4.4 pandas 布尔过滤和数据离散化分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5561d0e8",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28121ffe",
   "metadata": {},
   "source": [
    "# 布尔过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a93ae9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series(np.random.randn(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b3021a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      0.319255\n",
       "1      0.288620\n",
       "2      1.989162\n",
       "3     -0.009239\n",
       "4      1.109716\n",
       "         ...   \n",
       "995   -0.392609\n",
       "996   -0.050110\n",
       "997    0.727632\n",
       "998    1.925536\n",
       "999   -1.468915\n",
       "Length: 1000, dtype: float64>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2daf7006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13    -3.056372\n",
       "890    3.045602\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[np.abs(data) > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2401e045",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.random.randn(1000, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e149fc7",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.017157</td>\n",
       "      <td>-0.016438</td>\n",
       "      <td>0.037203</td>\n",
       "      <td>-0.026986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.985735</td>\n",
       "      <td>0.991331</td>\n",
       "      <td>1.014832</td>\n",
       "      <td>1.004448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.465450</td>\n",
       "      <td>-3.107359</td>\n",
       "      <td>-3.304523</td>\n",
       "      <td>-3.321672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.628826</td>\n",
       "      <td>-0.698963</td>\n",
       "      <td>-0.633764</td>\n",
       "      <td>-0.715244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.047201</td>\n",
       "      <td>-0.042555</td>\n",
       "      <td>0.068751</td>\n",
       "      <td>-0.047941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.664562</td>\n",
       "      <td>0.642463</td>\n",
       "      <td>0.687135</td>\n",
       "      <td>0.623631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.980401</td>\n",
       "      <td>3.241007</td>\n",
       "      <td>3.623015</td>\n",
       "      <td>3.259948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean     -0.017157    -0.016438     0.037203    -0.026986\n",
       "std       0.985735     0.991331     1.014832     1.004448\n",
       "min      -3.465450    -3.107359    -3.304523    -3.321672\n",
       "25%      -0.628826    -0.698963    -0.633764    -0.715244\n",
       "50%      -0.047201    -0.042555     0.068751    -0.047941\n",
       "75%       0.664562     0.642463     0.687135     0.623631\n",
       "max       2.980401     3.241007     3.623015     3.259948"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8042aa11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luhe\\AppData\\Local\\Temp\\ipykernel_18156\\2969116195.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  data[(np.abs(data) > 3).any(1)]\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>-0.166104</td>\n",
       "      <td>3.241007</td>\n",
       "      <td>-1.524057</td>\n",
       "      <td>-0.475031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>-0.302468</td>\n",
       "      <td>0.303490</td>\n",
       "      <td>3.228771</td>\n",
       "      <td>-0.727210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.959152</td>\n",
       "      <td>0.410390</td>\n",
       "      <td>0.042127</td>\n",
       "      <td>-3.321672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1.107201</td>\n",
       "      <td>-0.735651</td>\n",
       "      <td>3.623015</td>\n",
       "      <td>-1.103185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>-3.465450</td>\n",
       "      <td>-0.817645</td>\n",
       "      <td>1.029253</td>\n",
       "      <td>-1.560918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>-0.317455</td>\n",
       "      <td>-3.107359</td>\n",
       "      <td>-0.721843</td>\n",
       "      <td>0.648579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>0.403942</td>\n",
       "      <td>-0.425567</td>\n",
       "      <td>3.119359</td>\n",
       "      <td>-0.628374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>-3.423714</td>\n",
       "      <td>-0.173714</td>\n",
       "      <td>0.516909</td>\n",
       "      <td>0.036071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>-0.580077</td>\n",
       "      <td>0.216130</td>\n",
       "      <td>0.173360</td>\n",
       "      <td>3.024298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>-0.093136</td>\n",
       "      <td>-1.641034</td>\n",
       "      <td>-3.304523</td>\n",
       "      <td>0.331643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>-0.798606</td>\n",
       "      <td>-0.913635</td>\n",
       "      <td>0.681662</td>\n",
       "      <td>3.259948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>0.951751</td>\n",
       "      <td>-1.119582</td>\n",
       "      <td>3.037259</td>\n",
       "      <td>0.976069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "80  -0.166104  3.241007 -1.524057 -0.475031\n",
       "87  -0.302468  0.303490  3.228771 -0.727210\n",
       "117  0.959152  0.410390  0.042127 -3.321672\n",
       "118  1.107201 -0.735651  3.623015 -1.103185\n",
       "138 -3.465450 -0.817645  1.029253 -1.560918\n",
       "294 -0.317455 -3.107359 -0.721843  0.648579\n",
       "447  0.403942 -0.425567  3.119359 -0.628374\n",
       "458 -3.423714 -0.173714  0.516909  0.036071\n",
       "679 -0.580077  0.216130  0.173360  3.024298\n",
       "747 -0.093136 -1.641034 -3.304523  0.331643\n",
       "758 -0.798606 -0.913635  0.681662  3.259948\n",
       "967  0.951751 -1.119582  3.037259  0.976069"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(np.abs(data) > 3).any(1)]  # 选取包含绝对值大于3的行 .any(1)->行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a709782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "any([True, False, False])  # 有一个为True，则返回为True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc33dfbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all([True, False, False]) # 全部为True，才返回True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ee58955",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</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>995</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3\n",
       "0    1.0  1.0  1.0  1.0\n",
       "1    1.0 -1.0  1.0 -1.0\n",
       "2    1.0 -1.0  1.0 -1.0\n",
       "3    1.0  1.0  1.0 -1.0\n",
       "4   -1.0  1.0  1.0 -1.0\n",
       "..   ...  ...  ...  ...\n",
       "995  1.0  1.0 -1.0 -1.0\n",
       "996  1.0 -1.0  1.0  1.0\n",
       "997 -1.0  1.0 -1.0 -1.0\n",
       "998  1.0  1.0 -1.0  1.0\n",
       "999  1.0  1.0  1.0 -1.0\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sign(data)  # 将数据大于零的置为1，小于零的置为-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a481ab",
   "metadata": {},
   "source": [
    "# 数据离散化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "75b88721",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.random.randint(1, 100, (50,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99d1239a",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>93</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>37</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>53</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>58</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>81</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>68</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>70</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>99</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>25</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>46</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>94</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>77</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>71</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>93</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>58</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>71</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1\n",
       "0   85  31\n",
       "1   70  13\n",
       "2   84  22\n",
       "3   73  78\n",
       "4   44  20\n",
       "5   33  72\n",
       "6   38  15\n",
       "7   75  89\n",
       "8   93  43\n",
       "9   41  32\n",
       "10   4  29\n",
       "11  37  82\n",
       "12  58  34\n",
       "13  16  49\n",
       "14   8  52\n",
       "15  27  98\n",
       "16  11  70\n",
       "17  83  51\n",
       "18  48  85\n",
       "19  36  44\n",
       "20  37  64\n",
       "21  23  75\n",
       "22  53  96\n",
       "23   9  84\n",
       "24  34  31\n",
       "25  63  80\n",
       "26  70  78\n",
       "27  58  98\n",
       "28  31  22\n",
       "29  75  35\n",
       "30  49  79\n",
       "31  26  78\n",
       "32  16  59\n",
       "33  81  16\n",
       "34  68  27\n",
       "35  70  82\n",
       "36  45  90\n",
       "37  99  60\n",
       "38  30   1\n",
       "39  38  20\n",
       "40  25  48\n",
       "41  46  97\n",
       "42  94  53\n",
       "43  77  40\n",
       "44  71  16\n",
       "45  93  27\n",
       "46  62  98\n",
       "47  63   2\n",
       "48  58  39\n",
       "49  71  40"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d04e47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [20, 40, 60, 80, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2240e688",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats  = pd.cut(data.iloc[:, 0], bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4d1eab35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (80.0, 100.0]\n",
       "1      (60.0, 80.0]\n",
       "2     (80.0, 100.0]\n",
       "3      (60.0, 80.0]\n",
       "4      (40.0, 60.0]\n",
       "5      (20.0, 40.0]\n",
       "6      (20.0, 40.0]\n",
       "7      (60.0, 80.0]\n",
       "8     (80.0, 100.0]\n",
       "9      (40.0, 60.0]\n",
       "10              NaN\n",
       "11     (20.0, 40.0]\n",
       "12     (40.0, 60.0]\n",
       "13              NaN\n",
       "14              NaN\n",
       "15     (20.0, 40.0]\n",
       "16              NaN\n",
       "17    (80.0, 100.0]\n",
       "18     (40.0, 60.0]\n",
       "19     (20.0, 40.0]\n",
       "20     (20.0, 40.0]\n",
       "21     (20.0, 40.0]\n",
       "22     (40.0, 60.0]\n",
       "23              NaN\n",
       "24     (20.0, 40.0]\n",
       "25     (60.0, 80.0]\n",
       "26     (60.0, 80.0]\n",
       "27     (40.0, 60.0]\n",
       "28     (20.0, 40.0]\n",
       "29     (60.0, 80.0]\n",
       "30     (40.0, 60.0]\n",
       "31     (20.0, 40.0]\n",
       "32              NaN\n",
       "33    (80.0, 100.0]\n",
       "34     (60.0, 80.0]\n",
       "35     (60.0, 80.0]\n",
       "36     (40.0, 60.0]\n",
       "37    (80.0, 100.0]\n",
       "38     (20.0, 40.0]\n",
       "39     (20.0, 40.0]\n",
       "40     (20.0, 40.0]\n",
       "41     (40.0, 60.0]\n",
       "42    (80.0, 100.0]\n",
       "43     (60.0, 80.0]\n",
       "44     (60.0, 80.0]\n",
       "45    (80.0, 100.0]\n",
       "46     (60.0, 80.0]\n",
       "47     (60.0, 80.0]\n",
       "48     (40.0, 60.0]\n",
       "49     (60.0, 80.0]\n",
       "Name: 0, dtype: category\n",
       "Categories (4, interval[int64, right]): [(20, 40] < (40, 60] < (60, 80] < (80, 100]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "54924170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 40]     13\n",
       "(60, 80]     13\n",
       "(40, 60]     10\n",
       "(80, 100]     8\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6afa5b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats  = pd.cut(data.iloc[:, 0], bins=bins, right=False) \n",
    "# 左闭右开，默认左开右闭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e5ae7ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[20, 40)     13\n",
       "[60, 80)     13\n",
       "[40, 60)     10\n",
       "[80, 100)     8\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7590dc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (75.25, 99.0]\n",
       "1      (51.5, 75.25]\n",
       "2      (75.25, 99.0]\n",
       "3      (51.5, 75.25]\n",
       "4      (27.75, 51.5]\n",
       "5      (27.75, 51.5]\n",
       "6      (27.75, 51.5]\n",
       "7      (51.5, 75.25]\n",
       "8      (75.25, 99.0]\n",
       "9      (27.75, 51.5]\n",
       "10    (3.905, 27.75]\n",
       "11     (27.75, 51.5]\n",
       "12     (51.5, 75.25]\n",
       "13    (3.905, 27.75]\n",
       "14    (3.905, 27.75]\n",
       "15    (3.905, 27.75]\n",
       "16    (3.905, 27.75]\n",
       "17     (75.25, 99.0]\n",
       "18     (27.75, 51.5]\n",
       "19     (27.75, 51.5]\n",
       "20     (27.75, 51.5]\n",
       "21    (3.905, 27.75]\n",
       "22     (51.5, 75.25]\n",
       "23    (3.905, 27.75]\n",
       "24     (27.75, 51.5]\n",
       "25     (51.5, 75.25]\n",
       "26     (51.5, 75.25]\n",
       "27     (51.5, 75.25]\n",
       "28     (27.75, 51.5]\n",
       "29     (51.5, 75.25]\n",
       "30     (27.75, 51.5]\n",
       "31    (3.905, 27.75]\n",
       "32    (3.905, 27.75]\n",
       "33     (75.25, 99.0]\n",
       "34     (51.5, 75.25]\n",
       "35     (51.5, 75.25]\n",
       "36     (27.75, 51.5]\n",
       "37     (75.25, 99.0]\n",
       "38     (27.75, 51.5]\n",
       "39     (27.75, 51.5]\n",
       "40    (3.905, 27.75]\n",
       "41     (27.75, 51.5]\n",
       "42     (75.25, 99.0]\n",
       "43     (75.25, 99.0]\n",
       "44     (51.5, 75.25]\n",
       "45     (75.25, 99.0]\n",
       "46     (51.5, 75.25]\n",
       "47     (51.5, 75.25]\n",
       "48     (51.5, 75.25]\n",
       "49     (51.5, 75.25]\n",
       "Name: 0, dtype: category\n",
       "Categories (4, interval[float64, right]): [(3.905, 27.75] < (27.75, 51.5] < (51.5, 75.25] < (75.25, 99.0]]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(data.iloc[:,0], 4)   # 分成4类，从最小值到最大值区间平均划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c1b04569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.999, 33.25]    13\n",
       "(51.0, 71.0]      13\n",
       "(33.25, 51.0]     12\n",
       "(71.0, 99.0]      12\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(data.iloc[:,0], 4).value_counts()  # 分成4类，样本个数等分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95e23996",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}