D002_selecting/selecting_loc.ipynb

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    }
   },
   "source": [
    "import pandas as pd"
   ],
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df = pd.DataFrame([\n",
    "    [1, 2, 3, 4],\n",
    "    [5, 6, 7, 8],\n",
    "    [9, 10, 11, 12]],\n",
    "    columns=list('ABCD'),\n",
    "    index=list('XYZ'))\n",
    "df"
   ],
   "metadata": {
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   },
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc['Y']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:35.962719Z",
     "start_time": "2024-06-25T10:22:35.957994Z"
    }
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc['Y', 'C']"
   ],
   "metadata": {
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    "pycharm": {
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    },
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     "end_time": "2024-06-25T10:22:35.970214Z",
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    }
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc[['X', 'Y'], ['B', 'D']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
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    }
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc['Y':'Z', 'B':'D']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
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    }
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc[:, 'B':'D']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:35.992130Z",
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   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc[:, ::-1]"
   ],
   "metadata": {
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     "end_time": "2024-06-25T10:22:36.000450Z",
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    }
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc[::2, ::2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:36.008579Z",
     "start_time": "2024-06-25T10:22:36.002463Z"
    }
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc[[True, False, True],\n",
    "       [True, False, True, False]]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:36.018411Z",
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   },
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc[lambda df: [True, False, True],\n",
    "       lambda df: [True, False, True, False]]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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    }
   },
   "execution_count": 11,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df = pd.read_csv(\n",
    "    'tips.csv',\n",
    "    usecols=['total_bill','day','time']\n",
    ")\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
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     "end_time": "2024-06-25T10:22:36.037640Z",
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    }
   },
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.groupby(['day','time']).sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:36.046625Z",
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    }
   },
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# df.groupby(['day','time']).sum()[\n",
    "#     df[df['total_bill']>100]\n",
    "# ]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
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    },
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     "end_time": "2024-06-25T10:22:36.051832Z",
     "start_time": "2024-06-25T10:22:36.048626Z"
    }
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.groupby(['day','time']).sum().loc[\n",
    "    lambda df: df['total_bill']>100\n",
    "]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:36.061933Z",
     "start_time": "2024-06-25T10:22:36.053833Z"
    }
   },
   "execution_count": 15,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df = df.groupby(['day','time']).sum()\n",
    "df = df[df['total_bill']>100]\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:36.072177Z",
     "start_time": "2024-06-25T10:22:36.064929Z"
    }
   },
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df.loc['X', 'B'] == df.at['X', 'B']  # 报错\n",
    "df.loc[1, 2] == df.iat[1, 2]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-06-25T10:22:36.865521Z",
     "start_time": "2024-06-25T10:22:36.075216Z"
    }
   },
   "execution_count": 17,
   "outputs": []
  }
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