Python数据分析_第09课_假设检验_笔记.ipynb

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   "outputs": [
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     "data": {
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     "execution_count": 1,
     "metadata": {},
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   "source": [
    "from pandas import Series, DataFrame\n",
    "import numpy as np\n",
    "\n",
    "arr = [98, 83, 65, 72, 79, 76, 75, 94, 91, 77, 63, 83, 89, 69, 64, 78, 63, 86, 91, 72, 71, 72, 70, 80, 65, 70, 62, 74, 71,\n",
    "       76]\n",
    "\n",
    "# 均值\n",
    "np.mean(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np.mean(np.sort(arr)[14:16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([62, 63, 63, 64, 65, 65, 69, 70, 70, 71, 71, 72, 72, 72, 74, 75, 76,\n",
       "       76, 77, 78, 79, 80, 83, 83, 86, 89, 91, 91, 94, 98])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 排序\n",
    "np.sort(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[72]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 求众数的函数\n",
    "def get_mode(arr):\n",
    "    mode = []\n",
    "    arr_appear = dict((a, arr.count(a)) for a in arr)  # 统计各个元素出现的次数\n",
    "    if max(arr_appear.values()) == 1:  # 如果最大的出现为1  \n",
    "        return  # 则没有众数\n",
    "    else:\n",
    "        for k, v in arr_appear.items():  # 否则，出现次数最大的数字，就是众数  \n",
    "            if v == max(arr_appear.values()):\n",
    "                mode.append(k)\n",
    "    return mode\n",
    "\n",
    "\n",
    "get_mode(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.298888888888868"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方差\n",
    "np.var(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.659134996928497"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 标准差\n",
    "np.std(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57428918733545919"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = Series(arr)\n",
    "# 偏度\n",
    "arr.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.42872374795373291"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 峰度\n",
    "arr.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30.000000\n",
       "mean     75.966667\n",
       "std       9.824260\n",
       "min      62.000000\n",
       "25%      70.000000\n",
       "50%      74.500000\n",
       "75%      82.250000\n",
       "max      98.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 描述\n",
    "arr.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      data1     data2\n",
      "0 -0.481723  1.021694\n",
      "1  0.628769 -1.134141\n",
      "2 -0.463298 -1.042589\n",
      "3  0.159722  0.914932\n",
      "4 -0.493772 -0.559481\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
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       "      <td>0.256699</td>\n",
       "      <td>-0.100965</td>\n",
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       "      <th>data2</th>\n",
       "      <td>-0.100965</td>\n",
       "      <td>1.109845</td>\n",
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       "          data1     data2\n",
       "data1  0.256699 -0.100965\n",
       "data2 -0.100965  1.109845"
      ]
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   ],
   "source": [
    "df = DataFrame({'data1': np.random.randn(5),\n",
    "                'data2': np.random.randn(5)})\n",
    "print(df)\n",
    "# 协方差\n",
    "df.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>data1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.189159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data2</th>\n",
       "      <td>-0.189159</td>\n",
       "      <td>1.000000</td>\n",
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       "          data1     data2\n",
       "data1  1.000000 -0.189159\n",
       "data2 -0.189159  1.000000"
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "# 相关系数\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=array([ 0.65465367]), pvalue=array([ 0.52906417]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#假设检验\n",
    "from scipy import stats as ss\n",
    "\n",
    "df = DataFrame({'data': [10.1, 10, 9.8, 10.5, 9.7, 10.1, 9.9, 10.2, 10.3, 9.9]})\n",
    "# T-检验\n",
    "ss.ttest_1samp(a=df, popmean=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考资料：炼数成金Python数据分析课程"
   ]
  }
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