "2023-04-09T03:01:00.682395Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .hist() 是这个对象的一个方法，用于创建一个直方图。\n",
    "# df['sepal length'] 表示 DataFrame 中的 'sepal length' 这一列数据，bins=20 表示将这些数据分成 20 个区间，\n",
    "# 并统计每个区间的频数，生成一个直方图，显示 'sepal length' 这一列数据的分布情况。\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "axs[0, 0].hist(df['sepal length'], bins=20)\n",
    "axs[0, 0].set_title('Sepal Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8980003",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:01.375969Z",
     "start_time": "2023-04-09T03:01:00.985843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 处理缺失值\n",
    "df = df.dropna()\n",
    "\n",
    "# 处理异常值\n",
    "df = df[(df['sepal length'] > 0) & (df['sepal width'] > 0) & (df['petal length'] > 0) & (df['petal width'] > 0)]\n",
    "\n",
    "# 数据可视化\n",
    "# plt.subplots 是 Matplotlib 库中的一个函数，用于创建包含多个子图的图形。\n",
    "# 具体来说，plt.subplots 函数可以指定图形的行数和列数，然后返回一个包含整个图形以及每个子图的 axis 对象的元组。\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "\n",
    "axs[0, 0].hist(df['sepal length'], bins=20)\n",
    "axs[0, 0].set_title('Sepal Length')\n",
    "\n",
    "axs[0, 1].hist(df['sepal width'], bins=20)\n",
    "axs[0, 1].set_title('Sepal Width')\n",
    "\n",
    "axs[1, 0].hist(df['petal length'], bins=20)\n",
    "axs[1, 0].set_title('Petal Length')\n",
    "\n",
    "axs[1, 1].hist(df['petal width'], bins=20)\n",
    "axs[1, 1].set_title('Petal Width')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e415f2c4",
   "metadata": {},
   "source": [
    "# 练习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08727734",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:01.391967Z",
     "start_time": "2023-04-09T03:01:01.378967Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd3b39db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:03.735140Z",
     "start_time": "2023-04-09T03:01:01.393967Z"
    }
   },
   "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>class</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\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\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\", \n",
    "                      names=['sepal length', 'sepal width', 'petal length', 'petal width', 'class'])\n",
    "df_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e34ac0b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:03.750537Z",
     "start_time": "2023-04-09T03:01:03.736504Z"
    }
   },
   "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>class</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\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\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 处理缺失值\n",
    "df_iris = df_iris.dropna()\n",
    "df_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4cdfcfef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:09:58.826361Z",
     "start_time": "2023-04-09T03:09:58.808362Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>class</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>NaN</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>NaN</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>NaN</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>NaN</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>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width  class\n",
       "0             5.1          3.5           1.4          0.2    NaN\n",
       "1             4.9          3.0           1.4          0.2    NaN\n",
       "2             4.7          3.2           1.3          0.2    NaN\n",
       "3             4.6          3.1           1.5          0.2    NaN\n",
       "4             5.0          3.6           1.4          0.2    NaN\n",
       "..            ...          ...           ...          ...    ...\n",
       "145           6.7          3.0           5.2          2.3    NaN\n",
       "146           6.3          2.5           5.0          1.9    NaN\n",
       "147           6.5          3.0           5.2          2.0    NaN\n",
       "148           6.2          3.4           5.4          2.3    NaN\n",
       "149           5.9          3.0           5.1          1.8    NaN\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 处理异常值\n",
    "df_iris = df_iris[(df_iris['sepal length'] > 0)& (df_iris['sepal width'] > 0)& (df_iris['petal length']>0)&(df_iris['petal width'] > 0)]\n",
    "df_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a21402e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:04.063853Z",
     "start_time": "2023-04-09T03:01:03.766908Z"
    }
   },
   "outputs": [
    {
     "data": {
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qAwAAQMDz6DWkpaWl+uCDD3TxxRcrKSlJUVFR6tGjR7kf6wMAAACShwPpwYMHdezYMU2dOlWDBg3Sf//7X11//fUaPny4cnJyyn2Mw+FQUVGRywQAAIDqw6O/ZV9aWipJuu666zRhwgRJUufOnfX5559r9uzZ6tevX5nHZGZmKiMjw5Nl+JzmEz9w+zG7pw72QiX4I14XAP6CfoVA59EzpA0bNlRwcLDatm3rsvzSSy+t8Fv26enpKiwsdE75+fmeLAkAAAA+zqNnSENCQtS9e3ft2LHDZfl3332nZs2alfsYu90uu93uyTIAAADgR9wOpMeOHdPOnTud83l5edqyZYsiIyPVtGlTPfLII7rpppvUt29f9e/fX8uWLdOSJUuUnZ3tyboBAAAQINwOpBs3blT//v2d82lpaZKklJQUZWVl6frrr9fs2bOVmZmpBx98UJdccon+85//qHfv3p6rGgAAAAHD7WtIExISZFlWmSkrK8s55q677lJubq5+/fVXbdmyRdddd50nawYAv5WZmanu3bsrLCxMUVFRGjZsWJnLnBISEmSz2Vym+++/31DFAOB9/JY9AFShnJwcpaamat26dVq+fLlOnTqlgQMHqri42GXcPffco/379zunP/7YCAAEGo9+qQkAcHbLli1zmc/KylJUVJQ2bdqkvn37OpfXqVNHMTExVV0eABjBGVIAMKiwsFCSFBkZ6bL8X//6lxo2bKj27dsrPT1dx48fN1EeAFQJzpACgCGlpaUaP368evXqpfbt2zuX33rrrWrWrJliY2O1detWPfbYY9qxY4cWLFhQ4bYcDoccDodznl+9A+BPCKQAYEhqaqq2b9+uNWvWuCy/9957nf/doUMHNW7cWAMGDNCuXbvUsmXLcrdVHX71DkDg4iN7ADBg7NixWrp0qVauXKkmTZqcdWyPHj0kyeUe0GfiV+8A+DPOkAJAFbIsS+PGjdPChQuVnZ2t+Pj4cz5my5YtkqTGjRtXOIZfvQPgzwikAFCFUlNTNW/ePL3//vsKCwtTQUGBJCkiIkK1a9fWrl27NG/ePF1zzTVq0KCBtm7dqgkTJqhv377q2LGj4eoBwDsIpABQhWbNmiXp95vf/9GcOXM0atQohYSE6JNPPtGMGTNUXFysuLg4jRgxQk888YSBagGgahBIAaAKWZZ11vVxcXHKycmpomoAwDfwpSYAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRbgfSVatWaciQIYqNjZXNZtOiRYsqHHv//ffLZrNpxowZF1AiAAAAApnbgbS4uFidOnXSzJkzzzpu4cKFWrdunWJjYytdHAAAAAJfsLsPSE5OVnJy8lnH7N27V+PGjdPHH3+swYMHV7o4AAAABD6PX0NaWlqqO+64Q4888ojatWvn6c0DAAAgwLh9hvRcpk2bpuDgYD344IPnNd7hcMjhcDjni4qKPF0SAAAAfJhHA+mmTZv017/+VZs3b5bNZjuvx2RmZiojI8OTZcCLmk/8wO3H7J7KZRsAAKBiHv3IfvXq1Tp48KCaNm2q4OBgBQcH64cfftCf/vQnNW/evNzHpKenq7Cw0Dnl5+d7siQAAAD4OI+eIb3jjjuUmJjosiwpKUl33HGHRo8eXe5j7Ha77Ha7J8sAAACAH3E7kB47dkw7d+50zufl5WnLli2KjIxU06ZN1aBBA5fxNWvWVExMjC655JILrxYAAAABx+1AunHjRvXv3985n5aWJklKSUlRVlaWxwoDAABA9eB2IE1ISJBlWec9fvfu3e7uAgAAANUIv2UPAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACj3P4tewSO5hM/MF0CAAAAZ0gBAABgFoEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBoAplZmaqe/fuCgsLU1RUlIYNG6YdO3a4jDlx4oRSU1PVoEED1a1bVyNGjNCBAwcMVQwA3kcgBYAqlJOTo9TUVK1bt07Lly/XqVOnNHDgQBUXFzvHTJgwQUuWLNG7776rnJwc7du3T8OHDzdYNQB4FzfGB4AqtGzZMpf5rKwsRUVFadOmTerbt68KCwv1j3/8Q/PmzdNVV10lSZozZ44uvfRSrVu3TldccYWJsgHAqzhDCgAGFRYWSpIiIyMlSZs2bdKpU6eUmJjoHNOmTRs1bdpUa9eurXA7DodDRUVFLhMA+AsCKQAYUlpaqvHjx6tXr15q3769JKmgoEAhISGqV6+ey9jo6GgVFBRUuK3MzExFREQ4p7i4OG+WDgAe5XYgXbVqlYYMGaLY2FjZbDYtWrTIue7UqVN67LHH1KFDB4WGhio2NlZ33nmn9u3b58maASAgpKamavv27Zo/f/4Fbys9PV2FhYXOKT8/3wMVAkDVcDuQFhcXq1OnTpo5c2aZdcePH9fmzZs1adIkbd68WQsWLNCOHTs0dOhQjxQLAIFi7NixWrp0qVauXKkmTZo4l8fExOjkyZM6cuSIy/gDBw4oJiamwu3Z7XaFh4e7TADgL9z+UlNycrKSk5PLXRcREaHly5e7LHvllVd0+eWXa8+ePWratGnlqgSAAGFZlsaNG6eFCxcqOztb8fHxLuu7du2qmjVrasWKFRoxYoQkaceOHdqzZ4969uxpomQA8Dqvf8u+sLBQNputzPVQpzkcDjkcDuc8F+IDCGSpqamaN2+e3n//fYWFhTmvC42IiFDt2rUVERGhu+++W2lpaYqMjFR4eLjGjRunnj178g17AAHLq19qOnHihB577DHdcsstFX58xIX4AKqTWbNmqbCwUAkJCWrcuLFzeuedd5xj/vKXv+jaa6/ViBEj1LdvX8XExGjBggUGqwYA7/LaGdJTp05p5MiRsixLs2bNqnBcenq60tLSnPNFRUWEUgABy7Ksc46pVauWZs6cWe61+gAQiLwSSE+H0R9++EGffvrpWS+ut9vtstvt3igDAAAAfsDjgfR0GM3NzdXKlSvVoEEDT+8CAAAAAcTtQHrs2DHt3LnTOZ+Xl6ctW7YoMjJSjRs31g033KDNmzdr6dKlKikpcV6wHxkZqZCQEM9VDgAAgIDgdiDduHGj+vfv75w/ff1nSkqKpkyZosWLF0uSOnfu7PK4lStXKiEhofKVAgAAr2o+8QO3xu+eOthLlaC6cTuQJiQknPWi/PO5YB8AAAA4jd+yBwAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAY5fZPhwJwj7u/DS3x+9AAgOqFM6QAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADDK7UC6atUqDRkyRLGxsbLZbFq0aJHLesuy9OSTT6px48aqXbu2EhMTlZub66l6AQAAEGDcDqTFxcXq1KmTZs6cWe766dOn6+WXX9bs2bO1fv16hYaGKikpSSdOnLjgYgEAABB4gt19QHJyspKTk8tdZ1mWZsyYoSeeeELXXXedJGnu3LmKjo7WokWLdPPNN19YtQAAAAg4Hr2GNC8vTwUFBUpMTHQui4iIUI8ePbR27VpP7goAAAABwu0zpGdTUFAgSYqOjnZZHh0d7Vx3JofDIYfD4ZwvKiryZEkAAADwcR4NpJWRmZmpjIwM02XAi5pP/MDtx+yeOtjr+wAAAL7Box/Zx8TESJIOHDjgsvzAgQPOdWdKT09XYWGhc8rPz/dkSQAAAPBxHg2k8fHxiomJ0YoVK5zLioqKtH79evXs2bPcx9jtdoWHh7tMAAAAqD7c/sj+2LFj2rlzp3M+Ly9PW7ZsUWRkpJo2barx48frmWeeUevWrRUfH69JkyYpNjZWw4YN82TdAAAACBBuB9KNGzeqf//+zvm0tDRJUkpKirKysvToo4+quLhY9957r44cOaLevXtr2bJlqlWrlueqBgAAQMBwO5AmJCTIsqwK19tsNj311FN66qmnLqgwAAAAVA/8lj0AAACMIpACAADAKAIpAFSxVatWaciQIYqNjZXNZtOiRYtc1o8aNUo2m81lGjRokJliAaAKEEgBoIoVFxerU6dOmjlzZoVjBg0apP379zunt99+uworBICqZfyXmgCguklOTlZycvJZx9jt9gp/UAQAAg1nSAHAB2VnZysqKkqXXHKJxowZo0OHDp11vMPhUFFRkcsEAP6CQAoAPmbQoEGaO3euVqxYoWnTpiknJ0fJyckqKSmp8DGZmZmKiIhwTnFxcVVYMQBcGD6yBwAfc/PNNzv/u0OHDurYsaNatmyp7OxsDRgwoNzHpKenO3+oRPr9Z5sJpQD8BWdIAcDHtWjRQg0bNnT52eYz2e12hYeHu0wA4C8IpADg43788UcdOnRIjRs3Nl0KAHgFH9n7qOYTP3Br/O6pg71UCQBPO3bsmMvZzry8PG3ZskWRkZGKjIxURkaGRowYoZiYGO3atUuPPvqoWrVqpaSkJINVA4D3EEgBoIpt3LhR/fv3d86fvvYzJSVFs2bN0tatW/Xmm2/qyJEjio2N1cCBA/X000/LbrebKhkAvIpACgBVLCEhQZZlVbj+448/rsJqAMA8riEFAACAUQRSAAAAGEUgBQAAgFEEUgAAABjFl5oAAPj/uXvLvcrgNn1AWZwhBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAY5fFAWlJSokmTJik+Pl61a9dWy5Yt9fTTT5/1V0kAAABQfXn8W/bTpk3TrFmz9Oabb6pdu3bauHGjRo8erYiICD344IOe3h0AAAD8nMcD6eeff67rrrtOgwf/fluL5s2b6+2339YXX3zh6V0BAAAgAHj8I/srr7xSK1as0HfffSdJ+vLLL7VmzRolJyd7elcAAAAIAB4/Qzpx4kQVFRWpTZs2CgoKUklJiZ599lnddttt5Y53OBxyOBzO+aKiIk+XBAAAAB/m8TOk//73v/Wvf/1L8+bN0+bNm/Xmm2/qhRde0Jtvvlnu+MzMTEVERDinuLg4T5cEAAAAH+bxQPrII49o4sSJuvnmm9WhQwfdcccdmjBhgjIzM8sdn56ersLCQueUn5/v6ZIAAADgwzz+kf3x48dVo4Zrzg0KClJpaWm54+12u+x2u6fLAAAAgJ/weCAdMmSInn32WTVt2lTt2rXT//73P7300ku66667PL0rAAAABACPB9K//e1vmjRpkh544AEdPHhQsbGxuu+++/Tkk096elcAAAAIAB4PpGFhYZoxY4ZmzJjh6U0DAAAgAPFb9gAAADCKQAoAAACjCKQAAAAwikAKAAAAozz+pSYAAGBe84kf+OQ+dk8d7IVK4O84QwoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCK2z65qSpuowHfxesPAIDncYYUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFACq2KpVqzRkyBDFxsbKZrNp0aJFLusty9KTTz6pxo0bq3bt2kpMTFRubq6ZYgGgChBIAaCKFRcXq1OnTpo5c2a566dPn66XX35Zs2fP1vr16xUaGqqkpCSdOHGiiisFgKrBT4cCQBVLTk5WcnJyuessy9KMGTP0xBNP6LrrrpMkzZ07V9HR0Vq0aJFuvvnmqiwVAKqEV86Q7t27V7fffrsaNGig2rVrq0OHDtq4caM3dgUAASUvL08FBQVKTEx0LouIiFCPHj20du1ag5UBgPd4/Azp4cOH1atXL/Xv318fffSRGjVqpNzcXNWvX9/TuwKAgFNQUCBJio6OdlkeHR3tXFceh8Mhh8PhnC8qKvJOgQDgBR4PpNOmTVNcXJzmzJnjXBYfH+/p3QAA/iAzM1MZGRmmy8B5aD7xA9MlAD7H4x/ZL168WN26ddONN96oqKgodenSRW+88UaF4x0Oh4qKilwmAKiuYmJiJEkHDhxwWX7gwAHnuvKkp6ersLDQOeXn53u1TgDwJI+fIf3+++81a9YspaWl6c9//rM2bNigBx98UCEhIUpJSSkznnf1KA9nEFBdxcfHKyYmRitWrFDnzp0l/f7x+/r16zVmzJgKH2e322W326uoSgDwLI8H0tLSUnXr1k3PPfecJKlLly7avn27Zs+eXW4gTU9PV1pamnO+qKhIcXFxni4LAHzGsWPHtHPnTud8Xl6etmzZosjISDVt2lTjx4/XM888o9atWys+Pl6TJk1SbGyshg0bZq5oAPAijwfSxo0bq23bti7LLr30Uv3nP/8pdzzv6gFUNxs3blT//v2d86fflKekpCgrK0uPPvqoiouLde+99+rIkSPq3bu3li1bplq1apkqGQC8yuOBtFevXtqxY4fLsu+++07NmjXz9K4AwC8lJCTIsqwK19tsNj311FN66qmnqrAqADDH419qmjBhgtatW6fnnntOO3fu1Lx58/T6668rNTXV07sCAABAAPB4IO3evbsWLlyot99+W+3bt9fTTz+tGTNm6LbbbvP0rgAAABAAvPLToddee62uvfZab2waAAAAAcYrPx0KAAAAnC+vnCEFAAAwqTL3s949dbAXKsH54AwpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwKhqfWP8ytw011cF0rEgsHBzagDAuVTrQAoACFy8UQf8Bx/ZAwAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACM8nognTp1qmw2m8aPH+/tXQEAAMAPeTWQbtiwQa+99po6duzozd0AAADAj3ktkB47dky33Xab3njjDdWvX99buwEAAICf81ogTU1N1eDBg5WYmOitXQAAACAABHtjo/Pnz9fmzZu1YcOGc451OBxyOBzO+aKiIm+UBAAAAB/l8UCan5+vhx56SMuXL1etWrXOOT4zM1MZGRkXvN/mEz+44G0AvsLdv+fdUwd7fR+V3Q8AAOfi8Y/sN23apIMHD+qyyy5TcHCwgoODlZOTo5dfflnBwcEqKSlxGZ+enq7CwkLnlJ+f7+mSAAAA4MM8foZ0wIAB2rZtm8uy0aNHq02bNnrssccUFBTkss5ut8tut3u6DAAAAPgJjwfSsLAwtW/f3mVZaGioGjRoUGY5AAAAwC81AQAAwCivfMv+TNnZ2VWxGwAA4OP4QiXKwxlSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAB8zZcoU2Ww2l6lNmzamywIAr6mS2z4BANzTrl07ffLJJ8754GDaNYDARYcDAB8UHBysmJgY02UAQJXgI3sA8EG5ubmKjY1VixYtdNttt2nPnj1nHe9wOFRUVOQyAYC/IJACgI/p0aOHsrKytGzZMs2aNUt5eXnq06ePjh49WuFjMjMzFRER4Zzi4uKqsGIAuDAEUgDwMcnJybrxxhvVsWNHJSUl6cMPP9SRI0f073//u8LHpKenq7Cw0Dnl5+dXYcUAcGG4hhQAfFy9evV08cUXa+fOnRWOsdvtstvtVVgVAHgOZ0gBwMcdO3ZMu3btUuPGjU2XAgBeQSAFAB/z8MMPKycnR7t379bnn3+u66+/XkFBQbrllltMlwYAXsFH9gDgY3788UfdcsstOnTokBo1aqTevXtr3bp1atSokenSAMArCKQA4GPmz59vugQAqFJ8ZA8AAACjCKQAAAAwikAKAAAAo7iGFABQpZpP/MDtx+yeOtgLlQDwFZwhBQAAgFEEUgAAABjl8UCamZmp7t27KywsTFFRURo2bJh27Njh6d0AAAAgQHg8kObk5Cg1NVXr1q3T8uXLderUKQ0cOFDFxcWe3hUAAAACgMe/1LRs2TKX+aysLEVFRWnTpk3q27evp3cHAAAAP+f1a0gLCwslSZGRkd7eFQAAAPyQV2/7VFpaqvHjx6tXr15q3759uWMcDoccDodzvqioyJslAQAAwMd4NZCmpqZq+/btWrNmTYVjMjMzlZGR4c0ygIBXmfs6AgDgK7z2kf3YsWO1dOlSrVy5Uk2aNKlwXHp6ugoLC51Tfn6+t0oCAACAD/L4GVLLsjRu3DgtXLhQ2dnZio+PP+t4u90uu93u6TIAAADgJzweSFNTUzVv3jy9//77CgsLU0FBgSQpIiJCtWvX9vTuAAAGVdXlIlyWUr3x+gc+j39kP2vWLBUWFiohIUGNGzd2Tu+8846ndwUAAIAA4JWP7AEAAIDzxW/ZAwAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCiP34cUAADAH7n7i1C7pw72UiXVD2dIAQAAYBSBFAAAAEYRSAEAAGAU15ACOG/uXl9VVfvhOi4A8G+cIQUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFF8yx4AIKnq7qIABIpA+zdj8o4lnCEFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRXgukM2fOVPPmzVWrVi316NFDX3zxhbd2BQABiT4KoLrwSiB95513lJaWpsmTJ2vz5s3q1KmTkpKSdPDgQW/sDgACDn0UQHXilUD60ksv6Z577tHo0aPVtm1bzZ49W3Xq1NE///lPb+wOAAIOfRRAdeLxG+OfPHlSmzZtUnp6unNZjRo1lJiYqLVr15YZ73A45HA4nPOFhYWSpKKiIrf2W+o4XsmKAfg7d/vF6fGWZXmjnAvmbh+VPNNL6aNA9eZOv/B0H/V4IP35559VUlKi6Ohol+XR0dH69ttvy4zPzMxURkZGmeVxcXGeLg1AgIqYUbnHHT16VBERER6txRPc7aMSvRTAhatML/VUHzX+06Hp6elKS0tzzpeWluqXX35RgwYNZLPZDFZW9YqKihQXF6f8/HyFh4ebLsen8NxUjOfm7Mp7fizL0tGjRxUbG2u4Os+50F4aqH9HgXhcgXhMUmAeVyAf0549e2Sz2TzWRz0eSBs2bKigoCAdOHDAZfmBAwcUExNTZrzdbpfdbndZVq9ePU+X5VfCw8MD5g/X03huKsZzc3ZnPj++eGb0NHf7qOS5Xhqof0eBeFyBeExSYB5XIB5TRESER4/J419qCgkJUdeuXbVixQrnstLSUq1YsUI9e/b09O4AIODQRwFUN175yD4tLU0pKSnq1q2bLr/8cs2YMUPFxcUaPXq0N3YHAAGHPgqgOvFKIL3pppv0008/6cknn1RBQYE6d+6sZcuWlblAH67sdrsmT55c5mM38NycDc/N2fnr81PVfdRfn6dzCcTjCsRjkgLzuDim82ezfPW+JwAAAKgW+C17AAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIfMGXKFNlsNpepTZs2psvyGXv37tXtt9+uBg0aqHbt2urQoYM2btxouizjmjdvXubvxmazKTU11XRpxpWUlGjSpEmKj49X7dq11bJlSz399NM++9v1VSEzM1Pdu3dXWFiYoqKiNGzYMO3YseOcj3v33XfVpk0b1apVSx06dNCHH35YBdWen8ocU1ZWVpl/M7Vq1aqiis9t1qxZ6tixo/NG6j179tRHH3101sf48mt0mrvH5euvU3mmTp0qm82m8ePHn3WcP7xep53PMXnqtSKQ+oh27dpp//79zmnNmjWmS/IJhw8fVq9evVSzZk199NFH+vrrr/Xiiy+qfv36pkszbsOGDS5/M8uXL5ck3XjjjYYrM2/atGmaNWuWXnnlFX3zzTeaNm2apk+frr/97W+mSzMmJydHqampWrdunZYvX65Tp05p4MCBKi4urvAxn3/+uW655Rbdfffd+t///qdhw4Zp2LBh2r59exVWXrHKHJP0+6/m/PHfzg8//FBFFZ9bkyZNNHXqVG3atEkbN27UVVddpeuuu05fffVVueN9/TU6zd3jknz7dTrThg0b9Nprr6ljx45nHecvr5d0/sckeei1smDc5MmTrU6dOpkuwyc99thjVu/evU2X4Rceeughq2XLllZpaanpUowbPHiwddddd7ksGz58uHXbbbcZqsj3HDx40JJk5eTkVDhm5MiR1uDBg12W9ejRw7rvvvu8XV6lnM8xzZkzx4qIiKi6ojygfv361t///vdy1/nba/RHZzsuf3qdjh49arVu3dpavny51a9fP+uhhx6qcKy/vF7uHJOnXivOkPqI3NxcxcbGqkWLFrrtttu0Z88e0yX5hMWLF6tbt2668cYbFRUVpS5duuiNN94wXZbPOXnypN566y3dddddstlspssx7sorr9SKFSv03XffSZK+/PJLrVmzRsnJyYYr8x2FhYWSpMjIyArHrF27VomJiS7LkpKStHbtWq/WVlnnc0ySdOzYMTVr1kxxcXHnPEtnUklJiebPn6/i4uIKfzLW314j6fyOS/Kf1yk1NVWDBw8u8zqUx19eL3eOSfLMa+WVX2qCe3r06KGsrCxdcskl2r9/vzIyMtSnTx9t375dYWFhpssz6vvvv9esWbOUlpamP//5z9qwYYMefPBBhYSEKCUlxXR5PmPRokU6cuSIRo0aZboUnzBx4kQVFRWpTZs2CgoKUklJiZ599lnddtttpkvzCaWlpRo/frx69eql9u3bVziuoKCgzC9DRUdHq6CgwNsluu18j+mSSy7RP//5T3Xs2FGFhYV64YUXdOWVV+qrr75SkyZNqrDiim3btk09e/bUiRMnVLduXS1cuFBt27Ytd6w/vUbuHJc/vE6SNH/+fG3evFkbNmw4r/H+8Hq5e0wee60u+BwrPO7w4cNWeHh4hR9lVCc1a9a0evbs6bJs3Lhx1hVXXGGoIt80cOBA69prrzVdhs94++23rSZNmlhvv/22tXXrVmvu3LlWZGSklZWVZbo0n3D//fdbzZo1s/Lz8886rmbNmta8efNcls2cOdOKioryZnmVcr7HdKaTJ09aLVu2tJ544gkvVeY+h8Nh5ebmWhs3brQmTpxoNWzY0Prqq6/KHetPr5E7x3UmX3yd9uzZY0VFRVlffvmlc9m5Pt729derMsd0psq+Vpwh9UH16tXTxRdfrJ07d5ouxbjGjRuXeQd96aWX6j//+Y+hinzPDz/8oE8++UQLFiwwXYrPeOSRRzRx4kTdfPPNkqQOHTrohx9+UGZmZrU/sz527FgtXbpUq1atOufZi5iYGB04cMBl2YEDBxQTE+PNEt3mzjGdqWbNmurSpYtP9duQkBC1atVKktS1a1dt2LBBf/3rX/Xaa6+VGesvr5Hk3nGdyRdfp02bNungwYO67LLLnMtKSkq0atUqvfLKK3I4HAoKCnJ5jK+/XpU5pjNV9rXiGlIfdOzYMe3atUuNGzc2XYpxvXr1KnMbl++++07NmjUzVJHvmTNnjqKiojR48GDTpfiM48ePq0YN1/YWFBSk0tJSQxWZZ1mWxo4dq4ULF+rTTz9VfHz8OR/Ts2dPrVixwmXZ8uXLz3rdX1WqzDGdqaSkRNu2bfPpfltaWiqHw1HuOl9/jc7mbMd1Jl98nQYMGKBt27Zpy5Ytzqlbt2667bbbtGXLlnKDm6+/XpU5pjNV+rVy63wqvOJPf/qTlZ2dbeXl5VmfffaZlZiYaDVs2NA6ePCg6dKM++KLL6zg4GDr2WeftXJzc61//etfVp06day33nrLdGk+oaSkxGratKn12GOPmS7Fp6SkpFgXXXSRtXTpUisvL89asGCB1bBhQ+vRRx81XZoxY8aMsSIiIqzs7Gxr//79zun48ePOMXfccYc1ceJE5/xnn31mBQcHWy+88IL1zTffWJMnT7Zq1qxpbdu2zcQhlFGZY8rIyLA+/vhja9euXdamTZusm2++2apVq9Z5f3TsbRMnTrRycnKsvLw8a+vWrdbEiRMtm81m/fe//7Usy/9eo9PcPS5ff50qcubH2/76ev3RuY7JU68VgdQH3HTTTVbjxo2tkJAQ66KLLrJuuukma+fOnabL8hlLliyx2rdvb9ntdqtNmzbW66+/brokn/Hxxx9bkqwdO3aYLsWnFBUVWQ899JDVtGlTq1atWlaLFi2sxx9/3HI4HKZLM0ZSudOcOXOcY/r162elpKS4PO7f//63dfHFF1shISFWu3btrA8++KBqCz+LyhzT+PHjraZNm1ohISFWdHS0dc0111ibN2+u+uIrcNddd1nNmjWzQkJCrEaNGlkDBgxwhjbL8r/X6DR3j8vXX6eKnBne/PX1+qNzHZOnXiubZVXjny4BAACAcVxDCgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACp9ls9k0ZcqUs47ZvXu3bDabsrKyqqSmC9G8eXNde+21pssAUM3QS+EPCKTwinnz5mnGjBmmy6hyX3/9taZMmaLdu3ebLgVAAKCX7jZdCqoIgRReUZ2baEZGBk0UgEfQS3ebLgVVhEAKAAAAowik0JQpU2Sz2fTtt99q5MiRCg8PV4MGDfTQQw/pxIkTZca/9dZb6tq1q2rXrq3IyEjdfPPNys/Pd65PSEjQBx98oB9++EE2m002m03NmzeXJJ08eVJPPvmkunbtqoiICIWGhqpPnz5auXKlR4/p22+/1Q033KDIyEjVqlVL3bp10+LFi13GZGVlyWaz6bPPPlNaWpoaNWqk0NBQXX/99frpp59cxpaWlmrKlCmKjY1VnTp11L9/f3399ddq3ry5Ro0a5dzejTfeKEnq37+/89izs7NdtrVmzRpdfvnlqlWrllq0aKG5c+d69NgBmEEvpZei8gikcBo5cqROnDihzMxMXXPNNXr55Zd17733uox59tlndeedd6p169Z66aWXNH78eK1YsUJ9+/bVkSNHJEmPP/64OnfurIYNG+r//u//9H//93/Oj5yKior097//XQkJCZo2bZqmTJmin376SUlJSdqyZYtHjuOrr77SFVdcoW+++UYTJ07Uiy++qNDQUA0bNkwLFy4sM37cuHH68ssvNXnyZI0ZM0ZLlizR2LFjXcakp6crIyND3bp10/PPP6/WrVsrKSlJxcXFzjF9+/bVgw8+KEn685//7Dz2Sy+91Dlm586duuGGG3T11VfrxRdfVP369TVq1Ch99dVXHjl2AObRS+mlqAQL1d7kyZMtSdbQoUNdlj/wwAOWJOvLL7+0LMuydu/ebQUFBVnPPvusy7ht27ZZwcHBLssHDx5sNWvWrMy+fvvtN8vhcLgsO3z4sBUdHW3dddddLsslWZMnTz5r7Xl5eZYka86cOc5lAwYMsDp06GCdOHHCuay0tNS68sorrdatWzuXzZkzx5JkJSYmWqWlpc7lEyZMsIKCgqwjR45YlmVZBQUFVnBwsDVs2DCXfU+ZMsWSZKWkpDiXvfvuu5Yka+XKlWVqbdasmSXJWrVqlXPZwYMHLbvdbv3pT38663EC8H30UnopKo8zpHBKTU11mR83bpwk6cMPP5QkLViwQKWlpRo5cqR+/vln5xQTE6PWrVuf10dFQUFBCgkJkfT7Rze//PKLfvvtN3Xr1k2bN2++4GP45Zdf9Omnn2rkyJE6evSos8ZDhw4pKSlJubm52rt3r8tj7r33XtlsNud8nz59VFJSoh9++EGStGLFCv3222964IEHXB53+vlxR9u2bdWnTx/nfKNGjXTJJZfo+++/d3tbAHwTvfR39FK4I9h0AfAdrVu3dplv2bKlatSo4fyWY25urizLKjPutJo1a57Xft588029+OKL+vbbb3Xq1Cnn8vj4+MoV/gc7d+6UZVmaNGmSJk2aVO6YgwcP6qKLLnLON23a1GV9/fr1JUmHDx+WJGczbdWqlcu4yMhI59jzdea+Tu/v9L4A+D966e/opXAHgRQV+uM7Xen3d+E2m00fffSRgoKCyoyvW7fuObf51ltvadSoURo2bJgeeeQRRUVFKSgoSJmZmdq1a9cF11xaWipJevjhh5WUlFTumDObYXnHIkmWZV1wPWeqyn0B8A30Unopzo1ACqfc3FyXd9Y7d+5UaWmp81udLVu2lGVZio+P18UXX3zWbZ3ZgE9777331KJFCy1YsMBlzOTJky/8ACS1aNFC0u9nGBITEz2yzWbNmkn6/fn44/Nz6NChMu/GKzpuANUHvbR89FKcDdeQwmnmzJku83/7298kScnJyZKk4cOHKygoSBkZGWXehVqWpUOHDjnnQ0NDVVhYWGYfp9/V/vHx69ev19q1az1yDFFRUUpISNBrr72m/fv3l1l/5i1IzseAAQMUHBysWbNmuSx/5ZVXyowNDQ2VJOe3ZAFUP/TS8tFLcTacIYVTXl6ehg4dqkGDBmnt2rV66623dOutt6pTp06Sfn9X/8wzzyg9PV27d+/WsGHDFBYWpry8PC1cuFD33nuvHn74YUlS165d9c477ygtLU3du3dX3bp1NWTIEF177bVasGCBrr/+eg0ePFh5eXmaPXu22rZtq2PHjnnkOGbOnKnevXurQ4cOuueee9SiRQsdOHBAa9eu1Y8//qgvv/zSre1FR0froYce0osvvuh8fr788kt99NFHatiwocs7+c6dOysoKEjTpk1TYWGh7Ha7rrrqKkVFRXnk2AD4Pnpp+eilOKuq/2I/fM3pW5V8/fXX1g033GCFhYVZ9evXt8aOHWv9+uuvZcb/5z//sXr37m2FhoZaoaGhVps2bazU1FRrx44dzjHHjh2zbr31VqtevXqWJOdtS0pLS63nnnvOatasmWW3260uXbpYS5cutVJSUsrc2kSVvFWJZVnWrl27rDvvvNOKiYmxatasaV100UXWtddea7333nvOMadvVbJhwwaXx65cubLM7UZ+++03a9KkSVZMTIxVu3Zt66qrrrK++eYbq0GDBtb999/v8vg33njDatGihRUUFOSynWbNmlmDBw8ucwz9+vWz+vXrd9bjBOD76KX0UlSezbK4Ari6mzJlijIyMvTTTz+pYcOGpsvxG0eOHFH9+vX1zDPP6PHHHzddDgDD6KWVQy+FxDWkwHn59ddfyyw7/YspCQkJVVsMAPgpeikqwjWkwHl45513lJWVpWuuuUZ169bVmjVr9Pbbb2vgwIHq1auX6fIAwC/QS1ERAilwHjp27Kjg4GBNnz5dRUVFzovzn3nmGdOlAYDfoJeiIlxDCgAAAKO4hhQAAABGEUgBAABglM9dQ1paWqp9+/YpLCyMnw4D4BWWZeno0aOKjY1VjRqB+b6cXgrAmzzdR30ukO7bt09xcXGmywBQDeTn56tJkyamy/AKeimAquCpPupzgTQsLEzS7wcYHh5uuBoAgaioqEhxcXHOfhOI6KUAvMnTfdTnAunpj5bCw8NpogC8KpA/yqaXAqgKnuqjgXnxFAAAAPwGgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYJTP/ZZ9VWo+8QO3H7N76mAvVAIA/ok+CsATOEMKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwyq1AOmvWLHXs2FHh4eEKDw9Xz5499dFHHznXnzhxQqmpqWrQoIHq1q2rESNG6MCBAx4vGgD8FX0UAMpyK5A2adJEU6dO1aZNm7Rx40ZdddVVuu666/TVV19JkiZMmKAlS5bo3XffVU5Ojvbt26fhw4d7pXAA8Ef0UQAoy2ZZlnUhG4iMjNTzzz+vG264QY0aNdK8efN0ww03SJK+/fZbXXrppVq7dq2uuOKK89peUVGRIiIiVFhYqPDw8Asp7ZyaT/zA7cfsnjrYC5UAqEpV2WfOh6f7qFR1x0gfBaonT/eYSl9DWlJSovnz56u4uFg9e/bUpk2bdOrUKSUmJjrHtGnTRk2bNtXatWsvuFAACDT0UQD4XbC7D9i2bZt69uypEydOqG7dulq4cKHatm2rLVu2KCQkRPXq1XMZHx0drYKCggq353A45HA4nPNFRUXulgQAfsXTfVSilwLwb26fIb3kkku0ZcsWrV+/XmPGjFFKSoq+/vrrSheQmZmpiIgI5xQXF1fpbQGAP/B0H5XopQD8m9uBNCQkRK1atVLXrl2VmZmpTp066a9//atiYmJ08uRJHTlyxGX8gQMHFBMTU+H20tPTVVhY6Jzy8/PdPggA8Cee7qMSvRSAf7vg+5CWlpbK4XCoa9euqlmzplasWOFct2PHDu3Zs0c9e/as8PF2u915+5PTEwBUJxfaRyV6KQD/5tY1pOnp6UpOTlbTpk119OhRzZs3T9nZ2fr4448VERGhu+++W2lpaYqMjFR4eLjGjRunnj17uvXNUAAIZPRRACjLrUB68OBB3Xnnndq/f78iIiLUsWNHffzxx7r66qslSX/5y19Uo0YNjRgxQg6HQ0lJSXr11Ve9UjgA+CP6KACUdcH3IfU07kMKwNt87T6k3sB9SAF4k8/chxQAAADwBAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMciuQZmZmqnv37goLC1NUVJSGDRumHTt2uIxJSEiQzWZzme6//36PFg0A/oo+CgBluRVIc3JylJqaqnXr1mn58uU6deqUBg4cqOLiYpdx99xzj/bv3++cpk+f7tGiAcBf0UcBoKxgdwYvW7bMZT4rK0tRUVHatGmT+vbt61xep04dxcTEeKZCAAgg9FEAKOuCriEtLCyUJEVGRros/9e//qWGDRuqffv2Sk9P1/HjxyvchsPhUFFRkcsEANWFJ/qoRC8F4N/cOkP6R6WlpRo/frx69eql9u3bO5ffeuutatasmWJjY7V161Y99thj2rFjhxYsWFDudjIzM5WRkVHZMgDAb3mqj0r0UgD+zWZZllWZB44ZM0YfffSR1qxZoyZNmlQ47tNPP9WAAQO0c+dOtWzZssx6h8Mhh8PhnC8qKlJcXJwKCwsVHh5emdLOW/OJH7j9mN1TB3uhEgBVqaioSBEREVXSZ87GU31UMtdL6aNA9eTpPlqpM6Rjx47V0qVLtWrVqrM2UUnq0aOHJFXYSO12u+x2e2XKAAC/5ck+KtFLAfg3twKpZVkaN26cFi5cqOzsbMXHx5/zMVu2bJEkNW7cuFIFAkAgoY8CQFluBdLU1FTNmzdP77//vsLCwlRQUCBJioiIUO3atbVr1y7NmzdP11xzjRo0aKCtW7dqwoQJ6tu3rzp27OiVAwAAf0IfBYCy3Aqks2bNkvT7TZv/aM6cORo1apRCQkL0ySefaMaMGSouLlZcXJxGjBihJ554wmMFA4A/o48CQFluf2R/NnFxccrJybmgggAgkNFHAaAsfsseAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGuRVIMzMz1b17d4WFhSkqKkrDhg3Tjh07XMacOHFCqampatCggerWrasRI0bowIEDHi0aAPwVfRQAynIrkObk5Cg1NVXr1q3T8uXLderUKQ0cOFDFxcXOMRMmTNCSJUv07rvvKicnR/v27dPw4cM9XjgA+CP6KACUFezO4GXLlrnMZ2VlKSoqSps2bVLfvn1VWFiof/zjH5o3b56uuuoqSdKcOXN06aWXat26dbriiis8VzkA+CH6KACUdUHXkBYWFkqSIiMjJUmbNm3SqVOnlJiY6BzTpk0bNW3aVGvXrr2QXQFAQKKPAoCbZ0j/qLS0VOPHj1evXr3Uvn17SVJBQYFCQkJUr149l7HR0dEqKCgodzsOh0MOh8M5X1RUVNmSAMCveKqPSvRSAP6t0mdIU1NTtX37ds2fP/+CCsjMzFRERIRziouLu6DtAYC/8FQfleilAPxbpQLp2LFjtXTpUq1cuVJNmjRxLo+JidHJkyd15MgRl/EHDhxQTExMudtKT09XYWGhc8rPz69MSQDgVzzZRyV6KQD/5lYgtSxLY8eO1cKFC/Xpp58qPj7eZX3Xrl1Vs2ZNrVixwrlsx44d2rNnj3r27FnuNu12u8LDw10mAAhU3uijEr0UgH9z6xrS1NRUzZs3T++//77CwsKc1zNFRESodu3aioiI0N133620tDRFRkYqPDxc48aNU8+ePflmKACIPgoA5XErkM6aNUuSlJCQ4LJ8zpw5GjVqlCTpL3/5i2rUqKERI0bI4XAoKSlJr776qkeKBQB/Rx8FgLLcCqSWZZ1zTK1atTRz5kzNnDmz0kUBQKCijwJAWfyWPQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwKhg0wUAga75xA/cfszuqYO9UAkA+Cf6aODjDCkAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIxyO5CuWrVKQ4YMUWxsrGw2mxYtWuSyftSoUbLZbC7ToEGDPFUvAPg9+igAuHI7kBYXF6tTp06aOXNmhWMGDRqk/fv3O6e33377gooEgEBCHwUAV8HuPiA5OVnJyclnHWO32xUTE1PpogAgkNFHAcCVV64hzc7OVlRUlC655BKNGTNGhw4dqnCsw+FQUVGRywQA1Z07fVSilwLwbx4PpIMGDdLcuXO1YsUKTZs2TTk5OUpOTlZJSUm54zMzMxUREeGc4uLiPF0SAPgVd/uoRC8F4N/c/sj+XG6++Wbnf3fo0EEdO3ZUy5YtlZ2drQEDBpQZn56errS0NOd8UVERjRRAteZuH5XopQD8m9dv+9SiRQs1bNhQO3fuLHe93W5XeHi4ywQA+H/O1UcleikA/+b1QPrjjz/q0KFDaty4sbd3BQABiT4KINC5/ZH9sWPHXN6l5+XlacuWLYqMjFRkZKQyMjI0YsQIxcTEaNeuXXr00UfVqlUrJSUlebRwAPBX9FEAcOV2IN24caP69+/vnD99zVJKSopmzZqlrVu36s0339SRI0cUGxurgQMH6umnn5bdbvdc1QDgx+ijAODK7UCakJAgy7IqXP/xxx9fUEEAEOjoowDgit+yBwAAgFEev+0T4C+aT/zA7cfsnjrYC5UAgH+ij8JTOEMKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKOCTRcAeELziR8E1H7cVZm6dk8d7IVKAPizquhxvtpHJfdro496DmdIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBT3IXUT93sEgAtDHwVwJs6QAgAAwCgCKQAAAIwikAIAAMAotwPpqlWrNGTIEMXGxspms2nRokUu6y3L0pNPPqnGjRurdu3aSkxMVG5urqfqBQC/Rx8FAFduB9Li4mJ16tRJM2fOLHf99OnT9fLLL2v27Nlav369QkNDlZSUpBMnTlxwsQAQCOijAODK7W/ZJycnKzk5udx1lmVpxowZeuKJJ3TddddJkubOnavo6GgtWrRIN99884VVCwABgD4KAK48eg1pXl6eCgoKlJiY6FwWERGhHj16aO3ateU+xuFwqKioyGUCgOqqMn1UopcC8G8eDaQFBQWSpOjoaJfl0dHRznVnyszMVEREhHOKi4vzZEkA4Fcq00cleikA/2b8W/bp6ekqLCx0Tvn5+aZLAgC/Qy8F4M88GkhjYmIkSQcOHHBZfuDAAee6M9ntdoWHh7tMAFBdVaaPSvRSAP7No4E0Pj5eMTExWrFihXNZUVGR1q9fr549e3pyVwAQkOijAKojt79lf+zYMe3cudM5n5eXpy1btigyMlJNmzbV+PHj9cwzz6h169aKj4/XpEmTFBsbq2HDhnmybgDwW/RRAHDldiDduHGj+vfv75xPS0uTJKWkpCgrK0uPPvqoiouLde+99+rIkSPq3bu3li1bplq1anmuagDwY/RRAHDldiBNSEiQZVkVrrfZbHrqqaf01FNPXVBhABCo6KMA4Mr4t+wBAABQvRFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgVLDpAgCY0XziB24/ZvfUwV6oBAD8E33UczhDCgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKO4DynggypzbzsAwP9DH/UvnCEFAACAUQRSAAAAGOXxQDplyhTZbDaXqU2bNp7eDQAELPoogOrGK9eQtmvXTp988sn/20kwl6oCgDvoowCqE690uODgYMXExHhj0wBQLdBHAVQnXrmGNDc3V7GxsWrRooVuu+027dmzxxu7AYCARR8FUJ14/Axpjx49lJWVpUsuuUT79+9XRkaG+vTpo+3btyssLKzMeIfDIYfD4ZwvKirydEkA4Ffc7aMSvRSAf/N4IE1OTnb+d8eOHdWjRw81a9ZM//73v3X33XeXGZ+ZmamMjAxPlwE/x/3jUJ2520cleinKoo/Cn3j9tk/16tXTxRdfrJ07d5a7Pj09XYWFhc4pPz/f2yUBgF85Vx+V6KUA/JvXA+mxY8e0a9cuNW7cuNz1drtd4eHhLhMA4P85Vx+V6KUA/JvHA+nDDz+snJwc7d69W59//rmuv/56BQUF6ZZbbvH0rgAgINFHAVQ3Hr+G9Mcff9Qtt9yiQ4cOqVGjRurdu7fWrVunRo0aeXpXABCQ6KMAqhuPB9L58+d7epMAUK3QRwFUN/yWPQAAAIwikAIAAMCogPlxZO63BnhfZf6d7Z462AuVwBvoo4D30UfLxxlSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGBUw9yGF7+LehnCXu38z1eEefaje6KNwl7/d75QzpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCK+5BWAX+7F9jZcC88uIu/GXgCfRTVWXX4m+EMKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIziPqTVWHW4rxkAeBN9FPAMzpACAADAKAIpAAAAjCKQAgAAwCivBdKZM2eqefPmqlWrlnr06KEvvvjCW7sCgIBEHwVQXXglkL7zzjtKS0vT5MmTtXnzZnXq1ElJSUk6ePCgN3YHAAGHPgqgOvFKIH3ppZd0zz33aPTo0Wrbtq1mz56tOnXq6J///Kc3dgcAAYc+CqA68fhtn06ePKlNmzYpPT3duaxGjRpKTEzU2rVry4x3OBxyOBzO+cLCQklSUVGRW/stdRyvZMW+yd3jr4xAe85Qfbn77+X0eMuyvFHOBXO3j0qe6aWB1hPoo4B73Pk34+k+6vFA+vPPP6ukpETR0dEuy6Ojo/Xtt9+WGZ+ZmamMjIwyy+Pi4jxdml+JmGG6AsB/VPbfy9GjRxUREeHRWjzB3T4q0UvLQx8F3FOZfzOe6qPGb4yfnp6utLQ053xpaal++eUXNWjQQDabzWBl3lNUVKS4uDjl5+crPDzcdDkBhefWOwLtebUsS0ePHlVsbKzpUjymuvXSQPub9CU8t94TSM+tp/uoxwNpw4YNFRQUpAMHDrgsP3DggGJiYsqMt9vtstvtLsvq1avn6bJ8Unh4uN//QfoqnlvvCKTn1RfPjJ7mbh+Vqm8vDaS/SV/Dc+s9gfLcerKPevxLTSEhIeratatWrFjhXFZaWqoVK1aoZ8+ent4dAAQc+iiA6sYrH9mnpaUpJSVF3bp10+WXX64ZM2aouLhYo0eP9sbuACDg0EcBVCdeCaQ33XSTfvrpJz355JMqKChQ586dtWzZsjIX6FdXdrtdkydPLvPxGi4cz6138LxWPfro2fE36T08t97Dc1sxm+Wr9z0BAABAtcBv2QMAAMAoAikAAACMIpACAADAKAIpAAAAjCKQVpHMzEx1795dYWFhioqK0rBhw7Rjxw7TZQWkqVOnymazafz48aZLCQh79+7V7bffrgYNGqh27drq0KGDNm7caLosVFP00qpBH/Us+ui5EUirSE5OjlJTU7Vu3TotX75cp06d0sCBA1VcXGy6tICyYcMGvfbaa+rYsaPpUgLC4cOH1atXL9WsWVMfffSRvv76a7344ouqX7++6dJQTdFLvY8+6ln00fPDbZ8M+emnnxQVFaWcnBz17dvXdDkB4dixY7rsssv06quv6plnnlHnzp01Y8YM02X5tYkTJ+qzzz7T6tWrTZcClIte6ln0Uc+jj54fzpAaUlhYKEmKjIw0XEngSE1N1eDBg5WYmGi6lICxePFidevWTTfeeKOioqLUpUsXvfHGG6bLApzopZ5FH/U8+uj5IZAaUFpaqvHjx6tXr15q37696XICwvz587V582ZlZmaaLiWgfP/995o1a5Zat26tjz/+WGPGjNGDDz6oN99803RpAL3Uw+ij3kEfPT9e+elQnF1qaqq2b9+uNWvWmC4lIOTn5+uhhx7S8uXLVatWLdPlBJTS0lJ169ZNzz33nCSpS5cu2r59u2bPnq2UlBTD1aG6o5d6Dn3Ue+ij54czpFVs7NixWrp0qVauXKkmTZqYLicgbNq0SQcPHtRll12m4OBgBQcHKycnRy+//LKCg4NVUlJiukS/1bhxY7Vt29Zl2aWXXqo9e/YYqgj4Hb3Us+ij3kMfPT+cIa0ilmVp3LhxWrhwobKzsxUfH2+6pIAxYMAAbdu2zWXZ6NGj1aZNGz322GMKCgoyVJn/69WrV5lb6nz33Xdq1qyZoYpQ3dFLvYM+6j300fNDIK0iqampmjdvnt5//32FhYWpoKBAkhQREaHatWsbrs6/hYWFlbl+LDQ0VA0aNOC6sgs0YcIEXXnllXruuec0cuRIffHFF3r99df1+uuvmy4N1RS91Dvoo95DHz0/3PapithstnKXz5kzR6NGjaraYqqBhIQEblfiIUuXLlV6erpyc3MVHx+vtLQ03XPPPabLQjVFL6069FHPoY+eG4EUAAAARvGlJgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFH/H7Y3+iJKvrbRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据可视化\n",
    "\n",
    "fig,ax = plt.subplots(2,2,figsize=(8,8))\n",
    "ax[0,0].hist(df['sepal length'],bins=20)\n",
    "ax[0,0].set_title('sepal length')\n",
    "\n",
    "ax[0,1].hist(df['sepal width'],bins=20)\n",
    "ax[0,1].set_title('sepal width')\n",
    "\n",
    "ax[1,0].hist(df['petal length'],bins=20)\n",
    "ax[1,0].set_title('petal length')\n",
    "\n",
    "ax[1,1].hist(df['petal length'],bins=20)\n",
    "ax[1,1].set_title('petal length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e0f64de6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:04.078865Z",
     "start_time": "2023-04-09T03:01:04.064860Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.843333333333334\n",
      "4.3\n",
      "7.9\n"
     ]
    }
   ],
   "source": [
    "print(df['sepal length'].mean())  # 查看平均数\n",
    "print(df['sepal length'].min())   # 查询最小值\n",
    "print(df['sepal length'].max())   # 查询最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a4b44e24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:01:04.094679Z",
     "start_time": "2023-04-09T03:01:04.080679Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              sepal length  sepal width  petal length  petal width\n",
      "sepal length      0.685694    -0.039268      1.273682     0.516904\n",
      "sepal width      -0.039268     0.188004     -0.321713    -0.117981\n",
      "petal length      1.273682    -0.321713      3.113179     1.296387\n",
      "petal width       0.516904    -0.117981      1.296387     0.582414\n",
      "              sepal length  sepal width  petal length  petal width\n",
      "sepal length      1.000000    -0.109369      0.871754     0.817954\n",
      "sepal width      -0.109369     1.000000     -0.420516    -0.356544\n",
      "petal length      0.871754    -0.420516      1.000000     0.962757\n",
      "petal width       0.817954    -0.356544      0.962757     1.000000\n"
     ]
    }
   ],
   "source": [
    "print(df.cov())     # 协方差矩阵\n",
    "print(df.corr())    # 相关系数矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ec36a6c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:17:08.000241Z",
     "start_time": "2023-04-09T03:17:07.994245Z"
    }
   },
   "outputs": [],
   "source": [
    "class_dict = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2, 'New-label': 3}\n",
    "df_14['class'] =df_14['class'].map(class_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d9d7452f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T03:17:38.797294Z",
     "start_time": "2023-04-09T03:17:38.778736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_14['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "649c48d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56df9295",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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