From 7ac217dd91107af4fec40ad56f4bb9f3e37c2b5b Mon Sep 17 00:00:00 2001
From: XRubberDuck <xujingqiu@ruc.edu.cn>
Date: Sat, 23 Sep 2023 11:20:35 +0800
Subject: [PATCH 1/2] label

---
 ch-pandas/data-preprocessing.ipynb | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ch-pandas/data-preprocessing.ipynb b/ch-pandas/data-preprocessing.ipynb
index c042c80..2dab553 100644
--- a/ch-pandas/data-preprocessing.ipynb
+++ b/ch-pandas/data-preprocessing.ipynb
@@ -6,7 +6,6 @@
    "metadata": {},
    "source": [
     "# 数据处理\n",
-    ":label:`data-preprocessing`\n",
     "\n",
     "数据处理工作包括处理重复值、缺失值和异常值，生成新的列或者行等。"
    ]
@@ -3423,4 +3422,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}

From efa3ec9ee95ec1ddd8b72b5b404d89344953687b Mon Sep 17 00:00:00 2001
From: XRubberDuck <xujingqiu@ruc.edu.cn>
Date: Sat, 23 Sep 2023 11:28:00 +0800
Subject: [PATCH 2/2] remove label

---
 ch-pandas/dataframe-groupby.ipynb      | 1 -
 ch-pandas/dataframe-merge-concat.ipynb | 1 -
 ch-pandas/dataframe-slicing.ipynb      | 3 +--
 ch-pandas/series-dataframe.ipynb       | 3 +--
 4 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/ch-pandas/dataframe-groupby.ipynb b/ch-pandas/dataframe-groupby.ipynb
index fdda7b3..ef0615a 100644
--- a/ch-pandas/dataframe-groupby.ipynb
+++ b/ch-pandas/dataframe-groupby.ipynb
@@ -6,7 +6,6 @@
    "metadata": {},
    "source": [
     "# 分组汇总\n",
-    ":label:`dataframe-groupby`\n",
     "\n",
     "实际的数据分析中，经常需要对某一类数据进行统计分析。比如，假如我们拥有全国所有人的身高和体重数据，我们想按照省份分组，统计每个省的平均身高和平均体重，这时候就需要使用分组操作。pandas 提供了 `groupby` 函数进行类似的分组汇总操作。:numref:`groupby-img` 计算平均身高的分组汇总流程，主要包括两部分：分组与汇总。其中分组阶段将同一类的内容归结到相同的组中；汇总阶段将所关心的数据进行计算，比如求和、求平均等。\n",
     "\n",
diff --git a/ch-pandas/dataframe-merge-concat.ipynb b/ch-pandas/dataframe-merge-concat.ipynb
index 91e4f8a..ba65ce1 100644
--- a/ch-pandas/dataframe-merge-concat.ipynb
+++ b/ch-pandas/dataframe-merge-concat.ipynb
@@ -6,7 +6,6 @@
    "metadata": {},
    "source": [
     "# 多表操作\n",
-    ":label:`dataframe-merge-concat`\n",
     "\n",
     "之前的操作主要在单个 `DataFrame` ，实际上，我们经常需要对多个 `DataFrame` 联合起来进行分析。pandas 提供了多 `DataFrame` 之间的合并和连接的操作，分别是 `merge()` 和 `concat()` 函数。比如，我们可以将两个 `DataFrame` 合并成一个，且保留所有的列。\n",
     "\n",
diff --git a/ch-pandas/dataframe-slicing.ipynb b/ch-pandas/dataframe-slicing.ipynb
index be5c7ef..4f772b7 100644
--- a/ch-pandas/dataframe-slicing.ipynb
+++ b/ch-pandas/dataframe-slicing.ipynb
@@ -6,7 +6,6 @@
    "metadata": {},
    "source": [
     "# 数据切片\n",
-    ":label:`dataframe-slicing`\n",
     "\n",
     "实际中，我们常常不是分析整个数据，而是数据中的部分子集。如何根据特定的条件获得所需要的数据是本节的主要内容。"
    ]
@@ -2453,4 +2452,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ch-pandas/series-dataframe.ipynb b/ch-pandas/series-dataframe.ipynb
index 2afeb5d..b44f6b7 100644
--- a/ch-pandas/series-dataframe.ipynb
+++ b/ch-pandas/series-dataframe.ipynb
@@ -6,7 +6,6 @@
    "metadata": {},
    "source": [
     "# Series 与 DataFrame\n",
-    ":label:`series-dataframe`\n",
     "\n",
     "pandas 的核心数据结构有两个： Series 和 DataFrame。"
    ]
@@ -1218,4 +1217,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}