diff --git a/19_PPP.ipynb b/19_PPP.ipynb
index 419d8e90..1b3645b9 100644
--- a/19_PPP.ipynb
+++ b/19_PPP.ipynb
@@ -34,7 +34,7 @@
     "import statsmodels.formula.api as smf\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\n",
+    "np.set_printoptions(legacy='1.21')\n",
     "# 警告メッセージを非表示\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
@@ -44,7 +44,8 @@
    "cell_type": "markdown",
    "id": "7c0ddd8c",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## はじめに"
@@ -84,7 +85,8 @@
    "cell_type": "markdown",
    "id": "3647d0a7",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 国内での一物一価"
@@ -145,7 +147,8 @@
    "id": "b0c31a03",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### チョコレート"
@@ -379,7 +382,7 @@
    },
    "outputs": [],
    "source": [
-    "choco['mean_deviation'].hist(bins=12, ec='white')\n",
+    "choco.plot(y='mean_deviation', kind='hist', bins=12, ec='white')\n",
     "pass"
    ]
   },
@@ -513,7 +516,8 @@
    "id": "99fbafd9",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### アイスクリーム"
@@ -676,7 +680,7 @@
    "source": [
     "ice['mean_deviation'] =  ice['price'] / ice['price'].mean() - 1\n",
     "\n",
-    "ice['mean_deviation'].hist(bins=16, ec='white')\n",
+    "ice.plot(y='mean_deviation', kind='hist', bins=16, ec='white')\n",
     "pass"
    ]
   },
@@ -771,7 +775,8 @@
    "id": "b9a17c97",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### ガソリン"
@@ -934,7 +939,7 @@
    "source": [
     "gas['mean_deviation'] = gas['price'] / gas['price'].mean() - 1\n",
     "\n",
-    "gas['mean_deviation'].hist(ec='white')\n",
+    "gas.plot(y='mean_deviation', kind='hist', ec='white')\n",
     "pass"
    ]
   },
@@ -1013,7 +1018,8 @@
    "id": "cb38c1aa",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 結論"
@@ -1059,7 +1065,8 @@
    "cell_type": "markdown",
    "id": "0f726eb0",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "(sec:19-bigmac)=\n",
@@ -1107,7 +1114,8 @@
    "id": "f65cfb13",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### データ"
@@ -1527,7 +1535,7 @@
    },
    "outputs": [],
    "source": [
-    "bigmac['price_yen_deviation'].hist(ec='white')\n",
+    "bigmac.plot(y='price_yen_deviation', kind='hist', ec='white')\n",
     "pass"
    ]
   },
@@ -1689,7 +1697,8 @@
    "id": "c2ef0b6b",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 所得の影響"
@@ -1797,7 +1806,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = bigmac.plot('gdppc_yen', 'price_yen', kind='scatter', logx=True)\n",
+    "ax = bigmac.plot(x='gdppc_yen', y='price_yen', kind='scatter', logx=True)\n",
     "ax.set_xlabel('一人当たり実質GDP（円，常用対数）', fontsize=15)\n",
     "ax.set_ylabel('Big Mac価格（円）', fontsize=15)\n",
     "pass"
@@ -1854,7 +1863,8 @@
    "id": "69ef4782",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### PPPとは"
@@ -2002,7 +2012,8 @@
    "id": "e0407287",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### PPPレートと市場為替レートのデータ"
@@ -2288,7 +2299,8 @@
    "id": "af458300",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 相対一般物価水準の分布"
@@ -2385,7 +2397,7 @@
    "source": [
     "pwt19['pl_gdpo_log'] = np.log( pwt19['pl_gdpo'] )\n",
     "\n",
-    "pwt19['pl_gdpo_log'].plot(kind='hist', ec='white')\n",
+    "pwt19.plot(y='pl_gdpo_log', kind='hist', ec='white')\n",
     "pass"
    ]
   },
@@ -2586,7 +2598,8 @@
    "id": "639f4d19",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 所得の影響"
@@ -2716,7 +2729,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = pwt19.plot('cgdpo_pc_log', 'pl_gdpo_log', kind='scatter')\n",
+    "ax = pwt19.plot(x='cgdpo_pc_log', y='pl_gdpo_log', kind='scatter')\n",
     "ax.set_xlabel('一人当たりGDP（対数，百万米ドル）', fontsize=15)\n",
     "ax.set_ylabel('米国の2017年価格を基準とした\\n2019年相対価格（対数）', fontsize=15)\n",
     "pass"
@@ -2741,7 +2754,8 @@
    "id": "76176a3d",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 所得効果の分離"
@@ -2874,7 +2888,8 @@
    "id": "05a96845",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 推定"
@@ -2997,13 +3012,15 @@
    "outputs": [],
    "source": [
     "# 散布図\n",
-    "ax = pwt19.plot('cgdpo_pc_ratio_log', 'pl_gdpo_log', kind='scatter')\n",
+    "ax = pwt19.plot(x='cgdpo_pc_ratio_log', y='pl_gdpo_log', kind='scatter')\n",
     "ax.set_xlabel('米国を基準とした一人当たりGDP（対数，2019年）', fontsize=15)\n",
     "ax.set_ylabel('米国の2017年価格を基準とした\\n2019年相対価格（対数）', fontsize=15)\n",
     "\n",
     "# 回帰曲線\n",
-    "ax.plot('cgdpo_pc_ratio_log', 'fitted', color='red',\n",
-    "        data=pwt19.sort_values('cgdpo_pc_ratio_log'))  #1\n",
+    "pwt19.sort_values('cgdpo_pc_ratio_log').plot(x='cgdpo_pc_ratio_log',  #1\n",
+    "                                             y='fitted',\n",
+    "                                             color='red',\n",
+    "                                             ax=ax)\n",
     "pass"
    ]
   },
@@ -3024,7 +3041,8 @@
    "cell_type": "markdown",
    "id": "35b819d4",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### ヒストグラムの比較"
@@ -3111,7 +3129,8 @@
    "id": "9b98e6dd",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 所得効果の大きさ"
@@ -3351,7 +3370,8 @@
    "cell_type": "markdown",
    "id": "33fc0f87",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 所得効果の含意"
@@ -3412,7 +3432,8 @@
    "id": "bc5e0889",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### ペン効果"
@@ -3490,7 +3511,8 @@
    "id": "6d069838",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 一人当たりGDPの分布"
@@ -3581,7 +3603,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = pwt19.plot('cgdpo_pc_log','cgdpo_pc_xr_log', kind='scatter')\n",
+    "ax = pwt19.plot(x='cgdpo_pc_log', y='cgdpo_pc_xr_log', kind='scatter')\n",
     "ax.axline((9, 9), (10, 10), color='red', label='45度線')  #1\n",
     "ax.set_xlabel('一人当たりGDP（PPPレート）', fontsize=13)\n",
     "ax.set_ylabel('一人当たりGDP（市場為替レート）', fontsize=13)\n",
@@ -3631,7 +3653,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = pwt19[['cgdpo_pc_log','cgdpo_pc_xr_log']].plot(kind='kde')\n",
+    "ax = pwt19.plot(y=['cgdpo_pc_log','cgdpo_pc_xr_log'], kind='kde')\n",
     "ax.legend(['PPPレート','市場為替レート'], fontsize=13)\n",
     "ax.set_title('一人当たりGDP（米ドル，対数）', fontsize=20)\n",
     "pass"
@@ -3715,8 +3737,8 @@
    },
    "outputs": [],
    "source": [
-    "cc_ppp = pwt19['cgdpo_pc_log'].std()/pwt19['cgdpo_pc_log'].mean()\n",
-    "cc_xr = pwt19['cgdpo_pc_xr_log'].std()/pwt19['cgdpo_pc_xr_log'].mean()\n",
+    "cc_ppp = pwt19['cgdpo_pc_log'].std() / pwt19['cgdpo_pc_log'].mean()\n",
+    "cc_xr = pwt19['cgdpo_pc_xr_log'].std() / pwt19['cgdpo_pc_xr_log'].mean()\n",
     "\n",
     "print('--- GDPの変動係数 ---')\n",
     "print(f'PPPレート：{cc_ppp:>8.3f}')\n",
@@ -3737,7 +3759,8 @@
    "cell_type": "markdown",
    "id": "ed7a6eb0",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## まとめ"
diff --git a/5a_Development_Accounting.ipynb b/5a_Development_Accounting.ipynb
index f7e7868a..6878683d 100644
--- a/5a_Development_Accounting.ipynb
+++ b/5a_Development_Accounting.ipynb
@@ -34,7 +34,7 @@
     "import py4macro\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\n",
+    "np.set_printoptions(legacy='1.21')\n",
     "# 警告メッセージを非表示\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
diff --git a/5b_Growth_Accounting.ipynb b/5b_Growth_Accounting.ipynb
index fa9c511f..934eb9dd 100644
--- a/5b_Growth_Accounting.ipynb
+++ b/5b_Growth_Accounting.ipynb
@@ -30,7 +30,7 @@
     "import py4macro\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\n",
+    "np.set_printoptions(legacy='1.21')\n",
     "# 警告メッセージを非表示\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
@@ -497,7 +497,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_growth['gdppc'].plot(kind='hist',bins=15)\n",
+    "df_growth.plot(y='gdppc', kind='hist',bins=15)\n",
     "pass"
    ]
   },
@@ -590,7 +590,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "## 蓄積生産要素の成長率"
    ]
@@ -630,7 +632,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_growth['factors'].plot(kind='hist',bins=15)\n",
+    "df_growth.plot(y='factors', kind='hist',bins=15)\n",
     "pass"
    ]
   },
@@ -653,7 +655,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "## 全要素生産性"
    ]
@@ -691,7 +695,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_growth['tfp'].plot(kind='hist',bins=15)\n",
+    "df_growth.plot(y='tfp', kind='hist',bins=15)\n",
     "pass"
    ]
   },
@@ -745,7 +749,7 @@
    },
    "outputs": [],
    "source": [
-    "df_growth[['tfp','factors']].plot(kind='hist',bins=20,alpha=0.5)\n",
+    "df_growth.plot(y=['tfp','factors'], kind='hist',bins=20,alpha=0.5)\n",
     "pass"
    ]
   },
@@ -1258,7 +1262,7 @@
    },
    "outputs": [],
    "source": [
-    "df_jp['gdppc_growth'].plot(kind='bar')\n",
+    "df_jp.plot(y='gdppc_growth', kind='bar')\n",
     "pass"
    ]
   },
@@ -1281,7 +1285,8 @@
    },
    "outputs": [],
    "source": [
-    "df_jp.iloc[:,[0,-3,-2]].plot(kind='bar')\n",
+    "cols = ['gdppc_growth', 'factors_growth', 'tfp_growth']\n",
+    "df_jp.plot(y=cols, kind='bar')\n",
     "pass"
    ]
   },
@@ -1322,7 +1327,7 @@
    },
    "outputs": [],
    "source": [
-    "df_jp.iloc[:,[-3,-2]].plot(kind='bar', stacked=True)\n",
+    "df_jp.plot(y=cols[1:], kind='bar', stacked=True)\n",
     "pass"
    ]
   },
@@ -1343,8 +1348,8 @@
    },
    "outputs": [],
    "source": [
-    "ax_ = df_jp.iloc[:,0].plot(marker='o',color='k', legend=True)\n",
-    "df_jp.iloc[:,[-3,-2]].plot(kind='bar', stacked=True, legend=True, ax=ax_)\n",
+    "ax_ = df_jp.plot(y=cols[0], marker='o',color='k', legend=True)\n",
+    "df_jp.plot(y=cols[1:], kind='bar', stacked=True, legend=True, ax=ax_)\n",
     "pass"
    ]
   },
diff --git a/6_Regression.ipynb b/6_Regression.ipynb
index afd8b8cb..3b99d158 100644
--- a/6_Regression.ipynb
+++ b/6_Regression.ipynb
@@ -32,7 +32,7 @@
     "import statsmodels.formula.api as smf\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\n",
+    "np.set_printoptions(legacy='1.21')\n",
     "# 警告メッセージを非表示\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
@@ -80,7 +80,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### データ"
@@ -141,7 +142,7 @@
    "outputs": [],
    "source": [
     "# 資本の所得シャア\n",
-    "a=1/3.0\n",
+    "a = 1 / 3\n",
     "\n",
     "# 労働者一人当たりGDP\n",
     "df2019['gdp_pc'] = df2019['cgdpo'] / df2019['emp']\n",
@@ -185,7 +186,7 @@
    },
    "outputs": [],
    "source": [
-    "df2019.plot('tfp_relative', 'gdp_pc_relative', kind='scatter')\n",
+    "df2019.plot(x='tfp_relative', y='gdp_pc_relative', kind='scatter')\n",
     "pass"
    ]
   },
@@ -193,7 +194,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 回帰分析"
diff --git a/7_DifferenceEq.ipynb b/7_DifferenceEq.ipynb
index fa8dfc5d..401a8486 100644
--- a/7_DifferenceEq.ipynb
+++ b/7_DifferenceEq.ipynb
@@ -29,7 +29,7 @@
     "import statsmodels.formula.api as sm\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\n",
+    "np.set_printoptions(legacy='1.21')\n",
     "# 警告メッセージを非表示\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
@@ -246,7 +246,7 @@
     "\n",
     "for i in range(20):\n",
     "    \n",
-    "    x = 0.4*x + 3      # 3\n",
+    "    x = 0.4 * x + 3    # 3\n",
     "        \n",
     "    x_list.append(x)   # 4"
    ]
@@ -369,7 +369,7 @@
     "\n",
     "for i in range(20):\n",
     "    \n",
-    "    x = 0.4*x_list[i] + 3        # 2\n",
+    "    x = 0.4 * x_list[i] + 3      # 2\n",
     "        \n",
     "    x_list.append(x)             # 3\n",
     "\n",
@@ -419,14 +419,14 @@
    },
    "outputs": [],
    "source": [
-    "n = 20                          # 1\n",
+    "n = 20                           # 1\n",
     "\n",
-    "arr = np.zeros(1+n)             # 2\n",
-    "arr[0] = 1                      # 3\n",
+    "arr = np.zeros(1+n)              # 2\n",
+    "arr[0] = 1                       # 3\n",
     "\n",
     "for i in range(n):\n",
     "    \n",
-    "    arr[i+1] = 0.4*arr[i] + 3   # 4\n",
+    "    arr[i+1] = 0.4 * arr[i] + 3  # 4\n",
     "\n",
     "pd.DataFrame({'X':x_list}).plot()\n",
     "pass"
@@ -502,7 +502,7 @@
     "\n",
     "for i in range(50):\n",
     "    \n",
-    "    x = 1.2*x - 0.2\n",
+    "    x = 1.2 * x - 0.2\n",
     "        \n",
     "    x_list.append(x)\n",
     "\n",
@@ -656,7 +656,7 @@
    "outputs": [],
    "source": [
     "def model45(y0,a,b,d,f,n=10):\n",
-    "    \"\"\"引数                             # 1\n",
+    "    \"\"\"引数                                 #1\n",
     "            y0: GDPの初期値\n",
     "            a: 所得に依存しない消費\n",
     "            b: 限界消費性向\n",
@@ -666,18 +666,18 @@
     "        戻り値\n",
     "            yの値からなるDataFrame\"\"\"\n",
     "    \n",
-    "    y = y0                              # 2\n",
+    "    y = y0                                #2\n",
     "    \n",
-    "    y_list = [y0]                       # 3\n",
+    "    y_list = [y0]                         #3\n",
     "\n",
     "    for i in range(n):\n",
     "        \n",
-    "        y = y*f/(1-b) + (a+d)/(1-b)     # 4\n",
-    "        y_list.append(y)                # 5\n",
+    "        y = y * f / (1-b) + (a+d) / (1-b) # 4\n",
+    "        y_list.append(y)                  # 5\n",
     "\n",
-    "    yss = (a+d)/(1-b-f)                 # 6\n",
+    "    yss = (a+d) / (1-b-f)                 # 6\n",
     "    \n",
-    "    print(f'定常状態での産出量:{yss:.1f}') # 7\n",
+    "    print(f'定常状態での産出量:{yss:.1f}')   # 7\n",
     "    \n",
     "    return pd.DataFrame({'output':y_list})  # 8"
    ]
@@ -873,15 +873,15 @@
     "\n",
     "    for i in range(n):\n",
     "        \n",
-    "        p = (a+c)/b - (d/b)*p\n",
-    "        q = a-b*p\n",
+    "        p = (a+c) / b - (d/b) * p\n",
+    "        q = a - b * p\n",
     "\n",
     "        q_list.append(q)\n",
     "        p_list.append(p)\n",
     "\n",
     "    # 定常状態\n",
-    "    pss = (a+c)/(b+d)\n",
-    "    qss = a-b*pss\n",
+    "    pss = (a+c) / (b+d)\n",
+    "    qss = a - b * pss\n",
     "    \n",
     "    print(f'定常状態での価格:{pss:.1f}\\n定常状態での量:　{qss:.1f}')\n",
     "    \n",
@@ -906,7 +906,7 @@
    },
    "outputs": [],
    "source": [
-    "cobweb(45,a=100,b=1.1,c=1,d=1,n=50).plot(secondary_y='price')\n",
+    "cobweb(45, a=100, b=1.1, c=1, d=1, n=50).plot(secondary_y='price')\n",
     "pass"
    ]
   },
@@ -933,7 +933,7 @@
    },
    "outputs": [],
    "source": [
-    "cobweb(45,a=100,b=1,c=1,d=1.1,n=50).plot(secondary_y='price')\n",
+    "cobweb(45, a=100, b=1, c=1, d=1.1, n=50).plot(secondary_y='price')\n",
     "pass"
    ]
   }
diff --git a/8_Solow.ipynb b/8_Solow.ipynb
index 0639394a..16ed8b81 100644
--- a/8_Solow.ipynb
+++ b/8_Solow.ipynb
@@ -31,7 +31,7 @@
     "import statsmodels.formula.api as smf\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\n",
+    "np.set_printoptions(legacy='1.21')\n",
     "# 警告メッセージを非表示\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
@@ -40,8 +40,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "heading_collapsed": true
    },
    "source": [
     "## はじめに"
@@ -243,7 +242,7 @@
    },
    "outputs": [],
    "source": [
-    "def solow_model(k0, A,a,s,n,d,T=20):\n",
+    "def solow_model(k0, A=10, a=0.3, s=0.3, n=0.02, d=0.05, T=100):\n",
     "    \"\"\"引数\n",
     "            k0: 資本の初期値\n",
     "            A: 生産性\n",
@@ -256,15 +255,15 @@
     "            資本と産出量からなるDataFrame\"\"\"\n",
     "    \n",
     "    k = k0\n",
-    "    y = A*k0**a\n",
+    "    y = A * k0**a\n",
     "    \n",
     "    k_list = [k]\n",
     "    y_list = [y]\n",
     "\n",
     "    for t in range(T):\n",
     "        \n",
-    "        k = ( s*A*k**a+(1-d)*k )/( 1+n )\n",
-    "        y = A*k**a\n",
+    "        k = ( s * A * k**a + (1-d) * k )/( 1+n )\n",
+    "        y = A * k**a\n",
     "\n",
     "        k_list.append(k)\n",
     "        y_list.append(y)\n",
@@ -293,7 +292,7 @@
    },
    "outputs": [],
    "source": [
-    "df = solow_model(k0=200, A=10, a=0.3, s=0.3, n=0.02, d=0.05, T=100)\n",
+    "df = solow_model(k0=200)\n",
     "df.plot(subplots=True)\n",
     "pass"
    ]
@@ -310,18 +309,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "initial_list = range(100,300,10)      # 1\n",
     "\n",
-    "fig, ax = plt.subplots()              # 2\n",
+    "ax = pd.DataFrame({'capital':[],\n",
+    "                   'output':[]}).plot(legend=False)  # 2\n",
     "\n",
     "for i in initial_list:                # 3\n",
-    "    df_temp = solow_model(k0=i, A=10, a=0.3, s=0.3, n=0.02, d=0.05, T=150)\n",
-    "    ax.plot('capital', data=df_temp)\n",
+    "    \n",
+    "    solow_model(k0=i).plot(y='capital', legend=False, ax=ax)\n",
     "    \n",
     "ax.set(title='ソロー・モデルの移行過程',  # 4\n",
     "       xlabel='ループの回数',\n",
@@ -338,13 +336,12 @@
     "````{admonition} コードの説明\n",
     ":class: dropdown\n",
     "\n",
-    "1. `range(100,300,10)`は100から300-1=299までの間の数を10間隔で生成する。100，110，120，$cdots$，290となる。\n",
-    "1. `.subplots()`を使ってキャンバスを`fig`に，「軸」を`ax`に割り当てる。\n",
-    "1. `initial_list`に対して`for`ループを設定する。一回のループごとに以下をおこなう。\n",
+    "* `#1`：`range(100,300,10)`は100から300-1=299までの間の数を10間隔で生成する。100，110，120，$cdots$，290となる。\n",
+    "* `#2`：空の`DataFrame`を使って空の軸を作成し，`ax`に割り当てる。\n",
+    "* `#3`：`initial_list`に対して`for`ループを設定する。一回のループごとに以下をおこなう。\n",
     "    * `i`は`k0`の引数に使う値になる。\n",
-    "    * `solow_model()`で資本ストックの数値を計算し，その`DataFrame`を`df_temp`に割り当てる。\n",
     "    * `.plot()`を使い`ax`に図をプロットする。これにより図が重ねて描かれる。\n",
-    "1. `ax`のメソッド`set()`を使い，引数を使い以下を追加する。\n",
+    "* `#4`：`ax`のメソッド`set()`を使い，引数を使い以下を追加する。\n",
     "    * `title`：図のタイトル\n",
     "    * `xlabel`：横軸のラベル\n",
     "    * `ylabel`：縦軸のラベル\n",
@@ -370,7 +367,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 定常状態での変数の値"
@@ -393,10 +391,10 @@
    },
    "outputs": [],
    "source": [
-    "def calculate_steady_state(A,a,s,n,d):\n",
+    "def calculate_steady_state(A=10, a=0.3, s=0.3, n=0.02, d=0.05):\n",
     "    \n",
-    "    k_ss = ( s*A/(n+d) )**(1/(1-a))    \n",
-    "    y_ss = A*k_ss**(a/(1-a))    \n",
+    "    k_ss = ( s * A / (n+d) )**( 1/(1-a) )    \n",
+    "    y_ss = A * k_ss**( a/(1-a) )    \n",
     "    \n",
     "    return k_ss, y_ss"
    ]
@@ -409,7 +407,7 @@
    },
    "outputs": [],
    "source": [
-    "ss = calculate_steady_state(A=10, a=0.3, s=0.3, n=0.02, d=0.05)\n",
+    "ss = calculate_steady_state()\n",
     "\n",
     "print(f'定常状態での資本ストック:{ss[0]:.1f}'\n",
     "      f'\\n定常状態での産出量: {ss[1]:.1f}')"
@@ -418,7 +416,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 線形近似"
@@ -428,7 +427,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 説明"
@@ -524,7 +524,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### $\\lambda$の解釈"
@@ -599,7 +600,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 線形近似による誤差"
@@ -622,7 +624,7 @@
    },
    "outputs": [],
    "source": [
-    "def solow_model_approx(k0,A,a,s,n,d,T=20):\n",
+    "def solow_model_approx(k0, A=10, a=0.3, s=0.3, n=0.02, d=0.05, T=20):\n",
     "    \"\"\"引数\n",
     "            k0: 資本の初期値\n",
     "            A: 生産性\n",
@@ -635,7 +637,7 @@
     "            線形近似モデルを使い計算した資本と産出量からなるDataFrame\"\"\"\n",
     "    \n",
     "    k = k0\n",
-    "    y = A*k0**a\n",
+    "    y = A * k0**a\n",
     "    \n",
     "    k_list = [k]\n",
     "    y_list = [y]\n",
@@ -645,9 +647,9 @@
     "    \n",
     "    for t in range(T):\n",
     "        \n",
-    "        lamb = 1-(1-a)*(n+d)/(1+n)  # lambda\n",
-    "        k = lamb*k+(1-lamb)*k_ss  # 線形近似\n",
-    "        y = A*k**a\n",
+    "        lamb = 1 - (1-a) * (n+d) / (1+n)  # lambda\n",
+    "        k = lamb*k + (1-lamb) * k_ss  # 線形近似\n",
+    "        y = A * k**a\n",
     "\n",
     "        k_list.append(k)\n",
     "        y_list.append(y)\n",
@@ -656,7 +658,7 @@
     "    dic = {'capital':k_list, 'output':y_list}\n",
     "    df = pd.DataFrame(dic)\n",
     "    \n",
-    "    return df "
+    "    return df"
    ]
   },
   {
@@ -676,7 +678,7 @@
    },
    "outputs": [],
    "source": [
-    "df_approx = solow_model_approx(k0=200, A=10, a=0.3, s=0.3, n=0.02, d=0.05, T=150)"
+    "df_approx = solow_model_approx(k0=200, T=150)"
    ]
   },
   {
@@ -696,8 +698,8 @@
    },
    "outputs": [],
    "source": [
-    "df_approx['capital'].plot(label='線形近似モデル', legend=True)\n",
-    "df['capital'].plot(label='ソロー・モデル', legend=True)\n",
+    "ax = df_approx.plot(y='capital', label='線形近似モデル')\n",
+    "df.plot(y='capital', label='ソロー・モデル', ax=ax)\n",
     "pass"
    ]
   },
@@ -1183,7 +1185,7 @@
    },
    "outputs": [],
    "source": [
-    "ky_ratio['ky_ratio'] = np.log( ky_ratio['cn']/ky_ratio['cgdpo'] )"
+    "ky_ratio['ky_ratio'] = np.log( ky_ratio['cn'] / ky_ratio['cgdpo'] )"
    ]
   },
   {
@@ -1311,6 +1313,7 @@
    "outputs": [],
    "source": [
     "for df_right in [saving, depreciation, emp_growth]:  # 1\n",
+    "    \n",
     "    ky_ratio = pd.merge(ky_ratio, df_right,          # 2\n",
     "                        left_index=True,             # 3\n",
     "                        right_index=True,            # 4\n",
@@ -1429,26 +1432,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for var in ky_ratio.columns[-3:]:              # 1\n",
+    "for var in ky_ratio.columns[-3:]:              #1\n",
     "    \n",
-    "    df_temp = ky_ratio.copy()                  # 2\n",
-    "    res = smf.ols(f'ky_ratio ~ {var}',          # 3\n",
-    "                 data=df_temp).fit()           # 4\n",
-    "    bhat = res.params.iloc[1]                  # 5\n",
-    "    pval = res.pvalues.iloc[1]                 # 6\n",
+    "    df_tmp = ky_ratio.copy()                   #2\n",
+    "    res = smf.ols(f'ky_ratio ~ {var}',         #3\n",
+    "                  data=df_tmp).fit()           #4\n",
+    "    bhat = res.params.iloc[1]                  #5\n",
+    "    pval = res.pvalues.iloc[1]                 #6\n",
     "    \n",
-    "    df_temp['Trend'] = res.fittedvalues        # 7\n",
+    "    df_tmp['Trend'] = res.fittedvalues         #7\n",
     "\n",
-    "    fig, ax = plt.subplots(tight_layout=True)  # 8\n",
-    "    ax.scatter(var, 'ky_ratio', data=df_temp)  # 9\n",
-    "    ax.plot(var, 'Trend',                      # 10\n",
-    "            data=df_temp.sort_values('Trend'), # 11\n",
-    "            c='r')                             # 12\n",
-    "    ax.set_title(f'トレンドの傾き：{bhat:.2f}\\n'  # 13\n",
-    "                 f'p値：{pval:.3f}', size=20)   # 14\n",
-    "    ax.set_ylabel('資本ストック対GDP比（対数）',   # 15\n",
-    "                  size=15)                     # 16\n",
-    "    ax.set_xlabel(f'{var}', size=20)           # 17"
+    "    ax = df_tmp.plot(x=var, y='ky_ratio',      #8\n",
+    "                     kind='scatter')\n",
+    "    \n",
+    "    df_tmp.sort_values('Trend').plot(x=var,    #9\n",
+    "                                     y='Trend', \n",
+    "                                     color='red',\n",
+    "                                     ax=ax)\n",
+    "    ax.set_title(f'トレンドの傾き：{bhat:.2f}\\n'  #10\n",
+    "                 f'p値：{pval:.3f}', size=20)   #11\n",
+    "    ax.set_ylabel('資本ストック対GDP比（対数）',   #12\n",
+    "                  size=15)                     #13\n",
+    "    ax.set_xlabel(f'{var}', size=20)           #14"
    ]
   },
   {
@@ -1458,27 +1463,24 @@
     "```{admonition} コードの説明\n",
     ":class: dropdown\n",
     "\n",
-    "1. `ky_ratio`の最後の３列のラベルに対して`for`ループを組んで，`var`はその列ラベルを指す。\n",
-    "2. `ky_ratio`のコピーを作り`df_temp`に割り当てる。`.copy()`は実態としては別物のコピーを作成する。詳細は割愛するが`.copy()`がないと実態は同じで参照記号のみが異なることになり，予期しない結果につながることを防ぐために`.copy()`を使っている。\n",
-    "3. 回帰分析をおこなっているが，`f-string`を使い回帰式の説明変数を指定している。また回帰分析の結果を`res`に割り当てている。\n",
-    "4. `data`で回帰分析のデータの指定をおこない，`.fit()`で自動計算！\n",
-    "5. `res`の属性`.params`は推定値を２つ返すが，1番目に傾きの推定値が格納されているため`[1]`で抽出し，`bhat`に割り当てている。\n",
-    "6. `res`の属性`.pvalues`は$p$値を２つ返すが，1番目に傾きの$p$値が格納されているため`[1]`で抽出し，`pval`に割り当てている。\n",
-    "7. `res`の属性`fittedvalues`は予測値を返すが，それを新たな列（ラベルは`Trend`）として`df_temp`に追加している。\n",
-    "8. `.subplots()`によるキャンバスを`fig`に，「軸」を`ax`に割り当てている。引数の`tight_layout=True`はスペースに余裕がある様に表示にするためであり，なくても構わない。\n",
-    "9. `.scatter()`は散布図を描くメソッド。横軸は`var`，縦軸は`ky_ratio`とし，`df_temp`をデータとして使う。\n",
-    "10. `.plot()`を使いトレンド線を描く。横軸は`var`，縦軸は`Trend`。\n",
-    "11. データには`df_temp`を使うが，メソッド`.sort_values('Trend')`で列`Trend`に従って昇順に並び替える。これはトレンド線が正確に表示されるために必要。\n",
-    "12. トレンド線の色を赤に指定する。\n",
-    "13. 図のタイトルを設定する。\n",
+    "* `#1`：`ky_ratio`の最後の３列のラベルに対して`for`ループを組んで，`var`はその列ラベルを指す。\n",
+    "* `#2`：`ky_ratio`のコピーを作り`df_tmp`に割り当てる。`.copy()`は実態としては別物のコピーを作成する。詳細は割愛するが`.copy()`がないと実態は同じで参照記号のみが異なることになり，予期しない結果につながることを防ぐために`.copy()`を使っている。\n",
+    "* `#3`：回帰分析をおこなっているが，`f-string`を使い回帰式の説明変数を指定している。また回帰分析の結果を`res`に割り当てている。\n",
+    "* `#4`：`data`で回帰分析のデータの指定をおこない，`.fit()`で自動計算！\n",
+    "* `#5`：`res`の属性`.params`は推定値を２つ返すが，1番目に傾きの推定値が格納されているため`.iloc[1]`で抽出し，`bhat`に割り当てている。\n",
+    "* `#6`：`res`の属性`.pvalues`は$p$値を２つ返すが，1番目に傾きの$p$値が格納されているため`.iloc[1]`で抽出し，`pval`に割り当てている。\n",
+    "* `#7`：`res`の属性`fittedvalues`は予測値を返すが，それを新たな列（ラベルは`Trend`）として`df_tmp`に追加している。\n",
+    "* `#8`：`df_tmp`を使い横軸は`var`，縦軸は`ky_ratio`の散布図を表示し、その軸を`ax`に割り当てる。\n",
+    "* `#9`：`df_tmp`を使い横軸は`var`，縦軸は`ky_ratio`の回帰直線を表示。その際、メソッド`.sort_values('Trend')`で列`Trend`に従って昇順に並び替える。これはトレンド線が正確に表示されるために必要。\n",
+    "* `#10`：図のタイトルを設定する。\n",
     "    * `f-string`を使い`bhat`の値を代入する。\n",
     "    * `:.2f`は小数点第２位までの表示を指定。\n",
     "    * `\\n`は改行の意味。\n",
     "    * 行を変えるので`\\n`の後に`'`が必要となる。\n",
-    "14. `f-string`を使い`pval`の値を代入する。`:.3f`は小数点第３位までの表示を指定し，`size=20`はフォントの大きさの指定。\n",
-    "15. 縦軸のラベルの設定。\n",
-    "16. フォントの大きさの指定。\n",
-    "17. 横軸のラベルの設定であり，`f-string`を使い`var`の値を代入している。`size=20`はフォントの大きさの指定。\n",
+    "* `#11`：`f-string`を使い`pval`の値を代入する。`:.3f`は小数点第３位までの表示を指定し，`size=20`はフォントの大きさの指定。\n",
+    "* `#12`：縦軸のラベルの設定。\n",
+    "* `#13`：フォントの大きさの指定。\n",
+    "* `#14`：横軸のラベルの設定であり，`f-string`を使い`var`の値を代入している。`size=20`はフォントの大きさの指定。\n",
     "```"
    ]
   },
diff --git a/9_Convergence.ipynb b/9_Convergence.ipynb
index e465f67d..d0f09163 100644
--- a/9_Convergence.ipynb
+++ b/9_Convergence.ipynb
@@ -34,7 +34,7 @@
     "import statsmodels.formula.api as smf\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\n",
+    "np.set_printoptions(legacy='1.21')\n",
     "# 警告メッセージを非表示\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
@@ -44,7 +44,8 @@
    "cell_type": "markdown",
    "id": "twenty-speed",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## はじめに"
@@ -86,6 +87,7 @@
    "id": "conceptual-probe",
    "metadata": {
     "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": [
      "remove-cell"
     ]
@@ -98,7 +100,8 @@
    "cell_type": "markdown",
    "id": "magnetic-wrestling",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "(sec:9-distribution)=\n",
@@ -234,10 +237,10 @@
     "                                      density=True)\n",
     "\n",
     "# 2 密度関数\n",
-    "pwt.loc[cond,'gdp_pc_log'].plot(kind='density',ax=ax_)\n",
+    "pwt.loc[cond,'gdp_pc_log'].plot(kind='density', ax=ax_)\n",
     "\n",
     "# 3 日本\n",
-    "ax_.axvline(y_jp.iloc[0],color='red')\n",
+    "ax_.axvline(y_jp.iloc[0], color='red')\n",
     "\n",
     "# 4 横軸の表示範囲\n",
     "ax_.set_xlim(4,12)\n",
@@ -708,6 +711,7 @@
    "cell_type": "markdown",
    "id": "engaging-supplier",
    "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": [
      "remove-cell"
     ]
@@ -719,7 +723,9 @@
   {
    "cell_type": "markdown",
    "id": "decreased-copying",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "(sec:9-convergence)=\n",
     "## 所得収斂"
@@ -1035,8 +1041,10 @@
    "outputs": [],
    "source": [
     "def mean_nan(x):         # 1\n",
+    "    \n",
     "    if x.notna().all():  # 2\n",
     "        return x.mean()  # 3\n",
+    "        \n",
     "    else:\n",
     "        return np.nan    # 4"
    ]
@@ -1121,10 +1129,13 @@
    "outputs": [],
    "source": [
     "def mean_growth_nan(x):\n",
+    "    \n",
     "    t = len(x)-1\n",
+    "    \n",
     "    if x.notna().all():\n",
     "        x_growth = ( x.iloc[-1]/x.iloc[0] )**(1/t)-1  # 1\n",
     "        return x_growth\n",
+    "        \n",
     "    else:\n",
     "        return np.nan"
    ]
@@ -1416,22 +1427,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "statutory-frontier",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "3ddd3879-1e80-4fc9-b8a8-9c97a27b4705",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "growth_average = df_convergence.loc[:,'gdp_pc_growth'].mean() # 1\n",
+    "growth_avr = df_convergence.loc[:,'gdp_pc_growth'].mean()  #1\n",
     "\n",
-    "fig, ax = plt.subplots(tight_layout=True)\n",
-    "ax.hist('gdp_pc_growth', data=df_convergence,            # 2 \n",
-    "                         bins=25, edgecolor='k')         # 3\n",
-    "ax.axvline(growth_average , c='red')                     # 4\n",
-    "ax.set_ylabel('国の数', size=15)\n",
-    "ax.set_title('赤い線は平均', size=15)                      # 5\n",
-    "fig.suptitle(f'{len(df_convergence)}ヵ国の平均成長率 (%)',  # 6\n",
-    "             size=20)\n",
+    "ax = df_convergence.plot(y='gdp_pc_growth', kind='hist',   #2\n",
+    "                         bins=25, edgecolor='k')           #3\n",
+    "ax.axvline(growth_avr, color='red')                        #4\n",
+    "ax.set_ylabel('国の数', size=15)                            #5\n",
+    "ax.set_title(\n",
+    "    f'{len(df_convergence)}ヵ国の平均成長率\\n赤い線は平均',     #6\n",
+    "    size=17)                                               #7\n",
     "pass"
    ]
   },
@@ -1445,19 +1453,17 @@
     "```{admonition} コードの説明\n",
     ":class: dropdown\n",
     "\n",
-    "1. 平均成長率の平均を計算し，結果を変数`growth_average`に割り当てている。\n",
-    "2. `.hist`はヒストグラムをプロットする`ax`のメソッド。\n",
-    "    * 第一引数はプロットしたい列ラベル（ここでは`gdp_pc_growth`）\n",
-    "    * `data`は使用する`DataFrame`を指定する（ここでは`df_convergence`）\n",
-    "3. `bins`は階級の数を指定する引数（ここでは`25`）。`edgecolor`はヒストグラムの棒の枠線の色を指定する引数で，`k`は黒を示す。`black`としても構わない。\n",
-    "4. `axvline()`は縦の線を引く`ax`のメソッド\n",
+    "* `#1`：平均成長率の平均を計算し，結果を変数`growth_avr`に割り当てている。\n",
+    "* `#2`：`df_convergence`を使い`gdp_pc_growth`のヒストグラムをプロットし、軸を`ax`に割り当てる。\n",
+    "* `#3`：`bins`は階級の数を指定する引数（ここでは`25`）。`edgecolor`はヒストグラムの棒の枠線の色を指定する引数で，`k`は黒を示す。`black`としても構わない。\n",
+    "* `#4`：`axvline()`は縦の線を引く`ax`のメソッド\n",
     "    * 第一引数は横軸の値\n",
-    "    * `c`は色を指定する引数\n",
-    "5. `plt.subplots()`によって「軸」が`ax`に割り当てられるが，「軸」のタイトルを設定する`ax`のメソッドが`.set_title()`。\n",
+    "    * `color`は色を指定する引数\n",
+    "* `#5`：軸のタイトルを設定する`ax`のメソッドが`.set_title()`。\n",
     "    * `size`はフォントの大きさを指定する。\n",
-    "6. `plt.subplots()`によって「キャンバス」が`fig`に割り当てられるが，「キャンバス」のタイトルを設定する`fig`のメソッドが`.suptitle()`。\n",
+    "* `#6`：軸のタイトルを設定する。\n",
     "    * `f-string`を使って`{}`の中に`len(df_reg)`を代入している。\n",
-    "    * `size`はフォントの大きさを指定する。\n",
+    "* `#7`：`size`はフォントの大きさを指定する。\n",
     "    \n",
     "上のヒストグラムは[発展会計](chap:5a-accounting)の章の「はじめに」で表示したヒストグラムと次の点で異なる。\n",
     "* `df_convergence`は1970年以降のデータを使っている，「はじめに」のヒストグラムは1960年以降のデータを使っている。\n",
@@ -1485,7 +1491,7 @@
    },
    "outputs": [],
    "source": [
-    "( df_convergence.loc[:,'gdp_pc_growth'] < growth_average ).sum() / len(df_convergence)"
+    "( df_convergence.loc[:,'gdp_pc_growth'] < growth_avr ).sum() / len(df_convergence)"
    ]
   },
   {
@@ -1703,10 +1709,10 @@
    "source": [
     "def remaining_percent(n, s=speed1970):\n",
     "    \n",
-    "    x0 = gdp_pc_us-gdp_pc_mean\n",
-    "    x = ( 1-s )**n*x0\n",
+    "    x0 = gdp_pc_us - gdp_pc_mean\n",
+    "    x = ( 1-s )**n * x0\n",
     "    \n",
-    "    return 100*x/x0"
+    "    return 100 * x / x0"
    ]
   },
   {
@@ -2480,7 +2486,8 @@
    "cell_type": "markdown",
    "id": "fe333faa",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## まとめ"
@@ -2525,6 +2532,7 @@
    "id": "sealed-festival",
    "metadata": {
     "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": [
      "remove-cell"
     ]