From 018fc93fc09e9baf7a9f4d014c2c508457c474e1 Mon Sep 17 00:00:00 2001
From: Tetsu Haruyama <tetsu.yes@gmail.com>
Date: Mon, 19 Aug 2024 23:14:57 +0900
Subject: [PATCH] .plot.xxxx()

---
 11_Macro_Variables.ipynb        | 43 +++++++++++++++------------------
 12_IS-MP-PC.ipynb               | 12 ++++++---
 13_random_TFP.ipynb             | 19 +++++++++------
 19_PPP.ipynb                    | 35 +++++++++++++--------------
 5a_Development_Accounting.ipynb | 12 ++++-----
 5b_Growth_Accounting.ipynb      | 24 ++++++++----------
 6_Regression.ipynb              | 20 +++++++++------
 7_DifferenceEq.ipynb            | 12 ++++++---
 8_Solow.ipynb                   | 18 +++++---------
 9_Convergence.ipynb             | 25 +++++++++----------
 10 files changed, 109 insertions(+), 111 deletions(-)

diff --git a/11_Macro_Variables.ipynb b/11_Macro_Variables.ipynb
index aade6a80..27ff7461 100644
--- a/11_Macro_Variables.ipynb
+++ b/11_Macro_Variables.ipynb
@@ -39,8 +39,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "heading_collapsed": true
    },
    "source": [
     "## はじめに"
@@ -502,13 +501,11 @@
    },
    "outputs": [],
    "source": [
-    "ax_ = df.plot(x='u_rate_cycle',\n",
-    "              y='gdp_cycle',\n",
-    "              kind='scatter')\n",
+    "ax_ = df.plot.scatter(x='u_rate_cycle',\n",
+    "                      y='gdp_cycle')\n",
     "\n",
     "df.plot(x='u_rate_cycle',\n",
     "        y='fitted_okun',\n",
-    "        kind='line',\n",
     "        color='red',\n",
     "        ax=ax_)\n",
     "pass"
@@ -542,7 +539,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## フィリップス曲線"
@@ -711,7 +709,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### フラット化するフィリップス曲線"
@@ -734,7 +733,7 @@
    },
    "outputs": [],
    "source": [
-    "df.plot(x='unemployment_rate', y='inflation', kind='scatter')\n",
+    "df.plot.scatter(x='unemployment_rate', y='inflation')\n",
     "pass"
    ]
   },
@@ -909,13 +908,12 @@
     "    \n",
     "    cond = ( df['decade']==d )\n",
     "    \n",
-    "    df.loc[cond,:].plot(x='unemployment_rate',\n",
-    "                        y='inflation',\n",
-    "                        kind='scatter',\n",
-    "                        edgecolor=c,      #3\n",
-    "                        color='white',    #4\n",
-    "                        legend=False,\n",
-    "                        ax=ax)\n",
+    "    df.loc[cond,:].plot.scatter(x='unemployment_rate',\n",
+    "                                y='inflation',\n",
+    "                                edgecolor=c,      #3\n",
+    "                                color='white',    #4\n",
+    "                                legend=False,\n",
+    "                                ax=ax)\n",
     "\n",
     "    df.loc[cond,:].sort_values(f'{d}年代データ').plot(\n",
     "                        x='unemployment_rate',\n",
@@ -988,9 +986,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## インフレ率とマネーストックの増加率"
    ]
@@ -998,8 +994,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "heading_collapsed": true
    },
    "source": [
     "### 説明"
@@ -1539,7 +1534,7 @@
     "    res = smf.ols('inflation ~ money_growth', data=df).fit()        #2\n",
     "    df['トレンド'] = res.fittedvalues                                #3\n",
     "    \n",
-    "    ax_ = df.plot(x='money_growth', y='inflation', kind='scatter')  #4\n",
+    "    ax_ = df.plot.scatter(x='money_growth', y='inflation')          #4\n",
     "    df.sort_values('トレンド').plot(x='money_growth', y='トレンド',    #5\n",
     "                                   color='r', ax=ax_)               #6\n",
     "    ax_.set_title(f'{t}\\n'                                          #7\n",
@@ -2107,7 +2102,7 @@
    },
    "outputs": [],
    "source": [
-    "world.plot(x='money_growth', y='inflation', kind='scatter')\n",
+    "world.plot.scatter(x='money_growth', y='inflation')\n",
     "pass"
    ]
   },
@@ -2318,7 +2313,7 @@
    },
    "outputs": [],
    "source": [
-    "ax_ = world_mean.plot(x='money_growth_mean', y='inflation_mean', kind='scatter')\n",
+    "ax_ = world_mean.plot.scatter(x='money_growth_mean', y='inflation_mean')\n",
     "xpoints = ypoints = ax_.get_ylim()\n",
     "ax_.plot(xpoints,ypoints,'r-', label='45度線')\n",
     "ax_.set_title('平均インフレ率とマネーストトックの平均成長率', size='15')\n",
diff --git a/12_IS-MP-PC.ipynb b/12_IS-MP-PC.ipynb
index dfd0ee74..e212f157 100644
--- a/12_IS-MP-PC.ipynb
+++ b/12_IS-MP-PC.ipynb
@@ -81,6 +81,7 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": [
      "remove-cell"
     ]
@@ -227,7 +228,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### モデルの解"
@@ -405,7 +407,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### シミュレーション：インパルス反応"
@@ -532,7 +535,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### フィリップス曲線のショック"
@@ -1791,7 +1795,7 @@
     "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
-    "## IS-MP-PSモデルの問題点"
+    "## IS-MP-PCモデルの問題点"
    ]
   },
   {
diff --git a/13_random_TFP.ipynb b/13_random_TFP.ipynb
index bca5d7ce..55ee3c3b 100644
--- a/13_random_TFP.ipynb
+++ b/13_random_TFP.ipynb
@@ -264,7 +264,7 @@
    },
    "outputs": [],
    "source": [
-    "dfwn.plot(kind='hist', bins=30)\n",
+    "dfwn.plot.hist(bins=30)\n",
     "pass"
    ]
   },
@@ -333,7 +333,7 @@
    },
    "outputs": [],
    "source": [
-    "dfwn.plot(x='WNlag', y='WN', kind='scatter')\n",
+    "dfwn.plot.scatter(x='WNlag', y='WN')\n",
     "pass"
    ]
   },
@@ -1267,7 +1267,7 @@
    },
    "outputs": [],
    "source": [
-    "df.plot(x='tfp_cycle_lag', y='tfp_cycle', kind='scatter')\n",
+    "df.plot.scatter(x='tfp_cycle_lag', y='tfp_cycle')\n",
     "pass"
    ]
   },
@@ -1437,7 +1437,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "### 初期値の計算"
    ]
@@ -1671,7 +1673,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### シミュレーション"
@@ -1681,7 +1684,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### 関数"
@@ -1802,7 +1806,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### 実行"
diff --git a/19_PPP.ipynb b/19_PPP.ipynb
index 1b3645b9..8da143f1 100644
--- a/19_PPP.ipynb
+++ b/19_PPP.ipynb
@@ -382,7 +382,7 @@
    },
    "outputs": [],
    "source": [
-    "choco.plot(y='mean_deviation', kind='hist', bins=12, ec='white')\n",
+    "choco.plot.hist(y='mean_deviation', bins=12, ec='white')\n",
     "pass"
    ]
   },
@@ -680,7 +680,7 @@
    "source": [
     "ice['mean_deviation'] =  ice['price'] / ice['price'].mean() - 1\n",
     "\n",
-    "ice.plot(y='mean_deviation', kind='hist', bins=16, ec='white')\n",
+    "ice.plot.hist(y='mean_deviation', bins=16, ec='white')\n",
     "pass"
    ]
   },
@@ -939,7 +939,7 @@
    "source": [
     "gas['mean_deviation'] = gas['price'] / gas['price'].mean() - 1\n",
     "\n",
-    "gas.plot(y='mean_deviation', kind='hist', ec='white')\n",
+    "gas.plot.hist(y='mean_deviation', ec='white')\n",
     "pass"
    ]
   },
@@ -1052,7 +1052,6 @@
    "id": "cc3f4f60",
    "metadata": {
     "heading_collapsed": true,
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": [
      "remove-cell"
     ]
@@ -1229,7 +1228,8 @@
    "id": "c5a9085a",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 一物一価"
@@ -1535,7 +1535,7 @@
    },
    "outputs": [],
    "source": [
-    "bigmac.plot(y='price_yen_deviation', kind='hist', ec='white')\n",
+    "bigmac.plot.hist(y='price_yen_deviation', ec='white')\n",
     "pass"
    ]
   },
@@ -1806,7 +1806,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = bigmac.plot(x='gdppc_yen', y='price_yen', kind='scatter', logx=True)\n",
+    "ax = bigmac.plot.scatter(x='gdppc_yen', y='price_yen', 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"
@@ -2397,7 +2397,7 @@
    "source": [
     "pwt19['pl_gdpo_log'] = np.log( pwt19['pl_gdpo'] )\n",
     "\n",
-    "pwt19.plot(y='pl_gdpo_log', kind='hist', ec='white')\n",
+    "pwt19.plot.hist(y='pl_gdpo_log', ec='white')\n",
     "pass"
    ]
   },
@@ -2729,7 +2729,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = pwt19.plot(x='cgdpo_pc_log', y='pl_gdpo_log', kind='scatter')\n",
+    "ax = pwt19.plot.scatter(x='cgdpo_pc_log', y='pl_gdpo_log')\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"
@@ -3012,7 +3012,7 @@
    "outputs": [],
    "source": [
     "# 散布図\n",
-    "ax = pwt19.plot(x='cgdpo_pc_ratio_log', y='pl_gdpo_log', kind='scatter')\n",
+    "ax = pwt19.plot.scatter(x='cgdpo_pc_ratio_log', y='pl_gdpo_log')\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",
@@ -3098,12 +3098,11 @@
     "col_lst = ['pl_gdpo_log','income_effect','other_effect']\n",
     "title_lst = ['相対価格（対数）','所得効果（対数）','その他の効果（対数）']\n",
     "\n",
-    "ax = pwt19[col_lst].plot(kind='hist',\n",
-    "                         bins=20,\n",
-    "                         ec='white',\n",
-    "                         subplots=True,\n",
-    "                         legend=False,\n",
-    "                         figsize=(6,10))\n",
+    "ax = pwt19[col_lst].plot.hist(bins=20,\n",
+    "                              ec='white',\n",
+    "                              subplots=True,\n",
+    "                              legend=False,\n",
+    "                              figsize=(6,10))\n",
     "\n",
     "for i, s in enumerate(title_lst):\n",
     "    ax[i].set_title(s, fontsize=18)\n",
@@ -3603,7 +3602,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = pwt19.plot(x='cgdpo_pc_log', y='cgdpo_pc_xr_log', kind='scatter')\n",
+    "ax = pwt19.plot.scatter(x='cgdpo_pc_log', y='cgdpo_pc_xr_log')\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",
@@ -3653,7 +3652,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = pwt19.plot(y=['cgdpo_pc_log','cgdpo_pc_xr_log'], kind='kde')\n",
+    "ax = pwt19.plot.kde(y=['cgdpo_pc_log','cgdpo_pc_xr_log'])\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"
diff --git a/5a_Development_Accounting.ipynb b/5a_Development_Accounting.ipynb
index 6878683d..4bf32ca2 100644
--- a/5a_Development_Accounting.ipynb
+++ b/5a_Development_Accounting.ipynb
@@ -907,7 +907,7 @@
    },
    "outputs": [],
    "source": [
-    "df.plot(x='kpc_relative', y='gdppc_relative', kind='scatter')\n",
+    "df.plot.scatter(x='kpc_relative', y='gdppc_relative')\n",
     "pass"
    ]
   },
@@ -968,7 +968,7 @@
    },
    "outputs": [],
    "source": [
-    "df.plot(x='hc_relative', y='gdppc_relative', kind='scatter')\n",
+    "df.plot.scatter(x='hc_relative', y='gdppc_relative')\n",
     "pass"
    ]
   },
@@ -1031,7 +1031,7 @@
    },
    "outputs": [],
    "source": [
-    "df.plot(x='human_relative', y='gdppc_relative', kind='scatter')\n",
+    "df.plot.scatter(x='human_relative', y='gdppc_relative')\n",
     "pass"
    ]
   },
@@ -1054,7 +1054,7 @@
    "source": [
     "df['avh_relative'] = df['avh'] / us['avh'].iloc[0]\n",
     "\n",
-    "df.plot(x='avh_relative', y='gdppc_relative', kind='scatter')\n",
+    "df.plot.scatter(x='avh_relative', y='gdppc_relative')\n",
     "pass"
    ]
   },
@@ -1109,7 +1109,7 @@
    "source": [
     "df['tfp_relative'] = df['tfp'] / us['tfp'].iloc[0]\n",
     "\n",
-    "df.plot(x='tfp_relative', y='gdppc_relative', kind='scatter')\n",
+    "df.plot.scatter(x='tfp_relative', y='gdppc_relative')\n",
     "pass"
    ]
   },
@@ -1237,7 +1237,7 @@
    "source": [
     "df['factors_relative'] = df['factors'] / us['factors'].iloc[0]\n",
     "\n",
-    "df.plot(x='factors_relative', y='gdppc_relative', kind='scatter')\n",
+    "df.plot.scatter(x='factors_relative', y='gdppc_relative')\n",
     "pass"
    ]
   },
diff --git a/5b_Growth_Accounting.ipynb b/5b_Growth_Accounting.ipynb
index 934eb9dd..daf967e5 100644
--- a/5b_Growth_Accounting.ipynb
+++ b/5b_Growth_Accounting.ipynb
@@ -497,7 +497,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_growth.plot(y='gdppc', kind='hist',bins=15)\n",
+    "df_growth.plot.hist(y='gdppc', bins=15)\n",
     "pass"
    ]
   },
@@ -590,9 +590,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## 蓄積生産要素の成長率"
    ]
@@ -632,7 +630,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_growth.plot(y='factors', kind='hist',bins=15)\n",
+    "df_growth.plot.hist(y='factors', bins=15)\n",
     "pass"
    ]
   },
@@ -655,9 +653,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## 全要素生産性"
    ]
@@ -695,7 +691,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_growth.plot(y='tfp', kind='hist',bins=15)\n",
+    "df_growth.plot.hist(y='tfp', bins=15)\n",
     "pass"
    ]
   },
@@ -749,7 +745,7 @@
    },
    "outputs": [],
    "source": [
-    "df_growth.plot(y=['tfp','factors'], kind='hist',bins=20,alpha=0.5)\n",
+    "df_growth.plot.hist(y=['tfp','factors'], bins=20, alpha=0.5)\n",
     "pass"
    ]
   },
@@ -1262,7 +1258,7 @@
    },
    "outputs": [],
    "source": [
-    "df_jp.plot(y='gdppc_growth', kind='bar')\n",
+    "df_jp.plot.bar(y='gdppc_growth')\n",
     "pass"
    ]
   },
@@ -1286,7 +1282,7 @@
    "outputs": [],
    "source": [
     "cols = ['gdppc_growth', 'factors_growth', 'tfp_growth']\n",
-    "df_jp.plot(y=cols, kind='bar')\n",
+    "df_jp.plot.bar(y=cols)\n",
     "pass"
    ]
   },
@@ -1327,7 +1323,7 @@
    },
    "outputs": [],
    "source": [
-    "df_jp.plot(y=cols[1:], kind='bar', stacked=True)\n",
+    "df_jp.plot.bar(y=cols[1:], stacked=True)\n",
     "pass"
    ]
   },
@@ -1349,7 +1345,7 @@
    "outputs": [],
    "source": [
     "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",
+    "df_jp.plot.bar(y=cols[1:], stacked=True, legend=True, ax=ax_)\n",
     "pass"
    ]
   },
diff --git a/6_Regression.ipynb b/6_Regression.ipynb
index 3b99d158..6b6e6f69 100644
--- a/6_Regression.ipynb
+++ b/6_Regression.ipynb
@@ -41,7 +41,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## はじめに"
@@ -61,7 +62,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## `Python`を使った回帰分析 "
@@ -186,7 +188,7 @@
    },
    "outputs": [],
    "source": [
-    "df2019.plot(x='tfp_relative', y='gdp_pc_relative', kind='scatter')\n",
+    "df2019.plot.scatter(x='tfp_relative', y='gdp_pc_relative')\n",
     "pass"
    ]
   },
@@ -194,8 +196,7 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "hidden": true
    },
    "source": [
     "### 回帰分析"
@@ -643,7 +644,7 @@
    },
    "outputs": [],
    "source": [
-    "ax_ = df2019.plot(x='tfp_relative', y='gdp_pc_relative', kind='scatter')\n",
+    "ax_ = df2019.plot.scatter(x='tfp_relative', y='gdp_pc_relative')\n",
     "df2019.sort_values('OLS fitted').plot(x='tfp_relative',\n",
     "                                      y='OLS fitted',\n",
     "                                      color='r', ax=ax_)\n",
@@ -681,6 +682,7 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": [
      "remove-cell"
     ]
@@ -692,7 +694,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "(sec:6-regression-da)=\n",
@@ -704,7 +707,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 説明"
diff --git a/7_DifferenceEq.ipynb b/7_DifferenceEq.ipynb
index 401a8486..536946c8 100644
--- a/7_DifferenceEq.ipynb
+++ b/7_DifferenceEq.ipynb
@@ -38,7 +38,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## はじめに"
@@ -56,7 +57,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 1階差分方程式"
@@ -588,7 +590,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 45度線モデル"
@@ -740,7 +743,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 蜘蛛の巣モデル"
diff --git a/8_Solow.ipynb b/8_Solow.ipynb
index 16ed8b81..7a882f9b 100644
--- a/8_Solow.ipynb
+++ b/8_Solow.ipynb
@@ -367,8 +367,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "heading_collapsed": true
    },
    "source": [
     "## 定常状態での変数の値"
@@ -416,8 +415,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "heading_collapsed": true
    },
    "source": [
     "## 線形近似"
@@ -427,8 +425,7 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "hidden": true
    },
    "source": [
     "### 説明"
@@ -524,8 +521,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "hidden": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "hidden": true
    },
    "source": [
     "### $\\lambda$の解釈"
@@ -600,8 +596,7 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "hidden": true
    },
    "source": [
     "### 線形近似による誤差"
@@ -1442,8 +1437,7 @@
     "    \n",
     "    df_tmp['Trend'] = res.fittedvalues         #7\n",
     "\n",
-    "    ax = df_tmp.plot(x=var, y='ky_ratio',      #8\n",
-    "                     kind='scatter')\n",
+    "    ax = df_tmp.plot.scatter(x=var, y='ky_ratio') #8\n",
     "    \n",
     "    df_tmp.sort_values('Trend').plot(x=var,    #9\n",
     "                                     y='Trend', \n",
diff --git a/9_Convergence.ipynb b/9_Convergence.ipynb
index d0f09163..3978bf51 100644
--- a/9_Convergence.ipynb
+++ b/9_Convergence.ipynb
@@ -205,9 +205,8 @@
    "outputs": [],
    "source": [
     "cond = ( pwt.loc[:,'year']==2019 )\n",
-    "pwt.loc[cond,'gdp_pc_log'].plot(kind='hist',\n",
-    "                                bins=20,\n",
-    "                                edgecolor='white')\n",
+    "pwt.loc[cond,'gdp_pc_log'].plot.hist(bins=20,\n",
+    "                                     edgecolor='white')\n",
     "pass"
    ]
   },
@@ -231,13 +230,12 @@
    "outputs": [],
    "source": [
     "# 1 ヒストグラム\n",
-    "ax_ = pwt.loc[cond,'gdp_pc_log'].plot(kind='hist',\n",
-    "                                      bins=20,\n",
-    "                                      edgecolor='white',\n",
-    "                                      density=True)\n",
+    "ax_ = pwt.loc[cond,'gdp_pc_log'].plot.hist(bins=20,\n",
+    "                                           edgecolor='white',\n",
+    "                                           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.density(ax=ax_)\n",
     "\n",
     "# 3 日本\n",
     "ax_.axvline(y_jp.iloc[0], color='red')\n",
@@ -291,7 +289,7 @@
     "\n",
     "for y, c in zip(yr_list, color_arr):                      # 3\n",
     "    cond = ( pwt.loc[:,'year'] == y )                     # 4\n",
-    "    ax_ = pwt.loc[cond,'gdp_pc_log'].plot(kind='density', # 5\n",
+    "    ax_ = pwt.loc[cond,'gdp_pc_log'].plot.density(        # 5\n",
     "                                          label=str(y),   # 6\n",
     "                                          legend=True,    # 7\n",
     "                                          color=str(c))   # 8\n",
@@ -1433,8 +1431,8 @@
    "source": [
     "growth_avr = df_convergence.loc[:,'gdp_pc_growth'].mean()  #1\n",
     "\n",
-    "ax = df_convergence.plot(y='gdp_pc_growth', kind='hist',   #2\n",
-    "                         bins=25, edgecolor='k')           #3\n",
+    "ax = df_convergence.plot.hist(y='gdp_pc_growth',           #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",
@@ -1558,9 +1556,8 @@
     "df_convergence['fitted_absolute'] = res_absolute.fittedvalues\n",
     "\n",
     "# 図示\n",
-    "ax_ = df_convergence.plot(x='gdp_pc_init_log',\n",
-    "                          y='gdp_pc_growth',\n",
-    "                          kind='scatter')\n",
+    "ax_ = df_convergence.plot.scatter(x='gdp_pc_init_log',\n",
+    "                                  y='gdp_pc_growth')\n",
     "df_convergence.sort_values('fitted_absolute') \\\n",
     "              .plot(x='gdp_pc_init_log',\n",
     "                    y='fitted_absolute',\n",