From 13d461e87adbbdf76ad5be01fe709c112739ad40 Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Fri, 22 Dec 2023 21:44:30 +0800
Subject: [PATCH 01/16] Create python_submit.txt

---
 2023/homework/CaoXu/python_submit.txt | 107 ++++++++++++++++++++++++++
 1 file changed, 107 insertions(+)
 create mode 100644 2023/homework/CaoXu/python_submit.txt

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
new file mode 100644
index 00000000..0eddef83
--- /dev/null
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -0,0 +1,107 @@
+C
+B
+C
+C
+C
+A
+A
+B
+B
+B
+B
+C
+A
+D
+A
+A
+A
+C
+D
+B
+A
+D
+C
+A
+D
+D
+C
+B
+A
+C
+C
+C
+B
+C
+D
+B
+A
+C
+D
+A
+B
+A
+C
+A
+C
+D
+B
+A
+B
+A
+A
+A
+B
+D
+D
+A
+A
+D
+B
+C
+B
+B
+A
+D
+A
+D
+A
+B
+A
+A
+D
+B
+C
+A
+C
+A
+B
+B
+C
+A
+B
+A
+C
+B
+A
+C
+C
+B
+B
+B
+A
+C
+A
+B
+B
+A
+A
+A
+C
+C
+A
+B
+B
+A
+A
+
+

From fd4ce56cc7206ed92e45a34bf696993b662abca2 Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Fri, 22 Dec 2023 22:12:01 +0800
Subject: [PATCH 02/16] Update python_submit.txt

---
 2023/homework/CaoXu/python_submit.txt | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index 0eddef83..256722be 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -103,5 +103,6 @@ B
 B
 A
 A
-
-
+B
+A
+A

From 62bd63f98bc4e80c4c2c56ea4e4fb165dd2c8e3d Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Fri, 22 Dec 2023 22:36:16 +0800
Subject: [PATCH 03/16] Create numpy_submit.txt

---
 2023/homework/CaoXu/numpy_submit.txt | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 2023/homework/CaoXu/numpy_submit.txt

diff --git a/2023/homework/CaoXu/numpy_submit.txt b/2023/homework/CaoXu/numpy_submit.txt
new file mode 100644
index 00000000..c557c51f
--- /dev/null
+++ b/2023/homework/CaoXu/numpy_submit.txt
@@ -0,0 +1,10 @@
+C
+D
+B
+E
+C
+C
+E
+C
+C
+E

From 642ea9d5b02e7ffb38102caac720369dc86305ef Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 10:11:58 +0800
Subject: [PATCH 04/16] Update python_submit.txt

---
 2023/homework/CaoXu/python_submit.txt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index 256722be..f55b201d 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -23,8 +23,8 @@ D
 C
 A
 D
-D
-C
+B
+BC
 B
 A
 C
@@ -73,12 +73,12 @@ B
 C
 A
 C
-A
+C
 B
 B
 C
 A
-B
+C
 A
 C
 B

From 8ea9dad30bc64faeff78f7c8727246ea68571685 Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 10:19:05 +0800
Subject: [PATCH 05/16] Update python_submit.txt

---
 2023/homework/CaoXu/python_submit.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index f55b201d..67a7bd2e 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -24,7 +24,7 @@ C
 A
 D
 B
-BC
+C
 B
 A
 C
@@ -78,7 +78,7 @@ B
 B
 C
 A
-C
+B
 A
 C
 B

From 15ba364a7837bb75ab360d5a640b798a1488a377 Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 10:37:17 +0800
Subject: [PATCH 06/16] Update python_submit.txt

---
 2023/homework/CaoXu/python_submit.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index 67a7bd2e..bb19d9bc 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -97,7 +97,7 @@ A
 A
 A
 C
-C
+A
 A
 B
 B

From afd943c76d6a0824bcdfce8fda29586c149449fc Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 10:45:36 +0800
Subject: [PATCH 07/16] Update python_submit.txt
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

修改了29题
---
 2023/homework/CaoXu/python_submit.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index bb19d9bc..6b748f66 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -26,7 +26,7 @@ D
 B
 C
 B
-A
+B
 C
 C
 C
@@ -97,7 +97,7 @@ A
 A
 A
 C
-A
+C
 A
 B
 B

From 5c4ae42b7d90b300924c517c911789b369946f49 Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 11:30:18 +0800
Subject: [PATCH 08/16] Update python_submit.txt

45
---
 2023/homework/CaoXu/python_submit.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index 6b748f66..4abb8f79 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -26,7 +26,7 @@ D
 B
 C
 B
-B
+A
 C
 C
 C
@@ -42,7 +42,7 @@ B
 A
 C
 A
-C
+B
 D
 B
 A

From 037b46152ea59169cacda3baea2d7fc9ed67b81a Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 12:26:47 +0800
Subject: [PATCH 09/16] Update python_submit.txt

---
 2023/homework/CaoXu/python_submit.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index 4abb8f79..e6f8428f 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -54,7 +54,7 @@ B
 D
 D
 A
-A
+B
 D
 B
 C

From 00f2d5223dc70f3c5e0485464b15b012c8cd0f95 Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 13:36:28 +0800
Subject: [PATCH 10/16] Update python_submit.txt

47
---
 2023/homework/CaoXu/python_submit.txt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index e6f8428f..4a6326a3 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -42,9 +42,9 @@ B
 A
 C
 A
-B
+C
 D
-B
+A
 A
 B
 A
@@ -54,7 +54,7 @@ B
 D
 D
 A
-B
+A
 D
 B
 C

From 77a535173cdd37f84d8809717083ed437128375d Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sat, 23 Dec 2023 13:44:05 +0800
Subject: [PATCH 11/16] Update python_submit.txt

Fine
---
 2023/homework/CaoXu/python_submit.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index 4a6326a3..67a7bd2e 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -44,7 +44,7 @@ C
 A
 C
 D
-A
+B
 A
 B
 A

From 1365c153bd342037c9f106af7b2921b5807ab43e Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sun, 31 Dec 2023 19:03:08 +0800
Subject: [PATCH 12/16] Create pandas_submit.txt

---
 2023/homework/CaoXu/pandas_submit.txt | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 2023/homework/CaoXu/pandas_submit.txt

diff --git a/2023/homework/CaoXu/pandas_submit.txt b/2023/homework/CaoXu/pandas_submit.txt
new file mode 100644
index 00000000..b4df2b0a
--- /dev/null
+++ b/2023/homework/CaoXu/pandas_submit.txt
@@ -0,0 +1,12 @@
+b
+d
+c
+a
+b
+d
+a
+c
+c
+a
+b
+a

From 4d8da3f51b009895cde9c67f600ddaeb8a49b0ea Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sun, 31 Dec 2023 19:06:58 +0800
Subject: [PATCH 13/16] Update pandas_submit.txt
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

修改了10、12题
---
 2023/homework/CaoXu/pandas_submit.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2023/homework/CaoXu/pandas_submit.txt b/2023/homework/CaoXu/pandas_submit.txt
index b4df2b0a..bbf8ca6e 100644
--- a/2023/homework/CaoXu/pandas_submit.txt
+++ b/2023/homework/CaoXu/pandas_submit.txt
@@ -7,6 +7,6 @@ d
 a
 c
 c
-a
+d
+b
 b
-a

From 6202a867ffb12dfd265c7485d4aaac985073f57f Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sun, 31 Dec 2023 19:11:35 +0800
Subject: [PATCH 14/16] Update pandas_submit.txt
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

修改了第十题
---
 2023/homework/CaoXu/pandas_submit.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2023/homework/CaoXu/pandas_submit.txt b/2023/homework/CaoXu/pandas_submit.txt
index bbf8ca6e..c8c9f10e 100644
--- a/2023/homework/CaoXu/pandas_submit.txt
+++ b/2023/homework/CaoXu/pandas_submit.txt
@@ -7,6 +7,6 @@ d
 a
 c
 c
-d
+b
 b
 b

From 3dc1561575fc39095aff606350b96170a7b93c9c Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Sun, 31 Dec 2023 22:05:14 +0800
Subject: [PATCH 15/16] Update python_submit.txt

---
 2023/homework/CaoXu/python_submit.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2023/homework/CaoXu/python_submit.txt b/2023/homework/CaoXu/python_submit.txt
index 67a7bd2e..79d54642 100644
--- a/2023/homework/CaoXu/python_submit.txt
+++ b/2023/homework/CaoXu/python_submit.txt
@@ -53,7 +53,7 @@ A
 B
 D
 D
-A
+B
 A
 D
 B

From e73fc1a3c600901aaa117447409bf437d479c101 Mon Sep 17 00:00:00 2001
From: ShangTsing <145639022+ShangTsing@users.noreply.github.com>
Date: Thu, 4 Jan 2024 14:27:06 +0800
Subject: [PATCH 16/16] Add files via upload
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

matplotlib版
---
 .../CaoXu/homework-matplotlib_seaborn.ipynb   | 1467 +++++++++++++++++
 1 file changed, 1467 insertions(+)
 create mode 100644 2023/homework/CaoXu/homework-matplotlib_seaborn.ipynb

diff --git a/2023/homework/CaoXu/homework-matplotlib_seaborn.ipynb b/2023/homework/CaoXu/homework-matplotlib_seaborn.ipynb
new file mode 100644
index 00000000..1938b80c
--- /dev/null
+++ b/2023/homework/CaoXu/homework-matplotlib_seaborn.ipynb
@@ -0,0 +1,1467 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b8753dd8-7a83-4584-9360-954a9512007f",
+   "metadata": {},
+   "source": [
+    "# 数据可视化作业题目"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bda9f437-af21-4bc4-beb9-59034544e317",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67d732a4-44e5-4313-bf75-0b1b1d036aee",
+   "metadata": {},
+   "source": [
+    "## 练习1：航班乘客变化分析 (2个题)\n",
+    "\n",
+    "1. 分析年度乘客总量的变化情况（提示：折线图）\n",
+    "2. 分析乘客量在一年中12个月份的分布（提示：柱状图）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fbecfad8-61bd-483d-a1cc-e6cb6b69188a",
+   "metadata": {
+    "tags": []
+   },
+   "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>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>passengers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1949</td>\n",
+       "      <td>Jan</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1949</td>\n",
+       "      <td>Feb</td>\n",
+       "      <td>118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1949</td>\n",
+       "      <td>Mar</td>\n",
+       "      <td>132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1949</td>\n",
+       "      <td>Apr</td>\n",
+       "      <td>129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1949</td>\n",
+       "      <td>May</td>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year month  passengers\n",
+       "0  1949   Jan         112\n",
+       "1  1949   Feb         118\n",
+       "2  1949   Mar         132\n",
+       "3  1949   Apr         129\n",
+       "4  1949   May         121"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = sns.load_dataset(\"flights\")\n",
+    "data.head()\n",
+    "# 年份，月份，乘客数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "341a4d22-025f-486a-8e82-954fae22b177",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_passengers = data.groupby('year')['passengers'].sum()\n",
+    "plt.plot(yearly_passengers.index, yearly_passengers.values)\n",
+    "plt.xlabel('year')\n",
+    "plt.ylabel('number')\n",
+    "plt.title('The number of passengers in each year')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5e30df57-a5ac-41da-b1a1-b42b1baae88b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1742/1253088508.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  monthly_passengers = data.groupby('month')['passengers'].sum()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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PH47hw4cjMzMTPj4+mDFjBgICAkrlGHJxcUFeXh6uXr2qcbbk9TsXK1eurLXP0MbGBgMGDMCAAQOQnZ2N5s2bIzw8vFiBqLhq1KgBQRDg6uoqnvUrzNmzZ/H333/jp59+Qr9+/cTlr98Vl/9zde7cudIpWAsqV64MMzMzXLx4scC6v/76CwYGBgXOMhVFUR9kS6WPY4ioXPD19UWNGjXw3XffITs7u8B6bd6GWqNGDSQnJ+P58+fisri4uAK3E2tLUlKSxviRGzduYPPmzWjXrh0MDQ1haGiIoKAgrF+/vtD/MEqy7x06dEBubi4WLVqksXzevHmQyWTvvNPobW7duoWNGzeK82q1Gj///DO8vb2hVCoB/PvX7+t/6f7xxx+4efOmxrJ79+5pzBsbG8PDwwOCIODFixfIzc0VL7/ks7Ozg6Ojo/iog9I4hvz9/QH8+3C9Vy1cuFBjXluf4evvg4WFBdzc3N74OIfS0q1bNxgaGmLq1KkFPj9BEMQ6889uvNpGEAQsWLBA4zWVK1dG8+bNsWrVKqSlpRXoTx8YGhqiXbt22Lx5M65duyYuz8jIQGxsLD755JN3Xq4rjLm5eaF/gFHZ4xkiKhcMDAywYsUKBAQEoG7duhgwYACqVKmCmzdvYu/evVAoFNiyZYtWtjV48GCsW7cO7du3R48ePXDlyhX8+uuv4hkGbatXrx78/f01brsHIF4KAv59XMDevXvRqFEjfP755/Dw8MD9+/dx8uRJ7N69G/fv3y/Wtjt16oRWrVrhf//7H65duwYvLy/s2rULmzdvxujRo0u0z7Vq1cKgQYNw7Ngx2NvbY9WqVcjIyEB0dLTYpmPHjoiIiMCAAQPQpEkTnD17FqtXry5wJq5du3ZQKpVo2rQp7O3tceHCBSxatAiBgYGwtLTEw4cPUbVqVXTv3h1eXl6wsLDA7t27cezYMcyZMwdA6RxDvr6+CAoKwvz583Hv3j3xtvv8M2Gv/vWvjc/Qw8MDLVu2hK+vL2xsbHD8+HHxUQNlqUaNGpg+fTrCwsJw7do1dOnSBZaWlrh69So2btyIIUOG4KuvvkKdOnVQo0YNfPXVV7h58yYUCgXWr19f6PiX77//Hp988gl8fHwwZMgQuLq64tq1a9i6dStSUlLKdP/eZPr06YiPj8cnn3yC4cOHo0KFCvjhhx+Qk5PzXs9vepWvry+WLl2K6dOnw83NDXZ2dmjdurWWK6eiYCCicqNly5ZISkrCtGnTsGjRImRnZ0OpVKJRo0b473//q7Xt+Pv7Y86cOZg7dy5Gjx6NBg0aIC4uDl9++aXWtvGqFi1aQKVSYerUqUhLS4OHhwdiYmI0xpTY29vj6NGjiIiIwIYNG7BkyRLY2tqibt26RXr2ypsYGBjgzz//xOTJk7FmzRpER0ejWrVqmD17don3t2bNmli4cCHGjRuHixcvwtXVFWvWrBHPqgDA119/jcePHyM2NhZr1qyBj48Ptm7diokTJ2r09d///herV6/G3LlzkZ2djapVq2LUqFGYNGkSAMDMzAzDhw/Hrl27xDug3NzcsGTJEgwbNkzspzSOoZ9//hlKpRK//fYbNm7cCD8/P6xZs0Z80GY+bXyGo0aNwp9//oldu3YhJycHLi4umD59OsaNG1es2kti4sSJqFWrFubNmyeGdycnJ7Rr1w6ffvopgH8HV2/ZsgWjRo1CZGQkTExM0LVrV4wYMQJeXl4a/Xl5eSE5ORnffPMNli5dimfPnsHFxQU9evQo8317k7p16+LAgQMICwtDZGQk8vLy0KhRI/z6668FnkFUVJMnT8b169cRFRWFR48eoUWLFgxEOiIT9OV8JBF9MKpVq4Z69eohLi5O16XoREpKCj766CP8+uuvCA4O1nU5RFQEHENERFQChX0Fxfz582FgYIDmzZvroCIiKg5eMiMiKoGoqCicOHECrVq1QoUKFbB9+3Zs374dQ4YMKdZdR0SkGwxEREQl0KRJE8THx2PatGnIzs6Gs7MzwsPDC/1SUiLSXxxDRERERJLHMUREREQkeQxEREREJHkcQ1QEeXl5uHXrFiwtLfmYdSIionJCEAQ8evQIjo6OMDB4+zkgBqIiuHXrFu8WISIiKqdu3LiBqlWrvrUNA1ER5H/p5Y0bN4r1XTVERERU9tRqNZycnDS+vPpNGIiKIP8ymUKhYCAiIiIqZ4oy3IWDqomIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIq6LoAIiJ9U23iVp1t+9rMwDeu09e6iD4EPENEREREksdARERERJLHQERERESSx0BEREREksdARERERJLHQERERESSx0BEREREksdARERERJLHQERERESSx0BEREREksdARERERJLHQERERESSpzeBaObMmZDJZBg9erS47NmzZwgNDYWtrS0sLCwQFBSEjIwMjdelpaUhMDAQZmZmsLOzw7hx4/Dy5UuNNomJifDx8YFcLoebmxtiYmLKYI+IiIiovNCLQHTs2DH88MMP8PT01Fg+ZswYbNmyBX/88Qf27duHW7duoVu3buL63NxcBAYG4vnz5zh8+DB++uknxMTEYPLkyWKbq1evIjAwEK1atUJKSgpGjx6NwYMHY+fOnWW2f0RERKTfdB6IsrOzERwcjB9//BEVK1YUl2dlZWHlypWYO3cuWrduDV9fX0RHR+Pw4cNITk4GAOzatQvnz5/Hr7/+Cm9vbwQEBGDatGlYvHgxnj9/DgBYtmwZXF1dMWfOHLi7u2PEiBHo3r075s2bp5P9JSIiIv2j80AUGhqKwMBA+Pn5aSw/ceIEXrx4obG8Tp06cHZ2RlJSEgAgKSkJ9evXh729vdjG398farUaqampYpvX+/b39xf7ICIiIqqgy43//vvvOHnyJI4dO1ZgXXp6OoyNjWFtba2x3N7eHunp6WKbV8NQ/vr8dW9ro1ar8fTpU5iamhbYdk5ODnJycsR5tVr9/jtHRERE5YbOzhDduHEDX3zxBVavXg0TExNdlVGoyMhIWFlZiZOTk5OuSyIiIqJSpLNAdOLECWRmZsLHxwcVKlRAhQoVsG/fPnz//feoUKEC7O3t8fz5czx8+FDjdRkZGVAqlQAApVJZ4K6z/Pl3tVEoFIWeHQKAsLAwZGVlidONGze0sctERESkp3QWiNq0aYOzZ88iJSVFnBo0aIDg4GDx30ZGRkhISBBfc/HiRaSlpUGlUgEAVCoVzp49i8zMTLFNfHw8FAoFPDw8xDav9pHfJr+PwsjlcigUCo2JiIiIPlw6G0NkaWmJevXqaSwzNzeHra2tuHzQoEEYO3YsbGxsoFAoMHLkSKhUKjRu3BgA0K5dO3h4eKBv376IiopCeno6Jk2ahNDQUMjlcgDA0KFDsWjRIowfPx4DBw7Enj17sHbtWmzdurVsd5iIiIj0lk4HVb/LvHnzYGBggKCgIOTk5MDf3x9LliwR1xsaGiIuLg7Dhg2DSqWCubk5QkJCEBERIbZxdXXF1q1bMWbMGCxYsABVq1bFihUr4O/vr4tdIiIiIj0kEwRB0HUR+k6tVsPKygpZWVm8fEYkAdUm6u4M8rWZgW9cp691Eemr9/n/W+fPISIiIiLSNQYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpK8CrougIikq9rErTrb9rWZgTrbNhHpH54hIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyeOTqomIqMR09dRxPnGctIVniIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8nQaiJYuXQpPT08oFAooFAqoVCps375dXN+yZUvIZDKNaejQoRp9pKWlITAwEGZmZrCzs8O4cePw8uVLjTaJiYnw8fGBXC6Hm5sbYmJiymL3iIiIqJzQ6W33VatWxcyZM1GzZk0IgoCffvoJnTt3xqlTp1C3bl0AwOeff46IiAjxNWZmZuK/c3NzERgYCKVSicOHD+P27dvo168fjIyM8O233wIArl69isDAQAwdOhSrV69GQkICBg8eDAcHB/j7+5ftDhMREZFe0mkg6tSpk8b8jBkzsHTpUiQnJ4uByMzMDEqlstDX79q1C+fPn8fu3bthb28Pb29vTJs2DRMmTEB4eDiMjY2xbNkyuLq6Ys6cOQAAd3d3HDx4EPPmzWMgIiIiIgB6NIYoNzcXv//+Ox4/fgyVSiUuX716NSpVqoR69eohLCwMT548EdclJSWhfv36sLe3F5f5+/tDrVYjNTVVbOPn56exLX9/fyQlJZXyHhEREVF5ofMnVZ89exYqlQrPnj2DhYUFNm7cCA8PDwBAnz594OLiAkdHR5w5cwYTJkzAxYsXsWHDBgBAenq6RhgCIM6np6e/tY1arcbTp09hampaoKacnBzk5OSI82q1Wns7TERERHpH54Godu3aSElJQVZWFtatW4eQkBDs27cPHh4eGDJkiNiufv36cHBwQJs2bXDlyhXUqFGj1GqKjIzE1KlTS61/IiIi0i86v2RmbGwMNzc3+Pr6IjIyEl5eXliwYEGhbRs1agQAuHz5MgBAqVQiIyNDo03+fP64oze1USgUhZ4dAoCwsDBkZWWJ040bN4q/g0RERKT3dB6IXpeXl6dxuepVKSkpAAAHBwcAgEqlwtmzZ5GZmSm2iY+Ph0KhEC+7qVQqJCQkaPQTHx+vMU7pdXK5XHwUQP5EREREHy6dXjILCwtDQEAAnJ2d8ejRI8TGxiIxMRE7d+7ElStXEBsbiw4dOsDW1hZnzpzBmDFj0Lx5c3h6egIA2rVrBw8PD/Tt2xdRUVFIT0/HpEmTEBoaCrlcDgAYOnQoFi1ahPHjx2PgwIHYs2cP1q5di61bdfPNzERERKR/dBqIMjMz0a9fP9y+fRtWVlbw9PTEzp070bZtW9y4cQO7d+/G/Pnz8fjxYzg5OSEoKAiTJk0SX29oaIi4uDgMGzYMKpUK5ubmCAkJ0XhukaurK7Zu3YoxY8ZgwYIFqFq1KlasWMFb7omIiEik00C0cuXKN65zcnLCvn373tmHi4sLtm3b9tY2LVu2xKlTp967PiIiIpIGvRtDRERERFTWGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjyGIiIiIhI8hiIiIiISPIYiIiIiEjydPpt90RUNqpN3KqT7V6bGaiT7RIRvS+eISIiIiLJ4xkiKnd0dbYD4BkPIqIPFc8QERERkeQxEBEREZHk8ZIZkRZx8DIRUfnEQERERB8s/pFCRcVLZkRERCR5DEREREQkeQxEREREJHkcQ0REREQApP2cN54hIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsnTaSBaunQpPD09oVAooFAooFKpsH37dnH9s2fPEBoaCltbW1hYWCAoKAgZGRkafaSlpSEwMBBmZmaws7PDuHHj8PLlS402iYmJ8PHxgVwuh5ubG2JiYspi94iIiKic0Gkgqlq1KmbOnIkTJ07g+PHjaN26NTp37ozU1FQAwJgxY7Blyxb88ccf2LdvH27duoVu3bqJr8/NzUVgYCCeP3+Ow4cP46effkJMTAwmT54strl69SoCAwPRqlUrpKSkYPTo0Rg8eDB27txZ5vtLRERE+kmnT6ru1KmTxvyMGTOwdOlSJCcno2rVqli5ciViY2PRunVrAEB0dDTc3d2RnJyMxo0bY9euXTh//jx2794Ne3t7eHt7Y9q0aZgwYQLCw8NhbGyMZcuWwdXVFXPmzAEAuLu74+DBg5g3bx78/f3LfJ+JiIhI/+jNGKLc3Fz8/vvvePz4MVQqFU6cOIEXL17Az89PbFOnTh04OzsjKSkJAJCUlIT69evD3t5ebOPv7w+1Wi2eZUpKStLoI79Nfh+FycnJgVqt1piIiIjow6XzQHT27FlYWFhALpdj6NCh2LhxIzw8PJCeng5jY2NYW1trtLe3t0d6ejoAID09XSMM5a/PX/e2Nmq1Gk+fPi20psjISFhZWYmTk5OTNnaViIiI9JTOA1Ht2rWRkpKCI0eOYNiwYQgJCcH58+d1WlNYWBiysrLE6caNGzqth4iIiEqXzr/t3tjYGG5ubgAAX19fHDt2DAsWLEDPnj3x/PlzPHz4UOMsUUZGBpRKJQBAqVTi6NGjGv3l34X2apvX70zLyMiAQqGAqalpoTXJ5XLI5XKt7B8RERHpP52fIXpdXl4ecnJy4OvrCyMjIyQkJIjrLl68iLS0NKhUKgCASqXC2bNnkZmZKbaJj4+HQqGAh4eH2ObVPvLb5PdBREREpNMzRGFhYQgICICzszMePXqE2NhYJCYmYufOnbCyssKgQYMwduxY2NjYQKFQYOTIkVCpVGjcuDEAoF27dvDw8EDfvn0RFRWF9PR0TJo0CaGhoeIZnqFDh2LRokUYP348Bg4ciD179mDt2rXYunWrLnediIiI9IhOA1FmZib69euH27dvw8rKCp6enti5cyfatm0LAJg3bx4MDAwQFBSEnJwc+Pv7Y8mSJeLrDQ0NERcXh2HDhkGlUsHc3BwhISGIiIgQ27i6umLr1q0YM2YMFixYgKpVq2LFihW85Z6IiIhEOg1EK1eufOt6ExMTLF68GIsXL35jGxcXF2zbtu2t/bRs2RKnTp0qVo1ERET04dO7MUREREREZY2BiIiIiCRP57fdE1Btou4GeF+bGaizbRMREekLniEiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsnjXWb0Rrz7jYiIpIJniIiIiEjyeIaIiIiojPEMvP7hGSIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjydBqLIyEh8/PHHsLS0hJ2dHbp06YKLFy9qtGnZsiVkMpnGNHToUI02aWlpCAwMhJmZGezs7DBu3Di8fPlSo01iYiJ8fHwgl8vh5uaGmJiY0t49IiIiKid0Goj27duH0NBQJCcnIz4+Hi9evEC7du3w+PFjjXaff/45bt++LU5RUVHiutzcXAQGBuL58+c4fPgwfvrpJ8TExGDy5Mlim6tXryIwMBCtWrVCSkoKRo8ejcGDB2Pnzp1ltq9ERESkvyrocuM7duzQmI+JiYGdnR1OnDiB5s2bi8vNzMygVCoL7WPXrl04f/48du/eDXt7e3h7e2PatGmYMGECwsPDYWxsjGXLlsHV1RVz5swBALi7u+PgwYOYN28e/P39S28HiYiIqFx47zNEgiAgLS0Nz54903oxWVlZAAAbGxuN5atXr0alSpVQr149hIWF4cmTJ+K6pKQk1K9fH/b29uIyf39/qNVqpKamim38/Pw0+vT390dSUlKhdeTk5ECtVmtMRERE9OF67zNEgiDAzc0NqampqFmzptYKycvLw+jRo9G0aVPUq1dPXN6nTx+4uLjA0dERZ86cwYQJE3Dx4kVs2LABAJCenq4RhgCI8+np6W9to1ar8fTpU5iammqsi4yMxNSpU7W2b0RERKTf3jsQGRgYoGbNmrh3755WA1FoaCjOnTuHgwcPaiwfMmSI+O/69evDwcEBbdq0wZUrV1CjRg2tbf9VYWFhGDt2rDivVqvh5ORUKtsiIiIi3SvWoOqZM2di3LhxOHfunFaKGDFiBOLi4rB3715UrVr1rW0bNWoEALh8+TIAQKlUIiMjQ6NN/nz+uKM3tVEoFAXODgGAXC6HQqHQmIiIiOjDVaxA1K9fPxw9ehReXl4wNTWFjY2NxlRUgiBgxIgR2LhxI/bs2QNXV9d3viYlJQUA4ODgAABQqVQ4e/YsMjMzxTbx8fFQKBTw8PAQ2yQkJGj0Ex8fD5VKVeRaiYiI6MNVrLvM5s+fr5WNh4aGIjY2Fps3b4alpaU45sfKygqmpqa4cuUKYmNj0aFDB9ja2uLMmTMYM2YMmjdvDk9PTwBAu3bt4OHhgb59+yIqKgrp6emYNGkSQkNDIZfLAQBDhw7FokWLMH78eAwcOBB79uzB2rVrsXXrVq3sBxEREZVvxQpEISEhWtn40qVLAfz78MVXRUdHo3///jA2Nsbu3bsxf/58PH78GE5OTggKCsKkSZPEtoaGhoiLi8OwYcOgUqlgbm6OkJAQREREiG1cXV2xdetWjBkzBgsWLEDVqlWxYsUK3nJPREREAErwHKIrV64gOjoaV65cwYIFC2BnZ4ft27fD2dkZdevWLVIfgiC8db2TkxP27dv3zn5cXFywbdu2t7Zp2bIlTp06VaS6iIiISFqKNYZo3759qF+/Po4cOYINGzYgOzsbAHD69GlMmTJFqwUSERERlbZiBaKJEydi+vTpiI+Ph7Gxsbi8devWSE5O1lpxRERERGWhWIHo7Nmz6Nq1a4HldnZ2uHv3bomLIiIiIipLxQpE1tbWuH37doHlp06dQpUqVUpcFBEREVFZKlYg6tWrFyZMmID09HTIZDLk5eXh0KFD+Oqrr9CvXz9t10hERERUqooViL799lvUqVMHTk5OyM7OhoeHB5o3b44mTZpo3BJPREREVB4U67Z7Y2Nj/Pjjj/jmm29w7tw5ZGdn46OPPtLqd5sRERERlZViP4cIAJydncUvPZXJZFopiIiIiKisFeuSGQCsXLkS9erVg4mJCUxMTFCvXj2sWLFCm7URERERlYlinSGaPHky5s6di5EjR4pfkJqUlIQxY8YgLS1N42sziIiIiPRdsQLR0qVL8eOPP6J3797isk8//RSenp4YOXIkAxERERGVK8W6ZPbixQs0aNCgwHJfX1+8fPmyxEURERERlaViBaK+ffuK31T/quXLlyM4OLjERRERERGVpSJfMhs7dqz4b5lMhhUrVmDXrl1o3LgxAODIkSNIS0vjgxmJiIio3ClyIDp16pTGvK+vLwDgypUrAIBKlSqhUqVKSE1N1WJ5RERERKWvyIFo7969pVkHERERkc4U+zlERERERB+KYt12/+zZMyxcuBB79+5FZmYm8vLyNNafPHlSK8URERERlYViBaJBgwZh165d6N69Oxo2bMiv7SAiIqJyrViBKC4uDtu2bUPTpk21XQ8RERFRmSvWGKIqVarA0tJS27UQERER6USxAtGcOXMwYcIEXL9+Xdv1EBEREZW5Yl0ya9CgAZ49e4bq1avDzMwMRkZGGuvv37+vleKIiIiIykKxAlHv3r1x8+ZNfPvtt7C3t+egaiIiIirXihWIDh8+jKSkJHh5eWm7HiIiIqIyV6wxRHXq1MHTp0+1XQsRERGRThQrEM2cORNffvklEhMTce/ePajVao2JiIiIqDwp1iWz9u3bAwDatGmjsVwQBMhkMuTm5pa8MiIiIqIyUqxAxC96JSIiog9JsQJRixYttF0HERERkc4UawzR/v373zoVVWRkJD7++GNYWlrCzs4OXbp0wcWLFzXaPHv2DKGhobC1tYWFhQWCgoKQkZGh0SYtLQ2BgYEwMzODnZ0dxo0bh5cvX2q0SUxMhI+PD+RyOdzc3BATE1OcXSciIqIPULHOELVs2bLAslefRVTUMUT79u1DaGgoPv74Y7x8+RJff/012rVrh/Pnz8Pc3BwAMGbMGGzduhV//PEHrKysMGLECHTr1g2HDh0StxUYGAilUonDhw/j9u3b6NevH4yMjPDtt98CAK5evYrAwEAMHToUq1evRkJCAgYPHgwHBwf4+/sX5y0gIiKiD0ixAtGDBw805l+8eIFTp07hm2++wYwZM4rcz44dOzTmY2JiYGdnhxMnTqB58+bIysrCypUrERsbi9atWwMAoqOj4e7ujuTkZDRu3Bi7du3C+fPnsXv3btjb28Pb2xvTpk3DhAkTEB4eDmNjYyxbtgyurq6YM2cOAMDd3R0HDx7EvHnzGIiIiIioeJfMrKysNKZKlSqhbdu2mDVrFsaPH1/sYrKysgAANjY2AIATJ07gxYsX8PPzE9vUqVMHzs7OSEpKAgAkJSWhfv36sLe3F9v4+/tDrVYjNTVVbPNqH/lt8vsgIiIiaSvWGaI3sbe3LzAGqKjy8vIwevRoNG3aFPXq1QMApKenw9jYGNbW1gW2k56eLrZ5NQzlr89f97Y2arUaT58+hampqca6nJwc5OTkiPN8thIREdGHrViB6MyZMxrzgiDg9u3bmDlzJry9vYtVSGhoKM6dO4eDBw8W6/XaFBkZialTp+q6DCIiIiojxQpE3t7ekMlkEARBY3njxo2xatWq9+5vxIgRiIuLw/79+1G1alVxuVKpxPPnz/Hw4UONs0QZGRlQKpVim6NHj2r0l38X2qttXr8zLSMjAwqFosDZIQAICwvD2LFjxXm1Wg0nJ6f33i8iIiIqH4oViK5evaoxb2BggMqVK8PExOS9+hEEASNHjsTGjRuRmJgIV1dXjfW+vr4wMjJCQkICgoKCAAAXL15EWloaVCoVAEClUmHGjBnIzMyEnZ0dACA+Ph4KhQIeHh5im23btmn0HR8fL/bxOrlcDrlc/l77QkREROVXsQKRi4sLEhISkJCQgMzMTOTl5WmsL+pZotDQUMTGxmLz5s2wtLQUx/xYWVnB1NQUVlZWGDRoEMaOHQsbGxsoFAqMHDkSKpUKjRs3BgC0a9cOHh4e6Nu3L6KiopCeno5JkyYhNDRUDDVDhw7FokWLMH78eAwcOBB79uzB2rVrsXXr1uLsPhEREX1gihWIpk6dioiICDRo0AAODg4azyB6H0uXLgVQ8LlG0dHR6N+/PwBg3rx5MDAwQFBQEHJycuDv748lS5aIbQ0NDREXF4dhw4ZBpVLB3NwcISEhiIiIENu4urpi69atGDNmDBYsWICqVatixYoVvOWeiIiIABQzEC1btgwxMTHo27dviTb++hikwpiYmGDx4sVYvHjxG9u4uLgUuCT2upYtW+LUqVPvXSMRERF9+Ir1HKLnz5+jSZMm2q6FiIiISCeKFYgGDx6M2NhYbddCREREpBPFumT27NkzLF++HLt374anpyeMjIw01s+dO1crxRERERGVhWI/mDH/AYznzp3TWFfcAdZEREREulKsQLR3715t10FERESkM8UaQ0RERET0IWEgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsnTaSDav38/OnXqBEdHR8hkMmzatEljff/+/SGTyTSm9u3ba7S5f/8+goODoVAoYG1tjUGDBiE7O1ujzZkzZ9CsWTOYmJjAyckJUVFRpb1rREREVI7oNBA9fvwYXl5eWLx48RvbtG/fHrdv3xan3377TWN9cHAwUlNTER8fj7i4OOzfvx9DhgwR16vVarRr1w4uLi44ceIEZs+ejfDwcCxfvrzU9ouIiIjKlwq63HhAQAACAgLe2kYul0OpVBa67sKFC9ixYweOHTuGBg0aAAAWLlyIDh064LvvvoOjoyNWr16N58+fY9WqVTA2NkbdunWRkpKCuXPnagQnIiIiki69H0OUmJgIOzs71K5dG8OGDcO9e/fEdUlJSbC2thbDEAD4+fnBwMAAR44cEds0b94cxsbGYht/f39cvHgRDx48KHSbOTk5UKvVGhMRERF9uPQ6ELVv3x4///wzEhISMGvWLOzbtw8BAQHIzc0FAKSnp8POzk7jNRUqVICNjQ3S09PFNvb29hpt8ufz27wuMjISVlZW4uTk5KTtXSMiIiI9otNLZu/Sq1cv8d/169eHp6cnatSogcTERLRp06bUthsWFoaxY8eK82q1mqGIiIjoA6bXZ4heV716dVSqVAmXL18GACiVSmRmZmq0efnyJe7fvy+OO1IqlcjIyNBokz//prFJcrkcCoVCYyIiIqIPV7kKRP/88w/u3bsHBwcHAIBKpcLDhw9x4sQJsc2ePXuQl5eHRo0aiW3279+PFy9eiG3i4+NRu3ZtVKxYsWx3gIiIiPSSTgNRdnY2UlJSkJKSAgC4evUqUlJSkJaWhuzsbIwbNw7Jycm4du0aEhIS0LlzZ7i5ucHf3x8A4O7ujvbt2+Pzzz/H0aNHcejQIYwYMQK9evWCo6MjAKBPnz4wNjbGoEGDkJqaijVr1mDBggUal8SIiIhI2nQaiI4fP46PPvoIH330EQBg7Nix+OijjzB58mQYGhrizJkz+PTTT1GrVi0MGjQIvr6+OHDgAORyudjH6tWrUadOHbRp0wYdOnTAJ598ovGMISsrK+zatQtXr16Fr68vvvzyS0yePJm33BMREZFIp4OqW7ZsCUEQ3rh+586d7+zDxsYGsbGxb23j6emJAwcOvHd9REREJA3lagwRERERUWlgICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsljICIiIiLJYyAiIiIiyWMgIiIiIsnTaSDav38/OnXqBEdHR8hkMmzatEljvSAImDx5MhwcHGBqago/Pz9cunRJo839+/cRHBwMhUIBa2trDBo0CNnZ2Rptzpw5g2bNmsHExAROTk6Iiooq7V0jIiKickSngejx48fw8vLC4sWLC10fFRWF77//HsuWLcORI0dgbm4Of39/PHv2TGwTHByM1NRUxMfHIy4uDvv378eQIUPE9Wq1Gu3atYOLiwtOnDiB2bNnIzw8HMuXLy/1/SMiIqLyoYIuNx4QEICAgIBC1wmCgPnz52PSpEno3LkzAODnn3+Gvb09Nm3ahF69euHChQvYsWMHjh07hgYNGgAAFi5ciA4dOuC7776Do6MjVq9ejefPn2PVqlUwNjZG3bp1kZKSgrlz52oEJyIiIpIuvR1DdPXqVaSnp8PPz09cZmVlhUaNGiEpKQkAkJSUBGtrazEMAYCfnx8MDAxw5MgRsU3z5s1hbGwstvH398fFixfx4MGDQredk5MDtVqtMREREdGHS28DUXp6OgDA3t5eY7m9vb24Lj09HXZ2dhrrK1SoABsbG402hfXx6jZeFxkZCSsrK3FycnIq+Q4RERGR3tLbQKRLYWFhyMrKEqcbN27ouiQiIiIqRXobiJRKJQAgIyNDY3lGRoa4TqlUIjMzU2P9y5cvcf/+fY02hfXx6jZeJ5fLoVAoNCYiIiL6cOltIHJ1dYVSqURCQoK4TK1W48iRI1CpVAAAlUqFhw8f4sSJE2KbPXv2IC8vD40aNRLb7N+/Hy9evBDbxMfHo3bt2qhYsWIZ7Q0RERHpM50GouzsbKSkpCAlJQXAvwOpU1JSkJaWBplMhtGjR2P69On4888/cfbsWfTr1w+Ojo7o0qULAMDd3R3t27fH559/jqNHj+LQoUMYMWIEevXqBUdHRwBAnz59YGxsjEGDBiE1NRVr1qzBggULMHbsWB3tNREREekbnd52f/z4cbRq1Uqczw8pISEhiImJwfjx4/H48WMMGTIEDx8+xCeffIIdO3bAxMREfM3q1asxYsQItGnTBgYGBggKCsL3338vrreyssKuXbsQGhoKX19fVKpUCZMnT+Yt90RERCTSaSBq2bIlBEF443qZTIaIiAhERES8sY2NjQ1iY2Pfuh1PT08cOHCg2HUSERHRh01vxxARERERlRUGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjwGIiIiIpI8BiIiIiKSPAYiIiIikjy9DkTh4eGQyWQaU506dcT1z549Q2hoKGxtbWFhYYGgoCBkZGRo9JGWlobAwECYmZnBzs4O48aNw8uXL8t6V4iIiEiPVdB1Ae9St25d7N69W5yvUOH/lTxmzBhs3boVf/zxB6ysrDBixAh069YNhw4dAgDk5uYiMDAQSqUShw8fxu3bt9GvXz8YGRnh22+/LfN9ISIiIv2k94GoQoUKUCqVBZZnZWVh5cqViI2NRevWrQEA0dHRcHd3R3JyMho3boxdu3bh/Pnz2L17N+zt7eHt7Y1p06ZhwoQJCA8Ph7GxcVnvDhEREekhvb5kBgCXLl2Co6MjqlevjuDgYKSlpQEATpw4gRcvXsDPz09sW6dOHTg7OyMpKQkAkJSUhPr168Pe3l5s4+/vD7VajdTU1DduMycnB2q1WmMiIiKiD5deB6JGjRohJiYGO3bswNKlS3H16lU0a9YMjx49Qnp6OoyNjWFtba3xGnt7e6SnpwMA0tPTNcJQ/vr8dW8SGRkJKysrcXJyctLujhEREZFe0etLZgEBAeK/PT090ahRI7i4uGDt2rUwNTUtte2GhYVh7Nix4rxarWYoIiIi+oDp9Rmi11lbW6NWrVq4fPkylEolnj9/jocPH2q0ycjIEMccKZXKAned5c8XNi4pn1wuh0Kh0JiIiIjow1WuAlF2djauXLkCBwcH+Pr6wsjICAkJCeL6ixcvIi0tDSqVCgCgUqlw9uxZZGZmim3i4+OhUCjg4eFR5vUTERGRftLrS2ZfffUVOnXqBBcXF9y6dQtTpkyBoaEhevfuDSsrKwwaNAhjx46FjY0NFAoFRo4cCZVKhcaNGwMA2rVrBw8PD/Tt2xdRUVFIT0/HpEmTEBoaCrlcruO9IyIiIn2h14Hon3/+Qe/evXHv3j1UrlwZn3zyCZKTk1G5cmUAwLx582BgYICgoCDk5OTA398fS5YsEV9vaGiIuLg4DBs2DCqVCubm5ggJCUFERISudomIiIj0kF4Hot9///2t601MTLB48WIsXrz4jW1cXFywbds2bZdGREREH5ByNYaIiIiIqDQwEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5DEQERERkeQxEBEREZHkMRARERGR5EkqEC1evBjVqlWDiYkJGjVqhKNHj+q6JCIiItIDkglEa9aswdixYzFlyhScPHkSXl5e8Pf3R2Zmpq5LIyIiIh2TTCCaO3cuPv/8cwwYMAAeHh5YtmwZzMzMsGrVKl2XRkRERDomiUD0/PlznDhxAn5+fuIyAwMD+Pn5ISkpSYeVERERkT6ooOsCysLdu3eRm5sLe3t7jeX29vb466+/CrTPyclBTk6OOJ+VlQUAUKvVpVJfXs6TUum3KN62T6yroHcdA7qqjXW9v/J4jOlrXQCPsdfpa11A+T3GStKnIAjvbixIwM2bNwUAwuHDhzWWjxs3TmjYsGGB9lOmTBEAcOLEiRMnTpw+gOnGjRvvzAqSOENUqVIlGBoaIiMjQ2N5RkYGlEplgfZhYWEYO3asOJ+Xl4f79+/D1tYWMpms1OstKrVaDScnJ9y4cQMKhULX5Yj0tS5Af2tjXe9HX+sC9Lc21vV+WNf708faBEHAo0eP4Ojo+M62kghExsbG8PX1RUJCArp06QLg35CTkJCAESNGFGgvl8shl8s1lllbW5dBpcWjUCj05uB7lb7WBehvbazr/ehrXYD+1sa63g/ren/6VpuVlVWR2kkiEAHA2LFjERISggYNGqBhw4aYP38+Hj9+jAEDBui6NCIiItIxyQSinj174s6dO5g8eTLS09Ph7e2NHTt2FBhoTURERNIjmUAEACNGjCj0Ell5JZfLMWXKlAKX93RNX+sC9Lc21vV+9LUuQH9rY13vh3W9P32urShkglCUe9GIiIiIPlySeDAjERER0dswEBEREZHkMRARERGR5DEQUZmRyWTYtGmTrssg0gke/0T6jYFIj/Xv3198kKS+6N+/P2QyWYHp8uXLOq9p6NChBdaFhoZCJpOhf//+ZV/YK5KSkmBoaIjAwECd1lEe3itAP4/91+lLjfpybL3uzp07GDZsGJydnSGXy6FUKuHv749Dhw7pujQAwI0bNzBw4EA4OjrC2NgYLi4u+OKLL3Dv3r0ivT4xMREymQwPHz4scS35P5czZ87UWL5p0yadfzvCq7/zjYyMYG9vj7Zt22LVqlXIy8vTaW3axkBE7619+/a4ffu2xuTq6qrTmpycnPD777/j6dOn4rJnz54hNjYWzs7OJer7xYsXJS0PK1euxMiRI7F//37cunWrRH3l5uaW6BdRab5XVPa0eWxpU1BQEE6dOoWffvoJf//9N/7880+0bNmyyIGjNP3f//0fGjRogEuXLuG3337D5cuXsWzZMiQkJEClUuH+/ftlXpOJiQlmzZqFBw8elPm23yX/d/61a9ewfft2tGrVCl988QU6duyIly9f6ro8rWEgKid27NiBTz75BNbW1rC1tUXHjh1x5coVcf21a9cgk8mwYcMGtGrVCmZmZvDy8kJSUpLWa8n/a+/VydDQEJs3b4aPjw9MTExQvXp1TJ06tcAPy+3btxEQEABTU1NUr14d69at00pNPj4+cHJywoYNG8RlGzZsgLOzMz766CNxWVHfxzVr1qBFixYwMTHB6tWrS1RbdnY21qxZg2HDhiEwMBAxMTHiuvy/Mrdu3QpPT0+YmJigcePGOHfunNgmJiYG1tbW+PPPP+Hh4QG5XI60tLRi16Ot96p169YFnut1584dGBsbIyEhodj1va5atWqYP3++xjJvb2+Eh4eL8zKZDCtWrEDXrl1hZmaGmjVr4s8//9RaDdqosTS87djKP25eVdgZh+nTp8POzg6WlpYYPHgwJk6cCG9v7xLV9fDhQxw4cACzZs1Cq1at4OLigoYNGyIsLAyffvqp2Gbw4MGoXLkyFAoFWrdujdOnT4t9hIeHw9vbGz/88AOcnJxgZmaGHj16ICsrq0S1Af+eDTU2NsauXbvQokULODs7IyAgALt378bNmzfxv//9DwCQk5ODCRMmwMnJCXK5HG5ubli5ciWuXbuGVq1aAQAqVqyolTOrfn5+UCqViIyMfGOb9evXo27dupDL5ahWrRrmzJkjrvv666/RqFGjAq/x8vJCREREiWrL/51fpUoV+Pj44Ouvv8bmzZuxfft28Zh71+cJAFu2bMHHH38MExMTVKpUCV27di1RXdrGQFROPH78GGPHjsXx48eRkJAAAwMDdO3atcCZgv/973/46quvkJKSglq1aqF3795lkuAPHDiAfv364YsvvsD58+fxww8/ICYmBjNmzNBo98033yAoKAinT59GcHAwevXqhQsXLmilhoEDByI6OlqcX7VqVYGvZinq+zhx4kR88cUXuHDhAvz9/UtU19q1a1GnTh3Url0b//nPf7Bq1Sq8/vivcePGYc6cOTh27BgqV66MTp06aZyZevLkCWbNmoUVK1YgNTUVdnZ2JapJG+/V4MGDERsbi5ycHPE1v/76K6pUqYLWrVuXqL7imDp1Knr06IEzZ86gQ4cOCA4O1slf+mWpKMfW26xevRozZszArFmzcOLECTg7O2Pp0qUlrsvCwgIWFhbYtGmTxvHxqs8++wyZmZnYvn07Tpw4AR8fH7Rp00bjM7t8+TLWrl2LLVu2YMeOHTh16hSGDx9eotru37+PnTt3Yvjw4TA1NdVYp1QqERwcjDVr1kAQBPTr1w+//fYbvv/+e1y4cAE//PADLCws4OTkhPXr1wMALl68iNu3b2PBggUlqsvQ0BDffvstFi5ciH/++afA+hMnTqBHjx7o1asXzp49i/DwcHzzzTdiIAkODsbRo0c1/mhJTU3FmTNn0KdPnxLVVpjWrVvDy8tL/MPqXZ/n1q1b0bVrV3To0AGnTp1CQkICGjZsqPW6SkQgvRUSEiJ07ty50HV37twRAAhnz54VBEEQrl69KgAQVqxYIbZJTU0VAAgXLlzQak2GhoaCubm5OHXv3l1o06aN8O2332q0/eWXXwQHBwdxHoAwdOhQjTaNGjUShg0bVuKaOnfuLGRmZgpyuVy4du2acO3aNcHExES4c+eO0LlzZyEkJKTQ177pfZw/f36JanpVkyZNxP5evHghVKpUSdi7d68gCIKwd+9eAYDw+++/i+3v3bsnmJqaCmvWrBEEQRCio6MFAEJKSkqJa9Hme/X06VOhYsWKYp2CIAienp5CeHi41uoUBEFwcXER5s2bp7Hey8tLmDJlijgPQJg0aZI4n52dLQAQtm/fXuJatFnjxo0btVrD246t6OhowcrKSqP9xo0bhVd/7Tdq1EgIDQ3VaNO0aVPBy8urxLWtW7dOqFixomBiYiI0adJECAsLE06fPi0IgiAcOHBAUCgUwrNnzzReU6NGDeGHH34QBEEQpkyZIhgaGgr//POPuH779u2CgYGBcPv27WLXlZyc/NbPYu7cuQIA4ciRIwIAIT4+vtB2+T+7Dx48KHYt+V49lho3biwMHDhQEATNz6tPnz5C27ZtNV43btw4wcPDQ5z38vISIiIixPmwsDChUaNGWqvtdT179hTc3d2L9HmqVCohODi4RLWUNp4hKicuXbqE3r17o3r16lAoFKhWrRoAFLh04unpKf7bwcEBAJCZmanVWlq1aoWUlBRx+v7773H69GlERESIfxlaWFjg888/x+3bt/HkyRPxtSqVSqMvlUqltTNElStXFi8bREdHIzAwEJUqVdJoU9T3sUGDBlqp6eLFizh69Ch69+4NAKhQoQJ69uyJlStXarR79X2xsbFB7dq1Nd4XY2Njjc+2pLTxXpmYmKBv375YtWoVAODkyZM4d+6czgZlv/r+mJubQ6FQaP3Y1ydFPbbe1cfrf6Vr66/2oKAg3Lp1C3/++Sfat2+PxMRE+Pj4ICYmBqdPn0Z2djZsbW01fmdcvXpV4wyHs7MzqlSpIs6rVCrk5eXh4sWLJa5PeMeZtGvXrsHQ0BAtWrQo8bbex6xZs/DTTz8V+L144cIFNG3aVGNZ06ZNcenSJeTm5gL49yxRbGwsgH/377fffkNwcHCp1SoIAmQyWZE+z5SUFLRp06bUatEGSX2XWXnWqVMnuLi44Mcff4SjoyPy8vJQr149PH/+XKOdkZGR+O/8sQLavhPA3Nwcbm5uGsuys7MxdepUdOvWrUB7ExMTrW7/bQYOHCiOa1m8eHGB9UV9H83NzbVSz8qVK/Hy5Us4OjqKywRBgFwux6JFi4rcj6mpqdbvNtHGezV48GB4e3vjn3/+QXR0NFq3bg0XFxet1mlgYFDgP6/CBrq/euwD/x7/ZXUXTFFr1KZ3HVu6qOl1JiYmaNu2Ldq2bYtvvvkGgwcPxpQpUzB8+HA4ODggMTGxwGteH/ekbW5ubpDJZLhw4UKhY1guXLiAihUrFricVlaaN28Of39/hIWFvfcfF71798aECRNw8uRJPH36FDdu3EDPnj1Lp1D8+165uroiOzv7nZ+nrt7P98FAVA7cu3cPFy9exI8//ohmzZoBAA4ePKjjqjT5+Pjg4sWLBYLS65KTk9GvXz+N+VcH8pZU+/bt8fz5c8hksgJjf8r6fXz58iV+/vlnzJkzB+3atdNY16VLF/z222+oU6cOgH/fh/w7vB48eIC///4b7u7upVYboJ33qn79+mjQoAF+/PFHxMbGvlfIK6rKlSvj9u3b4rxarcbVq1e1vp2SKOsai3Jsubi44NGjR3j8+LEY8FNSUjTa1q5dG8eOHdP4mTx27Fip1e3h4YFNmzbBx8cH6enpqFChgnjmsTBpaWm4deuWGPqSk5NhYGCA2rVrF7sGW1tbtG3bFkuWLMGYMWM0/qNOT0/H6tWr0a9fP9SvXx95eXnYt28f/Pz8CvRjbGwMAOLZGW2aOXMmvL29NfbT3d29wCMLDh06hFq1asHQ0BAAULVqVbRo0QKrV6/G06dP0bZt2xKPN3yTPXv24OzZsxgzZgyqVq36zs/T09MTCQkJBcYq6hMGonKgYsWKsLW1xfLly+Hg4IC0tDRMnDhR12VpmDx5Mjp27AhnZ2d0794dBgYGOH36NM6dO4fp06eL7f744w80aNAAn3zyCVavXo2jR4++1yn+dzE0NBRPNef/kshX1u9jXFwcHjx4gEGDBsHKykpjXVBQEFauXInZs2cDACIiImBrawt7e3v873//Q6VKlUr9GTfaeq8GDx6MESNGwNzcvFTuGmndujViYmLQqVMnWFtbY/LkyQXq1bWyrrEox9bOnTthZmaGr7/+GqNGjcKRI0c07kIDgJEjR+Lzzz9HgwYN0KRJE6xZswZnzpxB9erVS1TfvXv38Nlnn2HgwIHw9PSEpaUljh8/jqioKHTu3Bl+fn5QqVTo0qULoqKiUKtWLdy6dUsceJt/ydrExAQhISH47rvvoFarMWrUKPTo0QNKpbJE9S1atAhNmjSBv78/pk+fDldXV6SmpmLcuHGoUqUKZsyYARsbG4SEhGDgwIH4/vvv4eXlhevXryMzMxM9evSAi4sLZDIZ4uLi0KFDB5iamsLCwqJEdeWrX78+goOD8f3334vLvvzyS3z88ceYNm0aevbsiaSkJCxatAhLlizReG1wcDCmTJmC58+fY968eVqpJycnB+np6cjNzUVGRgZ27NiByMhIdOzYEf369YOBgcE7P88pU6agTZs2qFGjBnr16oWXL19i27ZtmDBhglZq1ArdDV+id+nbt68QFBQkCIIgxMfHC+7u7oJcLhc8PT2FxMREjYGB+YOBT506Jb7+wYMHAgBxkKU2vG2A3Y4dO4QmTZoIpqamgkKhEBo2bCgsX75cXA9AWLx4sdC2bVtBLpcL1apV0xiQWxo1CYKgMVC4OO9jcXXs2FHo0KFDoevyB2wuWLBAACBs2bJFqFu3rmBsbCw0bNhQHHwqCIUPji0ubb5X+R49eiSYmZkJw4cP10qNgqB57GdlZQk9e/YUFAqF4OTkJMTExBRpwLKVlZUQHR2ttZpKo8biKsqxdfr0aWHjxo2Cm5ubYGpqKnTs2FFYvny58Pqv/YiICKFSpUqChYWFMHDgQGHUqFFC48aNS1Tfs2fPhIkTJwo+Pj6ClZWVYGZmJtSuXVuYNGmS8OTJE0EQBEGtVgsjR44UHB0dBSMjI8HJyUkIDg4W0tLSBEH4d1C1l5eXsGTJEsHR0VEwMTERunfvLty/f79EteW7du2aEBISItjb24vbHzlypHD37l2xzdOnT4UxY8YIDg4OgrGxseDm5iasWrVKXB8RESEolUpBJpO98WaEoijs5/Lq1auCsbGxxue1bt06wcPDQzAyMhKcnZ2F2bNnF+jrwYMHglwuF8zMzIRHjx4Vu6ZXawMgABAqVKggVK5cWfDz8xNWrVol5Obmiu3e9XkKgiCsX79e8Pb2FoyNjYVKlSoJ3bp1K3F92iQThPe4R5PKVPv27eHm5lYqlyFIPyQmJqJVq1Z48OBBqY+dKC3Xrl1DjRo1cOzYMfj4+Gilz/Jw7JeHGoujbdu2UCqV+OWXX3RaR3h4ODZt2lTgMh9RaeElMz304MEDHDp0CImJiYV+xQKRPnjx4gXu3buHSZMmoXHjxloJQ+Xh2C8PNRbVkydPsGzZMvj7+8PQ0BC//fYbdu/ejfj4eF2XRlTmGIj00MCBA3Hs2DF8+eWX6Ny5s67LISrUoUOH0KpVK9SqVUtrTxwvD8d+eaixqGQyGbZt24YZM2bg2bNnqF27NtavX1/oIGKiDx0vmREREZHk8cGMREREJHkMRERERCR5DEREREQkeQxEREREJHkMRERExRQeHg5vb29dl0FEWsBARERUBDKZDJs2bdJ1GURUShiIiIiISPIYiIioXGnZsiVGjhyJ0aNHo2LFirC3t8ePP/6Ix48fY8CAAbC0tISbmxu2b98uvmbfvn1o2LAh5HI5HBwcMHHiRLx8+VKjz1GjRmH8+PGwsbGBUqlEeHi4uD7/G7y7du0KmUxW4Bu9f/nlF1SrVg1WVlbo1asXHj16VJpvARGVAgYiIip3fvrpJ1SqVAlHjx7FyJEjMWzYMHz22Wdo0qQJTp48iXbt2qFv37548uQJbt68iQ4dOuDjjz/G6dOnsXTpUqxcuRLTp08v0Ke5uTmOHDmCqKgoREREiF9hcezYMQBAdHQ0bt++Lc4DwJUrV7Bp0ybExcUhLi4O+/btw8yZM8vuzSAireCTqomoXGnZsiVyc3Nx4MABAEBubi6srKzQrVs3/PzzzwCA9PR0ODg4ICkpCVu2bMH69etx4cIFyGQyAMCSJUswYcIEZGVlwcDAoECfANCwYUO0bt1aDDcymQwbN25Ely5dxDbh4eGYPXs20tPTYWlpCQAYP3489u/fj+Tk5LJ4O4hIS3iGiIjKHU9PT/HfhoaGsLW1Rf369cVl9vb2AIDMzExcuHABKpVKDEMA0LRpU2RnZ+Off/4ptE8AcHBwQGZm5jtrqVatmhiG3ud1RKRfGIiIqNwxMjLSmJfJZBrL8sNPXl5eifosyuuL+zoi0i8MRET0QXN3d0dSUhJeHR1w6NAhWFpaomrVqkXux8jICLm5uaVRIhHpAQYiIvqgDR8+HDdu3MDIkSPx119/YfPmzZgyZQrGjh0LA4Oi/wqsVq0aEhISkJ6ejgcPHpRixUSkCwxERPRBq1KlCrZt24ajR4/Cy8sLQ4cOxaBBgzBp0qT36mfOnDmIj4+Hk5MTPvroo1Kqloh0hXeZERERkeTxDBERERFJHgMRERERSR4DEREREUkeAxERERFJHgMRERERSR4DEREREUkeAxERERFJHgMRERERSR4DEREREUkeAxERERFJHgMRERERSR4DEREREUne/weV21lgU6gMfQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "monthly_passengers = data.groupby('month')['passengers'].sum()\n",
+    "plt.bar(monthly_passengers.index, monthly_passengers.values)\n",
+    "plt.xlabel('month')\n",
+    "plt.ylabel('number')\n",
+    "plt.title('The number of passengers in each month')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66bbde41-e2c6-4bb6-bc80-5c5f3dd75bf6",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 练习2：鸢尾花花型尺寸分析 (3个题)\n",
+    "\n",
+    "1. 萼片（sepal）和花瓣（petal）的大小关系（提示：散点图）\n",
+    "2. 不同种类（species）鸢尾花萼片和花瓣的大小关系（提示：箱图或者提琴图）\n",
+    "3. 不同种类鸢尾花萼片和花瓣大小的分布情况（六角箱图或者核密度估计）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b71328f2-0ae3-463a-a557-43506a54a8a4",
+   "metadata": {
+    "tags": []
+   },
+   "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>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width species\n",
+       "0           5.1          3.5           1.4          0.2  setosa\n",
+       "1           4.9          3.0           1.4          0.2  setosa\n",
+       "2           4.7          3.2           1.3          0.2  setosa\n",
+       "3           4.6          3.1           1.5          0.2  setosa\n",
+       "4           5.0          3.6           1.4          0.2  setosa"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = sns.load_dataset(\"iris\")\n",
+    "data.head()\n",
+    "# 萼片长度，萼片宽度，花瓣长度，花瓣宽度，种类"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d27db8fe-9406-4897-adc2-32a0a83f359a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.size'] = 10\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(10, 10))\n",
+    "\n",
+    "axes[0, 0].scatter(data['sepal_length'], data['petal_length'], s=5)\n",
+    "axes[0, 0].set_xlabel('s_l')\n",
+    "axes[0, 0].set_ylabel('p_l')\n",
+    "axes[0, 0].set_title('s_l VS p_l')\n",
+    "\n",
+    "axes[0, 1].scatter(data['sepal_length'], data['petal_width'], s=5)\n",
+    "axes[0, 1].set_xlabel('s_l')\n",
+    "axes[0, 1].set_ylabel('p_w')\n",
+    "axes[0, 1].set_title('s_l VS p_w')\n",
+    "\n",
+    "axes[1, 0].scatter(data['sepal_width'], data['petal_length'], s=5)\n",
+    "axes[1, 0].set_xlabel('s_w')\n",
+    "axes[1, 0].set_ylabel('p_l')\n",
+    "axes[1, 0].set_title('s_w VS p_l')\n",
+    "\n",
+    "axes[1, 1].scatter(data['sepal_width'], data['petal_width'], s=5)\n",
+    "axes[1, 1].set_xlabel('s_w')\n",
+    "axes[1, 1].set_ylabel('p_w')\n",
+    "axes[1, 1].set_title('s_w VS p_w')\n",
+    "plt.subplots_adjust(wspace=0.2,hspace=0.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.size'] = 8\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(10, 10))\n",
+    "\n",
+    "axes[0, 0].scatter(data[data['species'] == 'setosa']['sepal_length'], data[data['species'] == 'setosa']['petal_length'],s=5)\n",
+    "axes[0, 0].scatter(data[data['species'] == 'versicolor']['sepal_length'], data[data['species'] == 'versicolor']['petal_length'], s=5,c='red', label='versicolor')\n",
+    "axes[0, 0].scatter(data[data['species'] == 'virginica']['sepal_length'], data[data['species'] == 'virginica']['petal_length'],s=5, c='black', label='virginica')\n",
+    "axes[0, 0].set_xlabel('s_l')\n",
+    "axes[0, 0].set_ylabel('p_l')\n",
+    "axes[0, 0].set_title('s_l VS p_l')\n",
+    "\n",
+    "axes[0, 1].scatter(data[data['species'] == 'setosa']['sepal_length'], data[data['species'] == 'setosa']['petal_width'],s=5)\n",
+    "axes[0, 1].scatter(data[data['species'] == 'versicolor']['sepal_length'], data[data['species'] == 'versicolor']['petal_width'], s=5,c='red', label='versicolor')\n",
+    "axes[0, 1].scatter(data[data['species'] == 'virginica']['sepal_length'], data[data['species'] == 'virginica']['petal_width'],s=5, c='black', label='virginica')\n",
+    "axes[0, 1].set_xlabel('s_l')\n",
+    "axes[0, 1].set_ylabel('p_w')\n",
+    "axes[0, 1].set_title('s_l VS p_w')\n",
+    "\n",
+    "axes[1,0].scatter(data[data['species'] == 'setosa']['sepal_width'], data[data['species'] == 'setosa']['petal_length'],s=5)\n",
+    "axes[1,0].scatter(data[data['species'] == 'versicolor']['sepal_width'], data[data['species'] == 'versicolor']['petal_length'], s=5,c='red', label='versicolor')\n",
+    "axes[1,0].scatter(data[data['species'] == 'virginica']['sepal_width'], data[data['species'] == 'virginica']['petal_length'],s=5, c='black', label='virginica')\n",
+    "axes[1, 0].set_xlabel('s_w')\n",
+    "axes[1, 0].set_ylabel('p_l')\n",
+    "axes[1, 0].set_title('s_w VS p_l')\n",
+    "\n",
+    "axes[1,1].scatter(data[data['species'] == 'setosa']['sepal_width'], data[data['species'] == 'setosa']['petal_width'],s=5)\n",
+    "axes[1,1].scatter(data[data['species'] == 'versicolor']['sepal_width'], data[data['species'] == 'versicolor']['petal_width'], s=5,c='red', label='versicolor')\n",
+    "axes[1,1].scatter(data[data['species'] == 'virginica']['sepal_width'], data[data['species'] == 'virginica']['petal_width'],s=5, c='black', label='virginica')\n",
+    "axes[1, 1].set_xlabel('s_w')\n",
+    "axes[1, 1].set_ylabel('p_w')\n",
+    "axes[1, 1].set_title('s_w VS p_w')\n",
+    "plt.subplots_adjust(wspace=0.2,hspace=0.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2b3ab7b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+aty4sV555RVHb0hcXJwWLFigoKAgde3aVVJJz0d8fLz++7//W0eOHJFU0mP48ccfKyMj47I1fPXVV/riiy+Um5urRo0aycfHR25ubmV6UFF3EEhgGR8fHyUnJys9PV0RERHy9fXVgAEDrvoF2K9fPxUUFCgwMFCDBw9WfHy8468oX19fbdq0Sd9++60CAwPVq1cvhYaG6u2335YkPfbYY3Jzc9PLL78sqeR6F5GRkUpISLjsX4s2m03+/v7q2bOn/P39JUk33XSTMjMzr3i4pmvXrnrkkUc0bNgw+fv768UXX3RyC11enz59tGnTJr355pvq0KGDWrZsqbFjxzp+mU+cOFHDhw9XRESEAgIC9Nprr5U5U0YqOYNo3759jkM6UkkX+IwZMzRmzBi1atVK27Zt089//vOr1vPYY4/pueee04MPPqgWLVqoQ4cOevrppx29Ld9++61uvPFGx1lF/v7+WrhwYaXXt1+/flq3bp1eeOEFNW/eXHfccYceffRRPfbYY5V+DankENy8efN03333yd/fX5MnT67Uclfb3pcqKCjQCy+8oA4dOsjX11dTpkzRO++8oy5dusjDw0ObNm1SkyZN1K9fP/n4+CgqKkoffPCB43BEacgICAiQv7+/0tPTr7oNfvrpJ02cOFEtWrRQq1at9Pnnn2vdunWSpKeeekpBQUHq1KmT2rdvry1btig+Pt6pbXepq33WpJIejeHDhysnJ0fDhg2TVBJIsrKyyoX59957T7169dLIkSMdZwK9+eabV+zFOX/+vJ588km1bt1a/v7+euONN/TBBx+oSZMm17RusIabuZY+OwAAgGpADwkAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMsRSAAAgOUqf2UiC9ntdh0/ftxx0RsAAFD7GWOUnZ2t9u3bX/VKyXUikBw/flxBQUFWlwEAAKrg6NGjV7xvmOTiQJKfn68pU6bo008/VePGjRUVFVXuctYV8fHxkVSyQr6+vjVdJgAAqAZZWVkKCgpyfI9fiUsDybRp0+Tm5qbvv/9ebm5ul71T5qVKD9P4+voSSAAAqGMqM9zCZYHkwoUL+uMf/6hjx445Ciu9ARMAAGjYXHaWzcGDB9WiRQvNmzdPvXv3VmxsrLZs2VLhvPn5+crKyirzAwAA6i+XBZKioiIdOXJE3bt3144dO/S73/1Od955p06cOFFu3vnz58vPz8/xw4BWAADqN5fd7ffUqVNq06aNCgoK5OHhIanklt7z58/XiBEjysybn5+v/Px8x+PSQTGZmZmMIQEAoI7IysqSn59fpb6/XdZDEhAQoOHDh+vTTz+VJB0+fFiHDx9WeHh4uXm9vLwcA1gZyAoAQP3n0rNsXnvtNf3617/WM888I3d3d73++uvq0KGDK0sAAAC1kEsDSefOnbV161ZXNgkAAOoA7mUDAAAsRyABAACWI5AAAADLEUgAAIDlCCQAAMByBBIAAGA5l57221Dl5Eh791ZhwdxcKS1NCg6WvL2dXjwsTGrSpArtAgDgYgQSF9i7V+rVqypLeksqfyXbytq5U4qJqfLiAAC4DIHEBcLCSsKB01JTpYTx0spVUgWX2K9MuwAA1AUEEhdo0qSqPRW5klKk8FyJng4AQD3GoFYAAGA5AgkAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMsRSAAAgOUIJAAAwHIEEgAAYDkCCQAAsByBBAAAWI5AAgAALEcgAQAAliOQAAAAyxFIAACA5QgkAADAcgQSAABgOQIJAACwHIEEAABYjkACAAAsRyABAACWI5AAAADLEUgAAIDlCCQAAMByBBIAAGA5AgkAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMsRSAAAgOU8rS6gLkpPl06dqvl2vFOlcEmpqVJuzTcnSQoIkDp2dFFjAAD8B4HESenpUni3YuXkedR4W9GSvpE0PkFKqfHWSjRpXKzUfR6EEgCASxFInHTqlJST56GVGq9wpdZoW26yK1Xd9EfdK+OCo2upCldC3iqdOkUvCQDAtQgkVRSuVMW4rN8CAID6jUGtAADAcgQSAABgOQIJAACwHIEEAABYjkACAAAs59JAEhwcrG7duslms8lms2nt2rWubB4AANRSLj/td+3atbLZbK5uFgAA1GIcsgEAAJZzeQ/J3XffLWOM+vbtqxdffFGtWrUqN09+fr7y8/Mdj7OyslxZYqWkKszqEqpdfVwnAEDd4NJA8sUXX6hjx44qLCzUzJkzNWHCBH300Ufl5ps/f77mzJnjytKclqD3rC4BAIB6w80YY6xoOCMjQ127dlV2dna5aRX1kAQFBSkzM1O+vr6uLLOcb76RevWSVmqcwrXX0lqqW6rClKD3tHOnFBNjdTUAgLouKytLfn5+lfr+dlkPyYULF1RYWCh/f39J0urVqxUdHV3hvF5eXvLy8nJVaVUSrr3cywYAgGriskBy4sQJ/fznP1dxcbGMMercubPeeecdVzUPAABqMZcFks6dOyslhR4FAABQHqf9AgAAyxFIAACA5QgkAADAcgQSAABgOQIJAACwHIEEAABYzuX3sqkvUhVe4224ya7GylOeGsu4IDu6Yp0AAKgIgcRJAQFSk8bFSshbVeNtResbfaNeitFOpcg113Jv0rhYAQEeLmkLAIBSBBIndewope7z0KlTNd+Wd6qkBGnVSinXRZ0XAQEe6tjRNW0BAFCKQFIFHTvKpV/a4eGSizpIAACwBINaAQCA5QgkAADAcgQSAABgOQIJAACwHIEEAABYjkACAAAsRyABAACWI5AAAADLEUgAAIDlCCQAAMByBBIAAGA5AgkAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMt5Wl0AAKm4uFhJSUnKyMhQu3btFBsbKw8PD6vLAgCXoYcEsFhiYqJCQ0M1bNgwjRs3TsOGDVNoaKgSExOtLg0AXIZAAlgoMTFRY8eOVUREhJKTk5Wdna3k5GRFRERo7NixhBIADYabMcZYXcTVZGVlyc/PT5mZmfL19bW6HNf55hupVy9p504pJsbqalDNiouLFRoaqoiICG3YsEHu7v//94Hdbld8fLz27Nmj/fv3c/gGQJ3kzPc3PSSARZKSkpSWlqYZM2aUCSOS5O7urunTp+vw4cNKSkqyqEIAcB0CCWCRjIwMSVLPnj0rnF76fOl8AFCfEUgAi7Rr106StGfPngqnlz5fOh8A1GcEEsAisbGxCg4O1rx582S328tMs9vtmj9/vkJCQhQbG2tRhQDgOgQSwCIeHh5atGiRNm7cqPj4+DJn2cTHx2vjxo1auHAhA1oBNAhcGM0FcnKkvXursGCqt6To//zrvLAwqUmTKi0KFxkzZozWrVunKVOmaMCAAY7nQ0JCtG7dOo0ZM8bC6gDAdTjt1wVKz951Nc4Wrju4UiuA+siZ7296SFwgLKwkHDgtN1dKS5OCgyVv53tJwsKq0CYs4eHhoaFDh1pdBgBYhkDiAk2aVLWnwlsaGF7d5QAAUOswqBUAAFiOQAIAACzHIRugmlX5rKpqGDPEWVUA6ioCCVDN9u6t6llV3pKqPmaIs6oA1GUEEqCaVfmsqtRUKWG8tHKVFO58MOGsKgB1GYEEqGZVP6sqV1KKFJ4r0dMBoIFhUCsAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMtZEkiWL18uNzc3bdiwwYrmAQBALePyQJKWlqY333xT/fv3d3XTAACglnJpILHb7brvvvu0dOlSeXl5ubJpAABQi7n0wmiLFy/WwIED1esq19XOz89Xfn6+43FWVlZNlwYAACzkskCyZ88erV+/Xl988cVV550/f77mzJnjgqoAAEBt4LJDNklJSUpLS9P111+v4OBgbd++XZMmTdIf/vCHcvNOnz5dmZmZjp+jR4+6qkwAAGABl/WQPPTQQ3rooYccj4cOHarHH39c8fHx5eb18vJijAkAAA0I1yEBAACWs+xuv9u2bbOqaQAAUMvQQwIAACxHIAEAAJYjkAAAAMsRSAAAgOUIJAAAwHIEEgAAYDkCCQAAsByBBAAAWI5AAgAALEcgAQAAliOQAAAAyxFIAACA5QgkAADAcpbd7ReoK9LTpVOnar4d71QpXFJqqpRb881JkgICpI4dXdQYAFwBgQS4gvR0KbxbsXLyPGq8rWhJ30ganyCl1HhrJZo0LlbqPg9CCQDLEUiAKzh1SsrJ89BKjVe4Umu0LTfZlapu+qPulXHB0dRUhSshb5VOnaKXBID1CCRAJYQrVTEu67cAgIaHQa0AAMByBBIAAGA5AgkAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMsRSAAAgOUIJAAAwHIEEgAAYDkCCQAAsByBBAAAWI6b6wGVkKowq0uodvVxnQDUXQQSoBIS9J7VJQBAvUYgASphpcYpXHutLqNapSqMoAWg1iCQAJUQrr2KUYrVZQBAvcWgVgAAYDkCCQAAsByBBAAAWI5AAgAALEcgAQAAliOQAAAAyxFIAACA5QgkAADAck5dGO3ZZ5/Vk08+KT8/P91yyy365z//qddff10///nPa6o+oFZIVXiNt+EmuxorT3lqLOOCvxVcsU4AUFlOBZIPP/xQc+fO1aZNm+Tp6al//OMf+uUvf0kgQb0VECA1aVyshLxVNd5WtL7RN+qlGO1UimJqvD2pZN0CAjxc0hYAXIlTgcTdveSvts8//1x33HGHunXrJjc3txopDKgNOnaUUvd56NSpmm/LO1VSgrRqpZTros6LgAAPdezomrYA4EqcCiRNmzbVb3/7W61Zs0b/+Mc/ZIxRQUFBTdUG1AodO8qlX9rh4ZKLOkgAoNZw6kD1ihUrlJGRoQULFqhNmzY6ePCgEhISaqo2AADQQLgZY4wzCxQWFio9PV1dunSpqZrKycrKkp+fnzIzM+Xr6+uydgGX+uYbqVcvaedOKYYuEgB1nzPf3071kGzbtk2dOnXSsGHDJElff/01PSQAAOCaORVIpk2bpqSkJLVs2VKS1KdPH6WkpNRIYQAAoOFwKpAUFxeXO1TTqFGjai0IAAA0PE6dZdO4cWOdP3/ecarv7t275e3tXenl4+Li9OOPP8rd3V0+Pj763e9+p+joaOcqBgAA9Y5TgWTWrFmKi4vTDz/8oISEBG3evFnvvfdepZf/85//LH9/f0nSBx98oIkTJ+rbb791qmAAAFD/OBVI4uLidP311+uTTz6RMUZz5sxx6myb0jAiSZmZmVxUDQAASHIykEhSSEiIHnrooSo3ePfdd2vr1q2SpI8++qjCefLz85Wfn+94nJWVVeX2AABA7edUIAkJCamwV+PQoUOVfo133nlHkvT222/rmWeeqTCUzJ8/X3PmzHGmNAAAUIc5dWG0f//7347/5+Xl6d1331XLli01a9asKjXu7e2tY8eOOU4jLlVRD0lQUBAXRkP9xoXRANQzzlwYzakekh49epR53KtXLw0YMKBSgeTcuXPKyclR+/btJUkbNmxQy5Yt1aJFi3Lzenl5ycvLy5nSAABAHeb0GJKLnT59Wj/++GOl5s3MzNQdd9yh3Nxcubu7q1WrVtq4cSMDWwEAgHOBJDo62hEgiouLdeTIEU2dOrVSy3bq1ElfffWV8xUCAIB6z6lA8vLLL///gp6e6ty5s9q1a1fdNQF1Wk6OtHdvFRZM9ZYU/Z9/nRcWJjVpUqVFAcByTt/t1wrc7Rd1SenYVFdjLCyA2qbaB7XefvvtVxzrkZiY6FyFQD0WFlYSDpyWmyulpUnBwZITt2S4uF0AqKsqFUji4+NruAyg/mjSpKo9Fd7SwPDqLgcA6oRKBZIJEybUdB0AAKABc/q03z//+c/atWuX8vLyHM8tXry4WosCAAANi7szMz/66KN69913tWLFCrm5uWndunXKzMysqdoAAEAD4VQg2bp1qz788EO1atVKixYt0ldffaVjx47VVG0AAKCBcCqQNG7cWO7u7nJzc1NhYaHatm2r48eP11RtAACggajUGJINGzbo1ltvlY+Pj3JycjRw4EAlJCSobdu2asKVmAAAwDWqVA/Jc889pw4dOig0NFTHjh3TwoULFRkZqeuuu07r1q2r6RoBAEA9V6lAsmvXLm3cuFHXXXed+vfvr5EjR6p9+/aaO3eugoKCarpGAABQz1V6DEnv3r31+9//XhkZGZo8ebLee+89tW/fXpMmTarJ+gAAQAPg9HVIvLy89Itf/EKNGzfW2bNntWbNGr3xxhs1URsAAGggnDrLZs+ePXriiSfUoUMHLVq0SA899BBn2QAAgGtWqR6SZcuW6U9/+pOOHTumu+66S9u2bVN4OPfcAAAA1aNSgeSvf/2rZsyYodGjR8vT0+mjPAAAAFdU6UACAABQU5waQwIAAFATCCQAAMByBBIAAGA5AgkAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMsRSAAAgOUIJAAAwHIEEgAAYDkCCQAAsByBBAAAWK5Sd/sFULOKi4uVlJSkjIwMtWvXTrGxsfLw8LC6LABwGXpIAIslJiYqNDRUw4YN07hx4zRs2DCFhoYqMTHR6tIAwGUIJICFEhMTNXbsWEVERCg5OVnZ2dlKTk5WRESExo4dSygB0GC4GWOM1UVcTVZWlvz8/JSZmSlfX1+rywGqRXFxsUJDQxUREaENGzbI3f3//z6w2+2Kj4/Xnj17tH//fg7fAKiTnPn+pocEsEhSUpLS0tI0Y8aMMmFEktzd3TV9+nQdPnxYSUlJFlUIAK5DIAEskpGRIUnq2bNnhdNLny+dDwDqMwIJYJF27dpJkvbs2VPh9NLnS+cDgPqMQAJYJDY2VsHBwZo3b57sdnuZaXa7XfPnz1dISIhiY2MtqhAAXIdAAljEw8NDixYt0saNGxUfH1/mLJv4+Hht3LhRCxcuZEArgAaBC6MBFhozZozWrVunKVOmaMCAAY7nQ0JCtG7dOo0ZM8bC6gDAdTjtF6gFuFIrgPrIme9vekiAWsDDw0NDhw61ugwAsAxjSAAAgOUIJAAAwHIEEgAAYDkCCQAAsByBBAAAWI5AAgAALEcgAQAAlnNZIMnLy1N8fLy6du2qqKgojRw5UgcOHHBV8wAAoBZzaQ/JpEmTtG/fPn377be67bbbdN9997myeQAAUEu5LJA0btxYN998s9zc3CRJ/fv3V1pamquaBwAAtZhll45/5ZVXdNttt1U4LT8/X/n5+Y7HWVlZrioLAABYwJJAMm/ePB04cEBbtmypcPr8+fM1Z84cF1cFAACs4vK7/S5cuFBr1qzR5s2b5e/vX+E8FfWQBAUFcbdfAPVCQUGBli1bpoMHD6pLly6aPHmyGjVqZHVZQLVz5m6/Lg0kixcv1qpVq7R582Y1b9680ss5s0IAUJtNnTpVS5YsUVFRkeM5T09PPfHEE1qwYIGFlQHVz5nvb5cNaj127JimTJmic+fOadiwYbLZbOrXr5+rmgcAy02dOlUvvfSSWrZsqTfffFMZGRl688031bJlS7300kuaOnWq1SUClnH5IZuqoIcEQF1XUFCgpk2bqmXLljp27Jg8Pf9/CF9RUZECAwN1+vRpXbhwgcM3qDdqZQ8JADRky5YtU1FRkZ5//vkyYUQqOWQzd+5cFRUVadmyZRZVCFiLQAIALnDw4EFJ0i233FLh9NLnS+cDGhoCCQC4QJcuXSRJGzdurHB66fOl8wENDWNIAMAFGEOChogxJABQyzRq1EhPPPGETpw4ocDAQL3xxhs6fvy43njjDQUGBurEiRN64oknCCNosCy7dDwANDSl1xlZsmSJHnjgAcfznp6eevrpp7kOCRo0DtkAgItxpVY0FLX2Sq1VRSABAKDuYQwJAACoUwgkAADAcgQSAABgOQIJAACwHKf9AsA1yMmR9u6twoK5uVJamhQcLHl7O714WJjUpEkV2gVqKQIJAFyDvXulXr2qsqS3pPAqt7tzpxQTU+XFgVqHQAIA1yAsrCQcOC01VUoYL61cJYU7H0zCwqrQJlCLEUgA4Bo0aVLVnopcSSlSeK5ETwfAoFYAAGA9AgkAALAcgQQAAFiOMSQAcJH0dOnUqZpvxzu15Byb1NSS0SSuEBAgdezoosYAJxFIAOA/0tOl8G7FysnzqPG2oiV9I2l8gpRS462VaNK4WKn7PAglqJUIJADwH6dOSTl5Hlqp8QpXao225Sa7UtVNf9S9Mi44ep6qcCXkrdKpU/SSoHYikADAJcKVqhiX9VsAkAgkAFBOqurfVcfq4zqhfiGQAMAlEvSe1SUADQ6BBAAusVLjFK6q3DGv9kpVGEELtRqBBAAuEa69jCEBXIwLowEAAMsRSAAAgOU4ZAMAl0hVeI234Sa7GitPeWrssuuQALUZgQQA/iMgoORqpgl5q2q8rWh9o2/USzHaqRTF1Hh7Usm6BQTU/FVogaogkADAf3TsKKXu83DZvWyUIK1aKeW6qPMiIIDLxqP2IpAAwEU6dnTtpdXDwyUXdZAAtRqDWgEAgOUIJAAAwHIEEgAAYDkCCQAAsByBBAAAWI5AAgAALEcgAQAAliOQAAAAy3FhNAC4Bjk50t69VVgw1VtS9H/+dV5YmNSkSZUWBWolAgkAXIO9e6VevaqyZLikb6SEqrW7c6cUwxVeUY8QSADgGoSFlYQDZxQXFytl+2ad2rtbAWERiu4/Qh4ezt30LizMuTaB2o5AAgDXoEkT53oqEhMTNWXKFKWlpTmeCw4O1qJFizRmzJjqLxCoIxjUCgAukpiYqLFjxyoiIkLJycnKzs5WcnKyIiIiNHbsWCUmJlpdImAZN2OMsbqIq8nKypKfn58yMzPl6+trdTkA4LTi4mKFhoYqIiJCGzZskLv7//89aLfbFR8frz179mj//v1OH74Baitnvr/pIQEAF0hKSlJaWppmzJhRJoxIkru7u6ZPn67Dhw8rKSnJogoBaxFIAMAFMjIyJEk9e/ascHrp86XzAQ0NgQQAXKBdu3aSpD179qi4uFjbtm3T6tWrtW3bNhUXF2vPnj1l5gMaGpeOIXn00Uf1v//7vzpy5IhSUlJks9kqtRxjSADUdaVjSAICAnTq1KlyZ9kEBATo9OnTjCFBvVJrx5CMHTtWf//739WpUydXNgsAlvPw8NAdd9yhHTt2KDc3V2+88YaOHz+uN954Q7m5udqxY4fGjh1LGEGDZclZNsHBwdqwYQM9JAAajIt7SE6ePKkjR444ptFDgvrKme/vWnlhtPz8fOXn5zseZ2VlWVgNAFy70rNsVq9erT59+igpKUkZGRlq166dYmNj9dVXX2nAgAFKSkrS0KFDrS4XcLlaGUjmz5+vOXPmWF0GAFSbi8+y8fDwKBc6OMsGDV2tPMtm+vTpyszMdPwcPXrU6pIA4JpcfJZNRTjLBg1drQwkXl5e8vX1LfMDAHVZbGysgoODNW/ePNnt9jLT7Ha75s+fr5CQEMXGxlpUIWAtlwaSBx54QIGBgTp27JhuuukmhYaGurJ5ALCMh4eHFi1apI0bNyo+Pr7MvWzi4+O1ceNGLVy4kAGtaLC4lw0AuFBFd/sNCQnRwoULudsv6h1nvr8JJADgYsXFxeXOsqFnBPVRnT/tFwDqs4rOsgEaulo5qBUAADQsBBIAAGA5AgkAALAcgQQAAFiOQAIAACxHIAEAAJYjkAAAAMsRSAAAgOUIJAAAwHIEEgAAYDkCCQAAsByBBAAAWI6b6wEAUIO4u3Pl0EMCAEANSUxMVGhoqIYNG6Zx48Zp2LBhCg0NVWJiotWl1ToEEgAAakBiYqLGjh2riIgIJScnKzs7W8nJyYqIiNDYsWMJJZdwM8YYq4u4mqysLPn5+SkzM1O+vr5WlwMAwBUVFxcrNDRUERER2rBhg9zd///vf7vdrvj4eO3Zs0f79++v14dvnPn+pocEAIBqlpSUpLS0NM2YMaNMGJEkd3d3TZ8+XYcPH1ZSUpJFFdY+DGoFAKCaZWRkSJJ69uxZ4aDWnj17lpkPBBIAAKpdu3btJEmvvvqqXn/9daWlpTmmBQcHa9KkSWXmA4dsAACodrGxsWrdurWmT5+unj17lhnU2rNnT82YMUOtW7dWbGys1aXWGgQSAABqwMXnjBhjHD+oGIEEAIBqlpSUpJMnT2r+/Pnas2ePBgwYIF9fXw0YMED//ve/NW/ePP30008Mar0IgQQAgGpWOlj14Ycf1oEDB7R161a999572rp1q/bv36+HH364zHxgUCsAANWudLDqnj171L9/fw0dOrTM9D179pSZD/SQAABQ7WJjYxUcHKx58+bJbreXmWa32zV//nyFhIQwqPUiBBIAAKqZh4eHFi1apI0bNyo+Pr7MWTbx8fHauHGjFi5cWK+v0uosDtkAAFADxowZo3Xr1mnKlCkaMGCA4/mQkBCtW7dOY8aMsbC62od72QAAUEk5OdLevc4tU1xcrJTtm3Vq724FhEUouv8Ip3tGwsKkJk2ca7c2cOb7mx4SAAAqae9eqVcvZ5fykHTTf36qZudOKSamyovXCQQSAAAqKSysJBw4LTVVShgvrVwlhYdXqd36jkACAEAlNWlS1Z6KXEkpUniuVM97OqqKs2wAAIDlCCQAAMByHLIBADRY6enSqVM13453qhSukqEkuTXfnCQpIEDq2NFFjVUDAgkAoEFKT5fCuxUrJ6/mL04WLekbSeMTpJQab61Ek8bFSt3nUWdCCYEEANAgnTol5eR5aKXGK1ypNdqWm+xKVTf9UffKuGC0RKrClZC3SqdO1Z1eEgIJAKBBC1eqYlzWb4HLYVArAACwHIEEAABYjkM2AIAG7SONUqpq9lKobrLLSwXKVyOXjCE5rJAab6O6EUgAAA1SQIDk6WHXrOIXrC6lRnh62BUQUHcOhBBIAAANUseO0pfJ7vr++5pvq/HhVIXMGq/Dv1mlvBDn72VTFV27uteZM2wkAgkAoAHr06fkp8Z9kyvNSlHMzdzL5nLqTl8OAACotwgkAADAchyyAQCgknJypL17q7Bgqrek6P/867ywMKlJkyotWmcQSAAAqKS9e6VevaqyZLikb6SEqrW7c6cUU8/HnhBIAACopLCwknDgtNxcKS1NCg6WvJ3vJQmr2cuk1AouDST79+/XhAkTdOrUKfn5+WnFihXq0aOHK0sAAKDKmjSpak+FtzTQNaf71lUuHdT6wAMPaNKkSfr+++/1zDPPaOLEia5sHgAA1FIuCyQ//fSTduzYoYSEkgNoP//5z3X06FEdOHDAVSUAAIBaymWB5OjRo2rXrp08PUuOErm5ualjx45KT08vN29+fr6ysrLK/AAAgPqrVl6HZP78+fLz83P8BAUFWV0SAACoQS4LJEFBQcrIyFBRUZEkyRij9PR0dazgQvvTp09XZmam4+fo0aOuKhMAAFjAZYGkdevWiomJ0cqVKyVJ69evV2BgoEJDQ8vN6+XlJV9f3zI/AACg/nIzxhhXNbZv3z5NnDhRp0+flq+vr5YvX66IiIirLpeVlSU/Pz9lZmYSTgAAqCOc+f526XVIunXrpuTkZFc2CQAA6oBaOagVAAA0LAQSAABgOQIJAACwHIEEAABYjkACAAAs59KzbKqq9MxkLiEPAEDdUfq9XZkrjNSJQJKdnS1JXEIeAIA6KDs7W35+flecx6UXRqsqu92u48ePy8fHR25ublaX4zJZWVkKCgrS0aNHuSBcA8D+bljY3w1LQ93fxhhlZ2erffv2cne/8iiROtFD4u7ursDAQKvLsAyXz29Y2N8NC/u7YWmI+/tqPSOlGNQKAAAsRyABAACWI5DUYl5eXpo9e7a8vLysLgUuwP5uWNjfDQv7++rqxKBWAABQv9FDAgAALEcgAQAAliOQAAAAyxFIAACA5QgkFnruuef0+OOPX3GeoUOHasOGDTVWw7Zt2/TJJ584Hqelpcnf37/G2kOJyuz7ynj22We1atWqCqe9+uqrmjhxoiRp165dWrNmTZnpbm5uOnfu3DXX0JCcO3dOL774YqXnDw4O1q5duy473RWft5dfflk//vij43F1vfcakure75Vls9kct065VO/evbVt2zZJ0ooVK7R3717HtBUrVig+Pv6a23c1AkkDd2kgQd0yd+5cjR8//qrzVRRI4Dxnv5hqg0sDCZxn1X7ftWuXfHx8rjrfpYGkriKQVCA3N1d33nmnunfvrqioKMXFxUmS3n33XfXr108xMTEaPHiwvv32W0klb4Ybb7xRo0ePVvfu3TV48GClpaVJknbv3q1BgwYpJiZG3bt31/PPP1/lurKzs3X//ferb9++ioyM1KRJk1RQUCCppCflqaeeUmxsrLp06aIHH3zQsVxGRobi4uLUvXt3xcXF6Ze//KWee+457dq1S6+99ppWrVolm82muXPnOpaZPXu2evXqpdDQUH300UdVrrmuqS37/vvvv1fXrl0lldwLok2bNpoxY4Yk6YsvvtCNN94oSZo4caJefvllSSXvjzvvvFPdunXToEGDtHv3bknSTz/9pGeffVZbt26VzWYr895YtmyZ+vbtq5CQEC1fvrzqG64OcnNz08yZMxUdHa2uXbuW6Wn6+uuvdeONN6p3796Kjo7W+++/L0l68MEHlZ2dLZvNpt69e0uSFi9erD59+shms6lPnz5KTk6uck2Xa7e0J+Vyn8sPP/xQ4eHhioqK0jPPPKOAgAClpaVp7ty5On78uO68807ZbDbHX+0ZGRm69dZb1b17d9144406c+ZMlWuua2rDfp8xY4bmzZsnSfroo4/k5uam77//XpJ077336p133nHUWtqL+eWXX8pms6lnz5665557VFRUJEl66623tGPHDj3xxBOy2WyO98X58+f1q1/9ShEREerdu7cOHTp0DVvNRQzKSUxMNHFxcY7Hp0+fNn//+9/NqFGjTF5enjHGmC+++MJ0797dGGPM8uXLTaNGjcx3331njDHmt7/9rRk5cqQxxpisrCzHMjk5OcZms5nk5GRjjDGzZ882jz322BVrGTJkiPnggw+MMcbcf//95u233zbGGGO3282vf/1rs2DBAsd88fHxprCw0OTk5Jjg4GDz5ZdfGmOMGTt2rHn22WeNMcZkZGSYNm3amNmzZ1dYw+HDh40ks27dOmOMMR9//LHp2rWrcxuwDqtN+75jx47myJEjZteuXaZ///6mb9++xhhjZs6caV544QVjjDETJkwwS5YsMcYY89RTT5m77rrL2O12c+7cORMWFmYmTJjgqPO2224r8/qSzMKFC40xxqSmpppmzZqZwsJCZzdZnSXJzJw50xhjzMGDB03z5s3N4cOHzdmzZ43NZjPHjx83xhhz8uRJExQUZI4dO2YOHz5s/Pz8yrzOTz/95Ph/cnKy6datm+Nxp06dTEpKymVruPj1rtbu5T6XJ06cMC1atDCpqanGGGP+9Kc/GUnm8OHDFdYwe/Zs06lTJ3Pq1CljjDF33nmnmTdvnhNbrm6rDft9y5YtZtiwYcYYYx5//HFzww03mN///vfGGGOCgoLMDz/84Kj17NmzJj8/3wQGBppNmzYZY4z59NNPjSSzdetWY0zZ7wljSj7vvr6+5tChQ8YYY5555hkzadIkJ7eU69WJm+u5WlRUlFJTUzV58mQNGTJEN998sz788EN9++236tevn2O+M2fOKDc3V5I0YMAAhYeHS5ImTZqkmTNnqri4WLm5uZo8ebJ27dold3d3HT16VLt27VL//v2drmvDhg1KTk7W4sWLJZX8Ne/h4eGYfuedd8rT01Oenp6y2Ww6ePCgbrjhBm3ZskULFy6UJLVt21a33HLLFdtp3LixxowZI0m64YYbdPDgQadrratq074fPny4Nm/erLNnz+quu+7SG2+8oXPnzmnz5s2OXpGLbdmyRUuWLJGbm5v8/Pw0bty4q+670sM9YWFh8vT01I8//tigbmR53333SZI6d+6swYMH64svvlBAQIAOHTqkUaNGlZl337596ty5c7nXSElJ0QsvvKDTp0/L09NT+/btU25urry9vZ2q5csvv7xiu5f7XG7fvl2RkZEKCwuTJE2YMKFML1hFfvazn6lly5aO1yrtTWsorN7vAwcO1K5du5Sbm6vPP/9cCxcu1Kuvvqrhw4eradOmat++fZn59+7dK09PT40YMUKSFBcXV2FNF7vhhhsUEhLi+P/SpUuvWpfVCCQV6Ny5s7777jv97W9/0+bNmzV16lTFxcVpwoQJjm62ypoxY4YCAgKUkpIiT09PjRkzRnl5eVWqyxij9evXO7ryL9W4cWPH/z08PBxdepdyc3O7YjteXl6OeTw8PFRcXFyleuui2rTvR4wYoY0bN+rs2bN65ZVXtH//fn3wwQfav3+/o9v4Sq62n6XKv2caCjc3Nxlj1KNHD3355ZflppcejitVUFCgMWPGaOvWrerTp4+ysrLk5+en/Px8pwPJ1dqtzs8l+70sV+93Ly8v9e7dW++//768vb01dOhQTZo0SZ9++qmGDx9e6ZqvpC7uY8aQVODYsWNyc3PT6NGjtXDhQhljlJCQoJUrVyo9PV2SZLfbtWPHDscyycnJjkFFb731loYNGyYPDw+dPXtWgYGBjgS9adOmKtcVHx+v3/72t4431tmzZ3XgwIGrLnfjjTdqxYoVkqQTJ05o48aNjmm+vr7KzMysck31TW3a98OHD9eWLVuUlpamrl27asSIEZozZ44GDRpUpmes1IgRI7R8+XIZY5SVlaXVq1c7prGfK1Y6biYtLU1JSUmKjY3VgAEDdPjwYW3evNkx365du1RQUCBfX1/l5uY6xm7l5eWpoKBAHTt2lKRr+iv0Su1eSf/+/fWvf/1L+/btkyStXLmyzDLs+/Jqw34fMWKEnn32WQ0fPlzu7u6Kjo7W4sWLHb0gFwsLC1NRUZG2bt0qSdq8eXOZ3s/6so8JJBXYvXu3Bg4cqKioKEVHR+uuu+7SkCFDtGDBAt1+++2KiopSjx49ypy1MGDAAD3zzDPq0aOH/vd//1evv/66JGnmzJlavny5IiMjNW3aNMdgxKpYsmSJvL29ZbPZFBkZqeHDh5dL7hV55ZVXlJSUpO7du2v8+PHq16+f41TD22+/Xbt27So3qLWhqk37vk2bNmrTpo1uuOEGSdKQIUN0/Pjxy/4FNWvWLOXm5iosLEw333yzBg0a5Jg2fPhw5efnKzIy8qrd+Q1JcXGxoqOjFRcXp9/97ncKDg5W8+bN9de//lXz5s1TVFSUunfvrmnTpslut6tFixa6++67FRkZqd69e8vX11fPP/+8+vbtq169eqlRo0ZVruVK7V5J69at9dZbbyk+Pl42m027d+9Ws2bNHJ/xRx99VPfff3+ZQa0NXW3Y7yNGjNCRI0ccAWTkyJE6evSohg4dWm7eRo0aae3atXriiScUERGh9957T1FRUY7pkyZN0rx588oMaq2LuLleNVixYoU2bNhQo9cLuRa5ubm67rrr5OnpqdOnT6t///5auXJlmTERqJravu9xeW5ubjp79my9uO5Odna24/TQDRs2aPr06UpNTbW4qtqpPu33+oYxJA3A/v37dffdd8sYo4KCAk2ePJkwAtQjS5cu1dq1a1VcXCxfX9/LXiwPqM3oIakF3nrrLb366qvlnl+6dKliY2MtqAiuwr5vGEaPHu0Yg1SqefPmjjEBqJ/Y784hkAAAAMsxqBUAAFiOQAIAACxHIAEAAJYjkADXIDg4WN26dZPNZpPNZnNckroqavOdlw8ePKiYmBhFR0eXuwlfWlqaXnvttTLPVdft153lyrueFhQU6JZbblFERIT++7//W88995wef/xxl7QN1Eec9gtco7Vr18pms13z62zbtk3nzp3Tz372M6eXLSoqkqdnzX2c161bpz59+jgu+nax0kBSGy64tmLFCvn7+zvu61KTUlJStH//fscVUp977rkab/NSNb3fAVeihwSoAe+++6769eunmJgYDR48WN9++62kkivBDho0SDExMerevbuef/55SSWXqH7ttde0atUqx1VzS285X+r8+fNl7l/h5uam2bNnq0+fPpo+fbqys7N1//33q2/fvoqMjNSkSZMcl7p+/vnnFR4e7ujJOXLkSLmaz58/r3vvvVc9e/ZUz549NWfOHEnSO++8oyVLligxMVE2m03fffddmeUefPBB7du3TzabTaNHj3Y8n5iY6LjBV+l6StKPP/6oX/ziF+rbt68iIiI0c+bMCrfh9u3b1atXL8ct1//whz9I0mXXs6LbsBcXF+vpp592rNMjjzzi2CZvvfWWunfvLpvNpoiICP3zn/+UJD311FOO28oPHjzYETgu9t1332n8+PFKT0+XzWZz3C6+1OXavXDhglq0aKHCwkJJUt++fTVu3DhJUnp6urp06SJJKiws1LRp09S3b1/ZbDb94he/0NmzZyVJEydO1L333qvBgwerZ8+eys3N1Z133qnu3bsrKipKcXFxFW5PoNaz4hbDQH3RqVMn07VrVxMVFWWioqJMYmKi+fvf/25GjRpl8vLyjDHGfPHFF6Z79+7GGGOysrIcz+fk5BibzWaSk5ONMSW3hX/sscccr33pLc+zs7PNxR9ZSWbOnDmOx/fff795++23jTHG2O128+tf/9osWLDAnDlzxvj5+ZmcnBxjjDEXLlwwubm55dZl6tSpZty4caa4uNicP3/e2Gw2s2bNmgpru9jWrVtNVFRUue3yyCOPGGNKbuPu6+trjh07ZowxJi4uzmzbts0YY0xhYaG56aabzJ///Odyrzt69Gjz3nvvOR6fOXPmiutpTPnbsC9btswMGTLE5OXlmcLCQjNq1Cjz4osvGmOM8fX1ddxqvqCgwGRnZxtjyt5WfvXq1eamm26q1HpfvI2u1G5sbKz5/PPPzZkzZ0xUVJQJDQ01drvdvPXWW+b+++83xhjzwgsvmLlz5zpee+7cuWby5MnGGGMmTJhgIiMjTVZWljHGmMTERBMXF+eY9/Tp0xXWC9R29PUB1+jSQzZTp07Vt99+W+ZquGfOnFFubq5yc3M1efJk7dq1S+7u7jp69Kh27dql/v37V6nte++91/H/DRs2KDk5WYsXL5ZUcssADw8P+fr66vrrr1dCQoLi4uL0X//1XwoMDCz3Wps3b9aiRYvk7u6upk2b6u6779amTZt05513Vqm20r/8AwIC1LlzZx0+fFj+/v7asmWLTpw44Zjv/PnzFfZCDBs2TL/5zW+0f/9+3XjjjY5781xuPSuyefNmTZw4UV5eXpKk+++/X7///e/1zDPPaPjw4brrrrt06623atSoUY67aG/atElLly5Vdna27Ha7zpw54/S6X6ndESNGaPPmzTp58qTi4uKUmpqq3bt3a/PmzYqPj3esY2ZmptavXy+pZLxKcHCw4/XvuOMOx6Xio6KilJqaqsmTJ2vIkCG6+eabna4XqA0IJEA1M8ZowoQJmjdvXrlpjzzyiAICApSSkiJPT0+NGTNGeXl5Fb6Op6dnmVvMVzRfs2bNyrS7fv16xxfrxbZv364vv/xS27ZtU//+/bV69eqrXgn2arc3v5qKbn9u/nMdxu3bt5eZXpHHH39ct912mzZv3qwZM2aoZ8+eWrZs2RXX82ouXqf169dr586d2rZtm26++WY9//zzGjBggB5++GF9/fXX6tKli/71r39p8ODBTrdzpXZHjBihp556SidPntTtt9+uDh06aNOmTdq6dat+97vfSSrZl0uXLr3s4ZeL93vnzp313Xff6W9/+5s2b96sqVOnateuXWrevPk11w24EmNIgGo2evRorVy50nHJaLvdrh07dkiSzp49q8DAQHl6emrfvn3atGmTY7lLbyHetm1bGWMcYzYuHadwqfj4eP32t79VUVGRo60DBw4oOztbJ06cUGxsrGbNmqVBgwYpJSWl3PIjRozQH//4RxljdOHCBb377ruVGo/gzK3PmzVrpmHDhunFF190PHf8+HEdO3as3Lz79u1TSEiI7r//fs2YMUPbt2+/4npWVMuIESP0zjvvqKCgQEVFRXrrrbcUFxenoqIiHTx4UL1799ZTTz2lsWPH6quvvlJmZqauu+46tWvXTsaYCi/rXxmXa1cqGTdSuu9jY2M1YsQILV26VG3btlWrVq0c67hkyRLl5ORIknJycvTvf/+7wraOHTsmNzc3jR49WgsXLpQxRkePHq1S3YCVCCRANYuNjdWCBQt0++23KyoqSj169NCaNWskSTNnztTy5csVGRmpadOm6cYbb3Qsd/vtt2vXrl2OQa2enp5aunSpbrnlFvXp08cxEPJylixZIm9vb9lsNkVGRmr48OFKS0tTZmamxowZo4iICEVGRqqwsFATJkwot/ysWbN03XXXKSIiQv369dPo0aP1i1/84qrrGxkZqR49eqhnz55lBrVezqpVq3TgwAH17NlTERERGjNmjE6fPl1uvldffVU9evRQdHS0Zs6cqUWLFl1xPaXyt2GfNGmSYmJiFBMTI5vNpuDgYD3++OMqLi52DOC12WzauXOnnnzySUVEROiXv/ylevTooT59+qhjx45XXZ+KXK5dqaTna+DAgerYsaO8vb3Vo0cPFRYWavjw4Y7ln3nmGfXp00f9+vVTZGSk+vfvf9nTqHfv3q2BAwcqKipK0dHRuuuuuxQZGVmlugErcS8bAABgOXpIAACA5QgkAADAcgQSAABgOQIJAACwHIEEAABYjkACAAAsRyABAACWI5AAAADLEUgAAIDlCCQAAMByBBIAAGC5/wP6v3i5FX5iCgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# 将花瓣和萼片按照种类分组\n",
+    "setosa_data = [data[data['species'] == 'setosa']['sepal_length'], data[data['species'] == 'setosa']['sepal_width'], data[data['species'] == 'setosa']['petal_length'],data[data['species'] == 'setosa']['petal_width']]\n",
+    "versicolor_data = [data[data['species'] == 'versicolor']['sepal_length'], data[data['species'] == 'versicolor']['sepal_width'], data[data['species'] == 'versicolor']['petal_length'],data[data['species'] == 'versicolor']['petal_width']]\n",
+    "virginica_data = [data[data['species'] == 'virginica']['sepal_length'], data[data['species'] == 'virginica']['sepal_width'], data[data['species'] == 'virginica']['petal_length'],data[data['species'] == 'virginica']['petal_width']]\n",
+    "\n",
+    "# 使用箱图显示花瓣大小分布情况\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(setosa_data, labels=['sepal_length', 'sepal_width', 'petal_length','petal_width'],patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the setosa flowers')\n",
+    "plt.ylabel('Values')\n",
+    "plt.title('The box with features for the setosa flowers')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.boxplot(versicolor_data, labels=['sepal_length', 'sepal_width', 'petal_length','petal_width'],patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the versicolor flowers')\n",
+    "plt.ylabel('Values')\n",
+    "plt.title('The box with features for the versicolor flowers')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.boxplot(virginica_data,labels=['sepal_length', 'sepal_width', 'petal_length','petal_width'],patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the virginica flowers')\n",
+    "plt.ylabel('Values')\n",
+    "plt.title('The box with features for the virginica flowers')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43eaf74c-c25a-4841-a524-1474c1993aeb",
+   "metadata": {},
+   "source": [
+    "## 练习3：餐厅小费情况分析 (7个题)\n",
+    "\n",
+    "1. 小费和总消费之间的关系（提示：散点图+回归分析）\n",
+    "2. 男性顾客和女性顾客，谁更慷慨（提示：箱图或者提琴图）\n",
+    "3. 抽烟与否是否会对小费金额产生影响（提示：箱图或者提琴图）\n",
+    "4. 工作日和周末，什么时候顾客给的小费更慷慨（提示：箱图或者提琴图）\n",
+    "5. 午饭和晚饭，哪一顿顾客更愿意给小费（提示：箱图或者提琴图）\n",
+    "6. 就餐人数是否会对慷慨度产生影响（提示：箱图或者提琴图）\n",
+    "7. 性别+抽烟的组合因素对慷慨度的影响（提示：统计柱状图）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "899479bc-3d1b-4144-ac14-52d113ad26b5",
+   "metadata": {
+    "tags": []
+   },
+   "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>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_bill   tip     sex smoker  day    time  size\n",
+       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
+       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
+       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
+       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
+       "4       24.59  3.61  Female     No  Sun  Dinner     4"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = sns.load_dataset(\"tips\")\n",
+    "data.head()\n",
+    "# 总消费，小费，性别，吸烟与否，就餐星期，就餐时间，就餐人数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9050a71c-23c4-4286-8188-86b0fc107026",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#1.小费和总消费之间的关系（提示：散点图+回归分析）\n",
+    "plt.scatter(data['total_bill'], data['tip'],s=5)\n",
+    "plt.xlabel('total bill')\n",
+    "plt.ylabel('tip')\n",
+    "plt.title('total bill vs. tip')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2ecbc020",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#2. 男性顾客和女性顾客，谁更慷慨（提示：箱图或者提琴图）\n",
+    "sex_tipdata=[data[data['sex'] == 'Female']['tip'],data[data['sex'] == 'Male']['tip']]\n",
+    "\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(sex_tipdata, labels=['Female', 'Male'],patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the sex')\n",
+    "plt.ylabel('Values of tip')\n",
+    "plt.title('The box with 2 features for the the tip')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e038cdd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#3. 抽烟与否是否会对小费金额产生影响（提示：箱图或者提琴图）\n",
+    "smoker_tipdata=[data[data['smoker'] == 'No']['tip'],data[data['smoker'] == 'Yes']['tip']]\n",
+    "\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(smoker_tipdata, labels=['No', 'Yes'],patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the smoker')\n",
+    "plt.ylabel('Values of tip')\n",
+    "plt.title('The box with 2 features for the the tip')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8d1b9708-a824-4160-bcf7-9e9977183cae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#4. 工作日和周末，什么时候顾客给的小费更慷慨（提示：箱图或者提琴图）\n",
+    "day_tipdata=[data[data['day'] == 'Sun']['tip'],data[data['day'] == 'Sat']['tip'],data[data['day'] == 'Thur']['tip'],data[data['day'] == 'Fri']['tip']]\n",
+    "\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(day_tipdata, labels=['Sun', 'Sat','Thur','Fri'],patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the day')\n",
+    "plt.ylabel('Values of tip')\n",
+    "plt.title('The box with 4 features for the the tip')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "47193c0a-3a5a-4e9f-bda6-d5bfb603bbb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v7cqyYLnc3FxlZGQoPj5ehYWFqq2tVWFhoeLj45WRkUFgAYAezmGMMV290aioKK1fv15JSUn68ssvFRMTowMHDiggIEDGGIWHh+udd95RTEzMKfuqqalRSEiIqquruZBhD+R0OhUTE6P4+HitX79efn7/ztcul0tpaWkqLi7Wzp072SUEAN2IJ5/fPp9gW1FRofDwcAUEHJ8+43A4NHjwYJWXl7fZvqGhQTU1NS1u6LkKCgpUVlamefPmtQgqkuTn56e5c+eqtLRUBQUFPqoQAOBtPg8rnlqyZIlCQkLct8jISF+XBC+qqqqSJMXFxbW5vnl5czsAQM/j87ASGRmpqqoqNTU1SZKMMSovL9fgwYPbbD937lxVV1e7bxUVFV1ZLrpYeHi4JKm4uLjN9c3Lm9sBAHoen4eVc889V6NGjdLq1aslSevWrVNERMQJ56sEBgYqODi4xQ09V0pKiqKiopSVlSWXy9Vincvl0pIlSxQdHa2UlBQfVQgA8LYuDSt33nmnIiIitGfPHk2ZMsUdSH7zm9/oN7/5jYYNG6Zf/OIXWrlyZVeWBYv5+/srOztbeXl5SktLa3E0UFpamvLy8rRs2TIm1wJAD+aTo4E6E0cDnRlyc3OVmZmpsrIy97Lo6GgtW7ZM6enpvisMAHBaPPn8Jqyg2+AMtgDQc3jy+c3p9tFt+Pv7a+LEib4uAwDQxXw+wRYAAOBkCCsAAMBqhBUAAGA1wgoAALAaYQUAAFiNsAIAAKxGWAEAAFYjrAAAAKsRVgAAgNUIKwAAwGqEFQAAYDXCCgAAsBphBQAAWI2wAgAArEZYAQAAViOsAAAAqxFWAACA1QgrAADAaoQVAABgNcIKAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrEVYAAIDVAnxdANBeTqdTBQUFqqqqUnh4uFJSUuTv7+/rsgAAXsbICrqF3NxcxcTEKDU1VdOnT1dqaqpiYmKUm5vr69IAAF5GWIH1cnNzlZGRofj4eBUWFqq2tlaFhYWKj49XRkYGgQUAejiHMcb4uoiOqKmpUUhIiKqrqxUcHOzrctDJnE6nYmJiFB8fr/Xr18vP79/52uVyKS0tTcXFxdq5cye7hACgG/Hk85uRFVitoKBAZWVlmjdvXougIkl+fn6aO3euSktLVVBQ4KMKAQDeRliB1aqqqiRJcXFxba5vXt7cDgDQ8xBWYLXw8HBJUnFxcZvrm5c3twMA9DyEFVgtJSVFUVFRysrKksvlarHO5XJpyZIlio6OVkpKio8qBAB4G2EFVvP391d2drby8vKUlpbW4migtLQ05eXladmyZUyuBYAejJPCwXrp6enKyclRZmamkpOT3cujo6OVk5Oj9PR0H1YHAPA2Dl1Gt8EZbAGg5/Dk85uRFXQb/v7+mjhxoq/LAAB0MeasAAAAqxFWAACA1QgrAADAaoQVAABgNcIKAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrEVYAAIDVCCsAAMBqhBUAAGA1wgoAALAaYQUAAFiNsAIAAKxGWAEAAFYjrAAAAKsRVgAAgNUIKwAAwGqEFQAAYDXCCgAAsBphBQAAWI2wAgAArEZYAQAAViOsAAAAqxFWAACA1QgrAADAataElddee02jRo1SUlKS4uLi9MILL/i6JAAAYAGHMcb4ughjjAYMGKD8/HwlJCSorKxMF110kb766isFBQWd9LE1NTUKCQlRdXW1goODu6hiAADQEZ58flszsuJwOHTo0CFJx5/AgAEDFBgY6NuiAACAzwX4ugDpeFB55ZVXlJ6ern79+ungwYPKzc1Vr169WrVtaGhQQ0OD+35NTU1XlgoAALqYFSMrTU1NWrx4sXJzc/XPf/5TGzdu1Pe+9z3t37+/VdslS5YoJCTEfYuMjPRBxQAAoKtYEVaKiopUWVmpCRMmSJLGjBmjiIgIbdmypVXbuXPnqrq62n2rqKjo6nIBAEAXsmI3UGRkpKqqqlRSUqLY2Fjt2rVLu3fv1vDhw1u1DQwMZC4LAABnECvCynnnnadnn31WN954o/z8/ORyufTLX/5SgwcP9nVpAADAx6w4dLkjOHQZAIDup1seugwAANAWwgoAALAaYQUAAFiNsAIAAKxGWAEAAFaz4tBloD2cTqcKCgpUVVWl8PBwpaSkyN/f39dlAQC8jJEVdAu5ubmKiYlRamqqpk+frtTUVMXExCg3N9fXpQEAvIywAuvl5uYqIyND8fHxKiwsVG1trQoLCxUfH6+MjAwCCwD0cJwUDlZzOp2KiYlRfHy81q9fLz+/f+drl8ultLQ0FRcXa+fOnewSAoBuhJPCoccoKChQWVmZ5s2b1yKoSJKfn5/mzp2r0tJSFRQU+KhCAIC3EVZgtaqqKklSXFxcm+ublze3AwD0PIQVWC08PFySVFxc3Ob65uXN7QAAPQ9hBVZLSUlRVFSUsrKy5HK5WqxzuVxasmSJoqOjlZKS4qMKAQDeRliB1fz9/ZWdna28vDylpaW1OBooLS1NeXl5WrZsGZNrAaAH46RwsF56erpycnKUmZmp5ORk9/Lo6Gjl5OQoPT3dh9UBALyNQ5fRbXAGWwDoOTz5/GZkBd2Gv7+/Jk6c6OsyAABdjDkrAADAaoQVAABgNcIKAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrEVYAAIDVCCsAAMBqhBUAAGA1wgoAALAaYQUAAFiNsAIAAKxGWAEAAFYjrAAAAKsRVgAAgNUIKwAAwGqEFQAAYDXCCgAAsBphBQAAWC3A1wUA7eV0OlVQUKCqqiqFh4crJSVF/v7+vi4LAOBlhBV0C7m5ucrMzFRZWZl7WVRUlLKzs5Wenu67wgB0Cr6M4GTYDQTr5ebmKiMjQ/Hx8SosLFRtba0KCwsVHx+vjIwM5ebm+rpEAB2Qm5urmJgYpaamavr06UpNTVVMTAzvbbgRVmA1p9OpzMxMXXvttVq/fr3GjRun/v37a9y4cVq/fr2uvfZa/fjHP5bT6fR1qQBOA19G0B4OY4zxdREdUVNTo5CQEFVXVys4ONjX5aCT5efnKzU1VYWFhRo3blyr9YWFhUpOTtamTZs0ceLEri8QwGlzOp2KiYlRfHy81q9fLz+/f39/drlcSktLU3FxsXbu3MkuoR7Ik89vRlZgtaqqKklSXFxcm+ublze3A9B9FBQUqKysTPPmzWsRVCTJz89Pc+fOVWlpqQoKCnxUIWxBWIHVwsPDJUnFxcVtrm9e3twOQPfBlxG0F2EFVktJSVFUVJSysrLkcrlarHO5XFqyZImio6OVkpLiowoBnC6+jKC9CCuwmr+/v7Kzs5WXl6e0tLQWE/DS0tKUl5enZcuWsT8b6Ib4MoL2IqzAeunp6crJydHWrVuVnJys4OBgJScn6+9//7tycnI4zwrQTfFlBO1FWEG3sHnzZlVWVrZYtnfvXm3evNlHFQHoDM1fRrZt29biy0hxcTFfRuBGWIH1Zs+eraVLl2rAgAF67rnnVFVVpeeee04DBgzQ0qVLNXv2bF+XCKAD0tPTtWvXLm3atElr167Vpk2btHPnToIK3DjPCqx27Ngx9evXTwMGDNCePXsUEPDvK0Q0NTUpIiJC//rXv3T48GH16tXLh5UCADzBeVbQYzz99NNqamrS4sWLWwQVSQoICNDPfvYzNTU16emnn/ZRhQAAbyOswGq7d++WJF177bVtrm9e3twOANDzEFZgtQsuuECSlJeX1+b65uXN7QAAPQ9zVmA15qwAQM/EnBX0GL169dKDDz6offv2KSIiQs8++6wqKyv17LPPKiIiQvv27dODDz5IUAGAHizg1E1a27NnjxwOh84///zOrgdo5bHHHpMkPf7447rzzjvdywMCAvSTn/zEvR4A0DN5tBto69at+u53v6svvvhCDodDAwcO1EsvvaTExERv1nhS7AY6cxw7dkxPP/20du/erQsuuED33HMPIyoA0E158vntUVgZM2aMZs+erWnTpkmScnJy9Oijj+rDDz/sWMUdQFgBAKD78dqclfr6endQkaSMjAw1NDScXpUAAADt4FFYGTVqlPLz8933//a3v2n06NGdXRMAAICbRxNsP/nkE61evVpRUVGSpLKyMl188cUaNWqUez0AAEBn8iis/PKXv/RWHQAAAG3yKKxcccUV3qoDAACgTe0KK5mZmcrOztYNN9wgh8PRan1ubm6nFwYAACC1M6xMnDhRkpSWlubFUgAAAFprV1i57rrrJEnnnXeevvWtb7VY9/rrr3dKIQ0NDcrMzNQbb7yh3r17KzExUatXr+6UvgEAQPfl0aHL8+bNa9ey0zFnzhw5HA599tln2rZtm5YtW9Yp/QIAgO6tXSMrn332mbZv367q6mr97//+r3t5dXW1jhw50uEiDh8+rBUrVrivOSRJAwcO7HC/AACg+2tXWCksLNSqVav05Zdf6vHHH3cvDw4OVnZ2doeL2L17t77xjW8oKytLGzZsUJ8+ffTII49o0qRJrdo2NDS0OGtuTU1Nh7cPOxw5Im3ffopGR49KZWVSVJTUp88p+7zoIqlv386oDgDgKx5dG2jFihW6/fbbO72ITz75RKNHj9YLL7yg73//+9qyZYuuuuoqffrppzrvvPNatH3kkUe0aNGiVn1wbaDu75NPpM4+IfLHH0v/f85CAIBFvHYhQ2/Zv3+/zjvvPB07dkz+/v6Sjl80ccmSJbryyitbtG1rZCUyMpKw0gO0a2SlpESacbO0eo0UG3vKPhlZAboHp9OpgoICVVVVKTw8XCkpKe7PA/RMnoQVj04K5y1hYWGaNGmS3njjDV1zzTUqLS1VaWmpYtv4MAoMDFRgYKAPqoS39e3bnlGQo5K2SLFHJUZMgB4hNzdXmZmZKisrcy+LiopSdna20tPTfVcYrOHR0UDe9Mwzz2jp0qWKj49XWlqafvOb3+j888/3dVkAAC/Kzc1VRkaG4uPjVVhYqNraWhUWFio+Pl4ZGRmcdBSS2rkb6LLLLlNhYaEeeOABPfHEE11QVvt5MoyEHqB5YguTUYBuz+l0KiYmRvHx8Vq/fr38/P79/dnlciktLU3FxcXauXMnu4R6IE8+v9s1snLo0CHt27dPmzZtUm1trWpqalrcAADwVEFBgcrKyjRv3rwWQUWS/Pz8NHfuXJWWlqqgoMBHFcIW7ZqzcuONNyo6OloNDQ0KCQlpsc7hcMjpdHqlOABAz1VVVSVJiouLa3N98/LmdjhztWtkZdGiRTpy5IjGjRsnl8vV4kZQAQCcjvDwcElScXFxm+ublze3w5nLowm27777riSpsrJSlZWVXikIAHBmSElJUVRUlLKysuRyuVqsc7lcWrJkiaKjo5WSkuKjCmELj8LK9u3bNWLECPctPj5e2095YgwAAFrz9/dXdna28vLylJaW1uJooLS0NOXl5WnZsmVMroVnYeXuu+/W/PnzdfDgQR08eFDz58/X3Xff7a3aAAA9XHp6unJycrRt2zYlJycrODhYycnJKi4uVk5ODudZgSQPz2CblJSkoqKiUy7rShy6fIbh0GWgR+IMtmcer53B1t/fX//4xz908cUXS5L+8Y9/8J8JANBh/v7+mjhxoq/LgKU8CitZWVmaMGGCEhISJEnbtm3TmjVrvFIYAACA5GFYmTJlikpKSvT+++9LksaNG6ewsDCvFAYAACCdxoUMzznnHF177bXeqAUAAKAVay5kCAAA0BbCCgAAsBphBQAAWM2jsPLwww/r0KFDMsbo29/+tsLCwrRu3Tpv1QYAAOBZWHn11VcVGhqqDRs2KCAgQO+++64WL17srdoAAAA8Cyt+fseb/+1vf9O0adM0fPhwORwOrxQGAAAgeXjocr9+/fToo4/q5Zdf1rvvvitjjI4dO+at2gAAADwbWVm1apWqqqr02GOP6bzzztPu3bs1Y8YMb9UGAADg2YUMJamxsVHl5eW64IILvFWTR7iQ4RmGCxkCQI/gyee3RyMr+fn5GjJkiFJTUyVJH374ISMrAADAqzwKK3PmzFFBQYEGDBggSRozZoy2bNnilcIAAAAkD8OK0+lstfunV69enVoQAADAf/IorPTu3Vt1dXXuw5W3bdumPn36eKUwAAAAycNDlx966CFNnjxZe/fu1YwZM7RhwwatXbvWW7UBAAB4FlYmT56sCy+8UK+//rqMMVq0aJE1RwUBAOx15Ii0ffspGh09KpWVSVFRUjtG7S+6SOrbtzOqg+08CiuSFB0drbvvvtsbtQAAeqjt24+fdeDk+kiKbXefnMHgzOFRWImOjm7z9Pqff/55pxUEAOh5LrroeLg4qZISacbN0uo1UuypQ8tFF3VObbCfR2ElLy/P/XN9fb1efPFF92HMAACcSN++7RkFOSppixR7VGLEBP/Bo7AyYsSIFvdHjx6t5ORkPfTQQ51aFAAAQDOPDl3+un/961/64osvOqsWAACAVjwaWRk5cqR7zorT6dQ///lPzZ492yuFAQAASB6GlSeeeOLfDwwI0NChQxUeHt7ZNQEAALh5FFauuOIKb9UBAADQpnaFlRtuuKHNQ5ab5ebmdlpBAAAA/6ldYSUtLc3LZQAAALStXWHllltu8XYdAAAAbfL4dPu///3vVVRUpPr6evey5cuXd2pRAAAAzTw6z8p9992nF198UatWrZLD4VBOTo6qq6u9VRsAAIBnYWXTpk169dVXdc455yg7O1sffPCB9uzZ463aAAAAPAsrvXv3lp+fnxwOhxobGzVw4EBVVlZ6qzYAAADP5qwEBQXpyJEjGj9+vGbMmKGBAweqb9++3qoNAACgfSMr69evl9Pp1EsvvaSAgAAtXbpUCQkJOuuss5STk+PtGgEAwBmsXWHlkUce0fnnn6/s7GyVlpaqT58+mj9/vpYtW6bIyEhv1wgAAM5g7QorRUVFysvL0+HDh3XZZZdp/PjxWrVqlY4cOeLt+gAAwBmu3RNsL7nkEv3qV79SVVWV7rnnHq1Zs0aDBg3SHXfc4c36AADAGc7jk8IFBgbqxhtvVO/evXXw4EG9/PLLevbZZ71RGwAAgGeHLhcXF+vBBx90z1+5++67OXQZAAB4VbtGVp5++mk9//zz2rNnj773ve8pPz9fsbGx3q4NAACgfWHlz3/+s+bNm6frr79eAQEe7zkCAAA4be0OKwAAAL7g0ZwVAACArkZYAQAAViOsAAAAqxFWAACA1QgrAADAaoQVAABgNcIKAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrEVYAAIDVCCsAAMBqhBUAAGA1wgoAALCadWFl5cqVcjgcWr9+va9LAQAAFrAqrJSVlem5557TuHHjfF0KAACwhDVhxeVyaebMmXrqqacUGBjo63IAAIAlAnxdQLPly5fr8ssv1+jRo0/arqGhQQ0NDe77NTU13i4NAAD4kBVhpbi4WOvWrdPbb799yrZLlizRokWLuqAqAABgAyt2AxUUFKisrEwXXnihoqKitHnzZt1xxx369a9/3art3LlzVV1d7b5VVFT4oGIAANBVrBhZufvuu3X33Xe770+cOFEPPPCA0tLSWrUNDAxkTgsAAGcQK0ZWAAAATsSKkZWvy8/P93UJAADAEoysAAAAqxFWAACA1QgrAADAaoQVAABgNcIKAACwGmEFAABYzcpDl9EzlZdL+/d3rI8+JVKspJIS6Wgn1BQWJg0e3AkdAQC8hrCCLlFeLsUOd+pIvX+H+hkp6RNJN8+QtnRCXX17O1Wyw5/AAgAWI6ygS+zfLx2p99dq3axYlZx2Pw65VKLhWqHbZDq4F7NEsZpRv0b79zO6AgA2I6ygS8WqRKM6ZUwEAHCmYIItAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrEVYAAIDVCCsAAMBqhBUAAGA1wgoAALAaYQUAAFiNsAIAAKzGtYEAAB1WXn78gqUd0adEipVUUiId7YSawsK4SGlPQVgBAHRIebkUO9ypI/X+HepnpKRPJN08Q51yudO+vZ0q2eFPYOkBCCsAgA7Zv186Uu+v1bpZsSo57X4ccqlEw7VCt8l0cJZCiWI1o36N9u9ndKUnIKwAADpFrEo0qlPGRICWmGALAACsRlgBAABWI6wAAACrEVYAAIDVCCsAAMBqhBUAAGA1Dl1GlyrRRb4uwc2mWgAAJ0ZYQZeaobW+LgEA0M0QVtClVmu6YrXd12VIOj6yQngCAPsRVtClYrWdM1wCADzCBFsAAGA1wgoAALAaYQUAAFiNsAIAAKxGWAEAAFYjrAAAAKsRVgAAgNUIKwAAwGqEFQAAYDXCCgAAsBphBQAAWI2wAgAArEZYAQAAViOsAAAAqxFWAACA1QgrAADAaoQVAABgNcIKAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrEVYAAIDVCCsAAMBqAb4uAADQM5ToIl+X4GZTLeg4wgoAoFPM0Fpfl4AeirACAOgUqzVdsdru6zIkHR9ZITz1HIQVAECniNV2jdIWX5eBHogJtgAAwGqEFQAAYDXCCgAAsJoVYaW+vl5paWkaNmyYEhMTddVVV2nXrl2+LgsAAFjAirAiSXfccYd27NihrVu3aurUqZo5c6avSwIAABawIqz07t1b11xzjRwOhyRp3LhxKisr821RAADAClYeuvzkk09q6tSpba5raGhQQ0OD+35NTU1XlYVOUKLYDj3eIZd6q1716i3Twazd0VoAtMT7G95iXVjJysrSrl27tHHjxjbXL1myRIsWLeriqtBRYWFS395Ozahf06F+RuoTfaLRGqWPtUWjOlxX395OhYX5d7gf4EzG+xve5jDGGF8X0WzZsmV6+eWXtWHDBoWGhrbZpq2RlcjISFVXVys4OLiLKsXpKC+X9u/vWB99Sj5R7IzRKln9sY7GdvyPWViYNHhwh7sBzni8v+GpmpoahYSEtOvz25qRleXLl+ull146aVCRpMDAQAUGBnZdYeg0gwd33h+O2FipE754AegkvL/hTVaElT179igzM1NDhw5VamqqpOOh5P333/dxZQAAwNesCCsRERGyaG8UAACwiBWHLgMAAJwIYQUAAFiNsAIAAKxGWAEAAFYjrAAAAKsRVgAAgNUIKwAAwGqEFQAAYDXCCgAAsBphBQAAWI2wAgAArEZYAQAAViOsAAAAqxFWAACA1QgrAADAaoQVAABgNcIKAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrEVYAAIDVAnxdAACg5ztyRNq+/RSNSvpIGvn//57aRRdJfft2uDR0A4QVAIDXbd8ujR59qlaxkj6RZrSvz48/lkaN6mBh6BYIKwAAr7voouPh4qSOHpXKyqSoKKnPqUdXLrqoMypDd0BYAQB4Xd++7RkF6SNdHtsV5aCbYYItAACwGmEFAABYjbACAACsRlgBAABWI6wAAACrcTQQrMFJowAAbSGswBqcNAoA0BbCCqzBSaMAAG0hrMAanDQKANAWJtgCAACrEVYAAIDVCCsAAMBqhBUAAGA1wgoAALAaYQUAAFiNsAIAAKxGWAEAAFYjrAAAAKsRVgAAgNUIKwAAwGqEFQAAYDXCCgAAsFq3v+qyMUaSVFNT4+NKAABAezV/bjd/jp9Mtw8rtbW1kqTIyEgfVwIAADxVW1urkJCQk7ZxmPZEGou5XC5VVlYqKChIDofD1+XAy2pqahQZGamKigoFBwf7uhwAnYj395nFGKPa2loNGjRIfn4nn5XS7UdW/Pz8FBER4esy0MWCg4P5Ywb0ULy/zxynGlFpxgRbAABgNcIKAACwGmEF3UpgYKAWLlyowMBAX5cCoJPx/saJdPsJtgAAoGdjZAUAAFiNsAIAAKxGWAEAAFYjrKBLRUVFafjw4UpMTFRMTIymTp2q9957T5L0zDPPaOnSpT6uEEBHRUVFqaioyKvbuPXWW/XEE094dRuwR7c/KRy6n1deeUVJSUmSpNzcXF1zzTV64403dNddd3VpHU1NTQoI4C0AALZjZAU+lZ6errvuukvLli3TI488ogceeECStGrVKl155ZW66aabFB8fr0suuUSff/65JCk/P19xcXG65557lJiYqBEjRuijjz5y9/nGG29o/PjxGj16tC699FJt2rTJ/bgRI0bo9ttvV1JSkv74xz92+fMFzlQOh0OHDh1y3w8LC1NZWZmk4yMxDz/8sC677DJFR0dr8eLF7nZ79+5VRkaG4uPjlZCQoIceesi9rqSkRJMmTdKwYcOUnp6uY8eOddXTQRcjrMDnxo4dq08//bTV8g8//FBZWVnatm2brrzySj366KPuddu3b9ctt9yirVu3atasWZo/f74k6fPPP9cjjzyi1157TR9//LHWrl2r6dOnq6GhQdLxP27f//73VVRUpGnTpnXNEwRwSocOHVJhYaE+/PBDLV26VHv37pUkzZgxQ6NHj9a2bdv097//Xffdd5/7MUVFRfrTn/6kkpIS7du3T+vWrfNV+fAyxsDhcyc61U/zt6zmn5966in3upiYGI0dO9a9btmyZZKk119/Xbt27dKECRPcbf38/FReXi5JGjp0qK644gqvPA8Ap2/69OmSjo+4DB06VKWlpQoJCdE777yjN954w93unHPOcf98ww03qG/fvpKkSy+9VLt37+7aotFlCCvwuQ8//FBxcXGtlvfu3dv9s7+/v5qamk65zhijq666SmvXrm3V3969e9W/f//OLB1AO/n7+8vpdLrv19fXt1h/svf7iZzOY9A9sRsIPvXqq6/q17/+tTIzMzulvylTpmjDhg36+9//7l72wQcfdErfAE5fTEyM3n//fUnHJ9YfPnz4lI/p37+/JkyYoOzsbPeyr776yms1wl6EFXS573znO+5Dl1esWKHXXnvNvUuno2JiYrR27VrdeeedSkxMVGxsLIc3Aj4wZcoURUREuG/z58/X/fffr1GjRmnLli0aMGBAu/p58cUX9dFHH2nEiBFKSkrSL3/5Sy9XDhtxbSAAAGA1RlYAAIDVCCsAAMBqhBUAAGA1wgoAALAaYQUAAFiNsAIAAKxGWAF6gKioKA0fPlxJSUlKSkrSzJkzT7uv/Px8vf76651YXefZvXu3Ro0apZEjR2rlypUt1pWVlemZZ55psSwqKkpFRUUd2mZRUZFefvnlFsuSkpJUW1vboX4BtB9hBeghXnnlFRUVFamoqEi//e1vT7ufjoQVb5/uPCcnR2PGjNGWLVv0gx/8oMW6tsJKZ2grrBQVFSkoKKjTtwWgbYQVoAd78cUXNXbsWI0aNUoTJkzQ1q1bJUnbtm3T+PHjNWrUKF188cVavHixpOMfws8884zWrFmjpKQk/exnP1NZWZlCQ0PdfdbV1cnhcLjvOxwOLVy4UGPGjNHcuXNVW1urH/7wh7r00kuVkJCgO+64Q8eOHZMkLV68WLGxse4RoH/+85+taq6rq9Ntt92muLg4xcXFadGiRZKk3/3ud3r88ceVm5urpKQk/eMf/2jxuLvuuks7duxQUlKSrr/+evfy3Nxc90Uxm5+nJH3xxRe68cYbdemllyo+Pl4LFixoVcuXX36phx9+WJs2bVJSUpLuuusu93M+dOiQpOOjNwsWLFBycrIiIyP1zDPPaOXKlbrssssUFRXVIuh8+OGH+uY3v6lLLrlEI0eO1B/+8IdT/xIBSAZAtzdkyBAzbNgwk5iYaBITE01ubq555513zNVXX23q6+uNMca8/fbb5uKLLzbGGFNTU+NefuTIEZOUlGQKCwuNMcYsXLjQ3H///e6+S0tLTUhIiPt+bW2t+c8/HZLMokWL3Pd/+MMfmhdeeMEYY4zL5TK33367eeyxx8yBAwdMSEiIOXLkiDHGmMOHD5ujR4+2ei6zZ88206dPN06n09TV1ZmkpCTz8ssvt1nbf9q0aZNJTExs9brMmjXLGGPMV199ZYKDg82ePXuMMcZMnjzZ5OfnG2OMaWxsNFOmTDG///3vW/W7cuVKM3Xq1BbLJJmDBw+6t/HAAw8YY4zZuXOn6d27t/n5z39ujDHmgw8+MGFhYcYYYw4ePGiSkpJMZWWlu57IyEh3PQBOjKsuAz3EK6+8oqSkJPf92bNna+vWrS2uu3TgwAEdPXpUR48e1T333KOioiL5+fmpoqJCRUVFGjdu3Glt+7bbbnP/vH79ehUWFmr58uWSpKNHj8rf31/BwcG68MILNWPGDE2ePFnf/va3FRER0aqvDRs2KDs7W35+furXr5++//3v680339R3vvOd06pt+vTpkqSwsDANHTpUpaWlCg0N1caNG7Vv3z53u7q6Ou3YseO0ttFcW0xMjHr37q2MjAxJ0iWXXKIDBw7o0KFDeu+99/T555/r6quvbvHYHTt26Pzzzz+t7QJnCsIK0EMZY3TLLbcoKyur1bpZs2YpLCxMW7ZsUUBAgNLT01VfX99mPwEBAXI6ne77bbXr379/i+2uW7dOw4YNa9Vu8+bNeu+995Sfn69x48bppZdeUkpKykmfx3/ucjodvXv3dv/s7++vpqYmmf+/JNrmzZtbrO+sbTTfdzgccjgc7m2OGDFC7733Xoe3B5xpmLMC9FDXX3+9Vq9erfLyckmSy+XSRx99JEk6ePCgIiIiFBAQoB07dujNN990Py44OFjV1dXu+wMHDpQxxj1H5He/+91Jt5uWlqZHH33UPdn24MGD2rVrl2pra7Vv3z6lpKTooYce0vjx47Vly5ZWj7/yyiu1YsUKGWN0+PBhvfjii5o8efIpn+/X6z6Z/v37KzU1Vb/4xS/cyyorK7Vnz54O9XsyycnJKi0t1YYNG9zLioqK3PN5AJwYYQXooVJSUvTYY4/phhtuUGJiokaMGOGe7LlgwQKtXLlSCQkJmjNnjr75zW+6H3fDDTeoqKjIPcE2ICBATz31lK699lqNGTNGjY2NJ93u448/rj59+igpKUkJCQmaNGmSysrKVF1drfT0dMXHxyshIUGNjY265ZZbWj3+oYce0llnnaX4+HiNHTtW119/vW688cZTPt+EhASNGDFCcXFxLSbYnsiaNWu0a9cuxcXFKT4+Xunp6frXv/7Vqt2kSZPU0NCghIQE9wTb03H22Wfrz3/+s7KyspSYmKiLL75Yc+bMkcvlOu0+gTOFwzSPhwIAAFiIkRUAAGA1wgoAALAaYQUAAFiNsAIAAKxGWAEAAFYjrAAAAKsRVgAAgNUIKwAAwGqEFQAAYDXCCgAAsBphBQAAWO3/AE96fYE1N+xRAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#5. 午饭和晚饭，哪一顿顾客更愿意给小费（提示：箱图或者提琴图）\n",
+    "time_tipdata=[data[data['time'] == 'Dinner']['tip'],data[data['time'] == 'Lunch']['tip']]\n",
+    "\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(time_tipdata, labels=['Dinner', 'Lunch'],patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the time')\n",
+    "plt.ylabel('Values of tip')\n",
+    "plt.title('The box with 2 features for the the tip')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5cdcdc34-b582-4d18-b103-cd6728a727c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#6. 就餐人数是否会对慷慨度产生影响（提示：箱图或者提琴图）\n",
+    "size_tipdata=data.groupby('size')['tip'].apply(list)\n",
+    "labels = list(size_tipdata.index)\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(size_tipdata.values, labels=labels,patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the size')\n",
+    "plt.ylabel('Values of tip')\n",
+    "plt.title('The box with 6 features for the the tip')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "ac15ac1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "smoker        No       Yes\n",
+      "sex                       \n",
+      "Female  2.773519  2.931515\n",
+      "Male    3.113402  3.051167\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#7. 性别+抽烟的组合因素对慷慨度的影响（提示：统计柱状图）\n",
+    "import numpy as  np\n",
+    "def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
+    "    _, ax = plt.subplots()\n",
+    "    # 设置每一组柱状图的宽度\n",
+    "    total_width = 0.8\n",
+    "    # 设置每一个柱状图的宽度\n",
+    "    ind_width = total_width / len(y_data_list)\n",
+    "\n",
+    "    # 计算每一个柱状图的中心偏移\n",
+    "    alteration = np.arange(-total_width/2+ind_width/2, total_width/2+ind_width/2, ind_width)\n",
+    "\n",
+    "    # 分别绘制每一个柱状图\n",
+    "    for i in range(0, len(y_data_list)):\n",
+    "        # 横向散开绘制\n",
+    "        ax.bar(np.arange(len(x_data)) + alteration[i], y_data_list[i], color = colors[i], label = y_data_names[i], width = ind_width)\n",
+    "    ax.set_xticks(np.arange(len(x_data)))\n",
+    "    ax.set_xticklabels(x_data)\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_title(title)\n",
+    "    ax.legend(loc = 'upper right')\n",
+    "\n",
+    "#数据分类\n",
+    "grouped_data1 = data[['sex', 'smoker','tip']].groupby(['sex','smoker'])['tip'].mean()\n",
+    "\n",
+    "#整理一下\n",
+    "grouped_data = grouped_data1.unstack()\n",
+    "print(grouped_data)\n",
+    "\n",
+    "groupedbarplot(x_data = grouped_data.index.values\n",
+    "               , y_data_list = [grouped_data['Yes'].values, grouped_data['No'].values]\n",
+    "               , y_data_names = ['Yes', 'No']\n",
+    "               , colors = ['#539caf', '#7663b0']\n",
+    "               , x_label = 'Sex'\n",
+    "               , y_label = 'Value of tip'\n",
+    "               , title = 'Values by Sex (Male or Female) and Smoker (Yes or No)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aefe2f74-5174-42cd-b32d-83991f0f1980",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab2fba5b-ad22-43a3-a196-c0ae9a03a523",
+   "metadata": {},
+   "source": [
+    "## 练习4：泰坦尼克号海难幸存状况分析 (8个题)\n",
+    "\n",
+    "1. 不同仓位等级中幸存和遇难的乘客比例（提示：箱图或者提琴图）\n",
+    "2. 不同性别的幸存比例（提示：箱图或者提琴图）\n",
+    "3. 幸存和遇难乘客的票价分布（提示：箱图或者提琴图）\n",
+    "4. 幸存和遇难乘客的年龄分布（提示：箱图或者提琴图）\n",
+    "5. 不同上船港口的乘客仓位等级分布（提示：箱图或者提琴图）\n",
+    "6. 幸存和遇难乘客堂兄弟姐妹的数量分布（提示：箱图或者提琴图）\n",
+    "7. 幸存和遇难乘客父母子女的数量分布（提示：箱图或者提琴图）\n",
+    "8. 单独乘船与否和幸存之间有没有联系（提示：统计柱状图）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "171e617f-9983-445a-b872-ad925ee239a4",
+   "metadata": {
+    "tags": []
+   },
+   "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>survived</th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>class</th>\n",
+       "      <th>who</th>\n",
+       "      <th>adult_male</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alive</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
+       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
+       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
+       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
+       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
+       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
+       "\n",
+       "     who  adult_male deck  embark_town alive  alone  \n",
+       "0    man        True  NaN  Southampton    no  False  \n",
+       "1  woman       False    C    Cherbourg   yes  False  \n",
+       "2  woman       False  NaN  Southampton   yes   True  \n",
+       "3  woman       False    C  Southampton   yes  False  \n",
+       "4    man        True  NaN  Southampton    no   True  "
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = sns.load_dataset(\"titanic\")\n",
+    "data.head()\n",
+    "# 幸存与否，仓位等级，性别，年龄，堂兄弟姐妹数，父母子女数，票价，上船港口缩写，仓位等级，人员分类，是否成年男性，所在甲板，上船港口，是否幸存，是否单独乘船"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "40a087d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "survived  unservived  survived  total  survived_prop  unsurvived_prop\n",
+      "pclass                                                               \n",
+      "1                 80       136    216       0.629630         0.370370\n",
+      "2                 97        87    184       0.472826         0.527174\n",
+      "3                372       119    491       0.242363         0.757637\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "#1.不同仓位等级中幸存和遇难的乘客比例（提示：箱图或者提琴图）\n",
+    "def stackedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
+    "    _, ax = plt.subplots()\n",
+    "    # 循环绘制堆积柱状图\n",
+    "    for i in range(0, len(y_data_list)):\n",
+    "        if i == 0:\n",
+    "            ax.bar(x_data, y_data_list[i], color = colors[i], align = 'center', label = y_data_names[i])\n",
+    "        else:\n",
+    "            # 采用堆积的方式，除了第一个分类，后面的分类都从前一个分类的柱状图接着画\n",
+    "            # 用归一化保证最终累积结果为1，下面bottom参数表示纵向从哪里开始画\n",
+    "            ax.bar(x_data, y_data_list[i], color = colors[i], bottom = y_data_list[i - 1], align = 'center', label = y_data_names[i])\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_title(title)\n",
+    "    ax.legend(loc = 'upper right') # 设定图例位置\n",
+    "\n",
+    "# 按照pclass分类，计算survived中0和1出现的次数\n",
+    "pclass_data = data.groupby('pclass')['survived'].value_counts().unstack()\n",
+    "#计算比例\n",
+    "pclass_data['total']=pclass_data[1]+pclass_data[0]\n",
+    "pclass_data['survived_prop']=pclass_data[1]/pclass_data['total']\n",
+    "pclass_data['unsurvived_prop']=pclass_data[0]/pclass_data['total']\n",
+    "pclass_data.rename(columns={0: 'unservived', 1: 'survived'}, inplace=True)\n",
+    "print(pclass_data)\n",
+    "\n",
+    "stackedbarplot(x_data = pclass_data.index.values\n",
+    "               , y_data_list = [pclass_data['survived_prop'], pclass_data['unsurvived_prop']]\n",
+    "               , y_data_names = ['survived', 'unservived']\n",
+    "               , colors = ['#539caf', '#7663b0']\n",
+    "               , x_label = 'Pclass'\n",
+    "               , y_label = 'Proportion of survived/unsurvived'\n",
+    "               , title = 'Proportion of survived/unsurvived by Pclass (1, 2,3)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "3ff7ff60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "survived  unservived  survived  total  survived_prop  unsurvived_prop\n",
+      "sex                                                                  \n",
+      "female            81       233    314       0.742038         0.257962\n",
+      "male             468       109    577       0.188908         0.811092\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#2.不同性别的幸存比例（提示：箱图或者提琴图）\n",
+    "import pandas as pd\n",
+    "def stackedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
+    "    _, ax = plt.subplots()\n",
+    "    # 循环绘制堆积柱状图\n",
+    "    for i in range(0, len(y_data_list)):\n",
+    "        if i == 0:\n",
+    "            ax.bar(x_data, y_data_list[i], color = colors[i], align = 'center', label = y_data_names[i])\n",
+    "        else:\n",
+    "            # 采用堆积的方式，除了第一个分类，后面的分类都从前一个分类的柱状图接着画\n",
+    "            # 用归一化保证最终累积结果为1，下面bottom参数表示纵向从哪里开始画\n",
+    "            ax.bar(x_data, y_data_list[i], color = colors[i], bottom = y_data_list[i - 1], align = 'center', label = y_data_names[i])\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_title(title)\n",
+    "    ax.legend(loc = 'upper right') # 设定图例位置\n",
+    "\n",
+    "# 按照pclass分类，计算survived中0和1出现的次数\n",
+    "sex_data = data.groupby('sex')['survived'].value_counts().unstack()\n",
+    "#计算比例\n",
+    "sex_data['total']=sex_data[1]+sex_data[0]\n",
+    "sex_data['survived_prop']=sex_data[1]/sex_data['total']\n",
+    "sex_data['unsurvived_prop']=sex_data[0]/sex_data['total']\n",
+    "sex_data.rename(columns={0: 'unservived', 1: 'survived'}, inplace=True)\n",
+    "print(sex_data)\n",
+    "\n",
+    "stackedbarplot(x_data = sex_data.index.values\n",
+    "               , y_data_list = [sex_data['survived_prop'], sex_data['unsurvived_prop']]\n",
+    "               , y_data_names = ['survived', 'unservived']\n",
+    "               , colors = ['#539caf', '#7663b0']\n",
+    "               , x_label = 'Sex'\n",
+    "               , y_label = 'Proportion of survived/unsurvived'\n",
+    "               , title = 'Proportion of survived/unsurvived by Sex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "fb9c51de-39f4-432a-b877-74f5a31f5256",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3txMrRXVgRAYAYGkTJkyQJI0fP94hxEiSp6enHnroIYd+qL0IMgCAGicxMVGSNHLkyBLb7777bod+qL0IMgCAGiciIkKS9Prrr5fYvnjxYod+qL2YIwMAqHGYI1O7MUcGAGBp3t7eioqKsoeWSZMmadeuXZo0aZI9xERFRRFicGFHZMLCwuTl5WX/xZsyZYpuvvlmJSYm6o477tCxY8fk5+enN998U61bt5akUtvKwogMAFgb15GpnSry+e1xgWqy++CDD9S+fXuHZaNHj9aoUaMUExOj5cuXKyYmRps3by6zDQDg2uLj45Wdna0JEyYoMTFRERERmjNnDiMxsLvgIzLx8fEOQebIkSMKDw/XiRMn5OHhIWOMgoODtWHDBvn6+p6zLTw8vMztMSIDAID11OgRmdtvv13GGHXp0kXPPPOMUlJSFBwcLA+P06XYbDY1btxYycnJ8vPzO2dbSUEmNzdXubm59sfp6ekX5kUBAACnuKCTfb/++mv98ssv+umnnxQYGKg77rijStc/e/Zs+fn52b9CQ0OrdP0AAKBmcdrp16mpqWrRooX27NlTZYeWShqRCQ0N5dASAAAWUiNPvz516pT+/PNP++P33ntPHTp0UFBQkDp27KilS5dKkmJjYxUSEqLw8PBS20ri5eUlX19fhy8AAOC6LtiIzN69ezV48GAVFhbKGKNmzZpp4cKFCgsL086dOxUTE6Pjx4/L19dXS5YsUWRkpCSV2lYWJvsCAGA9Ffn85sq+AACgRqmRh5YAAACqGkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYllOCzJIlS2Sz2RQfHy9JOnLkiP7xj38oIiJCbdq00ddff23vW1obAACo3S54kElKStJrr72mbt262ZdNnjxZ3bp1U2JiopYsWaLhw4crPz+/zDYAAFC7XdAgU1RUpJEjR+qFF16Ql5eXffmHH36oe++9V5LUuXNnNWrUSOvWrSuzDQAA1G4eF3Jj8+fPV48ePdSpUyf7suPHjys/P18NGza0LwsLC1NycnKpbSXJzc1Vbm6u/XF6eno1vAoAAFBTXLAgs23bNsXGxlbrHJfZs2dr5syZ1bZ+AABQs1ywQ0vr169XUlKSIiIiFBYWpk2bNmnUqFH68MMP5eHhoUOHDtn7JiUlqXHjxgoICDhnW0mmTJmitLQ0+1dKSkq1vy4AAOA8FyzI3HfffUpNTVVSUpKSkpLUrVs3vfrqq7rvvvs0dOhQvfzyy5KkzZs368CBA+rdu7ckldr2V15eXvL19XX4AgAAruuCzpE5l2effVa33XabIiIi5OnpqaVLl+qiiy4qsw0AANRuNmOMcXYR1SU9PV1+fn5KS0tjdAYAAIuoyOc3V/YFAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWVaEgk5aWpvvvv18DBw6UJP3+++967733qqUwAACAslQoyIwePVoNGzZUUlKSJKlp06Z69tlnq6MuAACAMlUoyOzatUvTp0/XRRddJEny9vaWMaZaCgMAAChLhYKMp6enw+Ps7GyCDAAAcJoKBZm+ffvqqaeeUk5OjlavXq0hQ4Zo0KBB5X5+//791bZtW7Vv3169evXSli1bJEmJiYnq3r27WrRooc6dO+u3336zP6e0NgAAULtVKMg8+eSTcnNzk6+vr6ZOnaoePXro0UcfLffzP/zwQ/3yyy/aunWrxo8fr5iYGEmn596MGjVKu3bt0qRJk+zLy2oDAAC1m82U89hQYWGhYmJi9Pbbb1fJht988009//zz+vLLLxUeHq4TJ07Iw8NDxhgFBwdrw4YN8vX1PWdbeHh4mdtIT0+Xn5+f0tLS5OvrWyV1AwCA6lWRz2+P8q7U3d1du3btOu/ibr/9dq1du1aS9OmnnyolJUXBwcHy8Dhdis1mU+PGjZWcnCw/P79ztpUUZHJzc5Wbm2t/nJ6eft71AgCAmqvCc2RGjRqlb775Rr/88ov9qyLeeustpaSkaNasWZo0aVKFnluW2bNny8/Pz/4VGhpapesHAAA1S7kPLUmnrxtz1gpsNu3du7dSG/f29lZSUpIiIiKq5NBSSSMyoaGhHFoCAMBCquXQkiTt27ev0kX9+eefysrKUqNGjSRJ8fHxCggIUFBQkDp27KilS5cqJiZGsbGxCgkJsQeV0tr+ysvLS15eXpWuEQAAWEuFRmSK/XXkozyjHX/88YeGDh2q7Oxsubm56ZJLLtHcuXPVvn177dy5UzExMTp+/Lh8fX21ZMkSRUZGSlKpbWVhsi8AANZTkc/vCgWZTZs26c477zxr0m9hYWHlKq1mBBkAAKyn2g4tjR07Vm+++abuvfdeff311/r3v/+tOnXqnFexAAAAlVWhs5by8/PVtWtXFRQUyMfHR9OmTdP7779fXbUBAACUqkJBpvh6LgEBAfrpp5909OhRHT16tFoKAwAAKEuFDi0NGzZMx48f19SpU9W7d2/l5+dr1qxZ1VUbAABAqcoVZP7zn//ojjvu0LXXXquAgAD1799fJ06cUE5Ojnx8fKq7RgAAgBKV69DSv//9b0nS8OHD7csuuugiQgwAAHCqch9auu+++3TgwAGNHz/+rLb58+dXaVEAAADlUa4gs2zZMi1btkxubm7y8/Or7poAAADKpVxBplmzZpo0aZIaNWqk2267rbprAgAAKJcKnX5NiAEAADVJhYIMAABATUKQAQAAllWuIPP3v/9dkvTQQw9VZy0AAAAVUq7Jvn/++acOHz6stWvXKiMjQ3+9YTZ3lgYAAM5QriBz0003qWnTpsrNzT3r9GubzabCwsJqKQ4or7y8PC1atEh79uxR8+bNNWbMGHl6ejq7LABVgP0bpbGZvw6vlKJHjx7auHFjddZTpdLT0+Xn56e0tDRGjVzYxIkTtWDBAhUUFNiXeXh4aNy4cXruueecWBmA8zVx4kTNmzdPRUVF9mVubm56+OGH2b9dWEU+vys02bc4xBw8eFAHDx6sfIVAFZk4caLmzJmjgIAAvfbaa0pNTdVrr72mgIAAzZkzRxMnTnR2iQAqqXj/PjPESFJRURH7N+wqNCKzY8cODR482B5iQkJCtGzZMl1++eXVVuD5YETGteXl5alevXoKCAjQ/v375eHxf0dKCwoKFBISouPHj+vUqVMMQwMWk5eXpzp16sgYo6CgID311FMaOHCgVq5cqWnTpunIkSOy2WzKyclh/3ZB1TYic99992natGk6efKkTp48qWnTpum+++47r2KBylq0aJEKCgo0a9YshxAjnT609MQTT6igoECLFi1yUoUAKmvhwoUyxsjHx0cHDhzQyJEj1bBhQ40cOVIHDhyQj4+PjDFauHChs0uFk1UoyJw8edLhDti33HKLTp48WeVFAeWxZ88eSdLAgQNLbC9eXtwPgHWsWLFCkjRjxowS/6Myffp0h36ovSoUZNzd3fX777/bH//+++9yd3ev8qKA8mjevLkkaeXKlSW2Fy8v7gcAcD0VmiPzxRdf6NZbb1Xbtm0lSb/++qveeecd9e/fv9oKPB/MkXFtzJEBXFfxZF5fX18dP378rP374osvVkZGhp577jlNmDDBiZWiOlTbHJkBAwZo+/btGj9+vMaPH6/t27fX2BAD1+fp6alx48bp8OHDCgkJ0auvvqqDBw/q1VdfVUhIiA4fPqxx48YRYgALGjt2rGw2m9LT03XZZZc57N+XXXaZMjIyZLPZNHbsWGeXCier0IiM1TAiUztwHRnANRWffn0uEyZMYB93URX5/CbIwCVw5U/ANU2cOFHz5893uII8/1FxfQSZ/yHI1B6FhYVav369UlNTFRwcrF69ejERHXAR2dnZmjBhghITExUREaE5c+bI29vb2WWhGlXbHBmgJoqLi1N4eLj69u2r4cOHq2/fvgoPD1dcXJyzSwNwnuLi4nTFFVfopZde0pdffqmXXnpJV1xxBfs37CoUZB577DH9+eefMsbon//8pwIDAxUbG1tdtQFliouL05AhQxQZGalvv/1WGRkZ+vbbbxUZGakhQ4bwxw6wMPZvlEeFDi21a9dOP//8s1atWqUXX3xRzz33nG655RZt2bKlOmusNA4tubbCwkKFh4crMjJS8fHxcnP7v1xeVFSk6Ohobdu2TYmJiRxmAiyG/bt2q7ZDS8W/SOvWrdPQoUPVsmVL2Wy2ylcKnIf169crKSlJU6dOdfgjJ53+XZ0yZYr27dun9evXO6lCAJXF/o3y8ii7y/+pV6+enn32Wb3//vvauHGjjDHKy8urrtqAUqWmpkqS2rRpU2J78fLifgCsg/0b5VWhEZk333xTqampeu6553TppZdqz549GjFiRHXVBpQqODhYkrRt27YS24uXF/cDYB3s3yivCp9+nZ+fr+TkZEvcv4Y5Mq6NY+iA62L/rt2qbY5MQkKCmjRpor59+0qSNm/ezIgMnMbd3V3z5s3TypUrFR0d7XBWQ3R0tFauXKm5c+fyRw6wIPZvlFeFRmS6deumd955R0OGDLGfqdS6dWv99ttv1Vbg+WBEpnaIi4vTww8/rKSkJPuypk2bau7cuRo0aJDzCgNw3ti/a6eKfH5XaLJvYWHhWYeUuAw8nG3QoEGKioriyr6AC2L/RlkqFGTq1KmjzMxM+ynXv/76K5eJRo3g7u6uPn36OLsMANWA/RulqVCQefTRR9W/f38dOHBAI0aM0OrVq/Xuu+9WV20AAAClqvBZS/v27dPnn38uY4wGDBhQo89eYo5M7cFNIwHXxU0jax/ufv0/BJnaoaTJgGFhYZo3bx6TAQGLi46O1ooVK85aHhUVpfj4+AtfEC6Iajv9umnTpmrWrNlZX4CzcFM5wHUVhxhPT09NnjxZu3fv1uTJk+Xp6akVK1YoOjra2SWiBqjQiMyZp1nn5OTo7bffVkBAgB599NFqKe58MSLj2s68YFZsbKw2btxoP7TUo0cPDR48mAtmARaVnZ2tunXrytPTUxkZGQ5nyObl5cnHx0d5eXnKysriMJMLqrYRmdatW9u/OnXqpOeff16fffbZeRULVFbxTeW6d++uFi1aqG/fvho+fLj69u2rFi1a6O9//zs3lQMsasKECZKk8ePHn3WZD09PTz300EMO/VB7VSjI/NXx48d16NChqqoFqJDim8VNmTKlxENLU6dOdegHwDoSExMlSSNHjiyx/e6773boh9qrQkGmQ4cO6tixozp27Kh27dqpefPm5/wlA6pbUFCQJKlnz56Kj49Xt27dVL9+fXXr1k3x8fHq0aOHQz8A1hERESFJev3110tsX7x4sUM/1F4VmiOzbt06+/ceHh5q1qxZjb7zKHNkXNuaNWt09dVXq2fPnlq3bt1ZN5Xr3bu3NmzYoNWrV6tfv35OrBRARTFHpnartjkyvXv3tn/16NGjRocYuL4jR45IkjZu3FjiTeU2btzo0A+AdXh7eysqKsoeWiZNmqRdu3Zp0qRJ9hATFRVFiEH5RmRuvPFG+20JSlJTT3FlRMa1JSQkqG/fvpo9e7ZeeeWVs24qd88992jq1Klau3YtlzcHLIrryNROVX7TSM7VR03Uq1cvhYWF6ZtvvtH27dv18ssva8+ePWrevLnuvfde3XTTTWratKl69erl7FIBVFJ8fLxOnDih3r176+DBg2rUqJHWrVuniy++2NmloYbgyr6wtOIL4tWpU0fZ2dn25d7e3srJydHy5cu5ui9gYYzI1E5VPiJzpg8//FBbt25VTk6Ofdn8+fMrXiVQRUrK4jabrcTlAKzjzCv7jh8/XiNHjtTrr7+u+fPn26/sS5hBhUZkHnzwQe3bt08//vijhg0bpmXLlumaa66xnwZX0zAi49q4si/gujhrqXartrOW1q5dqxUrVuiSSy7RvHnz9P3332v//v3nVSxQWcVX9p06daouuugi9enTR8OGDVOfPn100UUXacqUKVzZF7AoruyL8qpQkKlTp47c3Nxks9mUn5+vhg0b6uDBg9VVG1Cq4iv2tmnTpsT24uVc2RewHq7si/KqUJDx8fFRVlaWevbsqREjRmjs2LGqW7duddUGlKr4Okbbtm0rsb14Odc7AqyHK/uivMo1RyY+Pl7XX3+9jh07pgYNGqiwsFDz58/XyZMnNXbsWIWGhl6IWiuMOTKu7cw5MvHx8Wdd2Tc6Opo5MoBFMUemdqvyOTKPP/64LrvsMs2bN0/79u2Tt7e3pk2bprlz59bYEAPX5+7urnnz5mnlypUlXtl35cqVmjt3LiEGsCCu7ItyM+W0efNmM2bMGNOgQQPTo0cPs2TJEnPq1KnyPt1kZ2ebqKgoExERYdq2bWuuvvpqk5iYaIwx5vDhw2bAgAEmPDzctG7d2qxbt87+vNLaypKWlmYkmbS0tHI/B9YTGxtrwsLCjCT7V9OmTU1sbKyzSwNwnqKiohz27eKvqKgoZ5eGalSRz+8KXxAvNzdXsbGxWrJkiTZv3qybbrpJr776apnPy8nJ0VdffaVrr71WNptNL774opYvX66EhATdddddaty4sR5//HFt3rxZN954o/bt26eLLrqo1LaycGip9igsLNT69evtp1/36tWLkRjARWRnZ2vChAlKTExURESE5syZw0iMi6vI53elruxbUFCgjz/+WE8//bR27dql9PT0Chf5ww8/aMiQIUpKSlL9+vW1e/duNWzYUJLUpUsXPf3007r66qtLbSsLQQYAAOuptiv7btu2TYsXL9a7776r8PBw3Xfffbr55psrVeTChQsVFRWl48eP20/lLhYWFqbk5ORS20qSm5ur3Nxc++PKBCwAAGAd5QoyixYt0htvvKH9+/frtttuU0JCglq1alXpjT799NPavXu31qxZ43B/nPM1e/ZszZw5s8rWBwAAarZynbX0ySefaOrUqdq/f7/mzJlzXiFm7ty5iouL02effaa6desqICBAHh4eOnTokL1PUlKSGjduXGpbSaZMmaK0tDT7V0pKSqXrBAAANV+5g8ygQYPk4VHhe0w6mD9/vt577z2tWrVK/v7+9uVDhw7Vyy+/LEnavHmzDhw4oN69e5fZ9ldeXl7y9fV1+AIAAK6rUpN9K2P//v0KDQ1Vs2bN5OPjI+l08Pjuu+90+PBh3Xbbbdq3b588PT314osvqm/fvpJUaltZmOwLAID1VPtZS1ZBkAEAwHqq7e7XAAAANQlBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWJaHswsAAKA0eXl5WrRokfbs2aPmzZtrzJgx8vT0dHZZqCEIMgCAGmvixIlasGCBCgoK7MsmTJigcePG6bnnnnNiZagpOLQEAKiRJk6cqDlz5iggIECvvfaaUlNT9dprrykgIEBz5szRxIkTnV0iagCbMcY4u4jqkp6eLj8/P6WlpcnX19fZ5QAAyikvL0/16tVTQECA9u/fLw+P/zuAUFBQoJCQEB0/flynTp3iMJMLqsjnNyMyAIAaZ9GiRSooKNCsWbMcQowkeXh46IknnlBBQYEWLVrkpApRUxBkAAA1zp49eyRJAwcOLLG9eHlxP9ReBBkAQI3TvHlzSdLKlStLbC9eXtwPtRdzZOASCgsLtX79eqWmpio4OFi9evWSu7u7s8sCUElnzpHZu3evXn31Vfvp16NGjVKzZs2YI+PCKvL5zenXsLy4uDg9/PDDSkpKsi8LCwvTvHnzNGjQIOcVBqDSPD09NW7cOM2ZM0f16tVzaBs3bpyk06dhE2JAkIGlxcXFaciQIbruuusUFRWl7OxseXt7a/fu3RoyZIiWL19OmAEAF8ahJVhWYWGhwsPD5e7urqSkJBUWFtrb3N3dFRYWpqKiIiUmJnKYCbCY4kNLnp6eysnJUVFRkb3Nzc1NderUUV5eHoeWXBSnX6NWWL9+vZKSkrRnzx4FBgY6XDArMDBQe/bs0b59+7R+/XpnlwqggopPv87KytIll1zisH9fcsklysrK4vRrSCLIwMJSUlIkSUFBQdq/f79Gjhyphg0bauTIkdq/f7+CgoIc+gGwjp07d0qSAgMDtXfvXmVmZuqpp55SZmam9u7dq8DAQId+qL0IMrCs7777TpJ01113lXjBrJiYGId+AKzj0KFDkk7/R8XPz0/jxo3Tiy++qHHjxsnPz0+XXHKJQz/UXgQZWFbx9K4ff/zR4fi5JBUVFWnLli0O/QBYR3BwsCTp999/V4MGDRwOLTVo0EDbt2936IfaiyADy4qIiJAkrVq1StHR0fr222+VkZGhb7/9VtHR0Vq1apVDPwDW0axZM/v3bm5uKioqsn+5ubmV2A+1E6dfw7LGjBmjCRMmqF69evrll1/UvXt3e1tYWJj8/Px06tQpjRkzxolVAjgfnp6eOnr0qEaPHm1f5u7uLk9PT+Xl5TmxMtQUBBlY1pkXzKpTp47Gjx+vZs2aae/evXrnnXeUlpbGBbMAi/rjjz8knT4NOygoSL1791a9evV06tQprVu3TkeOHHHoh9qLIANLe+655yRJCxYs0Pz58+3LPTw8NGHCBHs7AGspvofSgAEDtGbNGi1btsze5uHhoWuuuUarVq3iXkvggnhwDdnZ2ZowYYISExMVERGhOXPmyNvb29llAaikM++1tGPHDt155532ey0tWbJEl19+OfdacmEV+fwmyMDyuNcS4JomTpyoOXPmnLOdUVfXxZV9UWsU32spMjLS4aylyMhIDRkyRHFxcc4uEQBQjRiRgWUV32spMjJS8fHxDqdkFhUVKTo6Wtu2beNeS4AF5eXlycvLS5Jks9nUqVMnNW/eXHv27NGPP/5ovz5Ubm4uh5ZcUEU+v5nsC8sqvtfSe++95xBipNPXnZgyZYq6d++u9evXq0+fPs4pEkClzJs3z/59VlaW6tSpY3+ck5NjnwM3b948TZky5YLXh5qDQ0uwrNTUVElSmzZtSmwvXl7cD4B1vPrqq5KkoUOHOoQYSapTp459/ltxP9ReBBlYVvGlybdt21Zie/FyLmEOWE9OTo4kqVWrViW2Fy8v7ofaiyADy+rVq5fCwsL09NNPl3ivpdmzZ6tp06bq1auXkyoEUFmdO3eWJD377LMqKChwaCsoKNDcuXMd+qH2IsjAstzd3TVv3jytXLmyxHstrVy5UnPnzmWiL2BB7777rqTTk3mDg4P16quv6uDBg3r11VcVHBys3Nxch36ovThrCZZX0nVkmjZtqrlz53IdGcDCunTpos2bN5+zvXPnzvr+++8vYEW4ULgg3v8QZGqPwsJCrV+/XqmpqQoODlavXr0YiQFcwLnCDCHGtRFk/ocgAwDWl5mZqdtuu81+i4K3335b9evXd3ZZqEZcRwYA4DLq16+vjz76yNlloIZisi8AALAsRmQAAE6VlSXt2FFGp+xsKSlJCguTynFn+8svl+rWrYrqUNMRZOASmOwLWNeOHVKnTmX18pZU8sXxSvLjj1LHjudTFayCIAPLi4uL0/jx4/XHH3/YlzVp0kTz58/n9GvAAi6//HTwKNX27dKIW6Wl70jnuNrvX9eJ2oEgA0uLi4vT4MGD7TeQK3bkyBENHjxYsbGxhBmghqtbtzyjJ9mStkitsiVGWnAGggwsq7CwUPfee68k6aqrrtJ1110nb29vZWdn69NPP9Unn3yi++67T1FRURxmAgAXRZCBZSUkJOjo0aNq1aqVfv31V33yySf2tsaNG+vyyy/Xjh07lJCQoH79+jmxUgBAdeH0a1hWQkKCJGn79u06evSoQ9vRo0e143+nQRT3AwC4HoIMLOvMO17369fP4aaRZ47A/PXO2AAA18GhJViWv7+/JMnX11cfffSRPDxO/zp369ZNH330kS6++GJlZGTY+wEAXA8jMrCsP//8U9Lpe3LceOONDiMyN954ozIyMhz6AQBcDyMysCw3t//L4WvWrNHKlSvtj+uecUnPM/sBAFzLBf0L/+CDDyosLEw2m01bt261L09MTFT37t3VokULde7cWb/99lu52lC79enTR5J0+eWX69JLL3Vou/TSS3X5/66IVdwPAOB6LmiQGTJkiDZs2KAmTZo4LB89erRGjRqlXbt2adKkSYqJiSlXG2q3Pn36KCgoSDt27FDr1q314osvavHixXrxxRd1xRVXaMeOHQoKCiLIAIALsxljzIXeaFhYmOLj49W+fXsdOXJE4eHhOnHihDw8PGSMUXBwsDZs2CBfX99ztoWHh5+13tzcXOXm5tofp6enKzQ0VGlpafL19b2QLxEXSFxcnIYMGaI6deooOzvbvrxu3brKzs7W8uXLubIv4Ap++un0DZm4iVKtkJ6eLj8/v3J9fjt98kBKSoqCg4PtZ5zYbDY1btxYycnJpbaVZPbs2fLz87N/hYaGXrDXAecYNGiQli9fXuKhJUIMALg+l5rsO2XKFI0fP97+uHhEBq5t0KBBioqK4u7XAFALOT3IhIaGKjU1VQUFBfbDR8nJyWrcuLF8fX3P2VYSLy8veXl5XeBXAAAAnMXph5aCgoLUsWNHLV26VJIUGxurkJAQhYeHl9oGFIuLi1N4eLj69u2r4cOHq2/fvgoPD1dcXJyzSwMAVLMLGmRGjx6tkJAQ7d+/XwMGDLAHkldeeUWvvPKKWrRooWeeeUZLliyxP6e0NqB4sm9kZKTDBfEiIyM1ZMgQwgwAuDinnLV0oVRk1jOsp7CwUOHh4YqMjFR8fLzDhe+KiooUHR2tbdu2KTExkfkygNVx1lKtYqmzloDKWr9+vZKSkjR16lQZY5SQkKD33ntPCQkJMsZoypQp2rdvn9avX+/sUgEA1cTpk32BykpNTZUk7dmzR8OGDVNSUpK9LSwsTLNmzXLoBwBwPQQZWFZwcLAkacSIEfL29nZoO3z4sEaMGOHQDwDgeggysKzu3bvLzc1NRUVFuvLKK5Wdna1jx44pMDBQ3t7e+uKLL+Tm5qbu3bs7u1QAQDUhyMCy1q9fr6KiIknSF198UWKfoqIirV+/Xv369buQpQEALhAm+8KyEhISqrQfAMB6GJGBZeXk5Ni/v/jii3XXXXepWbNm2rt3r9544w2dOHHirH4AANdCkIFlbdiwwf79jh07dOONN+qDDz5Q48aNtWPHDgUFBZ3VDwDgWggysKwzT7cuDi3S6Tuqn/n4zH4AANfCHBlYVp06daq0HwDAeggysKzi68RUVT8AgPUQZGBZq1evdnjs5eWlHj16yMvLq9R+AADXwRwZWNbOnTsdHufm5mrjxo1l9gMAuA5GZGBZ6enpVdoPAGA9BBlYlofH/w0oHjlyRP/617/Uv39//etf/9KRI0dK7AcAcC38hYdl+fv76/Dhw5IcT7/+8ssv9dJLLzn0AwC4JkZkYFkzZ86s0n4AAOshyMCywsPDq7QfAMB6CDKwrLy8vCrtBwCwHoIMLOvdd9+t0n4AAOshyMCyMjMzq7QfAMB6CDKwrC5duti/z8zM1Nq1a/Xuu+9q7dq1DuHlzH4AANfC6dewrOTkZPv3N998s6ZNm6aBAwdq27Ztuvnmm0vsB+DCS06Wjh07v3V4b5daSdq+XcqugpoCA6XGjatgRXA6ggwsa+/evfbvP/vsM33yySf2x+7u7iX2A3BhJSdLrVoWKivHvezOpegg6SdJt46QtlRBXXXrFGr7TnfCjAsgyMASsrKkHTscl/n5RUj6Uq1bd9WOHT9KKjqj1abWrTvrt982y88vQj/9dPY6L79cqlu3GosGoGPHpKwcdy3VrWql7ZVej01F2q6WWqy7ZM5zVsR2tdKInHd07BijMq6AIANL2LFD6tTpr0vnSHpJv/22RdJJSa9L2iOpuQoLR+q33wIkScuWzdGyZWev88cfpY4dq7NqAMVaabs6VslYCuCIIANLuPzy08HDkbfGj4/SunUrdNFFARo+4DZFrdyoFQOz9e4XAcrPz1Pv3lGaP9/7nOsEAFgbQQaWULduyaMnCQnxio6O1ooVK/SflYv1H0laefoYVFRUlOLj4y9kmQCAC4zTr2F58fHxysrK0r+GDlV/Sf8aOlRZWVmEGACoBRiRgUvw9vbWi5MnS8uWSZMnS94lH04CALgWRmQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlcdNI1AjJydKxY+e3Du/tUitJ27dL2VVQU2Cg1LhxFawIgLbrcmeXYFeTasH5I8jA6ZKTpVYtC5WV435e6+kg6SdJt46QtlRBXXXrFGr7TnfCDFAFRuhdZ5cAF0WQgdMdOyZl5bhrqW5VK22v9HpsKtJ2tdRi3SVznkdNt6uVRuS8o2PHGJUBqsJSDVcr7XB2GZJOj8gQrFwHQQY1RittV8cqGUsBUNO00g72b1QLJvsCAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADL4qwlAEC1265W5/V8m4pURznKUZ0qubwCXAdBBjVGTbraZk2qBbCywMDTF5cckfPOea2ng37ST+qkjvpRW9TxvOuqW6dQgYHndxFO1AwEGdQYXKAKcD2NG0vbd7pXyS1INEJ6Z6mUXQUDKoGBXLXbVRBkUGM8qWlqqn2Vfr5NRfJSnnLled5Dz/vUVI/qqfNaB4DTGjeuuitkt2olVcGADFwIQQZOVzz0/GjO+QUHhp4BoPaxRJBJTEzUHXfcoWPHjsnPz09vvvmmWrdu7eyyUEUYegYAVJYlgszo0aM1atQoxcTEaPny5YqJidHmzZudXRaqEEPPAIDKqPHXkTly5Ih++OEHjRgxQpI0ePBgpaSkaPfu3U6uDAAAOFuNH5FJSUlRcHCwPDxOl2qz2dS4cWMlJycrPDzcoW9ubq5yc3Ptj9PT0y9orag+WVnSjh1ldNruLanD//4t2+WXS3XrnndpAM4T+zfOR40PMhUxe/ZszZw509lloBrs2CF16lRWr1aSfpJGlG+dP/4odeQQFOB07N84HzZjjHF2EaU5cuSIwsPDdeLECXl4eMgYo+DgYG3YsKFcIzKhoaFKS0uTr6/vhS4dVahc/2PLzpaSkqSwMMm77P+18T82oGZg/8Zfpaeny8/Pr1yf3zV+RCYoKEgdO3bU0qVLFRMTo9jYWIWEhJwVYiTJy8tLXl5eTqgS1a1u3fL878pb6sGlxwGrYf/G+ajxIzKStHPnTsXExOj48ePy9fXVkiVLFBkZWebzKpLoAABAzeBSIzKS1LJlS3377bfOLgMAANQwNf70awAAgHMhyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMuyxL2WKqv4fpjp6elOrgQAAJRX8ed2ee5r7dJBJiMjQ5IUGhrq5EoAAEBFZWRkyM/Pr9Q+NlOeuGNRRUVFOnjwoHx8fGSz2ZxdDqpZenq6QkNDlZKSUuZt3wFYC/t37WKMUUZGhho1aiQ3t9Jnwbj0iIybm5tCQkKcXQYuMF9fX/7QAS6K/bv2KGskphiTfQEAgGURZAAAgGURZOAyvLy8NGPGDHl5eTm7FABVjP0b5+LSk30BAIBrY0QGAABYFkEGAABYFkEGAABYFkEGLiMxMVHdu3dXixYt1LlzZ/3222/OLglAFXjwwQcVFhYmm82mrVu3Orsc1DAEGbiM0aNHa9SoUdq1a5cmTZqkmJgYZ5cEoAoMGTJEGzZsUJMmTZxdCmogzlqCSzhy5IjCw8N14sQJeXh4yBij4OBgbdiwQeHh4c4uD0AVCAsLU3x8vNq3b+/sUlCDMCIDl5CSkqLg4GB5eJy+64bNZlPjxo2VnJzs5MoAANWJIAMAACyLIAOXEBoaqtTUVBUUFEg6fefU5ORkNW7c2MmVAQCqE0EGLiEoKEgdO3bU0qVLJUmxsbEKCQlhfgwAuDgm+8Jl7Ny5UzExMTp+/Lh8fX21ZMkSRUZGOrssAOdp9OjR+uSTT3To0CEFBATIx8dHu3fvdnZZqCEIMgAAwLI4tAQAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIANYUFhYmFq2bKn27durffv2GjlyZKXXlZCQoM8//7wKq6s6e/bsUceOHdWhQwctWbLEoS0pKUkvv/yyw7KwsDBt3br1AlZYcT/88INuvvnmKl3nI488oscff7xK1wlYhYezCwBQOR988EGV3AU4ISFBf/75p/7xj39U+LkFBQX2G3VWh+XLl6tz58565ZVXzmorDjL33ntvtW2/MoqKiiRJbm4l/z/xb3/7mz744IMLWRLg0hiRAVzI22+/ra5du6pjx4668sor9fPPP0uSfv31V/Xs2VMdO3bUFVdcoVmzZkmStm7dqpdfflnvvPOO2rdvryeeeEJJSUny9/e3rzMzM1M2m83+2GazacaMGercubOmTJmijIwM3XPPPerSpYvatm2rUaNGKS8vT5I0a9YstWrVyj5y9Mcff5xVc2Zmpu666y61adNGbdq00cyZMyVJb731lhYsWKC4uDi1b99ev//+u8Pz7r33Xu3cuVPt27fXDTfcYF8eFxenv//972ratKn9dUrSoUOHdNNNN6lLly6KjIzU9OnTS3wPN23apE6dOql9+/Zq06aN/t//+3+SpJiYGD3//PP2fmeOgjz++OMaPHiwBgwYoDZt2ui9997TwIED7X2NMWrWrJl+/vlnJSQk2APoPffco7lz59r77du3Tw0bNlR+fr7y8/M1efJkdenSRe3bt9dNN92kkydPSpJSU1M1YMAAXXHFFbr66qu1f//+El8LUCsYAJbTpEkT06JFC9OuXTvTrl07ExcXZzZs2GCuvfZak5OTY4wx5uuvvzZXXHGFMcaY9PR0+/KsrCzTvn178+233xpjjJkxY4YZO3asfd379u0zfn5+9scZGRnmzD8VkszMmTPtj++55x7zn//8xxhjTFFRkbn77rvNc889Z06cOGH8/PxMVlaWMcaYU6dOmezs7LNey8SJE83w4cNNYWGhyczMNO3btzfvv/9+ibWdae3ataZdu3ZnvS8PPPCAMcaYo0ePGl9fX7N//35jjDH9+/c3CQkJxhhj8vPzzYABA8yHH3541npvuOEG8+6779ofnzhxwhhjzB133GEWLFhgX/7www+bGTNm2OsMDg42hw4dMsacfo8DAgJMamqqMcaYr776ynTs2PGsujdu3Ghat25tX+djjz1mxo8fb4wx5qmnnjJPPPGEve2JJ54wY8aMMcYYM2TIEDN9+nRjjDH79+83gYGB9lqA2oZDS4BF/fXQ0sSJE/Xzzz+ra9eu9mUnTpxQdna2srOzNWbMGG3dulVubm5KSUnR1q1b1a1bt0pt+6677rJ/Hx8fr2+//Vbz58+XJGVnZ8vd3V2+vr6KiIjQiBEj1L9/f/3zn/9USEjIWetavXq15s2bJzc3N9WrV0+33367Vq1aVel5JMOHD5ckBQYGqlmzZtq3b5/8/f21Zs0aHT582N4vMzNTO3fuPOv5ffv21ZNPPqnExERdddVV6tmzZ7m2e9111+nSSy+VJHl7e2vw4MF6++23NWHCBL355pu68847z3pO9+7dVVBQoM2bN+tvf/ub3nrrLf33v/+VdPp9TUtLU2xsrCQpLy9PYWFhkqQ1a9bYR3Iuu+wyhxEpoLYhyAAuwhijO+64Q08//fRZbQ888IACAwO1ZcsWeXh4aNCgQcrJySlxPR4eHiosLLQ/Lqlf/fr1HbYbGxurFi1anNVv06ZN+uabb5SQkKBu3brpvffeU69evUp9HWcexqqMOnXq2L93d3dXQUGBzP9uKbdp0yaH9pI89NBDioqK0urVqzV16lS1adNGixYtKvF9OfN9OPN76XTYu/POO3Xfffdp5cqVWrBgQYnbu/POO7VkyRJlZmYqMDBQbdq0kXT6fX3hhRfUv3//Ml/z+b5ngJUxRwZwETfccIOWLl2q5ORkSacnnf7www+SpJMnTyokJEQeHh7auXOnVq1aZX+er6+v0tLS7I8bNmwoY4x9Tspbb71V6najo6P17LPPqqCgwL6t3bt3KyMjQ4cPH1avXr306KOPqmfPntqyZctZz7/66qu1ePFiGWN06tQpvf322+X68P5r3aWpX7+++vbtq2eeeca+7ODBgyXOLdm5c6eaNm2qe+65R1OnTtWmTZskSeHh4fr+++8lScePH9enn35a6jaLR8YeeeQRXX311br44otL7Hfbbbdp2bJlevnllx1GuqKjo7VgwQJlZWVJkrKysvTbb79JOv2evfHGG5JOz5f5+OOPy/U+AK6IIAO4iF69eum5557TjTfeqHbt2ql169Z6//33JUnTp0/XkiVL1LZtW02ePFlXXXWV/Xk33nijtm7dap/s6+HhoRdeeEEDBw5U586dlZ+fX+p2FyxYIG9vb7Vv315t27ZVv379lJSUpLS0NA0aNEiRkZFq27at8vPzdccdd5z1/EcffVQXXXSRIiMj1bVrV91www266aabyny9bdu2VevWrdWmTZtyHVp55513tHv3brVp00aRkZEaNGiQjh8/fla/F198Ua1bt1aHDh00ffp0zZs3T5I0atQoHT16VK1atdLtt99ersNyd955p1555ZUSDysVa9Sokbp06aKPP/5Yw4YNsy+fNGmSOnfurK5du6pt27bq1q2b/dTyhQsXatOmTbriiit0++23O/w8gdrGZorHXAEAACyGERkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZ/x+gXMur/gEoZwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#3. 幸存和遇难乘客的票价分布（提示：箱图或者提琴图）\n",
+    "fare_data=data.groupby('survived')['fare'].apply(list)\n",
+    "labels = list(fare_data.index)\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(fare_data.values, labels=labels,patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the survived')\n",
+    "plt.ylabel('Values of fare')\n",
+    "plt.title('The box with 2 features for the the fare')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "6cdf21fe-898f-450d-b69a-107c481bc8eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#4. 幸存和遇难乘客的年龄分布（提示：箱图或者提琴图）\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "dropna_data=data.dropna()\n",
+    "age_data=dropna_data.groupby('survived')['age'].apply(list)\n",
+    "\n",
+    "labels = list(age_data.index)\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(age_data, labels=labels,patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the survived')\n",
+    "plt.ylabel('Values of age')\n",
+    "plt.title('The box with 2 features for the the age')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "id": "12dd5a11-623a-4c05-927e-355508daf357",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pclass      1    2    3\n",
+      "embarked               \n",
+      "C          85   17   66\n",
+      "Q           2    3   72\n",
+      "S         127  164  353\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#5. 不同上船港口的乘客仓位等级分布（提示：箱图或者提琴图）\n",
+    "import numpy as  np\n",
+    "def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
+    "    _, ax = plt.subplots()\n",
+    "    # 设置每一组柱状图的宽度\n",
+    "    total_width = 0.8\n",
+    "    # 设置每一个柱状图的宽度\n",
+    "    ind_width = total_width / len(y_data_list)\n",
+    "\n",
+    "    # 计算每一个柱状图的中心偏移\n",
+    "    alteration = np.arange(-total_width/2+ind_width/2, total_width/2+ind_width/2, ind_width)\n",
+    "\n",
+    "    # 分别绘制每一个柱状图\n",
+    "    for i in range(0, len(y_data_list)):\n",
+    "        # 横向散开绘制\n",
+    "        ax.bar(np.arange(len(x_data)) + alteration[i], y_data_list[i], color = colors[i], label = y_data_names[i], width = ind_width)\n",
+    "    ax.set_xticks(np.arange(len(x_data)))\n",
+    "    ax.set_xticklabels(x_data)\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_title(title)\n",
+    "    ax.legend(loc = 'best')\n",
+    "\n",
+    "#数据分类,并计算人数总数\n",
+    "grouped_data3 = data[['survived', 'embarked','pclass']].groupby(['embarked','pclass'])['survived'].count()\n",
+    "#整理一下\n",
+    "grouped_data3 = grouped_data3.unstack()\n",
+    "print(grouped_data3)\n",
+    "#可以按如下方法调用行数据\n",
+    "# grouped_data3.loc['C']\n",
+    "grouped_data3.columns.values\n",
+    "groupedbarplot(x_data = grouped_data3.columns.values\n",
+    "               , y_data_list = [grouped_data3.loc['C'].values, grouped_data3.loc['Q'].values,grouped_data3.loc['S'].values]\n",
+    "               , y_data_names = ['C', 'Q','S']\n",
+    "               , colors = ['#539caf', '#7663b0','purple']\n",
+    "               , x_label = 'Embarked'\n",
+    "               , y_label = 'Number of pepole for different kind of pclass'\n",
+    "               , title = 'Number of pepole for different kind of pclass by Embarked')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "7c1d49c4-9a90-420c-a477-cbbb6997e40b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#6. 幸存和遇难乘客堂兄弟姐妹的数量分布（提示：箱图或者提琴图）\n",
+    "sibsp_data=data.groupby('survived')['sibsp'].apply(list)\n",
+    "labels = list(sibsp_data.index)\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(sibsp_data.values, labels=labels,patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the survived')\n",
+    "plt.ylabel('Values of sibsp')\n",
+    "plt.title('The box with 2 features for the the sibsp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "6f788970-a071-4986-b529-694f85aeb2b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#7. 幸存和遇难乘客父母子女的数量分布（提示：箱图或者提琴图）\n",
+    "parch_data=data.groupby('survived')['parch'].apply(list)\n",
+    "labels = list(parch_data.index)\n",
+    "boxprops = {'facecolor': 'red', 'edgecolor': 'blue', 'color': 'blue'}  # 箱子的填充颜色和边线颜色\n",
+    "whiskerprops = {'color': 'red'}  # 框线的颜色\n",
+    "medianprops = {'color': 'blue'}  # 中位线的颜色\n",
+    "capprops={'color': 'blue'}\n",
+    "plt.boxplot(parch_data.values, labels=labels,patch_artist=True,boxprops=boxprops, whiskerprops=whiskerprops, medianprops=medianprops,capprops=capprops)\n",
+    "plt.xlabel('Features of the survived')\n",
+    "plt.ylabel('Values of parch')\n",
+    "plt.title('The box with 2 features for the the parch')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "6770cfff-e584-4d79-87b5-b37dce7e2cfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "survived    0    1\n",
+      "alone             \n",
+      "False     175  179\n",
+      "True      374  163\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#8. 单独乘船与否和幸存之间有没有联系（提示：统计柱状图）\n",
+    "import numpy as  np\n",
+    "def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
+    "    _, ax = plt.subplots()\n",
+    "    # 设置每一组柱状图的宽度\n",
+    "    total_width = 0.8\n",
+    "    # 设置每一个柱状图的宽度\n",
+    "    ind_width = total_width / len(y_data_list)\n",
+    "\n",
+    "    # 计算每一个柱状图的中心偏移\n",
+    "    alteration = np.arange(-total_width/2+ind_width/2, total_width/2+ind_width/2, ind_width)\n",
+    "\n",
+    "    # 分别绘制每一个柱状图\n",
+    "    for i in range(0, len(y_data_list)):\n",
+    "        # 横向散开绘制\n",
+    "        ax.bar(np.arange(len(x_data)) + alteration[i], y_data_list[i], color = colors[i], label = y_data_names[i], width = ind_width)\n",
+    "    ax.set_xticks(np.arange(len(x_data)))\n",
+    "    ax.set_xticklabels(x_data)\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_title(title)\n",
+    "    ax.legend(loc = 'best')\n",
+    "\n",
+    "#数据分类\n",
+    "alone_data = data.groupby(['alone'])['survived'].value_counts()\n",
+    "\n",
+    "#整理一下\n",
+    "alone_data = alone_data.unstack()\n",
+    "print(alone_data)\n",
+    "\n",
+    "groupedbarplot(x_data = alone_data.index.values\n",
+    "               , y_data_list = [alone_data[0].values, alone_data[1].values]\n",
+    "               , y_data_names = ['0', '1']\n",
+    "               , colors = ['#539caf', '#7663b0']\n",
+    "               , x_label = 'alone'\n",
+    "               , y_label = 'The number of pepole'\n",
+    "               , title = 'The number of pepole by whether be survived or alone or not')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa7a5851-e261-4048-b85d-96af26ff8a4a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "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,
+ "nbformat_minor": 5
+}