diff --git a/ch-pandas/data-preprocessing.ipynb b/ch-pandas/data-preprocessing.ipynb
index a535021..4823db9 100644
--- a/ch-pandas/data-preprocessing.ipynb
+++ b/ch-pandas/data-preprocessing.ipynb
@@ -68,7 +68,7 @@
    "source": [
     "## 处理重复值\n",
     "\n",
-    "检测数据集的记录是否存在重复，可以使用 `.duplicated` 函数进行验证，但是该函数返回的是数据集每一行的检测结果，即 n 行数据会返回 n 个布尔值。为了能够得到最直接的结果，可以使用 `any` 函数。该函数表示的是在多个条件判断中，只要有一个条件为 True，则 `any` 返回的结果为 True。"
+    "检测数据集的记录是否存在重复，可以使用 `.duplicated()` 函数进行验证，但是该函数返回的是数据集每一行的检测结果，即 n 行数据会返回 n 个布尔值。为了能够得到最直接的结果，可以使用 `any` 函数。该函数表示的是在多个条件判断中，只要有一个条件为 True，则 `any` 返回的结果为 True。"
    ]
   },
   {
@@ -104,7 +104,7 @@
    "id": "610f3c20",
    "metadata": {},
    "source": [
-    "如果有重复项，可以通过 `.drop_duplicated()` 删除。该函数有 inplace 参数，设置为 True 表示直接在原始数据集上做操作：`df.drop_duplicated(inplace = True)`。\n",
+    "如果有重复项，可以通过 `.drop_duplicated()` 删除。该函数有 `inplace` 参数，设置为 True 表示直接在原始数据集上做操作：`df.drop_duplicated(inplace = True)`。\n",
     "\n",
     "## 处理缺失值\n",
     "\n",
@@ -191,7 +191,7 @@
     "  \n",
     "可以使用 `.dropna()` 函数删除有缺失值的行或列。具体形式：`df.dropna(axis=0, how='any', inplace=False)`。\n",
     "\n",
-    "这个函数有参数 `axis`，`axis` 用来指定要删除的轴。`axis=0` 表示删除行（默认），axis=1 表示删除列。`how` 用来指定删除的条件。`how='any'` 表示删除包含任何缺失值的行（默认），`how='all'` 表示只删除所有值都是缺失值的行。`inplace` 用于指定是否在原始 `DataFrame` 上进行修改，默认为 False，表示不修改原始 `DataFrame`，而是返回一个新的 `DataFrame`。\n",
+    "这个函数有参数 `axis`，`axis` 用来指定要删除的轴。`axis=0` 表示删除行（默认），`axis=1` 表示删除列。`how` 用来指定删除的条件。`how='any'` 表示删除包含任何缺失值的行（默认），`how='all'` 表示只删除所有值都是缺失值的行。`inplace` 用于指定是否在原始 `DataFrame` 上进行修改，默认为 False，表示不修改原始 `DataFrame`，而是返回一个新的 `DataFrame`。\n",
     "\n",
     "例如，删除包含任何缺失值的行。"
    ]
@@ -697,7 +697,7 @@
    "id": "480ca978",
    "metadata": {},
    "source": [
-    "在这个例子中，`apply` 有个参数为 `axis`，`axis = 1` 设置函数对每一行操作；`axis = 0`` 设置函数对每一列操作；默认 axis = 0。\n",
+    "在这个例子中，`apply` 有个参数为 `axis`，`axis = 1` 设置函数对每一行操作；`axis = 0` 设置函数对每一列操作；默认 `axis = 0`。\n",
     "\n",
     "例：和 `.loc[]` 一起使用，进行更高级的数据切片。`.apply()` 返回对每一行做条件判断的一系列布尔值，以 `[]` 操作选择部分列。下面的选择条件为：如果 `country` 列属于特定国家，且 `POP > 40000`；如果 `country` 列不属于特定国家，且 `POP < 20000`"
    ]
diff --git a/ch-pandas/dataframe-slicing.ipynb b/ch-pandas/dataframe-slicing.ipynb
index b8552c0..950dc16 100644
--- a/ch-pandas/dataframe-slicing.ipynb
+++ b/ch-pandas/dataframe-slicing.ipynb
@@ -81,9 +81,16 @@
     "* 使用 `.iloc` 或者 `.loc` 函数\n",
     "* 使用 `.query` 函数\n",
     "\n",
-    "### 使用 [] 进行选择\n",
+    "### 使用 `[]` 进行选择\n",
+    "- 选择行\n",
     "\n",
-    "选择第 2 行到第 5 行（不包括第 5 行）的数据："
+    "直接使用数字索引即可，`df[a,b]`表示选择 `DataFrame` 的第`a`行到第`b-1`行。\n",
+    "\n",
+    "```{note}\n",
+    "Python中的索引区间都是左闭右开区间，这意味着左边端点可以取到，而右边端点取不到。\n",
+    "```\n",
+    "\n",
+    "例：对上一章节的PWT案例数据 df 选择第 2 行到第 5 行（不包括第 5 行）的数据。\n"
    ]
   },
   {
@@ -254,7 +261,11 @@
    "id": "eb81787d",
    "metadata": {},
    "source": [
-    "要选择列，我们可以传递一个列表，其中包含所需列的列名，为字符串形式。"
+    "- 选择列\n",
+    "\n",
+    "我们可以传递一个列表，其中包含所需列的列名，为字符串形式。\n",
+    "\n",
+    "例：选择 country 和 tcgdp 两列。"
    ]
   },
   {
@@ -389,7 +400,9 @@
    "source": [
     "如果只选取一列，`df['country']` 等价于 `df.country`。\n",
     "\n",
-    "`[]` 还可以用来选择符合特定条件的数据。 例如，选取 POP 大于 20000 的行。判断语句 `df.POP> 20000` 会返回一系列布尔值，符合 POP 大于 20000 条件的会返回为 `True`。如果想要选择这些符合条件的数据，则需要："
+    "- `[]` 选择符合特定条件的数据。 \n",
+    "\n",
+    "例如，选取 POP 大于 20000 的行。判断语句 `df.POP> 20000` 会返回一系列布尔值，符合 POP 大于 20000 条件的会返回为 `True`。如果想要选择这些符合条件的数据，则需要："
    ]
   },
   {
@@ -789,7 +802,7 @@
    "id": "9b41ebb1",
    "metadata": {},
    "source": [
-    "如果选择 cc 列和 cg 列的和大于 80 并且 POP 小于 20000 的行："
+    "例：选择 cc 列和 cg 列的和大于 80 并且 POP 小于 20000 的行。"
    ]
   },
   {
@@ -1287,7 +1300,7 @@
    "source": [
     "使用 `loc` 函数进行选择，与 `iloc` 的区别在于，`loc` 除了接受整数外，还可以接受标签（`a`、`b` 这样的列名）、表示整数位置的 index、`boolean` 。\n",
     "\n",
-    "选择第 2 行到第 5 行（不包括第 5 行），`country` 和 `tcgdp` 列："
+    "例：选择第 2 行到第 5 行（不包括第 5 行），country 和 tcgdp 列。"
    ]
   },
   {
@@ -1369,7 +1382,7 @@
    "id": "44f9c427",
    "metadata": {},
    "source": [
-    "使用 `loc` 函数选择 POP 列最大值的行："
+    "例：使用 `loc` 函数选择 POP 列最大值的行。"
    ]
   },
   {
@@ -1517,7 +1530,9 @@
    "id": "97dd2bd9",
    "metadata": {},
    "source": [
-    "还可以使用这种形式：`.loc[,]`，两个参数用逗号隔开，第一个参数接受条件，第二个参数接受我们想要返回的列名，得到的是符合条件的特定的列。"
+    "还可以使用这种形式：`.loc[,]`，两个参数用逗号隔开，第一个参数接受条件，第二个参数接受我们想要返回的列名，得到的是符合条件的特定的列。\n",
+    "\n",
+    "例：选择满足 cc 列加 cg 列大于等于80，POP小于等于20000条件的 country, year, POP 三列。"
    ]
   },
   {
@@ -2455,8 +2470,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "pyds",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/ch-pandas/series-dataframe.ipynb b/ch-pandas/series-dataframe.ipynb
index d579fa3..b17e0bf 100644
--- a/ch-pandas/series-dataframe.ipynb
+++ b/ch-pandas/series-dataframe.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 31,
    "id": "c3935c76",
    "metadata": {
     "execution": {
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 32,
    "id": "9f760577",
    "metadata": {
     "execution": {
@@ -66,9 +66,25 @@
      "shell.execute_reply": "2023-09-11T14:28:40.645276Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    3\n",
+       "3    4\n",
+       "Name: my_series, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "s = pd.Series([1, 2, 3, 4], name = 'my_series')"
+    "s = pd.Series([1, 2, 3, 4], name = 'my_series')\n",
+    "s"
    ]
   },
   {
@@ -78,12 +94,16 @@
    "source": [
     "`Series` 是一个数组状数据结构，其实就是 {numref}`numpy-ndarray` 中的 `ndarray`。 数组最重要的结构是索引（Index）。Index 主要用于标记第几个位置存储什么数据。`pd.Series()` 中不指定 Index 参数时，默认从 0 开始，逐一自增，形如： 0，1，...\n",
     "\n",
-    "- Series 支持计算操作。"
+    "- `Series` 支持计算操作。\n",
+    "\n",
+    "  可以对Series对象执行基本的数学运算，如加法、减法、乘法和除法。\n",
+    "\n",
+    "例：对上述构建的 s，进行乘法操作。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 33,
    "id": "94626599",
    "metadata": {
     "execution": {
@@ -104,7 +124,7 @@
        "Name: my_series, dtype: int64"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -113,17 +133,75 @@
     "s * 100"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以对多个 `Series` 对象进行数学操作。\n",
+    "```{note}\n",
+    "当多个 Series 对象操作时，如果形状不同，比如 s1 有 4 个数，s2 有 5 个数，s1 + s2 操作后，返回结果会有 5 个数，但是第 5 个数为 NaN 值。\n",
+    "```\n",
+    "\n",
+    "例：构建 s2，与 s 进行加减乘除操作。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "s+s2结果为\n",
+      "0    2\n",
+      "1    4\n",
+      "2    6\n",
+      "3    8\n",
+      "dtype: int64\n",
+      "s-s2结果为\n",
+      "0    0\n",
+      "1    0\n",
+      "2    0\n",
+      "3    0\n",
+      "dtype: int64\n",
+      "s*s2结果为\n",
+      "0     1\n",
+      "1     4\n",
+      "2     9\n",
+      "3    16\n",
+      "dtype: int64\n",
+      "s/s2结果为\n",
+      "0    1.0\n",
+      "1    1.0\n",
+      "2    1.0\n",
+      "3    1.0\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "s2 = pd.Series([1, 2, 3, 4])\n",
+    "print('s+s2结果为\\n{}'.format(s+s2))\n",
+    "print('s-s2结果为\\n{}'.format(s-s2))\n",
+    "print('s*s2结果为\\n{}'.format(s*s2))\n",
+    "print('s/s2结果为\\n{}'.format(s/s2))\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ee28bbf0",
    "metadata": {},
    "source": [
-    "- Series 支持描述性统计。比如，获得所有统计信息。"
+    "- `Series` 支持描述性统计，可以使用`.describe()`方法同时获取 [计数、均值、标准差、最小值，25%分位数，50%分位数，75%分位数和最大值] 的统计信息，也可以使用`.max()`等特定的统计量方法单独获取对应的信息。\n",
+    "\n",
+    "例：对上例 s 获得所有统计信息。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "id": "3ccd82a9",
    "metadata": {
     "execution": {
@@ -148,7 +226,7 @@
        "Name: my_series, dtype: float64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,12 +240,12 @@
    "id": "fb61cc9f",
    "metadata": {},
    "source": [
-    "计算平均值，中位数和标准差。"
+    "例：单独计算平均值，中位数和标准差。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "id": "4e7d31be",
    "metadata": {
     "execution": {
@@ -184,7 +262,7 @@
        "2.5"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "id": "2c7599d6",
    "metadata": {
     "execution": {
@@ -212,7 +290,7 @@
        "2.5"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "id": "0c3aab52",
    "metadata": {
     "execution": {
@@ -240,7 +318,7 @@
        "1.2909944487358056"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,12 +332,16 @@
    "id": "b9aafec8",
    "metadata": {},
    "source": [
-    "- Series 的索引很灵活。"
+    "- `Series` 的索引很灵活。\n",
+    "\n",
+    "除了上述默认的序数 index 作为索引，也可以自定义索引方式。\n",
+    "\n",
+    "例：将 s 的 0，1，2，3 的索引依次改为 number1, number2, number3，number4。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 63,
    "id": "55e7037b",
    "metadata": {
     "execution": {
@@ -279,12 +361,14 @@
    "id": "c3c4636d",
    "metadata": {},
    "source": [
-    "这时，`Series` 就像一个 Python 中的字典 `dict`，可以使用像 `dict` 一样的语法来访问 `Series` 中的元素，其中 `index` 相当于 `dict` 的键 `key`。例如，使用 `[]` 操作符访问 `number1` 对应的值。"
+    "这时，`Series` 就像一个 Python 中的字典 `dict`，可以使用像 `dict` 一样的语法来访问 `Series` 中的元素，其中 `index` 相当于 `dict` 的键 `key`。\n",
+    "\n",
+    "例如，使用 `[]` 操作符访问 `number1` 对应的值。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 64,
    "id": "a9287533",
    "metadata": {
     "execution": {
@@ -301,7 +385,7 @@
        "1"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -320,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 65,
    "id": "7cd8388f",
    "metadata": {
     "execution": {
@@ -337,7 +421,7 @@
        "True"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -346,6 +430,41 @@
     "'number1' in s"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- `value_counts()`计算`Series`中唯一值的频率，这个方法返回一个索引为唯一值，值为对应频率的新的`Series`。\n",
+    "\n",
+    "这种方法一方面可以快速了解数据集中各个值的分布情况，知道是否有异常值、缺失值或者某些值的频率很低，也有利于后续进行一些可视化处理。\n",
+    "\n",
+    "例：对s3序列进行计数，可见A出现的频率最高而C出现的频率最低。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    4\n",
+       "B    3\n",
+       "C    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s3 = pd.Series(['A', 'B', 'A', 'C', 'B', 'A', 'A', 'B']) \n",
+    "s3.value_counts()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "803dff93",
@@ -368,12 +487,14 @@
     "\n",
     "创建一个 `DataFrame` 有很多方式，比如从列表、字典、文件中读取数据，并创建一个 `DataFrame`。\n",
     "\n",
-    "- 基于列表创建"
+    "- 基于列表创建\n",
+    "\n",
+    "例：创建一个第一列为 Name，第二列为 Age，第三列为 City 的 `DataFrame`。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 66,
    "id": "29fda5e0",
    "metadata": {
     "execution": {
@@ -383,13 +504,75 @@
      "shell.execute_reply": "2023-09-11T14:28:40.759646Z"
     }
    },
-   "outputs": [],
+   "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>Name</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>City</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>25</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>30</td>\n",
+       "      <td>San Francisco</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Charlie</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name  Age           City\n",
+       "0    Alice   25       New York\n",
+       "1      Bob   30  San Francisco\n",
+       "2  Charlie   22    Los Angeles"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "names = ['Alice', 'Bob', 'Charlie']\n",
     "ages = [25, 30, 22]\n",
     "cities = ['New York', 'San Francisco', 'Los Angeles']\n",
     "data = {'Name': names, 'Age': ages, 'City': cities}\n",
-    "df = pd.DataFrame(data)"
+    "df = pd.DataFrame(data)\n",
+    "df"
    ]
   },
   {
@@ -397,12 +580,14 @@
    "id": "44c4ceb3",
    "metadata": {},
    "source": [
-    "- 基于字典创建"
+    "- 基于字典创建\n",
+    "\n",
+    "例：创建一个第一列为 Column1，第二列为 Column2 的 `DataFrame`。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 67,
    "id": "12cbb41a",
    "metadata": {
     "execution": {
@@ -412,10 +597,62 @@
      "shell.execute_reply": "2023-09-11T14:28:40.765960Z"
     }
    },
-   "outputs": [],
+   "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>Column1</th>\n",
+       "      <th>Column2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Column1  Column2\n",
+       "0        1        3\n",
+       "1        2        4"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data = {'Column1': [1, 2], 'Column2': [3, 4]}\n",
-    "df = pd.DataFrame(data)"
+    "df = pd.DataFrame(data)\n",
+    "df"
    ]
   },
   {
@@ -478,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -523,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -608,7 +845,7 @@
        "max    2.000000  4.000000"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -630,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -697,7 +934,7 @@
        "mean        NaN      3.5"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -720,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 71,
    "id": "ec624d37",
    "metadata": {
     "execution": {
@@ -777,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 72,
    "id": "c052691d",
    "metadata": {
     "execution": {
@@ -790,7 +1027,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "\n",
+    "# 注：直接输入文件绝对路径即可，这里的 os.path.join 是将文件夹的路径和文件名结合一起\n",
     "df = pd.read_csv(os.path.join(folder_path, \"pwt70_w_country_names.csv\"))"
    ]
   },
@@ -804,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 73,
    "id": "23a5ed20",
    "metadata": {
     "execution": {
@@ -1010,7 +1247,7 @@
        "[5 rows x 37 columns]"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1030,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 74,
    "id": "f84177de",
    "metadata": {
     "execution": {
@@ -1243,7 +1480,7 @@
        "[5 rows x 37 columns]"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1262,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 75,
    "id": "5f0544f4",
    "metadata": {
     "execution": {
@@ -1338,7 +1575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 76,
    "id": "821c9174",
    "metadata": {
     "execution": {
@@ -1392,7 +1629,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1411,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 77,
    "id": "aae06a0c",
    "metadata": {
     "execution": {
@@ -1433,7 +1670,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1448,18 +1685,434 @@
    "source": [
     "- `rename()` 函数既可以用于更改行标签，也可以用于列标签。传入一个字典，其中键为当前名称，值为新名称，以更新相应的名称。\n",
     "\n",
-    "例：\n",
-    "1. 将 year 改为 Year，country 改为 Country：\n",
-    "\n",
-    "```\n",
-    "df_renamed = df.rename(columns={'year':Year, 'country':'Country'})\n",
-    "```\n",
-    "\n",
-    "2. 将所有列名改为小写：\n",
-    "\n",
-    "```\n",
-    "df_renamed = df.rename(columns=str.lower)\n",
-    "```"
+    "例：将year改为Year，country改为Country。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "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>Country</th>\n",
+       "      <th>isocode</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>POP</th>\n",
+       "      <th>XRAT</th>\n",
+       "      <th>Currency_Unit</th>\n",
+       "      <th>ppp</th>\n",
+       "      <th>tcgdp</th>\n",
+       "      <th>cgdp</th>\n",
+       "      <th>cgdp2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>kg</th>\n",
+       "      <th>ki</th>\n",
+       "      <th>openk</th>\n",
+       "      <th>rgdpeqa</th>\n",
+       "      <th>rgdpwok</th>\n",
+       "      <th>rgdpl2wok</th>\n",
+       "      <th>rgdpl2pe</th>\n",
+       "      <th>rgdpl2te</th>\n",
+       "      <th>rgdpl2th</th>\n",
+       "      <th>rgdptt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1950</td>\n",
+       "      <td>8150.368</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>8284.473</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425.333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1953</td>\n",
+       "      <td>8573.217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1954</td>\n",
+       "      <td>8728.408</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Country isocode  Year       POP  XRAT Currency_Unit  ppp  tcgdp  cgdp  \\\n",
+       "0  Afghanistan     AFG  1950  8150.368   NaN           NaN  NaN    NaN   NaN   \n",
+       "1  Afghanistan     AFG  1951  8284.473   NaN           NaN  NaN    NaN   NaN   \n",
+       "2  Afghanistan     AFG  1952  8425.333   NaN           NaN  NaN    NaN   NaN   \n",
+       "3  Afghanistan     AFG  1953  8573.217   NaN           NaN  NaN    NaN   NaN   \n",
+       "4  Afghanistan     AFG  1954  8728.408   NaN           NaN  NaN    NaN   NaN   \n",
+       "\n",
+       "   cgdp2  ...  kg  ki  openk  rgdpeqa  rgdpwok  rgdpl2wok  rgdpl2pe  rgdpl2te  \\\n",
+       "0    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "1    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "2    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "3    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "4    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "\n",
+       "   rgdpl2th  rgdptt  \n",
+       "0       NaN     NaN  \n",
+       "1       NaN     NaN  \n",
+       "2       NaN     NaN  \n",
+       "3       NaN     NaN  \n",
+       "4       NaN     NaN  \n",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_renamed = df.rename(columns={'year':'Year','country':'Country'})\n",
+    "df_renamed.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  例：将所有列名改为小写。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "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>country</th>\n",
+       "      <th>isocode</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>xrat</th>\n",
+       "      <th>currency_unit</th>\n",
+       "      <th>ppp</th>\n",
+       "      <th>tcgdp</th>\n",
+       "      <th>cgdp</th>\n",
+       "      <th>cgdp2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>kg</th>\n",
+       "      <th>ki</th>\n",
+       "      <th>openk</th>\n",
+       "      <th>rgdpeqa</th>\n",
+       "      <th>rgdpwok</th>\n",
+       "      <th>rgdpl2wok</th>\n",
+       "      <th>rgdpl2pe</th>\n",
+       "      <th>rgdpl2te</th>\n",
+       "      <th>rgdpl2th</th>\n",
+       "      <th>rgdptt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1950</td>\n",
+       "      <td>8150.368</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>8284.473</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425.333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1953</td>\n",
+       "      <td>8573.217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>1954</td>\n",
+       "      <td>8728.408</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country isocode  year       pop  xrat currency_unit  ppp  tcgdp  cgdp  \\\n",
+       "0  Afghanistan     AFG  1950  8150.368   NaN           NaN  NaN    NaN   NaN   \n",
+       "1  Afghanistan     AFG  1951  8284.473   NaN           NaN  NaN    NaN   NaN   \n",
+       "2  Afghanistan     AFG  1952  8425.333   NaN           NaN  NaN    NaN   NaN   \n",
+       "3  Afghanistan     AFG  1953  8573.217   NaN           NaN  NaN    NaN   NaN   \n",
+       "4  Afghanistan     AFG  1954  8728.408   NaN           NaN  NaN    NaN   NaN   \n",
+       "\n",
+       "   cgdp2  ...  kg  ki  openk  rgdpeqa  rgdpwok  rgdpl2wok  rgdpl2pe  rgdpl2te  \\\n",
+       "0    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "1    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "2    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "3    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "4    NaN  ... NaN NaN    NaN      NaN      NaN        NaN       NaN       NaN   \n",
+       "\n",
+       "   rgdpl2th  rgdptt  \n",
+       "0       NaN     NaN  \n",
+       "1       NaN     NaN  \n",
+       "2       NaN     NaN  \n",
+       "3       NaN     NaN  \n",
+       "4       NaN     NaN  \n",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_renamed = df.rename(columns=str.lower)\n",
+    "df_renamed.head(5)"
    ]
   },
   {
@@ -1472,7 +2125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 80,
    "id": "9e1b3c2f",
    "metadata": {
     "execution": {
@@ -1489,7 +2142,7 @@
        "RangeIndex(start=0, stop=11400, step=1)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1508,7 +2161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 81,
    "id": "ccc0d0c6",
    "metadata": {
     "execution": {