数据分析考核.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一个一维数组，其中的元素是0到9的数字组成。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 创建一个有10个元素组成的数组，每个元素是10以内的随机整数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 5, 6, 1, 7, 2, 3, 5, 0, 5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, (10,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从数组a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])中，筛选出奇数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 3, 5, 7, 9], dtype=int64),)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "np.where(a % 2 == 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 将数组a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])中能被3整除的数替换为-1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(10)\n",
    "a[::3] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1,  2, -1,  4,  5, -1,  7,  8, -1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 将数组a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])中能被3整除的数替换为-1，但原数组a不能改变。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1,  2, -1,  4,  5, -1,  7,  8, -1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "np.where((a % 3 == 0), -1, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 生成一个有0到11（共计12个整数）组成的一维数组，并将这个数组做如下变形：\n",
    "a. 变换成形状为(3, 4)的二维数组\n",
    "b. 变换为只有一列的数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(12).reshape(3, 4)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0],\n",
       "       [ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10],\n",
       "       [11]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有两个数组a, b，请将这两个数组沿0轴方向连接起来。\n",
    "- a = np.arange(10).reshape((2, 5))\n",
    "- b = np.ones((2,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 2., 3., 4.],\n",
       "       [5., 6., 7., 8., 9.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10).reshape((2, 5))\n",
    "b = np.ones((2, 5))\n",
    "np.concatenate([a, b], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找出两个数组a = np.array([1,2,3,2,3,4,3,4,5,6])和b = np.array([7,2,10,2,7,4,9,4,9,8])的交集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 2, 3, 4, 3, 4, 5, 6])\n",
    "b = np.array([7, 2, 10, 2, 7, 4, 9, 4, 9, 8])\n",
    "np.intersect1d(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从数组a = np.array([1,2,3,4,5])中移除数组b=np.array([5,6,7,8,9])中的元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4, 5])\n",
    "b = np.array([5, 6, 7, 8, 9])\n",
    "np.setdiff1d(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于数组a = np.array([2,4,8,9,1,2,12,25,19,30])，从中选出大于5并且小于10的数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2, 3], dtype=int64),)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([2, 4, 8, 9, 1, 2, 12, 25, 19, 30])\n",
    "np.where(np.logical_and(a > 5, a < 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下所示，有两个数组，比较两个数组中对应索引的元素大小，将其中较大的元素组成新的数组。\n",
    "1. a = np.array([2, 4, 6, 8])\n",
    "2. b = np.array([1, 3, 7, 9])\n",
    "- 输出\n",
    "- np.array([2,4,7,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 7, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([2, 4, 6, 8])\n",
    "b = np.array([1, 3, 7, 9])\n",
    "np.where(a > b, a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据如下对象，创建Series对象。\n",
    "1. lst = list('abcedfghijklmnopqrstuvwxyz')\n",
    "2. arr = np.arange(26)\n",
    "3. dct = dict(zip(lst, arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "lst = list('abcedfghijklmnopqrstuvwxyz')\n",
    "arr = np.arange(26)\n",
    "dct = dict(zip(lst, arr))\n",
    "s_lst = pd.Series(lst)\n",
    "s_arr = pd.Series(arr)\n",
    "s_dct = pd.Series(dct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     a\n",
       "1     b\n",
       "2     c\n",
       "3     e\n",
       "4     d\n",
       "5     f\n",
       "6     g\n",
       "7     h\n",
       "8     i\n",
       "9     j\n",
       "10    k\n",
       "11    l\n",
       "12    m\n",
       "13    n\n",
       "14    o\n",
       "15    p\n",
       "16    q\n",
       "17    r\n",
       "18    s\n",
       "19    t\n",
       "20    u\n",
       "21    v\n",
       "22    w\n",
       "23    x\n",
       "24    y\n",
       "25    z\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      2\n",
       "3      3\n",
       "4      4\n",
       "5      5\n",
       "6      6\n",
       "7      7\n",
       "8      8\n",
       "9      9\n",
       "10    10\n",
       "11    11\n",
       "12    12\n",
       "13    13\n",
       "14    14\n",
       "15    15\n",
       "16    16\n",
       "17    17\n",
       "18    18\n",
       "19    19\n",
       "20    20\n",
       "21    21\n",
       "22    22\n",
       "23    23\n",
       "24    24\n",
       "25    25\n",
       "dtype: int32"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     0\n",
       "b     1\n",
       "c     2\n",
       "e     3\n",
       "d     4\n",
       "f     5\n",
       "g     6\n",
       "h     7\n",
       "i     8\n",
       "j     9\n",
       "k    10\n",
       "l    11\n",
       "m    12\n",
       "n    13\n",
       "o    14\n",
       "p    15\n",
       "q    16\n",
       "r    17\n",
       "s    18\n",
       "t    19\n",
       "u    20\n",
       "v    21\n",
       "w    22\n",
       "x    23\n",
       "y    24\n",
       "z    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_dct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从seaborn中读入iris数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "iris = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据第上题所得到的数据，生成一个不包含species特征的数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             5.1          3.5           1.4          0.2\n",
       "1             4.9          3.0           1.4          0.2\n",
       "2             4.7          3.2           1.3          0.2\n",
       "3             4.6          3.1           1.5          0.2\n",
       "4             5.0          3.6           1.4          0.2\n",
       "..            ...          ...           ...          ...\n",
       "145           6.7          3.0           5.2          2.3\n",
       "146           6.3          2.5           5.0          1.9\n",
       "147           6.5          3.0           5.2          2.0\n",
       "148           6.2          3.4           5.4          2.3\n",
       "149           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据第13题数据集，对各个特征进行描述性统计。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据第13题数据集，特征species标明了每个样本所属类别，查看共计多少种类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "versicolor    50\n",
       "setosa        50\n",
       "virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据第14题数据集，选出物种是setosa的所有样本，并统计样本数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    50\n",
       "sepal_width     50\n",
       "petal_length    50\n",
       "petal_width     50\n",
       "species         50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa = iris[iris['species'] == 'setosa']\n",
    "setosa.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载seaborn中集成的tatanic数据，并显示该数据集的基本信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = sns.load_dataset('titanic')\n",
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除age特征的缺失值，用可视化的方式显示获救与未获救人员的年龄、性别分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.dropna(subset=['age'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age              0\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           530\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\envs\\ml_env\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17316a22448>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='survived', y='age', hue='sex', split=True, data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1731bb3e948>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic['age2'] = titanic['age'] // 10\n",
    "sns.barplot(x='age2', y='survived', hue='sex', data=titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以sex特征为关键词分组统计survived特征的和，并绘制柱形图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1731bb0d5c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bm6rqtUleD3wwyZ8CZ1XV+yc3tjQ3T4XUopfk6cAtwFbgZuAbTL83z5eHTZYA+6vqpcP2m4FfZfqDn/ed+Iml+bnnrkWvqv4zySXAy4F3ALcCu6rqhUdum+Q04JnA/wJnA8ZdJyWPuWvRS/KTwHer6kPAu4DnA1NJXjg8f3qSi4bN3wzcCVwFfCDJ6ZOYWZqPe+4SPBt4Z5IfAA8DrwMOAX82HH9fCrwnycPAbwJrqurbST4FvBV424TmlubkMXdJasjDMpLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvo/aYduv8d3F2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.groupby(['sex']).count()['survived'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}