hw_pivot&plotly.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2nd part of homework (task2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) Pivot a DataFrame based on column values:\n",
    "Take a DataFrame with columns representing categories and values, and pivot it to reshape the data, with the category values as columns and corresponding values in the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pa\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_data = {'product':['rain poncho', 'umbrella']*6,\n",
    "        'quality':['low','medium','high']*4,\n",
    "        'units in stock':[10,25,20,30,5,7]*2,\n",
    "        'price':np.arange(3, 15, 1)}"
   ]
  },
  {
   "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>product</th>\n",
       "      <th>quality</th>\n",
       "      <th>units in stock</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rain poncho</td>\n",
       "      <td>low</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>umbrella</td>\n",
       "      <td>medium</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rain poncho</td>\n",
       "      <td>high</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>umbrella</td>\n",
       "      <td>low</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rain poncho</td>\n",
       "      <td>medium</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>umbrella</td>\n",
       "      <td>high</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rain poncho</td>\n",
       "      <td>low</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>umbrella</td>\n",
       "      <td>medium</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rain poncho</td>\n",
       "      <td>high</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>umbrella</td>\n",
       "      <td>low</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rain poncho</td>\n",
       "      <td>medium</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>umbrella</td>\n",
       "      <td>high</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product quality  units in stock  price\n",
       "0   rain poncho     low              10      3\n",
       "1      umbrella  medium              25      4\n",
       "2   rain poncho    high              20      5\n",
       "3      umbrella     low              30      6\n",
       "4   rain poncho  medium               5      7\n",
       "5      umbrella    high               7      8\n",
       "6   rain poncho     low              10      9\n",
       "7      umbrella  medium              25     10\n",
       "8   rain poncho    high              20     11\n",
       "9      umbrella     low              30     12\n",
       "10  rain poncho  medium               5     13\n",
       "11     umbrella    high               7     14"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fake_data = pa.DataFrame(fake_data)\n",
    "df_fake_data.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_df1 = df_fake_data.pivot_table(index='product', columns='quality', values=['units in stock', 'price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">units in stock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</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>rain poncho</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>umbrella</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            price            units in stock           \n",
       "quality      high low medium           high low medium\n",
       "product                                               \n",
       "rain poncho     8   6     10             20  10      5\n",
       "umbrella       11   9      7              7  30     25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8) Aggregate and pivot data:\n",
    "Given a DataFrame with multiple columns, apply aggregation functions (e.g., sum, average) to the values and pivot the data based on specific columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_df_sum = df_fake_data.pivot_table(index='product', columns='quality',\n",
    "                                        values=['units in stock', 'price'],\n",
    "                                        aggfunc=['sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">units in stock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</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>rain poncho</th>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>umbrella</th>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sum                                     \n",
       "            price            units in stock           \n",
       "quality      high low medium           high low medium\n",
       "product                                               \n",
       "rain poncho    16  12     20             40  20     10\n",
       "umbrella       22  18     14             14  60     50"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_df_mean = df_fake_data.pivot_table(index='product', columns='quality',\n",
    "                                        values=['units in stock', 'price'],aggfunc=['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">units in stock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</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>rain poncho</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>umbrella</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean                                     \n",
       "            price            units in stock           \n",
       "quality      high low medium           high low medium\n",
       "product                                               \n",
       "rain poncho     8   6     10             20  10      5\n",
       "umbrella       11   9      7              7  30     25"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot1 = df_fake_data.pivot_table(index='product', columns='quality',\n",
    "                                  values=['units in stock'],aggfunc=['sum'])\n",
    "pivot2 = df_fake_data.pivot_table(index='product', columns='quality',\n",
    "                                  values=['price'],aggfunc=['mean'])\n",
    "merged_df=pivot2.merge(pivot1, on=['product']).pivot_table(index=['Name', 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">units in stock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                       sum           \n",
       "        price            units in stock           \n",
       "quality  high low medium           high low medium\n",
       "0          11   9      7             14  60     50\n",
       "Name        8   6     10             40  20     10"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9) Pivot with multi-index columns:\n",
    "Perform a pivot operation on a DataFrame with multi-index columns, reshaping the data based on specific levels of the column index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pa.MultiIndex.from_tuples([('Lithuania', 2000), ('Lithuania', 2020),\n",
    "         ('Latvia', 2000), ('Latvia', 2020),\n",
    "         ('Estonia', 2000), ('Estonia', 2020)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pa.Series([3.5, 2.8,\n",
    "                2.4, 1.9,\n",
    "                1.4, 1.33],index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lithuania</th>\n",
       "      <th>2000</th>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Latvia</th>\n",
       "      <th>2000</th>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Estonia</th>\n",
       "      <th>2000</th>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population\n",
       "Lithuania 2000        3.50\n",
       "          2020        2.80\n",
       "Latvia    2000        2.40\n",
       "          2020        1.90\n",
       "Estonia   2000        1.40\n",
       "          2020        1.33"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa.DataFrame(data).set_axis(['population'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2000</th>\n",
       "      <th>2020</th>\n",
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       "  <tbody>\n",
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       "      <th>Estonia</th>\n",
       "      <td>1.4</td>\n",
       "      <td>1.33</td>\n",
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       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.90</td>\n",
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       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>3.5</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "           2000  2020\n",
       "Estonia     1.4  1.33\n",
       "Latvia      2.4  1.90\n",
       "Lithuania   3.5  2.80"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "data.unstack()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10) Take any data (your choice) and visualize the data using Ploty library. You can select any plot type,\n",
    "but you need to comment (write a comment in code) why the selected type of plot represents the data best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "el_data = pa.read_excel('nordpool_daily.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=31, step=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>LT</th>\n",
       "      <th>LV</th>\n",
       "      <th>EE</th>\n",
       "      <th>FI</th>\n",
       "      <th>SE4</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-02</th>\n",
       "      <td>80.05</td>\n",
       "      <td>80.05</td>\n",
       "      <td>79.53</td>\n",
       "      <td>23.08</td>\n",
       "      <td>80.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-03</th>\n",
       "      <td>56.06</td>\n",
       "      <td>56.06</td>\n",
       "      <td>56.06</td>\n",
       "      <td>16.54</td>\n",
       "      <td>56.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-04</th>\n",
       "      <td>52.94</td>\n",
       "      <td>52.94</td>\n",
       "      <td>52.94</td>\n",
       "      <td>9.16</td>\n",
       "      <td>52.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-05</th>\n",
       "      <td>92.37</td>\n",
       "      <td>92.37</td>\n",
       "      <td>92.37</td>\n",
       "      <td>4.04</td>\n",
       "      <td>89.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-06</th>\n",
       "      <td>98.47</td>\n",
       "      <td>98.47</td>\n",
       "      <td>98.47</td>\n",
       "      <td>17.42</td>\n",
       "      <td>96.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "               LT     LV     EE     FI    SE4\n",
       "Date                                         \n",
       "2023-06-02  80.05  80.05  79.53  23.08  80.05\n",
       "2023-06-03  56.06  56.06  56.06  16.54  56.06\n",
       "2023-06-04  52.94  52.94  52.94   9.16  52.93\n",
       "2023-06-05  92.37  92.37  92.37   4.04  89.94\n",
       "2023-06-06  98.47  98.47  98.47  17.42  96.45"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el_data = el_data.sort_values(by='Date', ascending=True)\n",
    "el_data = el_data.set_index('Date')\n",
    "el_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.Figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data  = px.line(el_data,\n",
    "                x = el_data.index,\n",
    "                y = el_data.LT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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          "text": "LT"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig  = go.Figure(data=data)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(el_data, x=el_data.index, y=['LT', 'LV', 'EE', 'FI', 'SE4'],\n",
    "              labels={'variable': 'Region'},\n",
    "              markers=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Line plots with markers offer a simple and straightforward visual representation, making them easy to interpret. The absence of clutter or complex elements allows viewers to focus on the main message of the data - the price of electricity over time.\n",
    "\n",
    "* Line plots also allow for the simultaneous representation of multiple series or lines, enabling viewers to compare electricity prices across different regions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.update_layout(title='Nord Pool daily day-ahead prices',\n",
    "                yaxis_title='Price, EUR/MWh',\n",
    "                xaxis=dict(type='date',tickformat='%Y-%m-%d'),\n",
    "                xaxis_title='Date',\n",
    "                legend_title='Region')\n",
    "\n",
    "hover_templates = {\n",
    "    'LT': '<b><i>Region</i>: LT<br><i>Date</i>: %{x}<br><i>Price</i>: %{y}</b>',\n",
    "    'LV': '<b><i>Region</i>: LV<br><i>Date</i>: %{x}<br><i>Price</i>: %{y}</b>',\n",
    "    'EE': '<b><i>Region</i>: EE<br><i>Date</i>: %{x}<br><i>Price</i>: %{y}</b>',\n",
    "    'FI': '<b><i>Region</i>: FI<br><i>Date</i>: %{x}<br><i>Price</i>: %{y}</b>',\n",
    "    'SE4': '<b><i>Region</i>: SE4<br><i>Date</i>: %{x}<br><i>Price</i>: %{y}</b>'\n",
    "}\n",
    "\n",
    "for trace_name in hover_templates:\n",
    "    fig.update_traces(hovertemplate=hover_templates[trace_name], selector=dict(name=trace_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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