2軸のグラフ Python Matplotlib

01-two_scales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Plots with different scales — Matplotlib 3.0.2 documentation](https://matplotlib.org/gallery/subplots_axes_and_figures/two_scales.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2018-09-18</th>\n",
       "      <td>2890.739990</td>\n",
       "      <td>2911.169922</td>\n",
       "      <td>2890.429932</td>\n",
       "      <td>2904.310059</td>\n",
       "      <td>2904.310059</td>\n",
       "      <td>3074610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19</th>\n",
       "      <td>2906.600098</td>\n",
       "      <td>2912.360107</td>\n",
       "      <td>2903.820068</td>\n",
       "      <td>2907.949951</td>\n",
       "      <td>2907.949951</td>\n",
       "      <td>3280020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-20</th>\n",
       "      <td>2919.729980</td>\n",
       "      <td>2934.800049</td>\n",
       "      <td>2919.729980</td>\n",
       "      <td>2930.750000</td>\n",
       "      <td>2930.750000</td>\n",
       "      <td>3337730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-21</th>\n",
       "      <td>2936.760010</td>\n",
       "      <td>2940.909912</td>\n",
       "      <td>2927.110107</td>\n",
       "      <td>2929.669922</td>\n",
       "      <td>2929.669922</td>\n",
       "      <td>5607610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-24</th>\n",
       "      <td>2921.830078</td>\n",
       "      <td>2923.790039</td>\n",
       "      <td>2912.629883</td>\n",
       "      <td>2919.370117</td>\n",
       "      <td>2919.370117</td>\n",
       "      <td>2105481492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2018-09-18  2890.739990  2911.169922  2890.429932  2904.310059  2904.310059   \n",
       "2018-09-19  2906.600098  2912.360107  2903.820068  2907.949951  2907.949951   \n",
       "2018-09-20  2919.729980  2934.800049  2919.729980  2930.750000  2930.750000   \n",
       "2018-09-21  2936.760010  2940.909912  2927.110107  2929.669922  2929.669922   \n",
       "2018-09-24  2921.830078  2923.790039  2912.629883  2919.370117  2919.370117   \n",
       "\n",
       "                Volume  \n",
       "Date                    \n",
       "2018-09-18  3074610000  \n",
       "2018-09-19  3280020000  \n",
       "2018-09-20  3337730000  \n",
       "2018-09-21  5607610000  \n",
       "2018-09-24  2105481492  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('~/Documents/1/historical-prices/^GSPC.csv', header=0, index_col=0, parse_dates=[0])\n",
    "df.head()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2017 = df[df.index.year == 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb03f8bcd30>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(df2017.index, df2017['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 251 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.bar(df2017.index, df2017['Volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "c = ax1.bar(df2017.index, df2017['Volume'])\n",
    "l = ax2.plot(df2017.index, df2017['Close'])\n",
    "le = ax1.legend((c, *l), ('Volume', 'Close'), loc='upper left')"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

02-matplotlib-style.ipynb

03-matplotlib-rcparams.ipynb