From 43bfae8d03f2e473ccea5962a75743b1fa864c21 Mon Sep 17 00:00:00 2001
From: Lu Weizheng <luweizheng36@hotmail.com>
Date: Wed, 29 Nov 2023 13:51:45 +0800
Subject: [PATCH] update pandas

---
 ch-pandas/dataframe-merge-concat.ipynb        |  1 +
 ch-pandas/dataframe-pivot-melt.ipynb          | 60 ++++++++-----------
 .../ch-pandas/dataframe-merge-concat.ipynb    |  1 +
 .../ch-pandas/dataframe-pivot-melt.ipynb      | 60 ++++++++-----------
 docs/ch-pandas/dataframe-merge-concat.html    |  3 +-
 docs/ch-pandas/dataframe-pivot-melt.html      | 12 ++--
 docs/searchindex.js                           |  2 +-
 7 files changed, 61 insertions(+), 78 deletions(-)

diff --git a/ch-pandas/dataframe-merge-concat.ipynb b/ch-pandas/dataframe-merge-concat.ipynb
index 335c76b..19edd66 100644
--- a/ch-pandas/dataframe-merge-concat.ipynb
+++ b/ch-pandas/dataframe-merge-concat.ipynb
@@ -15,6 +15,7 @@
     "\n",
     "```{figure} ../img/ch-pandas/merge.svg\n",
     "---\n",
+    "width: 500px\n",
     "name: merge-img\n",
     "---\n",
     "对两个 DataFrame 进行 merge 操作\n",
diff --git a/ch-pandas/dataframe-pivot-melt.ipynb b/ch-pandas/dataframe-pivot-melt.ipynb
index f8df793..100727c 100644
--- a/ch-pandas/dataframe-pivot-melt.ipynb
+++ b/ch-pandas/dataframe-pivot-melt.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 34,
    "metadata": {
     "tags": [
      "hide cell",
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -333,7 +333,7 @@
        "16      Monitor         2    5000  presented  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +484,7 @@
        "Trantow-Barrows                15000.0  1.333333"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -579,7 +579,7 @@
        "              Wendy Yule     44250.000000  3.000000"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -736,7 +736,7 @@
        "              Wendy Yule            3.0     2.0      NaN  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -765,7 +765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -835,7 +835,7 @@
        "              Wendy Yule     44250.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -857,17 +857,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_34035/1265594602.py:1: FutureWarning: The provided callable <function mean at 0x7fedb7434dc0> is currently using DataFrameGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
-      "  pd.pivot_table(sales_df, index=[\"Manager\",\"Rep\"], values=[\"Price\"], aggfunc=[np.mean, len])\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -953,13 +945,13 @@
        "              Wendy Yule     44250.000000     4"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pd.pivot_table(sales_df, index=[\"Manager\",\"Rep\"], values=[\"Price\"], aggfunc=[np.mean, len])"
+    "pd.pivot_table(sales_df, index=[\"Manager\",\"Rep\"], values=[\"Price\"], aggfunc=[\"mean\", len])"
    ]
   },
   {
@@ -973,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1185,7 +1177,7 @@
        "All                                      1.764706  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1207,7 +1199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1401,7 +1393,7 @@
        "              won                 1       0        0  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1429,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1562,7 +1554,7 @@
        "             won                 0       0        0  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1580,7 +1572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1714,7 +1706,7 @@
        "              won               1       0        0  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1734,7 +1726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1801,7 +1793,7 @@
        "2           3  John    88       79       90"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1820,7 +1812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1931,7 +1923,7 @@
        "8           3  John  History     90"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/_sources/ch-pandas/dataframe-merge-concat.ipynb b/docs/_sources/ch-pandas/dataframe-merge-concat.ipynb
index 335c76b..19edd66 100644
--- a/docs/_sources/ch-pandas/dataframe-merge-concat.ipynb
+++ b/docs/_sources/ch-pandas/dataframe-merge-concat.ipynb
@@ -15,6 +15,7 @@
     "\n",
     "```{figure} ../img/ch-pandas/merge.svg\n",
     "---\n",
+    "width: 500px\n",
     "name: merge-img\n",
     "---\n",
     "对两个 DataFrame 进行 merge 操作\n",
diff --git a/docs/_sources/ch-pandas/dataframe-pivot-melt.ipynb b/docs/_sources/ch-pandas/dataframe-pivot-melt.ipynb
index f8df793..100727c 100644
--- a/docs/_sources/ch-pandas/dataframe-pivot-melt.ipynb
+++ b/docs/_sources/ch-pandas/dataframe-pivot-melt.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 34,
    "metadata": {
     "tags": [
      "hide cell",
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -333,7 +333,7 @@
        "16      Monitor         2    5000  presented  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +484,7 @@
        "Trantow-Barrows                15000.0  1.333333"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -579,7 +579,7 @@
        "              Wendy Yule     44250.000000  3.000000"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -736,7 +736,7 @@
        "              Wendy Yule            3.0     2.0      NaN  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -765,7 +765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -835,7 +835,7 @@
        "              Wendy Yule     44250.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -857,17 +857,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_34035/1265594602.py:1: FutureWarning: The provided callable <function mean at 0x7fedb7434dc0> is currently using DataFrameGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
-      "  pd.pivot_table(sales_df, index=[\"Manager\",\"Rep\"], values=[\"Price\"], aggfunc=[np.mean, len])\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -953,13 +945,13 @@
        "              Wendy Yule     44250.000000     4"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pd.pivot_table(sales_df, index=[\"Manager\",\"Rep\"], values=[\"Price\"], aggfunc=[np.mean, len])"
+    "pd.pivot_table(sales_df, index=[\"Manager\",\"Rep\"], values=[\"Price\"], aggfunc=[\"mean\", len])"
    ]
   },
   {
@@ -973,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1185,7 +1177,7 @@
        "All                                      1.764706  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1207,7 +1199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1401,7 +1393,7 @@
        "              won                 1       0        0  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1429,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1562,7 +1554,7 @@
        "             won                 0       0        0  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1580,7 +1572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1714,7 +1706,7 @@
        "              won               1       0        0  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1734,7 +1726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1801,7 +1793,7 @@
        "2           3  John    88       79       90"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1820,7 +1812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1931,7 +1923,7 @@
        "8           3  John  History     90"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/ch-pandas/dataframe-merge-concat.html b/docs/ch-pandas/dataframe-merge-concat.html
index f5dc43c..79ac4e4 100644
--- a/docs/ch-pandas/dataframe-merge-concat.html
+++ b/docs/ch-pandas/dataframe-merge-concat.html
@@ -416,7 +416,8 @@ <h1><span class="section-number">2.6. </span>多表操作<a class="headerlink" h
 <h2><span class="section-number">2.6.1. </span>merge<a class="headerlink" href="#merge" title="Permalink to this heading">#</a></h2>
 <p>pandas 的 <code class="docutils literal notranslate"><span class="pre">merge()</span></code> 操作可以合并两个 <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>（或者称为表） ，类似于 SQL 中的 JOIN 操作。 我们可以想象成：一个大表被拆分成两个小表，两个小表都包含一些同样的数据。现在我们需要把两个小表合并，生成一个大表，大表包含了两个小表的字段。</p>
 <figure class="align-default" id="merge-img">
-<img alt="../_images/merge.svg" src="../_images/merge.svg" /><figcaption>
+<a class="reference internal image-reference" href="../_images/merge.svg"><img alt="../_images/merge.svg" src="../_images/merge.svg" width="500px" /></a>
+<figcaption>
 <p><span class="caption-number">图 2.8 </span><span class="caption-text">对两个 DataFrame 进行 merge 操作</span><a class="headerlink" href="#merge-img" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
diff --git a/docs/ch-pandas/dataframe-pivot-melt.html b/docs/ch-pandas/dataframe-pivot-melt.html
index adbcff1..1e915ee 100644
--- a/docs/ch-pandas/dataframe-pivot-melt.html
+++ b/docs/ch-pandas/dataframe-pivot-melt.html
@@ -1064,15 +1064,11 @@ <h3><span class="section-number">2.7.1.3. </span>aggfunc 参数<a class="headerl
 <p>例：对 Price 显示 <code class="docutils literal notranslate"><span class="pre">mean</span></code> 和 <code class="docutils literal notranslate"><span class="pre">len</span></code> 两列结果。</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">sales_df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Manager&quot;</span><span class="p">,</span><span class="s2">&quot;Rep&quot;</span><span class="p">],</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Price&quot;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="nb">len</span><span class="p">])</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">sales_df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Manager&quot;</span><span class="p">,</span><span class="s2">&quot;Rep&quot;</span><span class="p">],</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Price&quot;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_42863/1265594602.py:1: FutureWarning: The provided callable &lt;function mean at 0x10f8080e0&gt; is currently using DataFrameGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;mean&quot; instead.
-  pd.pivot_table(sales_df, index=[&quot;Manager&quot;,&quot;Rep&quot;], values=[&quot;Price&quot;], aggfunc=[np.mean, len])
-</pre></div>
-</div>
 <div class="output text_html"><div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -1157,9 +1153,9 @@ <h3><span class="section-number">2.7.1.4. </span>fill_value 和 margins 参数<a
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_42863/3052684454.py:1: FutureWarning: The provided callable &lt;function sum at 0x10b73afc0&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;sum&quot; instead.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_81386/3052684454.py:1: FutureWarning: The provided callable &lt;function sum at 0x110c4f060&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;sum&quot; instead.
   pd.pivot_table(sales_df,index=[&quot;Manager&quot;,&quot;Rep&quot;,&quot;Product&quot;],
-/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_42863/3052684454.py:1: FutureWarning: The provided callable &lt;function mean at 0x10f8080e0&gt; is currently using DataFrameGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;mean&quot; instead.
+/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_81386/3052684454.py:1: FutureWarning: The provided callable &lt;function mean at 0x110c60180&gt; is currently using DataFrameGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;mean&quot; instead.
   pd.pivot_table(sales_df,index=[&quot;Manager&quot;,&quot;Rep&quot;,&quot;Product&quot;],
 </pre></div>
 </div>
@@ -1338,7 +1334,7 @@ <h3><span class="section-number">2.7.1.5. </span>维度交叉<a class="headerlin
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_42863/1669582107.py:1: FutureWarning: The provided callable &lt;function mean at 0x10f8080e0&gt; is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;mean&quot; instead.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_81386/1669582107.py:1: FutureWarning: The provided callable &lt;function mean at 0x110c60180&gt; is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;mean&quot; instead.
   table = pd.pivot_table(sales_df,
 </pre></div>
 </div>
diff --git a/docs/searchindex.js b/docs/searchindex.js
index e07e2f2..c20b6d3 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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