4.1 pandas 合并数据集.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75123a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01170af6",
   "metadata": {},
   "source": [
    "# 横向合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7cb958b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.DataFrame({'one':['a','b','a','a','c','b'], 'two':range(6)})\n",
    "data2 = pd.DataFrame({'one':['a','b','c','d'],'two':range(10,14)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7cf3d6e",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
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       "      <th>2</th>\n",
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      "text/plain": [
       "  one  two\n",
       "0   a    0\n",
       "1   b    1\n",
       "2   a    2\n",
       "3   a    3\n",
       "4   c    4\n",
       "5   b    5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data1"
   ]
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  {
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      "text/plain": [
       "  one  two\n",
       "0   a   10\n",
       "1   b   11\n",
       "2   c   12\n",
       "3   d   13"
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     },
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   "source": [
    "data2"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3211732b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(data1, data2, on='one')  \n",
    "# 根据one列进行横向合并，只保留one中共有元素，交集"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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       "  one  two_x  two_y\n",
       "0   a      0     10\n",
       "1   a      2     10\n",
       "2   a      3     10\n",
       "3   b      1     11\n",
       "4   b      5     11\n",
       "5   c      4     12"
      ]
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    "data"
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   "cell_type": "code",
   "execution_count": 10,
   "id": "ca5b276c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(data1, data2, on='one', how='outer')\n",
    "# 根据one列进行横向合并，并集"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "id": "908c1f0c",
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       "  one  two_x  two_y\n",
       "0   a    0.0     10\n",
       "1   a    2.0     10\n",
       "2   a    3.0     10\n",
       "3   b    1.0     11\n",
       "4   b    5.0     11\n",
       "5   c    4.0     12\n",
       "6   d    NaN     13"
      ]
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     "execution_count": 11,
     "metadata": {},
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    "data"
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   "cell_type": "code",
   "execution_count": 12,
   "id": "66ff2d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(data1, data2, on='one', how='left')\n",
    "# 根据one列进行横向合并，以data1为准"
   ]
  },
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   "cell_type": "code",
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   "id": "c58e95e2",
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       "  one  two_x  two_y\n",
       "0   a      0     10\n",
       "1   b      1     11\n",
       "2   a      2     10\n",
       "3   a      3     10\n",
       "4   c      4     12\n",
       "5   b      5     11"
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   "cell_type": "code",
   "execution_count": 14,
   "id": "c3fd6407",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(data1, data2, on='one', how='right')\n",
    "# 根据one列进行横向合并，以data2为准"
   ]
  },
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   "execution_count": 15,
   "id": "4bff807b",
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       "  one  two_x  two_y\n",
       "0   a    0.0     10\n",
       "1   a    2.0     10\n",
       "2   a    3.0     10\n",
       "3   b    1.0     11\n",
       "4   b    5.0     11\n",
       "5   c    4.0     12\n",
       "6   d    NaN     13"
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     "execution_count": 15,
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   "execution_count": 16,
   "id": "518dbcd8",
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   "outputs": [],
   "source": [
    "data1 = pd.DataFrame({'one1':['a','b','a','a','c','b'], 'two1':range(6)})\n",
    "data2 = pd.DataFrame({'one2':['a','b','c','d'],'two2':range(10,14)})"
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       "  one1  two1\n",
       "0    a     0\n",
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       "5    b     5"
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       "      <td>13</td>\n",
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      "text/plain": [
       "  one2  two2\n",
       "0    a    10\n",
       "1    b    11\n",
       "2    c    12\n",
       "3    d    13"
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     },
     "execution_count": 18,
     "metadata": {},
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    "data2"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6fbdd775",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(data1, data2, left_on='one1', right_on='one2')\n",
    "# 根据one列进行横向合并，data1根据one1，data2根据one2"
   ]
  },
  {
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   "execution_count": 21,
   "id": "a19e3272",
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   "outputs": [
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       "4    b     5    b    11\n",
       "5    c     4    c    12"
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     "metadata": {},
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    "data"
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  {
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   "outputs": [
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  {
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   "execution_count": 23,
   "id": "af3b6e28",
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   "outputs": [
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       "  one2  two2\n",
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   "execution_count": 26,
   "id": "9f11369e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(data1, data2, left_index=True, right_index=True, how='outer')\n",
    "# 根据one列进行横向合并，根据index合并，并集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0ab8df6",
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       "  one1  two1 one2  two2\n",
       "0    a     0    a  10.0\n",
       "1    b     1    b  11.0\n",
       "2    a     2    c  12.0\n",
       "3    a     3    d  13.0\n",
       "4    c     4  NaN   NaN\n",
       "5    b     5  NaN   NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data"
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  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "97cade1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(data1, data2, left_on='two1', right_index=True, how='outer')\n",
    "# 根据one列进行横向合并，左边根据two1，右边根据index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f25ea6a2",
   "metadata": {},
   "outputs": [
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       "  one1  two1 one2  two2\n",
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       "3    a     3    d  13.0\n",
       "4    c     4  NaN   NaN\n",
       "5    b     5  NaN   NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data"
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  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "30819983",
   "metadata": {},
   "outputs": [
    {
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       "  one1  two1\n",
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       "3    a     3\n",
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       "5    b     5"
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     "execution_count": 36,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "11f3a1e7",
   "metadata": {},
   "outputs": [
    {
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       "  one2  two2\n",
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "545ddb8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data1.iloc[:4]"
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  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1766c271",
   "metadata": {},
   "outputs": [
    {
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       "      <td>b</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>d</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  one1  two1 one2  two2\n",
       "0    a     0    a    10\n",
       "1    b     1    b    11\n",
       "2    a     2    c    12\n",
       "3    a     3    d    13"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.join(data2)  #快速合并，根据index，且没有相同的列，index不同也可以"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd565d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.DataFrame({'one1':['a','b','a','a','c','b'], 'two1':range(6)})\n",
    "data2 = pd.DataFrame({'one2':['a','b','c','d'],'two2':range(10,14)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7aae6e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>b</td>\n",
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       "      <td>b</td>\n",
       "      <td>11.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>a</td>\n",
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       "      <th>3</th>\n",
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       "      <td>13.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>c</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  one1  two1 one2  two2\n",
       "0    a     0    a  10.0\n",
       "1    b     1    b  11.0\n",
       "2    a     2    c  12.0\n",
       "3    a     3    d  13.0\n",
       "4    c     4  NaN   NaN\n",
       "5    b     5  NaN   NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.join(data2)  # 以data1为准，join data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e7a1fce9",
   "metadata": {},
   "outputs": [
    {
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       "  one1  two1\n",
       "0    a     0\n",
       "1    b     1\n",
       "2    a     2\n",
       "3    a     3\n",
       "4    c     4\n",
       "5    b     5"
      ]
     },
     "execution_count": 44,
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   ],
   "source": [
    "data1"
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  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dc2a4549",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  one1  two1  three\n",
       "0    a     0      0\n",
       "1    b     1      1\n",
       "2    a     2      2\n",
       "3    a     3      3\n",
       "4    c     4      4\n",
       "5    b     5      5"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.assign(three=np.arange(6))  # 直接增加一列"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b231fea1",
   "metadata": {},
   "source": [
    "# 纵向合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ad0028ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.DataFrame(np.random.randn(3,4), columns=['a','b','c','d'])\n",
    "data2 = pd.DataFrame(np.random.randn(2,3), columns=['b','c','a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e614add6",
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       "      <td>1.307029</td>\n",
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       "          a         b         c         d\n",
       "0 -0.314425 -0.571201  0.202219  0.823889\n",
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      ]
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     "metadata": {},
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    "data1"
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  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "abd7a740",
   "metadata": {},
   "outputs": [
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       "          b         c         a\n",
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d77045eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data1, data2])"
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  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3e23716f",
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       "          a         b         c         d\n",
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       "1  0.956227 -0.681311  1.236160       NaN"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fef5d02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data1, data2], ignore_index=True)  # 更新index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ab40f1b1",
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   "outputs": [
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       "          a         b         c         d\n",
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     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dae63916",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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