{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 7 - Final\n",
    "\n",
    "This final exercise combines different parts from the previous exercises."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 Search Digitraffic's REST interface service (in JSON format) for active trains with the endpoint address `/live-trains` from Helsinki station with the following search parameters:\n",
    "* arrived_trains=10\n",
    "* departed_trains=10\n",
    "* include_nonstopping=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 Read the data into the Pandas dataframe without editing, so that the column headings are as follows:\n",
    "\n",
    " `cancelled`,\n",
    " `commuterLineID`,\n",
    " `departureDate`,\n",
    " `operatorShortCode`,\n",
    " `operatorUICCode`,\n",
    " `runningCurrently`,\n",
    " `timeTableRows`,\n",
    " `timetableAcceptanceDate`,\n",
    " `timetableType`,\n",
    " `trainCategory`,\n",
    " `trainNumber`,\n",
    " `trainType`,\n",
    " `version`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 Delete all other columns except `timeTableRows` and `trainNumber`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4 After this, extract the values ​​of the `timeTableRows` column from each train separately (the column value for each train contains several JSON objects) and add `trainNumber`, which identifies the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5 Then drop the following columns from the data: `causes`, `differenceInMinutes` and `commercialStop`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6 Fill in the missing values ​​of the `actualTime` column in each row with the values of the `scheduledTime` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 2\n",
    "\n",
    "7 Extract the cell values ​​of the `table` element of the `data.html` file using the regex syntax and connect them to the dataframe created in step 1. **Note**: only regex should be used in the task (E.g. the use of Pandas' read_html() method is prohibited)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3\n",
    "\n",
    "8 Make a SQLite database and insert the dataframe obtained as a result of step 2 to the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}