{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. `pandas`和`seaborn`: 統計圖表繪製\n",
    "\n",
    "xarray雖然可以輕易地開啟netCDF檔，繪製多種地圖，但目前沒有內建的統計圖表繪圖函數如盒鬚圖 (box plot)、散佈圖 (scatter plot) 等。`seaborn`是強大的統計資料視覺化工具，可以利用簡明的語言和高階功能的引數，繪製專業又美觀的統計圖表。由於氣候資料的解讀很仰賴統計方法，因此學習利用如何將氣候統計的結果，送進 `seaborn`的函數中繪圖，是很重要的。\n",
    "\n",
    "`seaborn`可以接受的資料格式主要為.csv資料檔，以及 `pandas.DataFrame`，寫入資料時必須寫成 `seaborn` 能辨識之 **「長表格 (long form)」** 和 **「寬表格 (wide form)」** ，有關表格的說明詳見[`seaborn`網頁的說明](https://seaborn.pydata.org/tutorial/data_structure.html)。本單元的重點在於如何建立正確的`pandas.DataFrame`格式並且送進`seaborn`的畫圖函數，有關繪圖的方法、引數等，[官方教學](https://seaborn.pydata.org/)已經有清楚的說明，此處不再一一介紹。\n",
    "\n",
    "![](https://seaborn.pydata.org/_images/data_structure_19_0.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `pandas`的資料架構\n",
    "\n",
    "按照資料的維度，`pandas`的資料結構分為Series和DataFrame兩種。和xarray類似，pandas資料帶有網格資訊 (或稱為標籤 labels)。\n",
    "\n",
    "### Series\n",
    "\n",
    "Series是一維、標籤化的陣列，可以儲存多元的變數種類。而座標軸或標籤稱為index。建立Series的方法如下：\n",
    "\n",
    "~~~\n",
    "s = pd.Series(data, index=index)\n",
    "~~~\n",
    "\n",
    "只要給定資料和座標軸標籤，就可以建立Series。以下提供一個範例，更多詳細的用法請參考[Pandas官網](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.362540\n",
       "b   -0.600362\n",
       "c   -0.681006\n",
       "d    0.676658\n",
       "e   -1.140541\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "s = pd.Series(np.random.randn(5), index=[\"a\", \"b\", \"c\", \"d\", \"e\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame\n",
    "\n",
    "DataFrame就是二維標籤化的資料陣列，可以想像成一個Excel的活頁簿表格。建立的方法如下\n",
    "\n",
    "~~~\n",
    "s = pd.DataFrame(data, index=index, column=None)\n",
    "~~~\n",
    "\n",
    "index可以想像成列的標籤，column是欄的標籤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.566387</td>\n",
       "      <td>1.185958</td>\n",
       "      <td>0.963150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.117395</td>\n",
       "      <td>0.295263</td>\n",
       "      <td>-0.438252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.455508</td>\n",
       "      <td>0.210906</td>\n",
       "      <td>-0.682973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.447051</td>\n",
       "      <td>0.110604</td>\n",
       "      <td>0.758892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.730785</td>\n",
       "      <td>0.984017</td>\n",
       "      <td>-0.412548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three\n",
       "a  0.566387  1.185958  0.963150\n",
       "b  0.117395  0.295263 -0.438252\n",
       "c  0.455508  0.210906 -0.682973\n",
       "d  0.447051  0.110604  0.758892\n",
       "e  0.730785  0.984017 -0.412548"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.random.randn(5,3)\n",
    "df = pd.DataFrame(d, index=['a','b','c','d','e'], columns=['one','two','three'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以利用 **字典 (Dictionary)**，而字典的key就會被當作欄的標籤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>bom</th>\n",
       "      <th>cma</th>\n",
       "      <th>ecmwf</th>\n",
       "      <th>ncep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0.675900</td>\n",
       "      <td>-0.418055</td>\n",
       "      <td>-0.432737</td>\n",
       "      <td>0.020010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>0.249387</td>\n",
       "      <td>-0.065724</td>\n",
       "      <td>-0.822159</td>\n",
       "      <td>-1.432853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1.001723</td>\n",
       "      <td>0.912889</td>\n",
       "      <td>0.334306</td>\n",
       "      <td>-0.259447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-0.443690</td>\n",
       "      <td>-0.620247</td>\n",
       "      <td>-0.286467</td>\n",
       "      <td>-0.057605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>-0.572469</td>\n",
       "      <td>1.221623</td>\n",
       "      <td>-0.745894</td>\n",
       "      <td>0.161164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.445694</td>\n",
       "      <td>0.451130</td>\n",
       "      <td>0.984434</td>\n",
       "      <td>-0.121749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>-0.850694</td>\n",
       "      <td>-1.457001</td>\n",
       "      <td>1.046043</td>\n",
       "      <td>-2.354296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1.047600</td>\n",
       "      <td>1.592595</td>\n",
       "      <td>1.380519</td>\n",
       "      <td>0.656482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.099556</td>\n",
       "      <td>-0.285461</td>\n",
       "      <td>-0.338598</td>\n",
       "      <td>-1.023319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>-2.668941</td>\n",
       "      <td>0.907000</td>\n",
       "      <td>-1.349901</td>\n",
       "      <td>0.041280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           bom       cma     ecmwf      ncep\n",
       "1998  0.675900 -0.418055 -0.432737  0.020010\n",
       "1999  0.249387 -0.065724 -0.822159 -1.432853\n",
       "2000  1.001723  0.912889  0.334306 -0.259447\n",
       "2001 -0.443690 -0.620247 -0.286467 -0.057605\n",
       "2002 -0.572469  1.221623 -0.745894  0.161164\n",
       "2003  0.445694  0.451130  0.984434 -0.121749\n",
       "2004 -0.850694 -1.457001  1.046043 -2.354296\n",
       "2005  1.047600  1.592595  1.380519  0.656482\n",
       "2006  1.099556 -0.285461 -0.338598 -1.023319\n",
       "2007 -2.668941  0.907000 -1.349901  0.041280"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dict(bom=np.random.randn(10),\n",
    "                       cma=np.random.randn(10),\n",
    "                       ecmwf=np.random.randn(10),\n",
    "                       ncep=np.random.randn(10)), \n",
    "                  index=range(1998,2008)\n",
    "                  )\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用`pandas`讀取`.csv`檔案\n",
    "\n",
    "利用`pandas.read_csv()`，就可以將.csv檔案轉換成 `pandas.DataFrame`。\n",
    "\n",
    "**Example 1:** `sns_sample_s2s_pr_rmse.csv`檔案中有BoM、CMA的S2S模式在前15個預報時 (lead time)，事後預報 (1998-2013) 某區域冬季季內高峰降雨事件的PR值的誤差 (PR_RMSE) 。(見[Tsai et al. (2021, Atmosphere)](https://www.mdpi.com/2073-4433/12/6/758))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>Models</th>\n",
       "      <th>Lead time (days)</th>\n",
       "      <th>Year</th>\n",
       "      <th>PR_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BoM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>21.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BoM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>36.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BoM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BoM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>13.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BoM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>19.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models  Lead time (days)    Year  PR_RMSE\n",
       "0    BoM               1.0  1998.0    21.78\n",
       "1    BoM               1.0  1999.0    36.98\n",
       "2    BoM               1.0  2000.0     7.25\n",
       "3    BoM               1.0  2001.0    13.18\n",
       "4    BoM               1.0  2002.0    19.64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"data/sns_sample_s2s_pr_rmse.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `pandas.DataFrame`與`seaborn`的Long Form繪圖\n",
    "\n",
    "只要將資料按照long form/wide form的需求排列好，就可以很輕易地將資料繪圖。以上的.csv檔案就是屬於Long form的形式。\n",
    "\n",
    "**Example 1:** 將`sns_sample_s2s_pr_rmse.csv`檔案繪圖，繪製x軸為預報時(Lead time)，縱軸是預報PR_RMSE，利用盒鬚圖表示多年PR_RMSE的分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "mpl.rcParams['figure.dpi'] = 100\n",
    "\n",
    "sns.set_theme(style=\"white\", palette=None)\n",
    "fig, ax = plt.subplots(figsize=(8,4)) \n",
    "bxplt = sns.boxplot(data=df,\n",
    "                    x='Lead time (days)', y='PR_RMSE', \n",
    "                    ax=ax,\n",
    "                    hue='Models',\n",
    "                    palette=\"Set3\")\n",
    "ax.set_ylabel(\"PR_RMSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以用Facet Grid，將兩個模式分為兩張圖畫。用Facet Grid繪製盒鬚圖要用`catplot()`這個函數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"white\", palette=None)\n",
    "bxplt = sns.catplot(data=df,\n",
    "                    x='Lead time (days)', y='PR_RMSE', \n",
    "                    kind='box', col='Models',\n",
    "                    hue='Models',\n",
    "                    palette=\"Set3\")\n",
    "ax.set_ylabel(\"PR_RMSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多層次標籤 (Multi-index) 的 DataFrame與Long Form繪圖\n",
    "\n",
    "**Example 2:** 分析S2S模式在15個預報時 (lead time, `lt=15`) 以及11個系集成員 (ensemble members, `ens=11`)在分為Hindcast、Forecast兩種cases的情形下，某變數`value`的分佈情形。\n",
    "\n",
    "由於`value`分類的層次較多，所以必須用`pandas.MultiIndex`建立起`(lead_time, number, case)`的索引。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <th>number</th>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>Hindcast</th>\n",
       "      <td>-0.426472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast</th>\n",
       "      <td>1.277017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Hindcast</th>\n",
       "      <td>-0.437499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast</th>\n",
       "      <td>0.002919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>Hindcast</th>\n",
       "      <td>1.562215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              value\n",
       "lead_time number case              \n",
       "1         1      Hindcast -0.426472\n",
       "                 Forecast  1.277017\n",
       "          2      Hindcast -0.437499\n",
       "                 Forecast  0.002919\n",
       "          3      Hindcast  1.562215"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lt = 15 \n",
    "ens = 4\n",
    "\n",
    "iterables = [range(1,lt+1), range(1,ens+1), [\"Hindcast\", \"Forecast\"]]\n",
    "tuples = pd.MultiIndex.from_product(iterables, names=[\"lead_time\", \"number\",\"case\"])  \n",
    "         # from_product 是將iterables中的標籤相乘，形成各lead time、number、case的組合。\n",
    "data = pd.DataFrame(data={'value': np.random.randn(lt*ens*2)}, index=tuples)  \n",
    "       # 先以亂數代表資料。資料取名為'value'。\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們發現表頭被分為兩行，這是因為在`DataFrame`結構中， `lead_time`、`number`、`case`稱為 **Index**，`value` 稱為 **Column**，如果直接放到`seaborn`函數中，coulmns的名稱是無法使用的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'lead_time'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/5f/jxsyfmmj2rsb4dk_z6d_34qw0000gn/T/ipykernel_3994/774566885.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_theme\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"white\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m bxplt = sns.catplot(data=data,\n\u001b[0m\u001b[1;32m      4\u001b[0m                     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lead_time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'value'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'box'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                     \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'case'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Hindcast'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Forecast'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/p3/lib/python3.9/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/p3/lib/python3.9/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcatplot\u001b[0;34m(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, seed, order, hue_order, row_order, col_order, kind, height, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)\u001b[0m\n\u001b[1;32m   3790\u001b[0m     \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_CategoricalPlotter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3791\u001b[0m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_numeric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplotter_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_numeric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3792\u001b[0;31m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3793\u001b[0m     if (\n\u001b[1;32m   3794\u001b[0m         \u001b[0morder\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/p3/lib/python3.9/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    151\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0;31m# Figure out the plotting orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret input 'lead_time'"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "sns.set_theme(style=\"white\", palette=None)\n",
    "bxplt = sns.catplot(data=data,\n",
    "                    x='lead_time', y='value', kind='box', \n",
    "                    hue='case', hue_order=['Hindcast','Forecast'],\n",
    "                    palette=['white','silver'])\n",
    "ax.set_ylabel(\"PR_RMSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要讓這個`DataFrame`變成`seaborn` 可讀取的long form格式，必須加上`data.reset_index()`，就會轉變成理想中的DataFrame了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>lead_time</th>\n",
       "      <th>number</th>\n",
       "      <th>case</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hindcast</td>\n",
       "      <td>-0.426472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>1.277017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hindcast</td>\n",
       "      <td>-0.437499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>0.002919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hindcast</td>\n",
       "      <td>1.562215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>2.074004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Hindcast</td>\n",
       "      <td>-0.182615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>-0.092137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>Hindcast</td>\n",
       "      <td>-0.322854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>0.360733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     lead_time  number      case     value\n",
       "0            1       1  Hindcast -0.426472\n",
       "1            1       1  Forecast  1.277017\n",
       "2            1       2  Hindcast -0.437499\n",
       "3            1       2  Forecast  0.002919\n",
       "4            1       3  Hindcast  1.562215\n",
       "..         ...     ...       ...       ...\n",
       "115         15       2  Forecast  2.074004\n",
       "116         15       3  Hindcast -0.182615\n",
       "117         15       3  Forecast -0.092137\n",
       "118         15       4  Hindcast -0.322854\n",
       "119         15       4  Forecast  0.360733\n",
       "\n",
       "[120 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864.375x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"white\", palette=None)\n",
    "bxplt = sns.catplot(data=data.reset_index(),\n",
    "                    x='lead_time', y='value', kind='box', \n",
    "                    hue='case', hue_order=['Hindcast','Forecast'],\n",
    "                    palette=['white','silver'],\n",
    "                    aspect=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 將`xarray.DataArray`轉換至`pandas.DataFrame`\n",
    "\n",
    "### 利用`xarray.to_pandas`\n",
    "\n",
    "根據[API reference](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.to_pandas.html)的說明，轉換後的格式和給定DataArray的維度有關。\n",
    "> Convert this array into a pandas object with the same shape.\n",
    "The type of the returned object depends on the number of DataArray dimensions:  \n",
    "0D -> `xarray.DataArray`  \n",
    "1D -> `pandas.Series`  \n",
    "2D -> `pandas.DataFrame`  \n",
    "Only works for arrays with 2 or fewer dimensions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 範例：繪製散布圖 (scatter plot) 以及回歸分析\n",
    "\n",
    "**Example 3:** 將夏季(五至七月)候平均副高指標和長江流域(105.5˚-122˚E, 27˚-33.5˚N)降雨資料在散布圖上，並且計算回歸線。副高指數定義為\n",
    "\n",
    "$$\\mathrm{WPSH} = U_{850}\\left[ (115˚-140˚\\mathrm{E}, 28˚-30˚\\mathrm{N})\\right] − U_{850}\\left[(115˚-140˚\\mathrm{E}, 15˚-17˚\\mathrm{N})\\right]$$\n",
    "\n",
    "我們要了解降雨和副高兩個變量之間的關係，最常使用散佈圖來表示。兩個變量會用DataArray儲存，將兩個變量合併成一個Dataset，再轉換成pandas.DataFrame，就可以放到seaborn去作圖了。\n",
    "\n",
    "**Step 1:** 讀取風場和降雨資料檔案。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;cmorph&#x27; (time: 7670, lat: 12, lon: 66)&gt;\n",
       "[6074640 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-01-01 1998-01-02 ... 2018-12-31\n",
       "  * lon      (lon) float32 105.6 105.9 106.1 106.4 ... 121.1 121.4 121.6 121.9\n",
       "  * lat      (lat) float32 27.12 27.38 27.62 27.88 ... 29.12 29.38 29.62 29.88\n",
       "Attributes:\n",
       "    standard_name:  lwe_precipitation_rate\n",
       "    long_name:      NOAA Climate Data Record (CDR) of CPC Morphing Technique ...\n",
       "    units:          mm/day\n",
       "    comment:        !!! CMORPH estimate is rainrate !!!</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'cmorph'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7670</li><li><span class='xr-has-index'>lat</span>: 12</li><li><span class='xr-has-index'>lon</span>: 66</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2b221741-1959-4fe1-ba28-900ec69d7032' class='xr-array-in' type='checkbox' checked><label for='section-2b221741-1959-4fe1-ba28-900ec69d7032' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[6074640 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-b8941e19-075b-457e-82d5-f42f42666cf8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8941e19-075b-457e-82d5-f42f42666cf8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1998-01-01 ... 2018-12-31</div><input id='attrs-7e600477-1a4e-4747-b736-374f11eaace6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e600477-1a4e-4747-b736-374f11eaace6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3e4ad05-6c7e-43e8-9f5a-7c67c424fb56' class='xr-var-data-in' type='checkbox'><label for='data-b3e4ad05-6c7e-43e8-9f5a-7c67c424fb56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>starting point of time period</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-03T00:00:00.000000000&#x27;, ..., &#x27;2018-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-30T00:00:00.000000000&#x27;, &#x27;2018-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>105.6 105.9 106.1 ... 121.6 121.9</div><input id='attrs-e18f8849-3ed0-4d5a-8ae9-362bd5fe255d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e18f8849-3ed0-4d5a-8ae9-362bd5fe255d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-776f3644-748e-42d8-b15b-6ca80d1dfb0c' class='xr-var-data-in' type='checkbox'><label for='data-776f3644-748e-42d8-b15b-6ca80d1dfb0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude of center of grid box</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([105.625, 105.875, 106.125, 106.375, 106.625, 106.875, 107.125, 107.375,\n",
       "       107.625, 107.875, 108.125, 108.375, 108.625, 108.875, 109.125, 109.375,\n",
       "       109.625, 109.875, 110.125, 110.375, 110.625, 110.875, 111.125, 111.375,\n",
       "       111.625, 111.875, 112.125, 112.375, 112.625, 112.875, 113.125, 113.375,\n",
       "       113.625, 113.875, 114.125, 114.375, 114.625, 114.875, 115.125, 115.375,\n",
       "       115.625, 115.875, 116.125, 116.375, 116.625, 116.875, 117.125, 117.375,\n",
       "       117.625, 117.875, 118.125, 118.375, 118.625, 118.875, 119.125, 119.375,\n",
       "       119.625, 119.875, 120.125, 120.375, 120.625, 120.875, 121.125, 121.375,\n",
       "       121.625, 121.875], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>27.12 27.38 27.62 ... 29.62 29.88</div><input id='attrs-fe1fbfa0-7a2f-45d1-ad5f-0e7c5c02f71b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe1fbfa0-7a2f-45d1-ad5f-0e7c5c02f71b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6d51bdf-5fb6-4ae5-88c2-a2967c49ed47' class='xr-var-data-in' type='checkbox'><label for='data-c6d51bdf-5fb6-4ae5-88c2-a2967c49ed47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude of center of grid box</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([27.125, 27.375, 27.625, 27.875, 28.125, 28.375, 28.625, 28.875, 29.125,\n",
       "       29.375, 29.625, 29.875], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b82012f-f543-42b0-92fc-31bffe8caccb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b82012f-f543-42b0-92fc-31bffe8caccb' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>long_name :</span></dt><dd>NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>comment :</span></dt><dd>!!! CMORPH estimate is rainrate !!!</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'cmorph' (time: 7670, lat: 12, lon: 66)>\n",
       "[6074640 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-01-01 1998-01-02 ... 2018-12-31\n",
       "  * lon      (lon) float32 105.6 105.9 106.1 106.4 ... 121.1 121.4 121.6 121.9\n",
       "  * lat      (lat) float32 27.12 27.38 27.62 27.88 ... 29.12 29.38 29.62 29.88\n",
       "Attributes:\n",
       "    standard_name:  lwe_precipitation_rate\n",
       "    long_name:      NOAA Climate Data Record (CDR) of CPC Morphing Technique ...\n",
       "    units:          mm/day\n",
       "    comment:        !!! CMORPH estimate is rainrate !!!"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "pcpds = xr.open_dataset('data/cmorph_sample.nc')\n",
    "pcp = (pcpds.sel(time=slice('1998-01-01','2018-12-31'),\n",
    "                  lat=slice(27,33.5),\n",
    "                  lon=slice(105.5,122)).cmorph)\n",
    "pcp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;uwnd&#x27; (time: 7670, lat: 7, lon: 11)&gt;\n",
       "dask.array&lt;getitem, shape=(7670, 7, 11), dtype=float32, chunksize=(366, 7, 11), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-01-01 1998-01-02 ... 2018-12-31\n",
       "  * lon      (lon) float32 115.0 117.5 120.0 122.5 ... 132.5 135.0 137.5 140.0\n",
       "  * lat      (lat) float32 30.0 27.5 25.0 22.5 20.0 17.5 15.0\n",
       "    level    float32 850.0\n",
       "Attributes:\n",
       "    standard_name:         eastward_wind\n",
       "    long_name:             Daily U-wind on Pressure Levels\n",
       "    units:                 m/s\n",
       "    unpacked_valid_range:  [-140.  175.]\n",
       "    actual_range:          [-78.96 110.35]\n",
       "    precision:             2\n",
       "    GRIB_id:               33\n",
       "    GRIB_name:             UGRD\n",
       "    var_desc:              u-wind\n",
       "    dataset:               NCEP/DOE AMIP-II Reanalysis (Reanalysis-2) Daily A...\n",
       "    level_desc:            Pressure Levels\n",
       "    statistic:             Mean\n",
       "    parent_stat:           Individual Obs\n",
       "    cell_methods:          time: mean (of 4 6-hourly values in one day)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'uwnd'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7670</li><li><span class='xr-has-index'>lat</span>: 7</li><li><span class='xr-has-index'>lon</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-40fc836e-8f98-4aa5-8b11-9df86459123d' class='xr-array-in' type='checkbox' checked><label for='section-40fc836e-8f98-4aa5-8b11-9df86459123d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(365, 7, 11), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
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       "                        <th> Bytes </th>\n",
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       "                        <td> 110.09 kiB </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-583d535c-2ac0-475a-951c-07982317ef7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-583d535c-2ac0-475a-951c-07982317ef7d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1998-01-01 ... 2018-12-31</div><input id='attrs-7de326e2-6db4-45c2-afe5-3d802e533a17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7de326e2-6db4-45c2-afe5-3d802e533a17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caecd526-1044-42d7-80d8-f641a46214ba' class='xr-var-data-in' type='checkbox'><label for='data-caecd526-1044-42d7-80d8-f641a46214ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-03T00:00:00.000000000&#x27;, ..., &#x27;2018-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-30T00:00:00.000000000&#x27;, &#x27;2018-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>115.0 117.5 120.0 ... 137.5 140.0</div><input id='attrs-d26b3956-9db6-42b9-9c23-82689622a90a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d26b3956-9db6-42b9-9c23-82689622a90a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa050dda-f809-4c83-b989-6de934e20d04' class='xr-var-data-in' type='checkbox'><label for='data-fa050dda-f809-4c83-b989-6de934e20d04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([115. , 117.5, 120. , 122.5, 125. , 127.5, 130. , 132.5, 135. , 137.5,\n",
       "       140. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>30.0 27.5 25.0 22.5 20.0 17.5 15.0</div><input id='attrs-ab979b9c-6404-43b2-995a-6aa173e3d3c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab979b9c-6404-43b2-995a-6aa173e3d3c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbf88e0e-cc7a-47a1-aad2-e3d33a368e8e' class='xr-var-data-in' type='checkbox'><label for='data-fbf88e0e-cc7a-47a1-aad2-e3d33a368e8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([30. , 27.5, 25. , 22.5, 20. , 17.5, 15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>level</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>850.0</div><input id='attrs-e527209f-84da-4976-91cf-38ff5981220a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e527209f-84da-4976-91cf-38ff5981220a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbcf3026-3584-4817-91a9-5685ba237c5e' class='xr-var-data-in' type='checkbox'><label for='data-dbcf3026-3584-4817-91a9-5685ba237c5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>Level</dd><dt><span>units :</span></dt><dd>millibar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>actual_range :</span></dt><dd>[1000.   10.]</dd><dt><span>GRIB_id :</span></dt><dd>100</dd><dt><span>GRIB_name :</span></dt><dd>hPa</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array(850., dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7527815-e0a7-417d-87f6-665666b73356' class='xr-section-summary-in' type='checkbox'  ><label for='section-c7527815-e0a7-417d-87f6-665666b73356' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>long_name :</span></dt><dd>Daily U-wind on Pressure Levels</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>unpacked_valid_range :</span></dt><dd>[-140.  175.]</dd><dt><span>actual_range :</span></dt><dd>[-78.96 110.35]</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>33</dd><dt><span>GRIB_name :</span></dt><dd>UGRD</dd><dt><span>var_desc :</span></dt><dd>u-wind</dd><dt><span>dataset :</span></dt><dd>NCEP/DOE AMIP-II Reanalysis (Reanalysis-2) Daily Averages</dd><dt><span>level_desc :</span></dt><dd>Pressure Levels</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>cell_methods :</span></dt><dd>time: mean (of 4 6-hourly values in one day)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'uwnd' (time: 7670, lat: 7, lon: 11)>\n",
       "dask.array<getitem, shape=(7670, 7, 11), dtype=float32, chunksize=(366, 7, 11), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-01-01 1998-01-02 ... 2018-12-31\n",
       "  * lon      (lon) float32 115.0 117.5 120.0 122.5 ... 132.5 135.0 137.5 140.0\n",
       "  * lat      (lat) float32 30.0 27.5 25.0 22.5 20.0 17.5 15.0\n",
       "    level    float32 850.0\n",
       "Attributes:\n",
       "    standard_name:         eastward_wind\n",
       "    long_name:             Daily U-wind on Pressure Levels\n",
       "    units:                 m/s\n",
       "    unpacked_valid_range:  [-140.  175.]\n",
       "    actual_range:          [-78.96 110.35]\n",
       "    precision:             2\n",
       "    GRIB_id:               33\n",
       "    GRIB_name:             UGRD\n",
       "    var_desc:              u-wind\n",
       "    dataset:               NCEP/DOE AMIP-II Reanalysis (Reanalysis-2) Daily A...\n",
       "    level_desc:            Pressure Levels\n",
       "    statistic:             Mean\n",
       "    parent_stat:           Individual Obs\n",
       "    cell_methods:          time: mean (of 4 6-hourly values in one day)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uds = xr.open_mfdataset( 'data/ncep_r2_uv850/u850.*.nc',                                       \n",
    "                          combine = \"nested\",               \n",
    "                          concat_dim='time',                               \n",
    "                          parallel=True                \n",
    "                         )\n",
    "u = uds.sel(time=slice('1998-01-01','2018-12-31'),\n",
    "            level=850,\n",
    "            lat=slice(30,15),\n",
    "            lon=slice(115,140)).uwnd\n",
    "u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** 計算候降雨區域平均和副高指標，並且取出所需要的季節。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;cmorph&#x27; (time: 399)&gt;\n",
       "array([ 6.1119447 ,  7.571944  ,  9.226313  ,  1.109394  ,  5.3176517 ,\n",
       "        2.2713385 ,  4.5563893 ,  5.548586  , 20.215153  , 19.05255   ,\n",
       "       19.18139   , 11.589319  ,  5.768939  ,  0.8520454 ,  2.0018182 ,\n",
       "        7.1484594 , 16.180103  , 15.215152  ,  9.107071  ,  2.5040152 ,\n",
       "        3.840025  ,  6.161818  , 10.612726  , 14.754066  ,  4.777803  ,\n",
       "        3.7596467 ,  4.2742677 ,  4.1311617 ,  9.817575  ,  9.265707  ,\n",
       "       19.125706  ,  5.8694196 ,  9.390833  ,  6.653005  , 11.306919  ,\n",
       "        4.9133587 ,  4.404621  ,  5.623662  ,  0.42189392,  4.163914  ,\n",
       "        0.89323235,  4.060303  ,  6.1958838 ,  7.9272475 ,  9.796288  ,\n",
       "       13.937828  ,  7.175     ,  4.0308585 , 17.810404  ,  2.4159596 ,\n",
       "        8.7743435 ,  5.2782073 ,  0.85707074,  3.5488381 ,  1.2096969 ,\n",
       "        6.624646  ,  1.9946716 ,  5.699394  ,  8.616186  ,  1.5915152 ,\n",
       "        4.1929545 ,  2.6942427 ,  3.4918437 , 13.028662  ,  8.653611  ,\n",
       "        7.7104044 ,  9.239192  ,  6.0609846 ,  4.4448996 ,  3.5450509 ,\n",
       "        5.1548486 ,  8.582021  ,  2.6875    ,  0.5927273 ,  0.6543434 ,\n",
       "        6.838661  ,  8.804344  , 10.843763  , 15.854294  ,  4.8490906 ,\n",
       "        1.5454293 ,  5.111869  ,  2.1185105 ,  5.9939146 ,  8.986516  ,\n",
       "       11.501137  ,  6.855783  , 19.229874  ,  4.8697224 ,  3.6527023 ,\n",
       "        0.28075758, 11.871238  , 13.896718  ,  5.5745454 ,  7.2881308 ,\n",
       "        8.425404  ,  5.2889137 , 12.673864  ,  3.7550251 ,  5.4035106 ,\n",
       "...\n",
       "        1.7411363 ,  2.8605304 ,  0.06626263,  3.5106819 ,  9.037273  ,\n",
       "        9.238309  ,  6.672399  ,  7.323561  , 14.975934  ,  1.794293  ,\n",
       "       13.95515   ,  2.2613385 ,  0.44108588, 11.281011  , 12.378283  ,\n",
       "        7.4113646 , 14.560858  ,  1.5172222 , 13.836011  , 10.90058   ,\n",
       "        4.5382323 ,  4.3567934 ,  2.0662374 ,  4.1724496 ,  9.48851   ,\n",
       "       16.287956  ,  6.195707  ,  2.3606565 , 11.566339  , 13.658864  ,\n",
       "        9.880757  , 11.507375  , 10.583131  ,  7.5609345 ,  1.6071465 ,\n",
       "        9.036112  ,  1.5086616 ,  3.748207  ,  4.2615147 , 11.935782  ,\n",
       "        4.1128035 ,  0.83159095, 12.750354  , 18.391438  ,  3.9217172 ,\n",
       "        7.9154043 ,  6.137349  ,  5.678056  , 17.364166  ,  3.0512373 ,\n",
       "        6.274015  , 20.679419  ,  4.495379  , 14.612981  , 17.417526  ,\n",
       "        4.1593685 ,  7.485732  , 14.482325  ,  0.45714647,  1.4413131 ,\n",
       "        5.4458075 ,  3.4082577 ,  6.1348233 , 10.790833  ,  0.6386616 ,\n",
       "        4.5670204 ,  1.3894949 , 14.749066  ,  8.999117  , 13.643537  ,\n",
       "        6.38601   , 24.602198  , 17.027979  , 12.688131  ,  4.9872475 ,\n",
       "        4.0856314 ,  1.6659092 ,  0.5755303 ,  1.7656313 ,  5.5999494 ,\n",
       "        5.241363  ,  5.9827776 ,  2.3927524 , 12.155732  ,  6.2022223 ,\n",
       "        7.9329047 ,  3.525303  ,  5.64601   ,  4.1817927 ,  5.8839903 ,\n",
       "        7.2206063 ,  3.9626517 , 15.497324  ,  9.463485  ,  2.8450253 ,\n",
       "        0.19522728,  2.8943431 ,  3.5896213 ,  6.607399  ], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-05-01 1998-05-06 ... 2018-07-30</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'cmorph'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 399</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c01c52e3-1bc9-46e4-9028-2c15edab8868' class='xr-array-in' type='checkbox' checked><label for='section-c01c52e3-1bc9-46e4-9028-2c15edab8868' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.112 7.572 9.226 1.109 5.318 2.271 ... 2.845 0.1952 2.894 3.59 6.607</span></div><div class='xr-array-data'><pre>array([ 6.1119447 ,  7.571944  ,  9.226313  ,  1.109394  ,  5.3176517 ,\n",
       "        2.2713385 ,  4.5563893 ,  5.548586  , 20.215153  , 19.05255   ,\n",
       "       19.18139   , 11.589319  ,  5.768939  ,  0.8520454 ,  2.0018182 ,\n",
       "        7.1484594 , 16.180103  , 15.215152  ,  9.107071  ,  2.5040152 ,\n",
       "        3.840025  ,  6.161818  , 10.612726  , 14.754066  ,  4.777803  ,\n",
       "        3.7596467 ,  4.2742677 ,  4.1311617 ,  9.817575  ,  9.265707  ,\n",
       "       19.125706  ,  5.8694196 ,  9.390833  ,  6.653005  , 11.306919  ,\n",
       "        4.9133587 ,  4.404621  ,  5.623662  ,  0.42189392,  4.163914  ,\n",
       "        0.89323235,  4.060303  ,  6.1958838 ,  7.9272475 ,  9.796288  ,\n",
       "       13.937828  ,  7.175     ,  4.0308585 , 17.810404  ,  2.4159596 ,\n",
       "        8.7743435 ,  5.2782073 ,  0.85707074,  3.5488381 ,  1.2096969 ,\n",
       "        6.624646  ,  1.9946716 ,  5.699394  ,  8.616186  ,  1.5915152 ,\n",
       "        4.1929545 ,  2.6942427 ,  3.4918437 , 13.028662  ,  8.653611  ,\n",
       "        7.7104044 ,  9.239192  ,  6.0609846 ,  4.4448996 ,  3.5450509 ,\n",
       "        5.1548486 ,  8.582021  ,  2.6875    ,  0.5927273 ,  0.6543434 ,\n",
       "        6.838661  ,  8.804344  , 10.843763  , 15.854294  ,  4.8490906 ,\n",
       "        1.5454293 ,  5.111869  ,  2.1185105 ,  5.9939146 ,  8.986516  ,\n",
       "       11.501137  ,  6.855783  , 19.229874  ,  4.8697224 ,  3.6527023 ,\n",
       "        0.28075758, 11.871238  , 13.896718  ,  5.5745454 ,  7.2881308 ,\n",
       "        8.425404  ,  5.2889137 , 12.673864  ,  3.7550251 ,  5.4035106 ,\n",
       "...\n",
       "        1.7411363 ,  2.8605304 ,  0.06626263,  3.5106819 ,  9.037273  ,\n",
       "        9.238309  ,  6.672399  ,  7.323561  , 14.975934  ,  1.794293  ,\n",
       "       13.95515   ,  2.2613385 ,  0.44108588, 11.281011  , 12.378283  ,\n",
       "        7.4113646 , 14.560858  ,  1.5172222 , 13.836011  , 10.90058   ,\n",
       "        4.5382323 ,  4.3567934 ,  2.0662374 ,  4.1724496 ,  9.48851   ,\n",
       "       16.287956  ,  6.195707  ,  2.3606565 , 11.566339  , 13.658864  ,\n",
       "        9.880757  , 11.507375  , 10.583131  ,  7.5609345 ,  1.6071465 ,\n",
       "        9.036112  ,  1.5086616 ,  3.748207  ,  4.2615147 , 11.935782  ,\n",
       "        4.1128035 ,  0.83159095, 12.750354  , 18.391438  ,  3.9217172 ,\n",
       "        7.9154043 ,  6.137349  ,  5.678056  , 17.364166  ,  3.0512373 ,\n",
       "        6.274015  , 20.679419  ,  4.495379  , 14.612981  , 17.417526  ,\n",
       "        4.1593685 ,  7.485732  , 14.482325  ,  0.45714647,  1.4413131 ,\n",
       "        5.4458075 ,  3.4082577 ,  6.1348233 , 10.790833  ,  0.6386616 ,\n",
       "        4.5670204 ,  1.3894949 , 14.749066  ,  8.999117  , 13.643537  ,\n",
       "        6.38601   , 24.602198  , 17.027979  , 12.688131  ,  4.9872475 ,\n",
       "        4.0856314 ,  1.6659092 ,  0.5755303 ,  1.7656313 ,  5.5999494 ,\n",
       "        5.241363  ,  5.9827776 ,  2.3927524 , 12.155732  ,  6.2022223 ,\n",
       "        7.9329047 ,  3.525303  ,  5.64601   ,  4.1817927 ,  5.8839903 ,\n",
       "        7.2206063 ,  3.9626517 , 15.497324  ,  9.463485  ,  2.8450253 ,\n",
       "        0.19522728,  2.8943431 ,  3.5896213 ,  6.607399  ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-c76b3485-1abc-4f56-b1a5-964b6f28a465' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c76b3485-1abc-4f56-b1a5-964b6f28a465' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1998-05-01 ... 2018-07-30</div><input id='attrs-34b0446d-40eb-4bd0-8ff1-e6db3f0519cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34b0446d-40eb-4bd0-8ff1-e6db3f0519cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e3adce4-a6e3-4683-8a01-29d568c714a7' class='xr-var-data-in' type='checkbox'><label for='data-2e3adce4-a6e3-4683-8a01-29d568c714a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>starting point of time period</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1998-05-01T00:00:00.000000000&#x27;, &#x27;1998-05-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-05-11T00:00:00.000000000&#x27;, ..., &#x27;2018-07-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-25T00:00:00.000000000&#x27;, &#x27;2018-07-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3fa49b54-9b84-45ce-be1a-6fc93163e2b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3fa49b54-9b84-45ce-be1a-6fc93163e2b0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'cmorph' (time: 399)>\n",
       "array([ 6.1119447 ,  7.571944  ,  9.226313  ,  1.109394  ,  5.3176517 ,\n",
       "        2.2713385 ,  4.5563893 ,  5.548586  , 20.215153  , 19.05255   ,\n",
       "       19.18139   , 11.589319  ,  5.768939  ,  0.8520454 ,  2.0018182 ,\n",
       "        7.1484594 , 16.180103  , 15.215152  ,  9.107071  ,  2.5040152 ,\n",
       "        3.840025  ,  6.161818  , 10.612726  , 14.754066  ,  4.777803  ,\n",
       "        3.7596467 ,  4.2742677 ,  4.1311617 ,  9.817575  ,  9.265707  ,\n",
       "       19.125706  ,  5.8694196 ,  9.390833  ,  6.653005  , 11.306919  ,\n",
       "        4.9133587 ,  4.404621  ,  5.623662  ,  0.42189392,  4.163914  ,\n",
       "        0.89323235,  4.060303  ,  6.1958838 ,  7.9272475 ,  9.796288  ,\n",
       "       13.937828  ,  7.175     ,  4.0308585 , 17.810404  ,  2.4159596 ,\n",
       "        8.7743435 ,  5.2782073 ,  0.85707074,  3.5488381 ,  1.2096969 ,\n",
       "        6.624646  ,  1.9946716 ,  5.699394  ,  8.616186  ,  1.5915152 ,\n",
       "        4.1929545 ,  2.6942427 ,  3.4918437 , 13.028662  ,  8.653611  ,\n",
       "        7.7104044 ,  9.239192  ,  6.0609846 ,  4.4448996 ,  3.5450509 ,\n",
       "        5.1548486 ,  8.582021  ,  2.6875    ,  0.5927273 ,  0.6543434 ,\n",
       "        6.838661  ,  8.804344  , 10.843763  , 15.854294  ,  4.8490906 ,\n",
       "        1.5454293 ,  5.111869  ,  2.1185105 ,  5.9939146 ,  8.986516  ,\n",
       "       11.501137  ,  6.855783  , 19.229874  ,  4.8697224 ,  3.6527023 ,\n",
       "        0.28075758, 11.871238  , 13.896718  ,  5.5745454 ,  7.2881308 ,\n",
       "        8.425404  ,  5.2889137 , 12.673864  ,  3.7550251 ,  5.4035106 ,\n",
       "...\n",
       "        1.7411363 ,  2.8605304 ,  0.06626263,  3.5106819 ,  9.037273  ,\n",
       "        9.238309  ,  6.672399  ,  7.323561  , 14.975934  ,  1.794293  ,\n",
       "       13.95515   ,  2.2613385 ,  0.44108588, 11.281011  , 12.378283  ,\n",
       "        7.4113646 , 14.560858  ,  1.5172222 , 13.836011  , 10.90058   ,\n",
       "        4.5382323 ,  4.3567934 ,  2.0662374 ,  4.1724496 ,  9.48851   ,\n",
       "       16.287956  ,  6.195707  ,  2.3606565 , 11.566339  , 13.658864  ,\n",
       "        9.880757  , 11.507375  , 10.583131  ,  7.5609345 ,  1.6071465 ,\n",
       "        9.036112  ,  1.5086616 ,  3.748207  ,  4.2615147 , 11.935782  ,\n",
       "        4.1128035 ,  0.83159095, 12.750354  , 18.391438  ,  3.9217172 ,\n",
       "        7.9154043 ,  6.137349  ,  5.678056  , 17.364166  ,  3.0512373 ,\n",
       "        6.274015  , 20.679419  ,  4.495379  , 14.612981  , 17.417526  ,\n",
       "        4.1593685 ,  7.485732  , 14.482325  ,  0.45714647,  1.4413131 ,\n",
       "        5.4458075 ,  3.4082577 ,  6.1348233 , 10.790833  ,  0.6386616 ,\n",
       "        4.5670204 ,  1.3894949 , 14.749066  ,  8.999117  , 13.643537  ,\n",
       "        6.38601   , 24.602198  , 17.027979  , 12.688131  ,  4.9872475 ,\n",
       "        4.0856314 ,  1.6659092 ,  0.5755303 ,  1.7656313 ,  5.5999494 ,\n",
       "        5.241363  ,  5.9827776 ,  2.3927524 , 12.155732  ,  6.2022223 ,\n",
       "        7.9329047 ,  3.525303  ,  5.64601   ,  4.1817927 ,  5.8839903 ,\n",
       "        7.2206063 ,  3.9626517 , 15.497324  ,  9.463485  ,  2.8450253 ,\n",
       "        0.19522728,  2.8943431 ,  3.5896213 ,  6.607399  ], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-05-01 1998-05-06 ... 2018-07-30"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcpts = (pcp.mean(axis=(1,2))\n",
    "            .sel(time=~((pcp.time.dt.month == 2) & (pcp.time.dt.day == 29)))\n",
    "            )\n",
    "pcp_ptd = pcpts.coarsen(time=5,side='left', coord_func={\"time\": \"min\"}).mean()  # 計算pentad mean\n",
    "pcp_ptd_mjj = pcp_ptd.sel(time=(pcp_ptd.time.dt.month.isin([5,6,7])))\n",
    "pcp_ptd_mjj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;uwnd&#x27; (time: 399)&gt;\n",
       "dask.array&lt;getitem, shape=(399,), dtype=float32, chunksize=(19,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-05-01 1998-05-06 ... 2018-07-30\n",
       "    level    float32 850.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'uwnd'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 399</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-559a2d88-e148-48d6-af1d-4bac8a0829d2' class='xr-array-in' type='checkbox' checked><label for='section-559a2d88-e148-48d6-af1d-4bac8a0829d2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(19,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <td> 1.56 kiB </td>\n",
       "                        <td> 76 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (399,) </td>\n",
       "                        <td> (19,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
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       "                        <td> 345 Tasks </td>\n",
       "                        <td> 21 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-0bf1db1f-982a-428e-9f3a-7fed8e8d28cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0bf1db1f-982a-428e-9f3a-7fed8e8d28cd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1998-05-01 ... 2018-07-30</div><input id='attrs-9e753245-31e1-40da-a50b-0689f97e52bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e753245-31e1-40da-a50b-0689f97e52bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-924a70c8-9d00-4515-854c-1a0ae9626801' class='xr-var-data-in' type='checkbox'><label for='data-924a70c8-9d00-4515-854c-1a0ae9626801' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1998-05-01T00:00:00.000000000&#x27;, &#x27;1998-05-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-05-11T00:00:00.000000000&#x27;, ..., &#x27;2018-07-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-25T00:00:00.000000000&#x27;, &#x27;2018-07-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>level</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>850.0</div><input id='attrs-d3cf9c9e-2778-4fd3-b4d4-f141f9e6b5f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3cf9c9e-2778-4fd3-b4d4-f141f9e6b5f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d8c0653-a6a2-4bba-bf3b-f6256c89f698' class='xr-var-data-in' type='checkbox'><label for='data-5d8c0653-a6a2-4bba-bf3b-f6256c89f698' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>Level</dd><dt><span>units :</span></dt><dd>millibar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>actual_range :</span></dt><dd>[1000.   10.]</dd><dt><span>GRIB_id :</span></dt><dd>100</dd><dt><span>GRIB_name :</span></dt><dd>hPa</dd><dt><span>coordinate_defines :</span></dt><dd>point</dd></dl></div><div class='xr-var-data'><pre>array(850., dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2b83e6f-374e-4c0c-b12e-841beb8e5ae7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a2b83e6f-374e-4c0c-b12e-841beb8e5ae7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'uwnd' (time: 399)>\n",
       "dask.array<getitem, shape=(399,), dtype=float32, chunksize=(19,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1998-05-01 1998-05-06 ... 2018-07-30\n",
       "    level    float32 850.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ushear =  ( u.sel(lat=slice(30,28)).mean(axis=(1,2)) -\n",
    "            u.sel(lat=slice(17,15)).mean(axis=(1,2)) )\n",
    "ushear_ts = ushear.sel(time=~((ushear.time.dt.month == 2) & (ushear.time.dt.day == 29)))\n",
    "\n",
    "us_ptd = ushear_ts.coarsen(time=5,side='left', coord_func={\"time\": \"min\"}).mean()  # 計算pentad mean\n",
    "us_ptd_mjj = us_ptd.sel(time=(us_ptd.time.dt.month.isin([5,6,7])))\n",
    "us_ptd_mjj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3:** 轉換成DataFrame的Long Form形式，並送入`seaborn`繪圖函數繪圖。繪製散布圖以及對應的迴歸線，使用[`seaborn.regplot`](https://seaborn.pydata.org/generated/seaborn.regplot.html#seaborn.regplot)這個函數。記得將dataset轉換成Long Form的DataFrame時，要加上`reset_index()`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>time</th>\n",
       "      <th>pcp</th>\n",
       "      <th>level</th>\n",
       "      <th>ushear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998-05-01</td>\n",
       "      <td>6.111945</td>\n",
       "      <td>850.0</td>\n",
       "      <td>10.862909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998-05-06</td>\n",
       "      <td>7.571944</td>\n",
       "      <td>850.0</td>\n",
       "      <td>11.484545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998-05-11</td>\n",
       "      <td>9.226313</td>\n",
       "      <td>850.0</td>\n",
       "      <td>12.718000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998-05-16</td>\n",
       "      <td>1.109394</td>\n",
       "      <td>850.0</td>\n",
       "      <td>7.670909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998-05-21</td>\n",
       "      <td>5.317652</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-9.089272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>2.845025</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-5.535454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>0.195227</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-14.066727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>2018-07-20</td>\n",
       "      <td>2.894343</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-11.340726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>2018-07-25</td>\n",
       "      <td>3.589621</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-6.049819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>2018-07-30</td>\n",
       "      <td>6.607399</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-6.609454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>399 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time       pcp  level     ushear\n",
       "0   1998-05-01  6.111945  850.0  10.862909\n",
       "1   1998-05-06  7.571944  850.0  11.484545\n",
       "2   1998-05-11  9.226313  850.0  12.718000\n",
       "3   1998-05-16  1.109394  850.0   7.670909\n",
       "4   1998-05-21  5.317652  850.0  -9.089272\n",
       "..         ...       ...    ...        ...\n",
       "394 2018-07-10  2.845025  850.0  -5.535454\n",
       "395 2018-07-15  0.195227  850.0 -14.066727\n",
       "396 2018-07-20  2.894343  850.0 -11.340726\n",
       "397 2018-07-25  3.589621  850.0  -6.049819\n",
       "398 2018-07-30  6.607399  850.0  -6.609454\n",
       "\n",
       "[399 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df = (xr.merge([pcp_ptd_mjj.rename('pcp'), us_ptd_mjj.rename('ushear')])\n",
    "                .to_dataframe()\n",
    "                .reset_index())\n",
    "scatter_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1.0, 1.0, '$R=$ 0.316, $p=$ 1.09e-10')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "def corr(x, y):\n",
    "    return stats.pearsonr(x, y)[0], stats.pearsonr(x, y)[1]\n",
    "\n",
    "# 計算相關係數和統計顯著性。\n",
    "r, p = corr(us_ptd_mjj.values, pcp_ptd_mjj.values)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "sns.set_theme()\n",
    "plot = sns.regplot(x=\"ushear\", y=\"pcp\",\n",
    "                  data=scatter_df,\n",
    "                  ci=95, ax=ax) # ci是信心水準\n",
    "ax.set_title(f'$R=$ {r:5.3f}, $p=$ {p:8.2e}', loc='right' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wide Form的Seaborn製圖\n",
    "\n",
    "**Example 4:** 繪製台灣－北南海 (18˚-24˚N, 116˚-126˚E) 區域平均 1998-2020 各年四至十一月逐候 (pentad) 累積降雨百分等級 (PR) 的Heat Map。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 台灣\n",
    "lats = 18          \n",
    "latn = 24          \n",
    "lon1 = 116         \n",
    "lon2 = 126 \n",
    "\n",
    "pcp = pcpds.sel(time=slice('1998-01-01','2020-12-31'),\n",
    "                  lat=slice(lats,latn),\n",
    "                  lon=slice(lon1,lon2)).cmorph\n",
    "\n",
    "pcp_ptd_ts = (pcp.mean(axis=(1,2))\n",
    "                 .sel(time=~((pcp.time.dt.month == 2) & (pcp.time.dt.day == 29)))\n",
    "                 .coarsen(time=5,side='left', coord_func={\"time\": \"min\"})\n",
    "                 .sum())\n",
    "pcp_season = pcp_ptd_ts.sel(time=(pcp_ptd_ts.time.dt.month.isin([4,5,6,7,8,9,10,11])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;precip&#x27; (year: 23, pentad: 49)&gt;\n",
       "array([[2.40461402e+01, 7.98580302e-01, 7.22271517e+01, ...,\n",
       "        6.67258208e+01, 8.33185448e+01, 3.62910382e+01],\n",
       "       [3.99290151e+01, 1.87222715e+01, 6.03371783e+01, ...,\n",
       "        2.72404614e+01, 1.30434783e+01, 7.00976043e+01],\n",
       "       [4.31233363e+01, 3.47826087e+01, 4.62289264e+01, ...,\n",
       "        6.34427684e+01, 4.42768412e+01, 6.29991127e+00],\n",
       "       ...,\n",
       "       [8.87311446e-02, 3.44276841e+01, 4.09937888e+01, ...,\n",
       "        1.56166815e+01, 5.49245785e+01, 3.02573203e+01],\n",
       "       [4.30346051e+01, 2.66193434e-01, 5.42147294e+01, ...,\n",
       "        9.13930790e+01, 3.16770186e+01, 1.20674357e+01],\n",
       "       [4.27684117e+01, 5.04880213e+01, 2.71517303e+01, ...,\n",
       "        1.48181012e+01, 1.27772848e+01, 4.57852706e+01]])\n",
       "Coordinates:\n",
       "  * year     (year) int64 1998 1999 2000 2001 2002 ... 2016 2017 2018 2019 2020\n",
       "  * pentad   (pentad) int64 19 20 21 22 23 24 25 26 ... 60 61 62 63 64 65 66 67</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'precip'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 23</li><li><span class='xr-has-index'>pentad</span>: 49</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-13148f02-3e30-4675-aeea-a5d595d2a55e' class='xr-array-in' type='checkbox' checked><label for='section-13148f02-3e30-4675-aeea-a5d595d2a55e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>24.05 0.7986 72.23 77.11 32.48 68.41 ... 76.13 77.82 14.82 12.78 45.79</span></div><div class='xr-array-data'><pre>array([[2.40461402e+01, 7.98580302e-01, 7.22271517e+01, ...,\n",
       "        6.67258208e+01, 8.33185448e+01, 3.62910382e+01],\n",
       "       [3.99290151e+01, 1.87222715e+01, 6.03371783e+01, ...,\n",
       "        2.72404614e+01, 1.30434783e+01, 7.00976043e+01],\n",
       "       [4.31233363e+01, 3.47826087e+01, 4.62289264e+01, ...,\n",
       "        6.34427684e+01, 4.42768412e+01, 6.29991127e+00],\n",
       "       ...,\n",
       "       [8.87311446e-02, 3.44276841e+01, 4.09937888e+01, ...,\n",
       "        1.56166815e+01, 5.49245785e+01, 3.02573203e+01],\n",
       "       [4.30346051e+01, 2.66193434e-01, 5.42147294e+01, ...,\n",
       "        9.13930790e+01, 3.16770186e+01, 1.20674357e+01],\n",
       "       [4.27684117e+01, 5.04880213e+01, 2.71517303e+01, ...,\n",
       "        1.48181012e+01, 1.27772848e+01, 4.57852706e+01]])</pre></div></div></li><li class='xr-section-item'><input id='section-fb36716f-eefe-4168-b08e-5c8883cc34da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb36716f-eefe-4168-b08e-5c8883cc34da' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1998 1999 2000 ... 2018 2019 2020</div><input id='attrs-ae8c3610-5ffa-4803-bd09-17746b71176b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae8c3610-5ffa-4803-bd09-17746b71176b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29d09dcc-133d-407a-a423-4258736beaa1' class='xr-var-data-in' type='checkbox'><label for='data-29d09dcc-133d-407a-a423-4258736beaa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pentad</span></div><div class='xr-var-dims'>(pentad)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>19 20 21 22 23 ... 63 64 65 66 67</div><input id='attrs-3c047bad-2f24-439a-9f33-87250e78389d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c047bad-2f24-439a-9f33-87250e78389d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeb816c4-0d1b-48be-a118-1a3ee2c02fd5' class='xr-var-data-in' type='checkbox'><label for='data-eeb816c4-0d1b-48be-a118-1a3ee2c02fd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02973168-a5f8-4566-80e4-4b3c04e5136e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02973168-a5f8-4566-80e4-4b3c04e5136e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'precip' (year: 23, pentad: 49)>\n",
       "array([[2.40461402e+01, 7.98580302e-01, 7.22271517e+01, ...,\n",
       "        6.67258208e+01, 8.33185448e+01, 3.62910382e+01],\n",
       "       [3.99290151e+01, 1.87222715e+01, 6.03371783e+01, ...,\n",
       "        2.72404614e+01, 1.30434783e+01, 7.00976043e+01],\n",
       "       [4.31233363e+01, 3.47826087e+01, 4.62289264e+01, ...,\n",
       "        6.34427684e+01, 4.42768412e+01, 6.29991127e+00],\n",
       "       ...,\n",
       "       [8.87311446e-02, 3.44276841e+01, 4.09937888e+01, ...,\n",
       "        1.56166815e+01, 5.49245785e+01, 3.02573203e+01],\n",
       "       [4.30346051e+01, 2.66193434e-01, 5.42147294e+01, ...,\n",
       "        9.13930790e+01, 3.16770186e+01, 1.20674357e+01],\n",
       "       [4.27684117e+01, 5.04880213e+01, 2.71517303e+01, ...,\n",
       "        1.48181012e+01, 1.27772848e+01, 4.57852706e+01]])\n",
       "Coordinates:\n",
       "  * year     (year) int64 1998 1999 2000 2001 2002 ... 2016 2017 2018 2019 2020\n",
       "  * pentad   (pentad) int64 19 20 21 22 23 24 25 26 ... 60 61 62 63 64 65 66 67"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立降雨的氣候基期\n",
    "pcp_rank = pcp_season.rank(dim='time',pct=True) * 100.  # 利用DataArray.rank計算排名，pct=True可將排名百分化\n",
    "pcp_rank_da = xr.DataArray(data=pcp_rank.values.reshape(23,49),  # reshape將矩陣重塑成(year, pentad)的形狀\n",
    "                        dims=[\"year\", \"pentad\"],\n",
    "                        coords=dict(\n",
    "                                   year = range(1998,2021,1),\n",
    "                                   pentad = range(19,68,1),\n",
    "                                   ),\n",
    "                        name='precip')\n",
    "pcp_rank_da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>pentad</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>...</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>24.046140</td>\n",
       "      <td>0.798580</td>\n",
       "      <td>72.227152</td>\n",
       "      <td>77.107365</td>\n",
       "      <td>32.475599</td>\n",
       "      <td>68.411713</td>\n",
       "      <td>51.197870</td>\n",
       "      <td>26.086957</td>\n",
       "      <td>45.519077</td>\n",
       "      <td>86.867791</td>\n",
       "      <td>...</td>\n",
       "      <td>94.942325</td>\n",
       "      <td>43.300799</td>\n",
       "      <td>98.225377</td>\n",
       "      <td>15.527950</td>\n",
       "      <td>66.104703</td>\n",
       "      <td>52.440106</td>\n",
       "      <td>20.585626</td>\n",
       "      <td>66.725821</td>\n",
       "      <td>83.318545</td>\n",
       "      <td>36.291038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>39.929015</td>\n",
       "      <td>18.722272</td>\n",
       "      <td>60.337178</td>\n",
       "      <td>5.501331</td>\n",
       "      <td>7.897072</td>\n",
       "      <td>81.366460</td>\n",
       "      <td>70.275067</td>\n",
       "      <td>76.220053</td>\n",
       "      <td>55.723159</td>\n",
       "      <td>49.778172</td>\n",
       "      <td>...</td>\n",
       "      <td>85.891748</td>\n",
       "      <td>64.951198</td>\n",
       "      <td>10.559006</td>\n",
       "      <td>24.312334</td>\n",
       "      <td>28.748891</td>\n",
       "      <td>13.487134</td>\n",
       "      <td>12.156167</td>\n",
       "      <td>27.240461</td>\n",
       "      <td>13.043478</td>\n",
       "      <td>70.097604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>43.123336</td>\n",
       "      <td>34.782609</td>\n",
       "      <td>46.228926</td>\n",
       "      <td>26.974268</td>\n",
       "      <td>34.605146</td>\n",
       "      <td>58.207631</td>\n",
       "      <td>39.219166</td>\n",
       "      <td>4.081633</td>\n",
       "      <td>70.541260</td>\n",
       "      <td>84.117125</td>\n",
       "      <td>...</td>\n",
       "      <td>69.565217</td>\n",
       "      <td>48.890861</td>\n",
       "      <td>25.554570</td>\n",
       "      <td>96.628217</td>\n",
       "      <td>88.553682</td>\n",
       "      <td>65.838509</td>\n",
       "      <td>65.306122</td>\n",
       "      <td>63.442768</td>\n",
       "      <td>44.276841</td>\n",
       "      <td>6.299911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>39.751553</td>\n",
       "      <td>37.267081</td>\n",
       "      <td>37.799468</td>\n",
       "      <td>40.195209</td>\n",
       "      <td>61.579414</td>\n",
       "      <td>22.448980</td>\n",
       "      <td>37.089618</td>\n",
       "      <td>58.385093</td>\n",
       "      <td>92.280390</td>\n",
       "      <td>77.639752</td>\n",
       "      <td>...</td>\n",
       "      <td>55.989352</td>\n",
       "      <td>5.944987</td>\n",
       "      <td>1.863354</td>\n",
       "      <td>26.619343</td>\n",
       "      <td>15.882875</td>\n",
       "      <td>57.409051</td>\n",
       "      <td>66.637090</td>\n",
       "      <td>6.654836</td>\n",
       "      <td>4.170364</td>\n",
       "      <td>2.040816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>4.259095</td>\n",
       "      <td>3.637977</td>\n",
       "      <td>21.916593</td>\n",
       "      <td>6.743567</td>\n",
       "      <td>3.992902</td>\n",
       "      <td>4.702751</td>\n",
       "      <td>0.177462</td>\n",
       "      <td>3.460515</td>\n",
       "      <td>33.895297</td>\n",
       "      <td>79.858030</td>\n",
       "      <td>...</td>\n",
       "      <td>11.002662</td>\n",
       "      <td>49.423248</td>\n",
       "      <td>43.212067</td>\n",
       "      <td>49.068323</td>\n",
       "      <td>34.072760</td>\n",
       "      <td>50.399290</td>\n",
       "      <td>17.125111</td>\n",
       "      <td>62.999113</td>\n",
       "      <td>26.796806</td>\n",
       "      <td>25.820763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>48.003549</td>\n",
       "      <td>56.255546</td>\n",
       "      <td>20.496894</td>\n",
       "      <td>31.322094</td>\n",
       "      <td>78.615794</td>\n",
       "      <td>7.808341</td>\n",
       "      <td>37.533274</td>\n",
       "      <td>6.122449</td>\n",
       "      <td>11.268855</td>\n",
       "      <td>47.914818</td>\n",
       "      <td>...</td>\n",
       "      <td>44.454303</td>\n",
       "      <td>11.712511</td>\n",
       "      <td>2.750665</td>\n",
       "      <td>61.490683</td>\n",
       "      <td>57.231588</td>\n",
       "      <td>27.861579</td>\n",
       "      <td>64.596273</td>\n",
       "      <td>50.842946</td>\n",
       "      <td>46.140195</td>\n",
       "      <td>8.340728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>44.099379</td>\n",
       "      <td>18.367347</td>\n",
       "      <td>21.118012</td>\n",
       "      <td>17.746229</td>\n",
       "      <td>5.678793</td>\n",
       "      <td>8.518190</td>\n",
       "      <td>9.671695</td>\n",
       "      <td>30.434783</td>\n",
       "      <td>19.698314</td>\n",
       "      <td>77.994676</td>\n",
       "      <td>...</td>\n",
       "      <td>15.705413</td>\n",
       "      <td>38.509317</td>\n",
       "      <td>62.910382</td>\n",
       "      <td>1.685892</td>\n",
       "      <td>3.194321</td>\n",
       "      <td>13.753327</td>\n",
       "      <td>19.343390</td>\n",
       "      <td>42.945874</td>\n",
       "      <td>56.965395</td>\n",
       "      <td>28.926353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>7.187223</td>\n",
       "      <td>1.419698</td>\n",
       "      <td>26.264419</td>\n",
       "      <td>8.074534</td>\n",
       "      <td>18.988465</td>\n",
       "      <td>11.801242</td>\n",
       "      <td>17.480035</td>\n",
       "      <td>86.956522</td>\n",
       "      <td>61.668146</td>\n",
       "      <td>27.595386</td>\n",
       "      <td>...</td>\n",
       "      <td>13.664596</td>\n",
       "      <td>9.405501</td>\n",
       "      <td>29.281278</td>\n",
       "      <td>56.166815</td>\n",
       "      <td>0.976043</td>\n",
       "      <td>42.502218</td>\n",
       "      <td>50.044366</td>\n",
       "      <td>66.015972</td>\n",
       "      <td>33.007986</td>\n",
       "      <td>50.931677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2.218279</td>\n",
       "      <td>15.350488</td>\n",
       "      <td>46.406389</td>\n",
       "      <td>34.960071</td>\n",
       "      <td>19.254658</td>\n",
       "      <td>49.334516</td>\n",
       "      <td>27.950311</td>\n",
       "      <td>3.105590</td>\n",
       "      <td>69.476486</td>\n",
       "      <td>81.543922</td>\n",
       "      <td>...</td>\n",
       "      <td>5.590062</td>\n",
       "      <td>11.446318</td>\n",
       "      <td>33.984028</td>\n",
       "      <td>87.133984</td>\n",
       "      <td>72.848270</td>\n",
       "      <td>29.547471</td>\n",
       "      <td>35.492458</td>\n",
       "      <td>54.303461</td>\n",
       "      <td>21.827862</td>\n",
       "      <td>33.096717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>51.020408</td>\n",
       "      <td>33.185448</td>\n",
       "      <td>5.767524</td>\n",
       "      <td>39.396628</td>\n",
       "      <td>27.772848</td>\n",
       "      <td>13.398403</td>\n",
       "      <td>32.386868</td>\n",
       "      <td>52.706300</td>\n",
       "      <td>30.967169</td>\n",
       "      <td>74.090506</td>\n",
       "      <td>...</td>\n",
       "      <td>30.612245</td>\n",
       "      <td>12.954747</td>\n",
       "      <td>20.053239</td>\n",
       "      <td>54.835847</td>\n",
       "      <td>91.481810</td>\n",
       "      <td>75.421473</td>\n",
       "      <td>7.364685</td>\n",
       "      <td>25.199645</td>\n",
       "      <td>87.488909</td>\n",
       "      <td>43.833185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>12.333629</td>\n",
       "      <td>3.904170</td>\n",
       "      <td>12.599823</td>\n",
       "      <td>48.269743</td>\n",
       "      <td>51.641526</td>\n",
       "      <td>43.389530</td>\n",
       "      <td>46.850044</td>\n",
       "      <td>30.079858</td>\n",
       "      <td>56.433008</td>\n",
       "      <td>71.960958</td>\n",
       "      <td>...</td>\n",
       "      <td>72.315883</td>\n",
       "      <td>7.630878</td>\n",
       "      <td>14.374445</td>\n",
       "      <td>10.825200</td>\n",
       "      <td>19.964508</td>\n",
       "      <td>93.611358</td>\n",
       "      <td>32.209406</td>\n",
       "      <td>54.037267</td>\n",
       "      <td>62.732919</td>\n",
       "      <td>47.204969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>30.700976</td>\n",
       "      <td>38.952972</td>\n",
       "      <td>50.754215</td>\n",
       "      <td>81.721384</td>\n",
       "      <td>90.683230</td>\n",
       "      <td>40.550133</td>\n",
       "      <td>12.688554</td>\n",
       "      <td>55.013310</td>\n",
       "      <td>0.532387</td>\n",
       "      <td>20.763088</td>\n",
       "      <td>...</td>\n",
       "      <td>59.982254</td>\n",
       "      <td>72.049689</td>\n",
       "      <td>70.629991</td>\n",
       "      <td>33.362910</td>\n",
       "      <td>60.425909</td>\n",
       "      <td>4.525288</td>\n",
       "      <td>48.624667</td>\n",
       "      <td>46.761313</td>\n",
       "      <td>22.626442</td>\n",
       "      <td>19.432121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>14.640639</td>\n",
       "      <td>35.314996</td>\n",
       "      <td>32.564330</td>\n",
       "      <td>24.134871</td>\n",
       "      <td>33.629104</td>\n",
       "      <td>76.841171</td>\n",
       "      <td>64.063886</td>\n",
       "      <td>21.472937</td>\n",
       "      <td>24.755989</td>\n",
       "      <td>9.849157</td>\n",
       "      <td>...</td>\n",
       "      <td>67.701863</td>\n",
       "      <td>96.716948</td>\n",
       "      <td>63.975155</td>\n",
       "      <td>23.336291</td>\n",
       "      <td>78.083407</td>\n",
       "      <td>72.759539</td>\n",
       "      <td>80.745342</td>\n",
       "      <td>36.645963</td>\n",
       "      <td>41.171251</td>\n",
       "      <td>10.204082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>10.647737</td>\n",
       "      <td>2.484472</td>\n",
       "      <td>1.153505</td>\n",
       "      <td>58.473824</td>\n",
       "      <td>18.101154</td>\n",
       "      <td>18.012422</td>\n",
       "      <td>10.026619</td>\n",
       "      <td>73.469388</td>\n",
       "      <td>26.530612</td>\n",
       "      <td>86.335404</td>\n",
       "      <td>...</td>\n",
       "      <td>87.577640</td>\n",
       "      <td>44.986690</td>\n",
       "      <td>29.458740</td>\n",
       "      <td>33.451642</td>\n",
       "      <td>67.435670</td>\n",
       "      <td>94.676131</td>\n",
       "      <td>80.035492</td>\n",
       "      <td>65.572316</td>\n",
       "      <td>50.221828</td>\n",
       "      <td>68.677906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>31.588287</td>\n",
       "      <td>36.557232</td>\n",
       "      <td>1.597161</td>\n",
       "      <td>56.699201</td>\n",
       "      <td>22.981366</td>\n",
       "      <td>64.507542</td>\n",
       "      <td>37.976930</td>\n",
       "      <td>19.875776</td>\n",
       "      <td>54.392192</td>\n",
       "      <td>97.249335</td>\n",
       "      <td>...</td>\n",
       "      <td>13.575865</td>\n",
       "      <td>3.726708</td>\n",
       "      <td>37.444543</td>\n",
       "      <td>59.804791</td>\n",
       "      <td>69.653949</td>\n",
       "      <td>12.244898</td>\n",
       "      <td>52.085182</td>\n",
       "      <td>59.094942</td>\n",
       "      <td>35.758651</td>\n",
       "      <td>39.041704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>59.538598</td>\n",
       "      <td>64.685004</td>\n",
       "      <td>31.410825</td>\n",
       "      <td>24.578527</td>\n",
       "      <td>29.724933</td>\n",
       "      <td>57.941437</td>\n",
       "      <td>75.332742</td>\n",
       "      <td>28.305235</td>\n",
       "      <td>66.193434</td>\n",
       "      <td>72.138421</td>\n",
       "      <td>...</td>\n",
       "      <td>39.840284</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>23.425022</td>\n",
       "      <td>76.929902</td>\n",
       "      <td>48.447205</td>\n",
       "      <td>35.048802</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>23.070098</td>\n",
       "      <td>41.259982</td>\n",
       "      <td>42.058563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>49.955634</td>\n",
       "      <td>24.489796</td>\n",
       "      <td>7.275954</td>\n",
       "      <td>0.709849</td>\n",
       "      <td>10.292813</td>\n",
       "      <td>30.346051</td>\n",
       "      <td>64.862467</td>\n",
       "      <td>73.824312</td>\n",
       "      <td>36.202307</td>\n",
       "      <td>37.000887</td>\n",
       "      <td>...</td>\n",
       "      <td>7.985803</td>\n",
       "      <td>35.403727</td>\n",
       "      <td>34.693878</td>\n",
       "      <td>4.436557</td>\n",
       "      <td>16.592724</td>\n",
       "      <td>9.937888</td>\n",
       "      <td>66.903283</td>\n",
       "      <td>51.286602</td>\n",
       "      <td>16.503993</td>\n",
       "      <td>41.881100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>25.643301</td>\n",
       "      <td>42.324756</td>\n",
       "      <td>39.574091</td>\n",
       "      <td>34.871340</td>\n",
       "      <td>41.703638</td>\n",
       "      <td>16.149068</td>\n",
       "      <td>10.115350</td>\n",
       "      <td>59.449867</td>\n",
       "      <td>38.065661</td>\n",
       "      <td>52.883762</td>\n",
       "      <td>...</td>\n",
       "      <td>52.173913</td>\n",
       "      <td>85.714286</td>\n",
       "      <td>27.417924</td>\n",
       "      <td>17.213842</td>\n",
       "      <td>54.569654</td>\n",
       "      <td>31.144632</td>\n",
       "      <td>20.408163</td>\n",
       "      <td>28.482697</td>\n",
       "      <td>41.614907</td>\n",
       "      <td>25.377107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.621118</td>\n",
       "      <td>40.638864</td>\n",
       "      <td>67.169476</td>\n",
       "      <td>9.228039</td>\n",
       "      <td>29.192547</td>\n",
       "      <td>37.355812</td>\n",
       "      <td>13.220941</td>\n",
       "      <td>20.230701</td>\n",
       "      <td>50.310559</td>\n",
       "      <td>58.651287</td>\n",
       "      <td>...</td>\n",
       "      <td>69.387755</td>\n",
       "      <td>80.390417</td>\n",
       "      <td>8.163265</td>\n",
       "      <td>46.938776</td>\n",
       "      <td>14.729370</td>\n",
       "      <td>25.022183</td>\n",
       "      <td>16.770186</td>\n",
       "      <td>45.430346</td>\n",
       "      <td>74.445430</td>\n",
       "      <td>81.810115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>19.165927</td>\n",
       "      <td>1.952085</td>\n",
       "      <td>47.116238</td>\n",
       "      <td>26.885537</td>\n",
       "      <td>59.361136</td>\n",
       "      <td>36.379769</td>\n",
       "      <td>10.913931</td>\n",
       "      <td>11.623780</td>\n",
       "      <td>50.133097</td>\n",
       "      <td>95.563443</td>\n",
       "      <td>...</td>\n",
       "      <td>90.417036</td>\n",
       "      <td>24.667258</td>\n",
       "      <td>11.978705</td>\n",
       "      <td>28.837622</td>\n",
       "      <td>86.069210</td>\n",
       "      <td>64.330080</td>\n",
       "      <td>47.648625</td>\n",
       "      <td>48.802130</td>\n",
       "      <td>46.583851</td>\n",
       "      <td>53.149956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.088731</td>\n",
       "      <td>34.427684</td>\n",
       "      <td>40.993789</td>\n",
       "      <td>18.456078</td>\n",
       "      <td>37.178350</td>\n",
       "      <td>6.921029</td>\n",
       "      <td>11.091393</td>\n",
       "      <td>65.394854</td>\n",
       "      <td>4.791482</td>\n",
       "      <td>1.064774</td>\n",
       "      <td>...</td>\n",
       "      <td>46.495120</td>\n",
       "      <td>28.393966</td>\n",
       "      <td>11.357587</td>\n",
       "      <td>91.215617</td>\n",
       "      <td>23.158829</td>\n",
       "      <td>1.774623</td>\n",
       "      <td>16.681455</td>\n",
       "      <td>15.616681</td>\n",
       "      <td>54.924579</td>\n",
       "      <td>30.257320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>43.034605</td>\n",
       "      <td>0.266193</td>\n",
       "      <td>54.214729</td>\n",
       "      <td>69.742680</td>\n",
       "      <td>8.606921</td>\n",
       "      <td>29.015084</td>\n",
       "      <td>78.172138</td>\n",
       "      <td>75.865129</td>\n",
       "      <td>47.293700</td>\n",
       "      <td>46.051464</td>\n",
       "      <td>...</td>\n",
       "      <td>24.223602</td>\n",
       "      <td>14.995563</td>\n",
       "      <td>19.520852</td>\n",
       "      <td>37.888199</td>\n",
       "      <td>88.819876</td>\n",
       "      <td>47.826087</td>\n",
       "      <td>38.243123</td>\n",
       "      <td>91.393079</td>\n",
       "      <td>31.677019</td>\n",
       "      <td>12.067436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>42.768412</td>\n",
       "      <td>50.488021</td>\n",
       "      <td>27.151730</td>\n",
       "      <td>1.330967</td>\n",
       "      <td>47.471162</td>\n",
       "      <td>29.103815</td>\n",
       "      <td>3.549246</td>\n",
       "      <td>4.880213</td>\n",
       "      <td>42.590949</td>\n",
       "      <td>89.263531</td>\n",
       "      <td>...</td>\n",
       "      <td>45.607808</td>\n",
       "      <td>84.472050</td>\n",
       "      <td>79.680568</td>\n",
       "      <td>53.238687</td>\n",
       "      <td>74.001775</td>\n",
       "      <td>76.131322</td>\n",
       "      <td>77.817214</td>\n",
       "      <td>14.818101</td>\n",
       "      <td>12.777285</td>\n",
       "      <td>45.785271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "pentad         19         20         21         22         23         24  \\\n",
       "year                                                                       \n",
       "1998    24.046140   0.798580  72.227152  77.107365  32.475599  68.411713   \n",
       "1999    39.929015  18.722272  60.337178   5.501331   7.897072  81.366460   \n",
       "2000    43.123336  34.782609  46.228926  26.974268  34.605146  58.207631   \n",
       "2001    39.751553  37.267081  37.799468  40.195209  61.579414  22.448980   \n",
       "2002     4.259095   3.637977  21.916593   6.743567   3.992902   4.702751   \n",
       "2003    48.003549  56.255546  20.496894  31.322094  78.615794   7.808341   \n",
       "2004    44.099379  18.367347  21.118012  17.746229   5.678793   8.518190   \n",
       "2005     7.187223   1.419698  26.264419   8.074534  18.988465  11.801242   \n",
       "2006     2.218279  15.350488  46.406389  34.960071  19.254658  49.334516   \n",
       "2007    51.020408  33.185448   5.767524  39.396628  27.772848  13.398403   \n",
       "2008    12.333629   3.904170  12.599823  48.269743  51.641526  43.389530   \n",
       "2009    30.700976  38.952972  50.754215  81.721384  90.683230  40.550133   \n",
       "2010    14.640639  35.314996  32.564330  24.134871  33.629104  76.841171   \n",
       "2011    10.647737   2.484472   1.153505  58.473824  18.101154  18.012422   \n",
       "2012    31.588287  36.557232   1.597161  56.699201  22.981366  64.507542   \n",
       "2013    59.538598  64.685004  31.410825  24.578527  29.724933  57.941437   \n",
       "2014    49.955634  24.489796   7.275954   0.709849  10.292813  30.346051   \n",
       "2015    25.643301  42.324756  39.574091  34.871340  41.703638  16.149068   \n",
       "2016     0.621118  40.638864  67.169476   9.228039  29.192547  37.355812   \n",
       "2017    19.165927   1.952085  47.116238  26.885537  59.361136  36.379769   \n",
       "2018     0.088731  34.427684  40.993789  18.456078  37.178350   6.921029   \n",
       "2019    43.034605   0.266193  54.214729  69.742680   8.606921  29.015084   \n",
       "2020    42.768412  50.488021  27.151730   1.330967  47.471162  29.103815   \n",
       "\n",
       "pentad         25         26         27         28  ...         58         59  \\\n",
       "year                                                ...                         \n",
       "1998    51.197870  26.086957  45.519077  86.867791  ...  94.942325  43.300799   \n",
       "1999    70.275067  76.220053  55.723159  49.778172  ...  85.891748  64.951198   \n",
       "2000    39.219166   4.081633  70.541260  84.117125  ...  69.565217  48.890861   \n",
       "2001    37.089618  58.385093  92.280390  77.639752  ...  55.989352   5.944987   \n",
       "2002     0.177462   3.460515  33.895297  79.858030  ...  11.002662  49.423248   \n",
       "2003    37.533274   6.122449  11.268855  47.914818  ...  44.454303  11.712511   \n",
       "2004     9.671695  30.434783  19.698314  77.994676  ...  15.705413  38.509317   \n",
       "2005    17.480035  86.956522  61.668146  27.595386  ...  13.664596   9.405501   \n",
       "2006    27.950311   3.105590  69.476486  81.543922  ...   5.590062  11.446318   \n",
       "2007    32.386868  52.706300  30.967169  74.090506  ...  30.612245  12.954747   \n",
       "2008    46.850044  30.079858  56.433008  71.960958  ...  72.315883   7.630878   \n",
       "2009    12.688554  55.013310   0.532387  20.763088  ...  59.982254  72.049689   \n",
       "2010    64.063886  21.472937  24.755989   9.849157  ...  67.701863  96.716948   \n",
       "2011    10.026619  73.469388  26.530612  86.335404  ...  87.577640  44.986690   \n",
       "2012    37.976930  19.875776  54.392192  97.249335  ...  13.575865   3.726708   \n",
       "2013    75.332742  28.305235  66.193434  72.138421  ...  39.840284  14.285714   \n",
       "2014    64.862467  73.824312  36.202307  37.000887  ...   7.985803  35.403727   \n",
       "2015    10.115350  59.449867  38.065661  52.883762  ...  52.173913  85.714286   \n",
       "2016    13.220941  20.230701  50.310559  58.651287  ...  69.387755  80.390417   \n",
       "2017    10.913931  11.623780  50.133097  95.563443  ...  90.417036  24.667258   \n",
       "2018    11.091393  65.394854   4.791482   1.064774  ...  46.495120  28.393966   \n",
       "2019    78.172138  75.865129  47.293700  46.051464  ...  24.223602  14.995563   \n",
       "2020     3.549246   4.880213  42.590949  89.263531  ...  45.607808  84.472050   \n",
       "\n",
       "pentad         60         61         62         63         64         65  \\\n",
       "year                                                                       \n",
       "1998    98.225377  15.527950  66.104703  52.440106  20.585626  66.725821   \n",
       "1999    10.559006  24.312334  28.748891  13.487134  12.156167  27.240461   \n",
       "2000    25.554570  96.628217  88.553682  65.838509  65.306122  63.442768   \n",
       "2001     1.863354  26.619343  15.882875  57.409051  66.637090   6.654836   \n",
       "2002    43.212067  49.068323  34.072760  50.399290  17.125111  62.999113   \n",
       "2003     2.750665  61.490683  57.231588  27.861579  64.596273  50.842946   \n",
       "2004    62.910382   1.685892   3.194321  13.753327  19.343390  42.945874   \n",
       "2005    29.281278  56.166815   0.976043  42.502218  50.044366  66.015972   \n",
       "2006    33.984028  87.133984  72.848270  29.547471  35.492458  54.303461   \n",
       "2007    20.053239  54.835847  91.481810  75.421473   7.364685  25.199645   \n",
       "2008    14.374445  10.825200  19.964508  93.611358  32.209406  54.037267   \n",
       "2009    70.629991  33.362910  60.425909   4.525288  48.624667  46.761313   \n",
       "2010    63.975155  23.336291  78.083407  72.759539  80.745342  36.645963   \n",
       "2011    29.458740  33.451642  67.435670  94.676131  80.035492  65.572316   \n",
       "2012    37.444543  59.804791  69.653949  12.244898  52.085182  59.094942   \n",
       "2013    23.425022  76.929902  48.447205  35.048802  42.857143  23.070098   \n",
       "2014    34.693878   4.436557  16.592724   9.937888  66.903283  51.286602   \n",
       "2015    27.417924  17.213842  54.569654  31.144632  20.408163  28.482697   \n",
       "2016     8.163265  46.938776  14.729370  25.022183  16.770186  45.430346   \n",
       "2017    11.978705  28.837622  86.069210  64.330080  47.648625  48.802130   \n",
       "2018    11.357587  91.215617  23.158829   1.774623  16.681455  15.616681   \n",
       "2019    19.520852  37.888199  88.819876  47.826087  38.243123  91.393079   \n",
       "2020    79.680568  53.238687  74.001775  76.131322  77.817214  14.818101   \n",
       "\n",
       "pentad         66         67  \n",
       "year                          \n",
       "1998    83.318545  36.291038  \n",
       "1999    13.043478  70.097604  \n",
       "2000    44.276841   6.299911  \n",
       "2001     4.170364   2.040816  \n",
       "2002    26.796806  25.820763  \n",
       "2003    46.140195   8.340728  \n",
       "2004    56.965395  28.926353  \n",
       "2005    33.007986  50.931677  \n",
       "2006    21.827862  33.096717  \n",
       "2007    87.488909  43.833185  \n",
       "2008    62.732919  47.204969  \n",
       "2009    22.626442  19.432121  \n",
       "2010    41.171251  10.204082  \n",
       "2011    50.221828  68.677906  \n",
       "2012    35.758651  39.041704  \n",
       "2013    41.259982  42.058563  \n",
       "2014    16.503993  41.881100  \n",
       "2015    41.614907  25.377107  \n",
       "2016    74.445430  81.810115  \n",
       "2017    46.583851  53.149956  \n",
       "2018    54.924579  30.257320  \n",
       "2019    31.677019  12.067436  \n",
       "2020    12.777285  45.785271  \n",
       "\n",
       "[23 rows x 49 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcp_rank_df = pcp_rank_da.to_pandas()\n",
    "pcp_rank_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上的DataFrame就是一個Wide Form的形式。\n",
    "\n",
    "Long Form的表格，索引標籤都只存在index裡；Wide Form的表格，則是由Column和Index共同組成資料的內容，並且以2維的形式呈現。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "sns.set_theme()\n",
    "ax = sns.heatmap(pcp_rank_df,\n",
    "                 cmap='jet_r',\n",
    "                 square=True,\n",
    "                 vmin=1,vmax=100,\n",
    "                 cbar_kws={\"shrink\": 0.55, 'extend':'neither'},\n",
    "                 xticklabels=2)\n",
    "plt.xlabel(\"Pentad\")\n",
    "plt.ylabel(\"Years\")\n",
    "ax.set_title(\"Taiwan-Northern SCS, April to November\",loc='left')\n",
    "plt.savefig(\"pcp_pr_heatmap_obs_chn.png\",orientation='portrait',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wide/Long form互相轉換\n",
    "\n",
    "利用`pandas.DataFrame.unstack`：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>pentad</th>\n",
       "      <th>year</th>\n",
       "      <th>PR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>1998</td>\n",
       "      <td>24.046140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>1999</td>\n",
       "      <td>39.929015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>2000</td>\n",
       "      <td>43.123336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>2001</td>\n",
       "      <td>39.751553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>2002</td>\n",
       "      <td>4.259095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>67</td>\n",
       "      <td>2016</td>\n",
       "      <td>81.810115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>67</td>\n",
       "      <td>2017</td>\n",
       "      <td>53.149956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>67</td>\n",
       "      <td>2018</td>\n",
       "      <td>30.257320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>67</td>\n",
       "      <td>2019</td>\n",
       "      <td>12.067436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>67</td>\n",
       "      <td>2020</td>\n",
       "      <td>45.785271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1127 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pentad  year         PR\n",
       "0         19  1998  24.046140\n",
       "1         19  1999  39.929015\n",
       "2         19  2000  43.123336\n",
       "3         19  2001  39.751553\n",
       "4         19  2002   4.259095\n",
       "...      ...   ...        ...\n",
       "1122      67  2016  81.810115\n",
       "1123      67  2017  53.149956\n",
       "1124      67  2018  30.257320\n",
       "1125      67  2019  12.067436\n",
       "1126      67  2020  45.785271\n",
       "\n",
       "[1127 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcp_rank_long = pcp_rank_df.unstack().reset_index(name='PR')\n",
    "pcp_rank_long"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8e905df1d4d920326545d879dea538d50859be77412bc9bf54949dad3bde9dd6"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}