Fish skibidi jupiter notebook.ipynb

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   "source": [
    "# Fish Data"
   ]
  },
  {
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   "source": [
    "# Identifying and Defining\n",
    "\n",
    "### Learning Goals\n",
    "- I can clearly state the purpose of a data analysis solution.\n",
    "- I can describe use cases that outline how the data analysis solution will be used.\n",
    "- I can develop test cases that include inputs and expected outputs for the data analysis solution.\n",
    "- I can specify non-functional requirements such as performance, scalability, security, and usability for a data analysis solution.\n",
    "- I can explain the importance of non-functional requirements in ensuring the overall quality and effectiveness of a data analysis solution.\n",
    "### Non-Functional Requirements\n",
    " - Has security e.g. Username and password\n",
    " - Graphical user interface\n",
    " - Is easy to operate\n",
    "### Data \n",
    "Data: I am going to analyse fish seafood production from all of the countries from 1961 to 2021.\n",
    "Goal: My goal is to find out how much seafood has been harvested since 1961, which countries harvested the most while it also being able to sort data.\n",
    "Source: The source is from “RamLegacy”,  a non-profit organisation whose data is primarily focused on commercially exploited marine populations worldwide.\n",
    "Access: The data is publicly avaible at https://ourworldindata.org/fish-and-overfishing.\n",
    "Access Method:The data is accessable from it's preview or you can download it as csv.\n",
    "\n",
    "### Functional Requirements\n",
    "\n",
    "Data Loading: I must be able to deal with errors in loading data and sort problemsm like incorrect format, missing files.\n",
    "Data Cleaning: The progam must be able to handle any missing values while also being able to filter, sort and group specific data like by date, name and values. \n",
    "Data Analysis: The application can also find the mean, median, mode, and quartiles. \n",
    "Data Visualisation: The data can be visualised in diffrent types charts like bar charts and line charts.\n",
    "Data Reporting: The final output can be exported as a .py file. \n",
    "\n",
    "### Functions\n",
    "\n",
    "It is recommended that you use the following template to do this:\n",
    "Function Name (e.g. Data Loading)\n",
    "Description: It must be able to effectivly anaylise the data, isolate data - e.g. by date or country, and give the mean ect.\n",
    "Input: The user can give basic commands like for example \"Isolate China\".\n",
    "Output: sort out china and output china's stastistics\n",
    "Use Cases\n",
    "Use cases essentially describe how users will interact with the system by providing an example of how it will work. The following example demonstrates how you create a use-case and the template should be used to create a use-case for each functional requirement.\n",
    "\n",
    "Actor: User\n",
    "Goal: Isolate china\n",
    "Preconditions: User has a dataset file ready.\n",
    "Main Flow: \"Isolate china\"\n",
    "User places the dataset for reading into the correct folder.\n",
    "System validates the file format.\n",
    "System loads the dataset and displays the information in a dataframe.\n",
    "Postconditions: Dataset is loaded and ready for analysis.\n",
    "\n",
    "Non-Functional Requirements\n",
    "Typically non-functional requirements would focus on performance, usability, reliability, scalability and security. However, for this task we are going to focus on usability and reliability, as the performance and scalability will not vary much due to only working with one or two datasets and security was a bit much to cram into the short time we have to complete the task. You are of course welcome to add a security layer (e.g. username + password) to your UI if you feel it's appropriate, and if you have time.\n",
    "\n",
    "For now, focus on the following:\n",
    "\n",
    "Usability: What is required from the User Interface and a 'README' document?\n",
    "\n",
    "The user interface document should include how to use the code and what functions the code has. \n",
    "\n",
    "Reliability: What is required from the system when providing information to the user on errors and ensuring data integrity?\n",
    "\n",
    "It should be able to run perfectly every single time and not crash while giving accurate and reliable data.\n"
   ]
  },
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   "source": [
    "# Researching and panning"
   ]
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    "## Learning Intentions:\n",
    "Collect and interpret data while adhering to privacy and cybersecurity principles.\n",
    "Represent and store data to facilitate computation, including selecting appropriate data types, understanding data type limitations, and structuring data systematically.\n",
    "Success Criteria:\n",
    "I can collect data following established privacy and cybersecurity principles to ensure data protection.\n",
    "I can interpret data while maintaining compliance with privacy regulations and cybersecurity practices.\n",
    "I can select appropriate data types for representing and storing data to facilitate computation.\n",
    "I can structure data systematically to support efficient computation and analysis.\n",
    "\n",
    "\n",
    "\n",
    "Learning Intentions:\t1\n",
    "Success Criteria:\t1\n",
    "Researching and Planning\t3\n",
    "Research of Chosen Issue\t3\n",
    "Privacy and Security\t3\n",
    "Data Dictionaries\t4\n",
    "Data Frame in VS Code\t4\n",
    "Big Mac Data Dictionary\t4\n",
    "Object Type\t5\n",
    "Date Format in Pandas\t5\n",
    "Your Data Dictionary\t5\n",
    "\n",
    "Researching and Planning\n",
    "Research of Chosen Issue\n",
    "Let's do a deep dive of the issue to further understand and explain why this data analysis needs to be carried out by exploring what is lacking about current information. Make sure to link any references.\n",
    "\n",
    "Purpose: The purpose is to analyze data about tonnes of seafood harvest per country, finding the mean, average and patterns in the data, and how declining fish stocks is affecting the harvest rate\n",
    "Missing Data: I believe that it is necessary to analyze this data as the oceans and fish stocks are in trouble with fish stocks declining. This analysis will give new information and proof why you have to act now. \n",
    "\n",
    "Stakeholders: The information will become public allowing everyone to use this information, from teaching resources to personal use. \n",
    "\n",
    "Use: It can be used in research tasks and studies about the declining stocks of our oceans, and teaching resources to raise awearness.\n",
    "\n",
    "Privacy and Security\n",
    "Have you given consideration to how you can adhere to privacy and cyber security principles? Think back to our scaffold for the written task where we explored government guidelines, and to your theory modules where we looked at these concepts:\n",
    "\n",
    "Data Privacy of Source: The data is an csv. file so It won't contain any malmare. It will also be on trusted and popular websites like kaggle or github, where they already have measures in place for data security. \n",
    "\n",
    "Application Data Privacy: My responsibilities in maintaining user privacy is making sure that the data is acessable by everyone on trusted and popular websites which iwll not take advantage of you like github and kaggle. This data will contain no person information and can only be acessed if you have a github or kaggle account. These companies have an obligation to protect the user and my job is to provide safe and easy to use data.\n",
    "\n",
    "Cyber Security: AFter the data is publicised, you will need an account to acess the data. This will include an username and a strong password, or 2 step authentification. \n",
    "\n",
    "Data Dictionaries\n",
    "A data dictionary is a centralized repository of information about data. It provides a detailed description of the data elements used in a system or database, including their meaning, relationships, format, and usage. In essence, it serves as a reference guide that helps users and developers understand the structure and content of the data. \n",
    "Each row in a data dictionary represents a field (or column) in a dataframe / database/\n",
    "Here is an example based on the Big Mac dataframe.\n",
    "Data Frame in VS Code\n",
    "\n",
    "Object Type\n",
    "Any string / raw character format is stored as an object in Pandas. This means that strings will be added as an object in your data dictionary.\n",
    "Date Format in Pandas\n",
    "To get the datetime format, I added this code:\n",
    "big_mac_df['date'] = pd.to_datetime(big_mac_df['date'])\n",
    "Your Data Dictionary\n",
    "Look at the datasets you have found. For each .csv file / API, consider the column headings you want in the data frames you create. For each separate data frame, you need a data dictionary (if you have more than one):\n",
    "\n",
    "\n",
    "| Field | Datatype | Format for Display | Description | Example | Validation |\n",
    "| ----- | -------- | ------------------ | ----------- | ------- | -----------| \n",
    "| name | object | XX…XX | Name of the country of the data | Afghanistan | Any characters expect numbers | \n",
    "| code | object | XXX | abbreviation of country name | AUS | Any characters expect numbers| \n",
    "| year | abstract data type | YYYY | Year of harvest | 1969 | Any number, must be in format YYYY, no characters|\n",
    "| Weight (tonnes) | Quantitative data | NN...N | weight of yearly harvest | 20480 | Any number with no characters|"
   ]
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   "execution_count": 49,
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fish_data_df = pd.read_csv('/Fish/FIsh-data-anaylisis/Fish2.csv',\n",
    "                               header=None,\n",
    "                               names=['Species', 'Weight', 'Length', 'Diagonal', 'Width'])\n",
    "def showCharts():\n",
    "    fish_data_df.plot(\n",
    "        kind='scatter',\n",
    "        x='Species',\n",
    "        y='Weight',\n",
    "        color='blue',\n",
    "        alpha=0.5,\n",
    "        title= 'weight of fish by species'\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "showCharts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysising data\n",
    "\n",
    "looking at the graph and data, in fish.py we can see that asia is harvesting mroe and more fish. I beleive that this is bad as our oceans are already suffering, and looking at my data if i isolate asia by year, we can see that over time, asia is harvestin more and more over the years. I also do not see any slowing down and believe that they will continue harvesting. Overall, i can see from this data that the oceans are being over harvested. the data set(Yearly Seafood Harvest Data for Asia:\n",
    "      Year  Fish and seafood\n",
    "0   1961.0        14741760.0\n",
    "1   1962.0        15261280.0\n",
    "2   1963.0        15661690.0\n",
    "3   1964.0        16143670.0\n",
    "4   1965.0        17241320.0\n",
    "..     ...               ...\n",
    "56  2017.0       120018610.0\n",
    "57  2018.0       122510730.0\n",
    "58  2019.0       124714280.0\n",
    "59  2020.0       124714270.0\n",
    "60  2021.0       124714270.0\n",
    "\n",
    "[61 rows x 2 columns]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Peer evaulation\n",
    "\n",
    "## Victor\n",
    "Kelvin's program is fully functional, following all of the functional reqirements. He has satisfied 2 out of three non functional requirements, he has a username and password and the program is easy to use. The program fully satisfies every purpose that he has asked of it and therefore is quite successful. \n",
    "\n",
    "## Levin Shao\n",
    "\n",
    "Kevin's program is very successful. His program demonstrated all the function and non-functional requirements. He is able to load data with Pandas, clean data, analyse data, visualize data with matplotlib, and report the data, creating a working software that meets all functional requirements and some non functional ones. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# evaulation\n",
    "\n",
    "Overall, I believe that i reached my goal by producing a software that gives the revelant information about my data set. My functions are \n",
    "    1 - Show the original dataset\n",
    "    2 - Show the updated Data Frame\n",
    "    3 - Visualize the Tonnes of Seafood Caught by Country\n",
    "    4 - Average Harvest\n",
    "    5 - Top 10 Seafood Harvest\n",
    "    6 - Isolate Country (total harvest)\n",
    "    7 - Isolate Country (yearly harvest)\n",
    "    8 - Quit Program.\n",
    "\n",
    "\n",
    " My program is reliable as I and others have tested it multiple times and have not once failed. I believe that i could of been more effienct at time management and be less distracted. becuase of this, I had to do some 3am grinds (Check git commits) in order to finish my code. I blame my valorant addiction for my 3am runs and I have to peactice better self control. I faced multiple challenges through out this task, with things like the program not identifying 3 as a number, I overcame this challenge by researching about it and found that it was pretty common on reddit. I then fixed my code by setting my data frame more specificly. \n",
    "\n",
    " Overall, i believe that i slacked off during the term but paid the price by working on my code at 3 am two days in a row. Overall, i am pround with what i have produced and learnt valuable lessons about python and life lessons. Next time, I would like to try and add more graphs but I could not this time because my data file was way too big with 13.8k lines of data. \n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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