{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your Student ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your student ID: 6204640178"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This task, you want to find out Today's date by using the library time\n",
    "\n",
    "Hint: Use time.FUNC_NAME.FUNC_NAME? (where FUNC_NAME is replaced with the function you found) to see information about that function and then call the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time.struct_time(tm_year=2022, tm_mon=8, tm_mday=23, tm_hour=10, tm_min=46, tm_sec=22, tm_wday=1, tm_yday=235, tm_isdst=0)\n"
     ]
    }
   ],
   "source": [
    "import time as t\n",
    "x = t.localtime()\n",
    "print(x)\n",
    "# your code here -- notice the comment!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question  2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.1 What is the standard deviation of the data:\n",
    "\n",
    "Hint: Using the function std in library Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [1,3,1,2,9,4,5,6,10,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9410882339705484"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here -- notice the comment!\n",
    "import numpy as np\n",
    "np.std(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 Then, you have the additional data as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_data =[8,11,2,5,6,4,3,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 1, 2, 9, 4, 5, 6, 10, 4, 8, 11, 2, 5, 6, 4, 3, 2]\n"
     ]
    }
   ],
   "source": [
    "#Add the new data to the original data, then re-calculate the standard deviation again.\n",
    "data.extend(add_data)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.954385733401851"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here -- notice the comment!\n",
    "np.std(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Consider the below information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Year': '2021', 'transactional data': {'transaction_id': 1000001, 'source_country': 'United Kingdom', 'target_country': 'Italy', 'send_currency': 'GBP', 'send_amount': 'GBP1000.00', 'target_currency': 'EUR', 'fx_rate EUR/GBP': 1.1648674, 'fee_pct': 0.5, 'platform': 'mobile'}}\n"
     ]
    }
   ],
   "source": [
    "data ={'Year':'2021','transactional data':{\n",
    " 'transaction_id': 1000001,\n",
    " 'source_country': 'United Kingdom',\n",
    " 'target_country': 'Italy',\n",
    " 'send_currency': 'GBP',\n",
    " 'send_amount': 'GBP1000.00',\n",
    " 'target_currency': 'EUR',\n",
    " 'fx_rate EUR/GBP': 1.1648674,\n",
    " 'fee_pct': 0.50, \n",
    " 'platform': 'mobile'}}\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1 Using the python code to pick up the foreign exchange rate  ('fx_rate EUR/GBP') from this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1648674"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here -- notice the comment!\n",
    "(data['transactional data']['fx_rate EUR/GBP'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 You now need to include the data for the \"fee_amount,\" which can be determined from the funds you send to the target country (send_amount) multiplied by the fee percentage (fee_pct). \n",
    "\n",
    "Create the new keyword \"fee_amount\" for this dataset, and then report its value.\n",
    "\n",
    "Hint: using replace() and float() to convert the 'send_amount' into a number.Then, multiply with fee percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBP1000.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here -- notice the comment!\n",
    "x = (data['transactional data']['send_amount'])\n",
    "print(x)\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'GBP1000.00'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [71]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m send_amount \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'GBP1000.00'"
     ]
    }
   ],
   "source": [
    "send_amount = float(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fee_pct' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [72]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m fee_amount \u001b[38;5;241m=\u001b[39m send_amount\u001b[38;5;241m*\u001b[39m\u001b[43mfee_pct\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(fee_amount)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'fee_pct' is not defined"
     ]
    }
   ],
   "source": [
    "fee_amount = send_amount*fee_pct\n",
    "print(fee_amount)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}