3. merge详解.ipynb

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   "source": [
    "# merge详解\n",
    "merge本来准备放在上一章的增那一节讲的，不过其算是关系数据库用得很多的一个操作，变化也较多，所以单独开一篇细讲这个函数。 "
   ]
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   "source": [
    "__auther__ = 'zhenhang.sun@gmail.com'"
   ]
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       "'D:\\\\github\\\\pandas-tutorial'"
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   "source": [
    "pwd"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 函数说明"
   ]
  },
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   "source": [
    "##### `pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False)`\n",
    "concat函数只能根据索引对齐，而如果想在任意**列**上对齐合并，则需要merge函数，其在sql应用很多。\n",
    "- left, right： 两个要对齐合并的DataFrame；\n",
    "- how： 先做笛卡尔积操作，然后按照要求，保留需要的，缺失的数据填充NaN；\n",
    "   - left: 以左DataFrame为基准，即左侧DataFrame的数据全部保留（不代表完全一致、可能会存在复制），保持原序;\n",
    "   - right: 以右DataFrame为基准，保持原序;\n",
    "   - inner: 交，保留左右DataFrame在on上完全一致的行，保持左DataFrame顺序;\n",
    "   - outer: 并，按照字典顺序重新排序;\n",
    "- on：对应列名或者行索引的名字，如果要在DataFrame相同的列索引做对齐，用这个参数；\n",
    "- left_on, right_on, left_index, right_index：\n",
    "   - on对应列名或者行索引的名字（所以行索引一般要跟列一样看待，有自己的名字），用这俩参数；\n",
    "   - index对应要使用的index,不建议使用，会搞晕。\n",
    "- sort: True or False，是否按字典序重新排序。"
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       "   A  B\n",
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   "source": [
    "df1 = pd.DataFrame([[1,2],[3,4]], index = ['a','b'],columns = ['A','B'])\n",
    "df2 = pd.DataFrame([[1,3],[4,8]], index = ['b','d'],columns = ['B','C'])\n",
    "df1"
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       "   B  C\n",
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    "df2"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如果单纯的按照index对齐，不如用concat方法。**"
   ]
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      "text/plain": [
       "   A  B_x  B_y  C\n",
       "b  3    4    1  3"
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   "source": [
    "pd.merge(left=df1, right= df2, how='inner' ,left_index=True, right_index=True)"
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   "execution_count": 7,
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       "   A  B  B  C\n",
       "b  3  4  1  3"
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   "source": [
    "# 小区别是concat对重复列没有重命名，但是重名的情况不多，而且重名了说明之前设计就不大合理。\n",
    "pd.concat([df1, df2], join='inner', axis =1)  "
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. on 用法\n",
    "设置 how='inner'"
   ]
  },
  {
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   "execution_count": 8,
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       "   A  B  C\n",
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   "source": [
    "#对于'B'列：df1的'b'行、df2的'd'行，是相同的，其他都不同。 \n",
    "pd.merge(left=df1, right=df2, how='inner', on=['B']) "
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   "execution_count": 9,
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       "   A  B_x  B_y  C\n",
       "0  3    4    1  3"
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   "source": [
    "# df1的'A'列'b'行，df2的'C'列'd'行是相同的，其他都不同。\n",
    "# 其他列如果同名会进行重命名。\n",
    "pd.merge(left=df1, right=df2, how='inner',left_on=['A'] ,right_on=['C'])"
   ]
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   "cell_type": "code",
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   "metadata": {},
   "source": [
    "# 3. how用法"
   ]
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       "   A  B    C\n",
       "0  1  2  NaN\n",
       "1  3  4  8.0"
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   "source": [
    "# 保持左侧DataFrame不变，用右侧来跟它对齐，对不上的填NaN。\n",
    "pd.merge(left=df1, right=df2, how='left', on=['B'] )"
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   "execution_count": 11,
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       "     A  B  C\n",
       "0  3.0  4  8\n",
       "1  NaN  1  3"
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     "execution_count": 11,
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   "source": [
    "# 保持右侧DataFrame不变，用右侧来跟它对齐，对不上的填NaN。\n",
    "pd.merge(left=df1, right=df2, how='right', on=['B'] )"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**对齐的列存在重复值，重复的也没关系，操作逻辑是一致的，完全可以假想不存在重复。**"
   ]
  },
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   "execution_count": 12,
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       "   A  B\n",
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    "df1.loc['a','B'] = 4  #改成重复\n",
    "df1"
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       "     A  B  C\n",
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       "1  3.0  4  8\n",
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   "source": [
    "### 保持右侧的列都在，如果左侧对齐的列存在重复值，那么对齐上后也存在重复。\n",
    "pd.merge(left=df1, right=df2, how='right', on=['B'] )"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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