{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Your Student ID:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6304640045 # your student ID" ] }, { "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": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "00:00:00\n" ] } ], "source": [ "import time as t\n", "\n", "import datetime as t \n", "\n", "time1=t.time()\n", "print(time1)\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": 15, "metadata": {}, "outputs": [], "source": [ "data = [1,3,1,2,9,4,5,6,10,4]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.9410882339705484\n" ] } ], "source": [ "import numpy as np\n", "print(np.std(data))\n", "# your code here -- notice the comment!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.2 Then, you have the additional data as shown below" ] }, { "cell_type": "code", "execution_count": null, "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": 17, "metadata": {}, "outputs": [], "source": [ "data.extend([8,11,2,5,6,4,3,2])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.954385733401851\n" ] } ], "source": [ "print(np.std(data))" ] }, { "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": 32, "metadata": {}, "outputs": [], "source": [ "data ={'Year':'2021',\n", " '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": 31, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'fx_rate EUR/GBP'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [31]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfx_rate EUR/GBP\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n", "\u001b[1;31mKeyError\u001b[0m: 'fx_rate EUR/GBP'" ] } ], "source": [ "data['fx_rate EUR/GBP']\n", " # your code here -- notice the comment!" ] }, { "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": 34, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can't multiply sequence by non-int of type 'float'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [34]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfee_amount\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGBP1000.00\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(data)\n", "\u001b[1;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'" ] } ], "source": [ "data[\"fee_amount\"]=0.5*\"GBP1000.00\"\n", "print(data)\n", "\n", "# your code here -- notice the comment!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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 }