Visualize array data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize array data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data = pd.read_csv('C:\\\\Users\\\\User\\\\Downloads\\\\iris.data') (แบบแรก)\n",
    "\n",
    "header = None>> บอกว่าไม่มีหัวตาราง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_PATH = 'C:\\\\Users\\\\User\\\\Downloads\\\\iris.data'\n",
    "iris_data = pd.read_csv(iris_PATH ,header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3               4\n",
       "0    5.1  3.5  1.4  0.2     Iris-setosa\n",
       "1    4.9  3.0  1.4  0.2     Iris-setosa\n",
       "2    4.7  3.2  1.3  0.2     Iris-setosa\n",
       "3    4.6  3.1  1.5  0.2     Iris-setosa\n",
       "4    5.0  3.6  1.4  0.2     Iris-setosa\n",
       "..   ...  ...  ...  ...             ...\n",
       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149  5.9  3.0  5.1  1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_np = iris_data.iloc[:,:-1].to_numpy()\n",
    "data_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_np_onlySeto = iris_data.iloc[:50,:-1].to_numpy()\n",
    "data_np_onlyversi = iris_data.iloc[50:100,:-1].to_numpy()\n",
    "data_np_onlyvirgi = iris_data.iloc[100:,:-1].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize array by heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as pp\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = [10,10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "['figure.figsize'] = [10,15]>> ขนาดกล่อง(ภาพ)\n",
    "\n",
    "สีเข้มค่าน้อย สี่สว่างค่ามาก"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.subplot(1,3,1)\n",
    "pp.imshow(data_np_onlySeto,interpolation = 'nearest')\n",
    "pp.subplot(1,3,2)\n",
    "pp.imshow(data_np_onlyversi,interpolation = 'nearest')\n",
    "pp.subplot(1,3,3)\n",
    "pp.imshow(data_np_onlyvirgi,interpolation = 'nearest')\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pp.subplot(1,3,2) plot เป็น array กี่ ถว กี่หลัก รูปนี้อยู่ที่เท่าไหร่(1,3,3) = 1 แถว 3 หลัก ตัวที่ 3 ต้องอยู่บรรทัดข้างบนที่เราต้องการชี้พิกัด"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = [15,5]\n",
    "pp.imshow(data_np_onlySeto,interpolation = 'nearest')\n",
    "pp.show()\n",
    "pp.imshow(data_np_onlyversi,interpolation = 'nearest')\n",
    "pp.show()\n",
    "pp.imshow(data_np_onlyvirgi,interpolation = 'nearest')\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18824e34a48>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.imshow(data_np,interpolation = 'nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ถ้าไม่ชอบสีนี้ เปลี่ยนสีได้โดยใช้คำสั่ง (เปิดดูใน maplotlib  coclormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Colormap red is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-4bc7b1c89fe8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_np_onlyversi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minterpolation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'nearest'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'red'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\matplotlib\\cm.py\u001b[0m in \u001b[0;36mget_cmap\u001b[1;34m(name, lut)\u001b[0m\n\u001b[0;32m    181\u001b[0m         raise ValueError(\n\u001b[0;32m    182\u001b[0m             \u001b[1;34m\"Colormap %s is not recognized. Possible values are: %s\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m             % (name, ', '.join(sorted(cmap_d))))\n\u001b[0m\u001b[0;32m    184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Colormap red is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r"
     ]
    }
   ],
   "source": [
    "pp.imshow(data_np_onlyversi,interpolation = 'nearest', cmap = pp.get_cmap('red'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x188241227c8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.imshow(data_np_onlyversi,interpolation = 'nearest', cmap = pp.get_cmap('twilight_shifted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ข้อมูลต่างชนิด มีคำสั่งสีต่างกัน ระวังนะ!!!!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}