Course-creator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "a7cbd03c-2f67-4587-b7c2-6028153d9d2e",
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   "outputs": [
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     "data": {
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       "<div>\n",
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       "    .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>title</th>\n",
       "      <th>duration</th>\n",
       "      <th>module</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Opening</td>\n",
       "      <td>20</td>\n",
       "      <td>Introduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Welcome &amp; Expectations</td>\n",
       "      <td>45</td>\n",
       "      <td>Introduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Program Overview and Introductions</td>\n",
       "      <td>60</td>\n",
       "      <td>Introduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Curriculum Overview</td>\n",
       "      <td>30</td>\n",
       "      <td>Introduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vendor Introduction</td>\n",
       "      <td>45</td>\n",
       "      <td>Introduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>Mock exam and review</td>\n",
       "      <td>90</td>\n",
       "      <td>Ending and exam preparation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Review product FAQ / review AWS /\\nAWS whitepa...</td>\n",
       "      <td>80</td>\n",
       "      <td>Ending and exam preparation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Review product FAQ / review AWS /\\nAWS whitepa...</td>\n",
       "      <td>85</td>\n",
       "      <td>Ending and exam preparation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Mock exam and review</td>\n",
       "      <td>80</td>\n",
       "      <td>Ending and exam preparation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Mock exam and review</td>\n",
       "      <td>85</td>\n",
       "      <td>Ending and exam preparation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  duration  \\\n",
       "0                                              Opening        20   \n",
       "1                               Welcome & Expectations        45   \n",
       "2                   Program Overview and Introductions        60   \n",
       "3                                  Curriculum Overview        30   \n",
       "4                                  Vendor Introduction        45   \n",
       "..                                                 ...       ...   \n",
       "306                               Mock exam and review        90   \n",
       "307  Review product FAQ / review AWS /\\nAWS whitepa...        80   \n",
       "308  Review product FAQ / review AWS /\\nAWS whitepa...        85   \n",
       "309                               Mock exam and review        80   \n",
       "310                               Mock exam and review        85   \n",
       "\n",
       "                          module  \n",
       "0                   Introduction  \n",
       "1                   Introduction  \n",
       "2                   Introduction  \n",
       "3                   Introduction  \n",
       "4                   Introduction  \n",
       "..                           ...  \n",
       "306  Ending and exam preparation  \n",
       "307  Ending and exam preparation  \n",
       "308  Ending and exam preparation  \n",
       "309  Ending and exam preparation  \n",
       "310  Ending and exam preparation  \n",
       "\n",
       "[311 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "import time\n",
    "from datetime import datetime, timedelta\n",
    "# from ics import Calendar, Event\n",
    "\n",
    "soft_acts = []\n",
    "hard_acts = []\n",
    "\n",
    "# Assuming your file is in the same directory as your Python script or Jupyter notebook\n",
    "file_path = \"data-2023.csv\"\n",
    "\n",
    "# Read the CSV file into a pandas DataFrame\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Set 'duration' as integers\n",
    "df['duration'] = df['duration'].astype(int)\n",
    "\n",
    "# Display rows where 'is_pro' is not NaN\n",
    "df_soft_skills = df[~df['is_pro'].isna()]\n",
    "\n",
    "# Reset the index after filtering\n",
    "df_soft_skills.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Drop rows where 'is_ignored' is True\n",
    "df = df[df['is_ignored'] != True]\n",
    "\n",
    "# Reset the index after dropping rows\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Drop rows where 'is_pro' is na\n",
    "df = df[df['is_pro'].isna()]\n",
    "\n",
    "# Reset the index after dropping rows\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Remove specified columns\n",
    "columns_to_remove = ['day_week_n', 'week', 'is_ignored', 'is_pro']\n",
    "df = df.drop(columns=columns_to_remove, axis=1)\n",
    "\n",
    "# Drop the last row (exam day)\n",
    "df_hard_skills = df.drop(df.index[-1])\n",
    "\n",
    "# Reset the index after dropping the last row\n",
    "df_hard_skills.reset_index(drop=True, inplace=True)\n",
    "\n",
    "soft_acts = df_soft_skills\n",
    "hard_acts = df_hard_skills\n",
    "\n",
    "# Display the modified DataFrame\n",
    "display(hard_acts)\n"
   ]
  },
  {
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     "text": [
      "Introduction - 2024-01-16 18:00:00\n",
      "Introduction - 2024-01-17 18:00:00\n",
      "Cloud Foundations - 2024-01-18 18:00:00\n",
      "Cloud Foundations - 2024-01-20 10:30:00\n",
      "Cloud Foundations - 2024-01-22 18:00:00\n",
      "Cloud Foundations - 2024-01-23 18:00:00\n",
      "Cloud Foundations - 2024-01-24 18:00:00\n",
      "Cloud Foundations - 2024-01-25 18:00:00\n",
      "Cloud Foundations - 2024-01-27 10:30:00\n",
      "Linux - 2024-01-29 18:00:00\n",
      "Linux - 2024-01-30 18:00:00\n",
      "Linux - 2024-01-31 18:00:00\n",
      "Linux - 2024-02-01 18:00:00\n",
      "Linux - 2024-02-03 10:30:00\n",
      "Linux - 2024-02-05 18:00:00\n",
      "Linux - 2024-02-06 18:00:00\n",
      "Linux - 2024-02-07 18:00:00\n",
      "Linux - 2024-02-08 18:00:00\n",
      "Linux - 2024-02-10 10:30:00\n",
      "Linux - 2024-02-12 18:00:00\n",
      "Networking - 2024-02-13 18:00:00\n",
      "Networking - 2024-02-14 18:00:00\n",
      "Networking - 2024-02-15 18:00:00\n",
      "Networking - 2024-02-17 10:30:00\n",
      "Networking - 2024-02-19 18:00:00\n",
      "Networking - 2024-02-20 18:00:00\n",
      "Networking - 2024-02-21 18:00:00\n",
      "Networking - 2024-02-22 18:00:00\n",
      "Security - 2024-02-24 10:30:00\n",
      "Security - 2024-02-26 18:00:00\n",
      "Security - 2024-02-27 18:00:00\n",
      "Security - 2024-02-28 18:00:00\n",
      "Security - 2024-02-29 18:00:00\n",
      "Security - 2024-03-02 10:30:00\n",
      "Security - 2024-03-04 18:00:00\n",
      "Security - 2024-03-05 18:00:00\n",
      "Security - 2024-03-06 18:00:00\n",
      "Python Programming - 2024-03-07 18:00:00\n",
      "Python Programming - 2024-03-09 10:30:00\n",
      "Python Programming - 2024-03-11 18:00:00\n",
      "Python Programming - 2024-03-12 18:00:00\n",
      "Python Programming - 2024-03-13 18:00:00\n",
      "Python Programming - 2024-03-14 18:00:00\n",
      "Python Programming - 2024-03-16 10:30:00\n",
      "Python Programming - 2024-03-18 18:00:00\n",
      "Python Programming - 2024-03-19 18:00:00\n",
      "Python Programming - 2024-03-20 18:00:00\n",
      "Python Programming - 2024-03-21 18:00:00\n",
      "Python Programming - 2024-03-23 10:30:00\n",
      "Python Programming - 2024-03-26 18:00:00\n",
      "Python Programming - 2024-03-27 18:00:00\n",
      "Python Programming - 2024-04-02 18:00:00\n",
      "Python Programming - 2024-04-03 18:00:00\n",
      "Databases - 2024-04-04 18:00:00\n",
      "Databases - 2024-04-06 10:30:00\n",
      "Databases - 2024-04-08 18:00:00\n",
      "Databases - 2024-04-09 18:00:00\n",
      "Databases - 2024-04-10 18:00:00\n",
      "Databases - 2024-04-11 18:00:00\n",
      "Databases - 2024-04-13 10:30:00\n",
      "Databases - 2024-04-15 18:00:00\n",
      "Databases - 2024-04-16 18:00:00\n",
      "AWS Architecture - 2024-04-17 18:00:00\n",
      "AWS Architecture - 2024-04-18 18:00:00\n",
      "Systems Operations - 2024-04-20 10:30:00\n",
      "Systems Operations - 2024-04-22 18:00:00\n",
      "Tooling and Automation - 2024-04-23 18:00:00\n",
      "Servers - 2024-04-24 18:00:00\n",
      "Servers - 2024-04-25 18:00:00\n",
      "Servers - 2024-04-27 10:30:00\n",
      "Scaling and Name Resolution - 2024-04-29 18:00:00\n",
      "Scaling and Name Resolution - 2024-04-30 18:00:00\n",
      "Scaling and Name Resolution - 2024-05-02 18:00:00\n",
      "Scaling and Name Resolution - 2024-05-04 10:30:00\n",
      "Serverless and Containers - 2024-05-06 18:00:00\n",
      "Serverless and Containers - 2024-05-07 18:00:00\n",
      "Serverless and Containers - 2024-05-08 18:00:00\n",
      "AWS Database Services - 2024-05-09 18:00:00\n",
      "AWS Database Services - 2024-05-11 10:30:00\n",
      "AWS Networking Services - 2024-05-13 18:00:00\n",
      "AWS Networking Services - 2024-05-14 18:00:00\n",
      "AWS Networking Services - 2024-05-15 18:00:00\n",
      "Storage and Archiving - 2024-05-16 18:00:00\n",
      "Storage and Archiving - 2024-05-21 18:00:00\n",
      "Storage and Archiving - 2024-05-22 18:00:00\n",
      "Storage and Archiving - 2024-05-23 18:00:00\n",
      "Storage and Archiving - 2024-05-25 10:30:00\n",
      "Monitoring and Security - 2024-05-27 18:00:00\n",
      "Monitoring and Security - 2024-05-28 18:00:00\n",
      "Managing Resource Consumption - 2024-05-29 18:00:00\n",
      "Managing Resource Consumption - 2024-05-30 18:00:00\n",
      "Managing Resource Consumption - 2024-06-01 10:30:00\n",
      "Creating Automated and Repeatable Deployments - 2024-06-03 18:00:00\n",
      "Creating Automated and Repeatable Deployments - 2024-06-04 18:00:00\n",
      "Creating Automated and Repeatable Deployments - 2024-06-05 18:00:00\n",
      "Creating Automated and Repeatable Deployments - 2024-06-06 18:00:00\n",
      "Ending and exam preparation - 2024-06-08 10:30:00\n",
      "Ending and exam preparation - 2024-06-10 18:00:00\n",
      "Ending and exam preparation - 2024-06-11 18:00:00\n",
      "Ending and exam preparation - 2024-06-12 18:00:00\n",
      "Ending and exam preparation - 2024-06-13 18:00:00\n",
      "Ending and exam preparation - 2024-06-15 10:30:00\n",
      "Ending and exam preparation - 2024-06-17 18:00:00\n",
      "Ending and exam preparation - 2024-06-18 18:00:00\n",
      "Soft skills - 2024-06-19 18:00:00\n",
      "Soft skills - 2024-06-20 18:00:00\n",
      "Soft skills - 2024-06-25 18:00:00\n",
      "Soft skills - 2024-06-26 18:00:00\n",
      "Soft skills - 2024-06-27 18:00:00\n",
      "DataFrame is empty., Total flex is  3590\n"
     ]
    }
   ],
   "source": [
    "MAX_MINUTES_DAY = 200\n",
    "TICKET_DURATION = 5\n",
    "RECAP_DURATION = 5\n",
    "TOTAL_FLEX_TIME = 0\n",
    "\n",
    "class Day:\n",
    "    def __init__(self, date_start, module_name):\n",
    "        self.date_start = date_start\n",
    "        self.date_end = date_start + timedelta(minutes=MAX_MINUTES_DAY)\n",
    "        self.module_name = module_name\n",
    "        self.activities = [{'title':'Recap', 'duration':RECAP_DURATION}]\n",
    "        self.total_duration = RECAP_DURATION\n",
    "\n",
    "    def can_add_hard(self, activity):\n",
    "        return (\n",
    "            self.total_duration + activity['duration'] <= MAX_MINUTES_DAY - TICKET_DURATION\n",
    "            and self.module_name == activity['module']\n",
    "        )\n",
    "\n",
    "    def can_add_soft(self, activity):\n",
    "        return (\n",
    "            self.total_duration + activity['duration'] <= MAX_MINUTES_DAY - TICKET_DURATION\n",
    "        )\n",
    "\n",
    "    def add_act(self, activity):\n",
    "        self.activities.append({'title':activity['title'], 'duration':activity['duration']})\n",
    "        self.total_duration += activity['duration']\n",
    "\n",
    "    def add_ticket(self):\n",
    "        self.activities.append({'title':'Exit Ticket', 'duration':TICKET_DURATION})\n",
    "        self.total_duration += TICKET_DURATION\n",
    "\n",
    "    def add_flex_time(self):\n",
    "        flex_time = MAX_MINUTES_DAY - self.total_duration\n",
    "        if flex_time > 0:\n",
    "            self.activities.append({'title':'Flex Time', 'duration':flex_time})\n",
    "            self.total_duration += flex_time\n",
    "        return(flex_time)\n",
    "        \n",
    "    def __str__(self):\n",
    "        return (\n",
    "            f\"{self.module_name} - {self.date_start}\"\n",
    "        )\n",
    "\n",
    "# Function to parse date from the CSV file\n",
    "def parse_date(date_str):\n",
    "    return datetime.strptime(date_str, '%d/%m/%Y %H:%M')\n",
    "\n",
    "# Function to check if a given date is a skipped day (Friday, Sunday or Holidays)\n",
    "def is_skipday(date):\n",
    "    # Check if it's a Friday or Sunday\n",
    "    if date.weekday() in [4, 6]:\n",
    "        return True\n",
    "    # Check if it's a holiday\n",
    "    holidays = [\n",
    "        '01/01/2024', '06/01/2024', '25/03/2024', '28/03/2024', '29/03/2024', \n",
    "        '01/04/2024', '01/05/2024',\n",
    "        '20/05/2024', '24/06/2024', '15/08/2024', '11/09/2024', '24/09/2024',\n",
    "        '12/11/2024', '01/11/2024', '06/12/2024', '25/12/2024', '26/12/2024',\n",
    "        '30/03/2024', '18/05/2024', '22/06/2024'\n",
    "    ]\n",
    "    return date.strftime('%d/%m/%Y') in holidays\n",
    "\n",
    "def is_saturday(date):\n",
    "    return date.weekday() == 5  # Saturday\n",
    "\n",
    "# Function to add a day to a given date, skipping skipdays\n",
    "def add_weekday(date, days):\n",
    "    new_date = date + timedelta(days)\n",
    "    while is_skipday(new_date):\n",
    "        new_date += timedelta(1)\n",
    "    if is_saturday(new_date):\n",
    "        new_date = new_date.replace(hour=10, minute=30)\n",
    "    else:\n",
    "        new_date = new_date.replace(hour=18, minute=0)\n",
    "    return new_date\n",
    "\n",
    "def get_first(activities):\n",
    "    if not activities.empty:\n",
    "        return (activities.iloc[0])\n",
    "\n",
    "def remove_first(activities):\n",
    "    return activities.iloc[1:]\n",
    "\n",
    "days = []\n",
    "\n",
    "start_date = parse_date('16/01/2024 18:00')\n",
    "\n",
    "# Iterate through the DataFrame until it's empty\n",
    "while not (hard_acts.empty and soft_acts.empty):\n",
    "    \n",
    "    hard_act = get_first(hard_acts)\n",
    "    soft_act = get_first(soft_acts)\n",
    "\n",
    "    if (not hard_acts.empty):\n",
    "        module_name = hard_act['module']\n",
    "    else:\n",
    "        module_name = \"Soft skills\"\n",
    "    day = Day(start_date, module_name)\n",
    "    \n",
    "    while (not hard_acts.empty and day.can_add_hard(hard_act)):\n",
    "        day.add_act(hard_act)\n",
    "        hard_acts = remove_first(hard_acts)\n",
    "        hard_act = get_first(hard_acts)\n",
    "    \n",
    "    while (not soft_acts.empty and day.can_add_soft(soft_act)):\n",
    "        day.add_act(soft_act)\n",
    "        soft_acts = remove_first(soft_acts)\n",
    "        soft_act = get_first(soft_acts)\n",
    "    \n",
    "    day.add_ticket()\n",
    "    TOTAL_FLEX_TIME += day.add_flex_time()\n",
    "    days.append(day)\n",
    "    \n",
    "    start_date = add_weekday(start_date, 1)\n",
    "    if (start_date.date() == datetime(2024, 3, 14).date()):\n",
    "        MAX_MINUTES_DAY = 220\n",
    "    print(str(day))\n",
    "\n",
    "\n",
    "\n",
    "# The DataFrame is now empty\n",
    "print(\"DataFrame is empty., Total flex is \", TOTAL_FLEX_TIME)\n",
    "\n",
    "# (total minutes, recap duration, ticket duration) total flex time in minutes. last day\n",
    "# (180 10 10) Case without separating soft and hard skills: 5855 total flex time. 2024-08-24\n",
    "# (180 10 10) Case with separating soft and hard skills: 5695 total flex time. 2024-08-24\n",
    "# (200 5 5) Case with separating soft and hard skills: 4715 total flex time. 2024-07-13\n",
    "# (200 5 5) Case with separating soft and hard skills: 4530 total flex time. 2024-07-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "348fc1a9-2c10-46e8-97cc-edcb8a41ad7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data has been written to output.csv.\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# CSV file path\n",
    "csv_file_path = 'output.csv'\n",
    "\n",
    "# Writing to CSV\n",
    "with open(csv_file_path, 'w', newline='') as csvfile:\n",
    "    fieldnames = ['module_name', 'full_date','day','time_start', 'time_end', 'day_plan']\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "    # Write header\n",
    "    writer.writeheader()\n",
    "\n",
    "    # Write data\n",
    "    for a_day in days:\n",
    "        writer.writerow({\n",
    "            'full_date': a_day.date_start.strftime('%d/%m/%Y'),\n",
    "            'time_start': a_day.date_start.strftime('%H:%M'),\n",
    "            'time_end': a_day.date_end.strftime('%H:%M'),\n",
    "            'day': a_day.date_start.strftime('%A'),\n",
    "            'module_name': a_day.module_name,\n",
    "            'day_plan': ' | '.join(f\"{d['title']} - {d['duration']} min\" for d in a_day.activities)\n",
    "        })\n",
    "\n",
    "print(f'Data has been written to {csv_file_path}.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0661b92b-e5e0-4005-abd2-8d051e9fd1b4",
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   "source": []
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