kitaichi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>white(1.32)</th>\n      <th>purple(1.39)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>280</th>\n      <td>369.6</td>\n      <td>389.20</td>\n    </tr>\n    <tr>\n      <th>285</th>\n      <td>376.2</td>\n      <td>396.15</td>\n    </tr>\n    <tr>\n      <th>290</th>\n      <td>382.8</td>\n      <td>403.10</td>\n    </tr>\n    <tr>\n      <th>295</th>\n      <td>389.4</td>\n      <td>410.05</td>\n    </tr>\n    <tr>\n      <th>300</th>\n      <td>396.0</td>\n      <td>417.00</td>\n    </tr>\n    <tr>\n      <th>305</th>\n      <td>402.6</td>\n      <td>423.95</td>\n    </tr>\n    <tr>\n      <th>310</th>\n      <td>409.2</td>\n      <td>430.90</td>\n    </tr>\n    <tr>\n      <th>315</th>\n      <td>415.8</td>\n      <td>437.85</td>\n    </tr>\n    <tr>\n      <th>320</th>\n      <td>422.4</td>\n      <td>444.80</td>\n    </tr>\n    <tr>\n      <th>325</th>\n      <td>429.0</td>\n      <td>451.75</td>\n    </tr>\n    <tr>\n      <th>330</th>\n      <td>435.6</td>\n      <td>458.70</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>442.2</td>\n      <td>465.65</td>\n    </tr>\n    <tr>\n      <th>340</th>\n      <td>448.8</td>\n      <td>472.60</td>\n    </tr>\n    <tr>\n      <th>345</th>\n      <td>455.4</td>\n      <td>479.55</td>\n    </tr>\n    <tr>\n      <th>350</th>\n      <td>462.0</td>\n      <td>486.50</td>\n    </tr>\n    <tr>\n      <th>355</th>\n      <td>468.6</td>\n      <td>493.45</td>\n    </tr>\n    <tr>\n      <th>360</th>\n      <td>475.2</td>\n      <td>500.40</td>\n    </tr>\n    <tr>\n      <th>365</th>\n      <td>481.8</td>\n      <td>507.35</td>\n    </tr>\n    <tr>\n      <th>370</th>\n      <td>488.4</td>\n      <td>514.30</td>\n    </tr>\n    <tr>\n      <th>375</th>\n      <td>495.0</td>\n      <td>521.25</td>\n    </tr>\n    <tr>\n      <th>380</th>\n      <td>501.6</td>\n      <td>528.20</td>\n    </tr>\n    <tr>\n      <th>385</th>\n      <td>508.2</td>\n      <td>535.15</td>\n    </tr>\n    <tr>\n      <th>390</th>\n      <td>514.8</td>\n      <td>542.10</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>521.4</td>\n      <td>549.05</td>\n    </tr>\n    <tr>\n      <th>400</th>\n      <td>528.0</td>\n      <td>556.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "     white(1.32)  purple(1.39)\n280        369.6        389.20\n285        376.2        396.15\n290        382.8        403.10\n295        389.4        410.05\n300        396.0        417.00\n305        402.6        423.95\n310        409.2        430.90\n315        415.8        437.85\n320        422.4        444.80\n325        429.0        451.75\n330        435.6        458.70\n335        442.2        465.65\n340        448.8        472.60\n345        455.4        479.55\n350        462.0        486.50\n355        468.6        493.45\n360        475.2        500.40\n365        481.8        507.35\n370        488.4        514.30\n375        495.0        521.25\n380        501.6        528.20\n385        508.2        535.15\n390        514.8        542.10\n395        521.4        549.05\n400        528.0        556.00"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "index = list(range(280,401,5))\n",
    "kireaji_white = list(map(lambda x: x * 1.32, index))\n",
    "kireaji_purple= list(map(lambda x: x * 1.39, index))\n",
    "\n",
    "df = pd.DataFrame([kireaji_white, kireaji_purple], columns=index, index=[\"white(1.32)\", \"purple(1.39)\"]).T\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df.plot(grid=True,figsize=[10,8],xticks=index)\n",
    "plt.show()"
   ]
  }
 ],
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