matplolib-plot-tutorial-02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9d5681c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f8f4116",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('s3://jupyter-plotting-01-123123123123/chocolatebars.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4949dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>snickers</th>\n",
       "      <th>mars</th>\n",
       "      <th>bounty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>110</td>\n",
       "      <td>100</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>113</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>114</td>\n",
       "      <td>107</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>116</td>\n",
       "      <td>118</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>120</td>\n",
       "      <td>122</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>120</td>\n",
       "      <td>124</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>123</td>\n",
       "      <td>127</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022</td>\n",
       "      <td>135</td>\n",
       "      <td>140</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  snickers  mars  bounty\n",
       "0  2015       110   100     106\n",
       "1  2016       113   105     108\n",
       "2  2017       114   107     110\n",
       "3  2018       116   118     113\n",
       "4  2019       120   122     117\n",
       "5  2020       120   124     118\n",
       "6  2021       123   127     121\n",
       "7  2022       135   140     128"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "445181ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'snickers', 'mars', 'bounty'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a6037595",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=8, step=1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2252196d",
   "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>year</th>\n",
       "      <th>snickers</th>\n",
       "      <th>mars</th>\n",
       "      <th>bounty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>110</td>\n",
       "      <td>100</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>116</td>\n",
       "      <td>118</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  snickers  mars  bounty\n",
       "0  2015       110   100     106\n",
       "3  2018       116   118     113"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[0,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1215e064",
   "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",
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       "    }\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>year</th>\n",
       "      <th>snickers</th>\n",
       "      <th>mars</th>\n",
       "      <th>bounty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>110</td>\n",
       "      <td>100</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>113</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>114</td>\n",
       "      <td>107</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  snickers  mars  bounty\n",
       "0  2015       110   100     106\n",
       "1  2016       113   105     108\n",
       "2  2017       114   107     110"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf1d44e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>year</th>\n",
       "      <th>snickers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  snickers\n",
       "0  2015       110\n",
       "3  2018       116\n",
       "7  2022       135"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[0,3,7], [\"year\",\"snickers\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3ba12277",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_snickers = df['snickers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "843e1f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015\n",
       "1    2016\n",
       "2    2017\n",
       "3    2018\n",
       "4    2019\n",
       "5    2020\n",
       "6    2021\n",
       "7    2022\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snickers_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f400c8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_year = df['year']\n",
    "bar_snickers = df['snickers']\n",
    "bar_mars = df['mars']\n",
    "bar_bounty = df['bounty']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9b21b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_offset = 0.25\n",
    "x_indexes = np.arange(len(bar_year))\n",
    "plt.bar(x_indexes - bar_offset, bar_snickers, width=bar_offset, label='Snickers')\n",
    "plt.bar(x_indexes, bar_mars, width=bar_offset,label='Mars')\n",
    "plt.bar(x_indexes + bar_offset, bar_bounty, width=bar_offset, label='Bounty')\n",
    "plt.title('Chocolate bar manufacturing cost ')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Manufacturing cost / cents')\n",
    "plt.xticks(ticks=x_indexes, labels=bar_year)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}