18-Pandas_bar_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bar plot\n",
    "\n",
    "* 막대 그래프(-graph), 바 차트(bar chart), 바 그래프(bar graph)는 표현 값에 비례하여 높이와 길이를 지닌 직사각형 막대로 범주형 데이터를 표현하는 차트나 그래프를 말한다. 막대는 수직으로나 수평으로 그릴 수 있다. 수직 막대 그래프는 선 그래프 또는 라인 그래프(line graph)라고도 한다. (출처 : [막대 그래프 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%A7%89%EB%8C%80_%EA%B7%B8%EB%9E%98%ED%94%84))\n",
    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# 한글폰트 사용시 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1.073231\n",
       "2000-01-02    0.284057\n",
       "2000-01-03   -0.020093\n",
       "2000-01-04   -1.836448\n",
       "2000-01-05    0.652448\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000),\n",
    "               index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts.head()"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.652573</td>\n",
       "      <td>0.078781</td>\n",
       "      <td>2.343246</td>\n",
       "      <td>-0.504927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.064216</td>\n",
       "      <td>-0.526081</td>\n",
       "      <td>0.387795</td>\n",
       "      <td>0.275044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>0.910131</td>\n",
       "      <td>-0.131730</td>\n",
       "      <td>3.129914</td>\n",
       "      <td>-0.113642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.549389</td>\n",
       "      <td>-0.344091</td>\n",
       "      <td>-1.209264</td>\n",
       "      <td>-0.326678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.015889</td>\n",
       "      <td>-1.071603</td>\n",
       "      <td>0.880289</td>\n",
       "      <td>-1.160906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.656085</td>\n",
       "      <td>0.640268</td>\n",
       "      <td>-1.599397</td>\n",
       "      <td>0.387572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -0.652573  0.078781  2.343246 -0.504927\n",
       "2000-01-02  0.064216 -0.526081  0.387795  0.275044\n",
       "2000-01-03  0.910131 -0.131730  3.129914 -0.113642\n",
       "2000-01-04 -0.549389 -0.344091 -1.209264 -0.326678\n",
       "2000-01-05 -0.015889 -1.071603  0.880289 -1.160906\n",
       "2000-01-06  0.656085  0.640268 -1.599397  0.387572"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4),\n",
    "                  index=ts.index, columns=list('ABCD'))\n",
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>0.674940</td>\n",
       "      <td>-0.001155</td>\n",
       "      <td>0.049131</td>\n",
       "      <td>-0.786572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>-1.000401</td>\n",
       "      <td>0.333387</td>\n",
       "      <td>0.722390</td>\n",
       "      <td>0.099241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2002-09-25  0.674940 -0.001155  0.049131 -0.786572\n",
       "2002-09-26 -1.000401  0.333387  0.722390  0.099241"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.656085\n",
       "B    0.640268\n",
       "C   -1.599397\n",
       "D    0.387572\n",
       "Name: 2000-01-06 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11388b550>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113913cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[5].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1139eea90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD5CAYAAAAk7Y4VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1121cc780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[5].plot.bar()\n",
    "plt.axhline(0, color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.498048</td>\n",
       "      <td>0.008850</td>\n",
       "      <td>0.202747</td>\n",
       "      <td>0.858336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.552843</td>\n",
       "      <td>0.867046</td>\n",
       "      <td>0.311755</td>\n",
       "      <td>0.430236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.252529</td>\n",
       "      <td>0.079763</td>\n",
       "      <td>0.779409</td>\n",
       "      <td>0.978289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0  0.498048  0.008850  0.202747  0.858336\n",
       "1  0.552843  0.867046  0.311755  0.430236\n",
       "2  0.252529  0.079763  0.779409  0.978289"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])\n",
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1138e7860>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ip4HpwI9HIK8kYOoNaw5Y3trVpiDqSJGZh94h4kZgd2Z+rb78eeC/MvOu+vIyasW/ur68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\nSc2z2CWpMBa7JBXGYpekwljsklSY/wf8kGcHi1lEtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131fc6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113a50518>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a3b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113a50668>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TjT8ihiLiuYjYPPG1qVd1NiEihifmduQYr7d27qcb+xyY91Mi4s6IeCQitkfEZ6Y4ppVz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11375d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.barh(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}