{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your Student ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6504930105"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time as t\n",
    "\n",
    "\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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [1,3,1,2,9,4,5,6,10,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [18]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# your code here -- notice the comment!\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<__array_function__ internals>:5\u001b[0m, in \u001b[0;36mstd\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3581\u001b[0m, in \u001b[0;36mstd\u001b[1;34m(a, axis, dtype, out, ddof, keepdims, where)\u001b[0m\n\u001b[0;32m   3578\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   3579\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m std(axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mdtype, out\u001b[38;5;241m=\u001b[39mout, ddof\u001b[38;5;241m=\u001b[39mddof, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m-> 3581\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _methods\u001b[38;5;241m.\u001b[39m_std(a, axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mdtype, out\u001b[38;5;241m=\u001b[39mout, ddof\u001b[38;5;241m=\u001b[39mddof,\n\u001b[0;32m   3582\u001b[0m                      \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:262\u001b[0m, in \u001b[0;36m_std\u001b[1;34m(a, axis, dtype, out, ddof, keepdims, where)\u001b[0m\n\u001b[0;32m    260\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_std\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, ddof\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m    261\u001b[0m          where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 262\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43m_var\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mddof\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mddof\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m               \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    265\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, mu\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m    266\u001b[0m         ret \u001b[38;5;241m=\u001b[39m um\u001b[38;5;241m.\u001b[39msqrt(ret, out\u001b[38;5;241m=\u001b[39mret)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:225\u001b[0m, in \u001b[0;36m_var\u001b[1;34m(a, axis, dtype, out, ddof, keepdims, where)\u001b[0m\n\u001b[0;32m    222\u001b[0m     arrmean \u001b[38;5;241m=\u001b[39m um\u001b[38;5;241m.\u001b[39mtrue_divide(arrmean, div, out\u001b[38;5;241m=\u001b[39marrmean, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    223\u001b[0m                              subok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    224\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 225\u001b[0m     arrmean \u001b[38;5;241m=\u001b[39m \u001b[43marrmean\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[38;5;241m.\u001b[39mtype(arrmean \u001b[38;5;241m/\u001b[39m rcount)\n\u001b[0;32m    227\u001b[0m \u001b[38;5;66;03m# Compute sum of squared deviations from mean\u001b[39;00m\n\u001b[0;32m    228\u001b[0m \u001b[38;5;66;03m# Note that x may not be inexact and that we need it to be an array,\u001b[39;00m\n\u001b[0;32m    229\u001b[0m \u001b[38;5;66;03m# not a scalar.\u001b[39;00m\n\u001b[0;32m    230\u001b[0m x \u001b[38;5;241m=\u001b[39m asanyarray(arr \u001b[38;5;241m-\u001b[39m arrmean)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'dict' object has no attribute 'dtype'"
     ]
    }
   ],
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_data =[8,11,2,5,6,4,3,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Add the new data to the original data, then re-calculate the standard deviation again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here -- notice the comment!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Consider the below information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "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'}}"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# your code here -- notice the comment!\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m (\u001b[38;5;28mlen\u001b[39m(data)):\n\u001b[1;32m----> 3\u001b[0m     \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m[\u001b[38;5;241m9\u001b[39m]\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "# your code here -- notice the comment!\n",
    "for i in range (len(data)):\n",
    "    data[i][9]"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here -- notice the comment!"
   ]
  }
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