12. Pandas的索引的用途.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas的索引index的用途"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把数据存储于普通的column列也能用于数据查询，那使用index有什么好处？\n",
    "\n",
    "index的用途总结：  \n",
    "1. 更方便的数据查询；\n",
    "2. 使用index可以获得性能提升；\n",
    "3. 自动的数据对齐功能；\n",
    "4. 更多更强大的数据结构支持；\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./datas/ml-latest-small/ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId       100836\n",
       "movieId      100836\n",
       "rating       100836\n",
       "timestamp    100836\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、使用index查询数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop==False，让索引列还保持在column\n",
    "df.set_index(\"userId\", inplace=True, drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating  timestamp\n",
       "userId                                    \n",
       "1            1        1     4.0  964982703\n",
       "1            1        3     4.0  964981247\n",
       "1            1        6     4.0  964982224\n",
       "1            1       47     5.0  964983815\n",
       "1            1       50     5.0  964982931"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
       "            ...\n",
       "            610, 610, 610, 610, 610, 610, 610, 610, 610, 610],\n",
       "           dtype='int64', name='userId', length=100836)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1005527755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1005528017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1005527926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1005527980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>104</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1005528065</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "userId                                     \n",
       "500        500        1     4.0  1005527755\n",
       "500        500       11     1.0  1005528017\n",
       "500        500       39     1.0  1005527926\n",
       "500        500      101     1.0  1005527980\n",
       "500        500      104     4.0  1005528065"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用index的查询方法\n",
    "df.loc[500].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1005527755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1005528017</td>\n",
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       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1005527926</td>\n",
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       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1005527980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>104</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1005528065</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "userId                                     \n",
       "500        500        1     4.0  1005527755\n",
       "500        500       11     1.0  1005528017\n",
       "500        500       39     1.0  1005527926\n",
       "500        500      101     1.0  1005527980\n",
       "500        500      104     4.0  1005528065"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用column的condition查询方法\n",
    "df.loc[df[\"userId\"] == 500].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 使用index会提升查询性能\n",
    "\n",
    "* 如果index是唯一的，Pandas会使用哈希表优化，查询性能为O(1);\n",
    "* 如果index不是唯一的，但是有序，Pandas会使用二分查找算法，查询性能为O(logN);\n",
    "* 如果index是完全随机的，那么每次查询都要扫描全表，查询性能为O(N);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./other_files/pandas-index-performance.png\" width=600/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实验1：完全随机的顺序查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据随机打散\n",
    "from sklearn.utils import shuffle\n",
    "df_shuffle = shuffle(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
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       "      <td>160</td>\n",
       "      <td>160</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>985383314</td>\n",
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       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>1136</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1167375403</td>\n",
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       "      <td>167</td>\n",
       "      <td>167</td>\n",
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       "      <td>1154718915</td>\n",
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       "      <td>536</td>\n",
       "      <td>536</td>\n",
       "      <td>276</td>\n",
       "      <td>3.0</td>\n",
       "      <td>832839990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>5952</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1501274082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "userId                                     \n",
       "160        160     2340     1.0   985383314\n",
       "129        129     1136     3.5  1167375403\n",
       "167        167    44191     4.5  1154718915\n",
       "536        536      276     3.0   832839990\n",
       "67          67     5952     2.0  1501274082"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shuffle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 索引是否是递增的\n",
    "df_shuffle.index.is_monotonic_increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shuffle.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "376 µs ± 52.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# 计时，查询id==500数据性能\n",
    "%timeit df_shuffle.loc[500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实验2：将index排序后的查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sorted = df_shuffle.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964983034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2617</td>\n",
       "      <td>2.0</td>\n",
       "      <td>964982588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3639</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>733</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating  timestamp\n",
       "userId                                    \n",
       "1            1     2985     4.0  964983034\n",
       "1            1     2617     2.0  964982588\n",
       "1            1     3639     4.0  964982271\n",
       "1            1        6     4.0  964982224\n",
       "1            1      733     4.0  964982400"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 索引是否是递增的\n",
    "df_sorted.index.is_monotonic_increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "203 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df_sorted.loc[500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 使用index能自动对齐数据\n",
    "\n",
    "包括series和dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([1,2,3], index=list(\"abc\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series([2,3,4], index=list(\"bcd\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    NaN\n",
       "b    4.0\n",
       "c    6.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1+s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 使用index更多更强大的数据结构支持"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***很多强大的索引数据结构*** \n",
    "* CategoricalIndex，基于分类数据的Index，提升性能；\n",
    "* MultiIndex，多维索引，用于groupby多维聚合后结果等；\n",
    "* DatetimeIndex，时间类型索引，强大的日期和时间的方法支持；"
   ]
  }
 ],
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