Week_4.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Week 4 handin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 01 Assignment\n",
    "\n",
    "Use only numpy (not pandas) in these exercises\n",
    "#### Exercise 1\n",
    "1. Open the file './data/befkbhalderstatkode.csv'\n",
    "2. Turn the csv file into a numpy ndarray with `np.genfromtxt(filename, delimiter=',', dtype=np.uint, skip_header=1)`\n",
    "3. Using this data:\n",
    "```python\n",
    "neighb = {1: 'Indre By', 2: 'Østerbro', 3: 'Nørrebro', 4: 'Vesterbro/Kgs. Enghave', \n",
    "          5: 'Valby', 6: 'Vanløse', 7: 'Brønshøj-Husum', 8: 'Bispebjerg', 9: 'Amager Øst', \n",
    "          10: 'Amager Vest', 99: 'Udenfor'}\n",
    "```\n",
    "Find out how many people lived in each of the 11 areas in 2015\n",
    "4. Make a bar plot to show the size of each city area from the smallest to the largest\n",
    "5. Create a boolean mask to find out how many people above 65 years lived in Copenhagen in 2015\n",
    "6. How many of those were from the other nordic countries (not dk)\n",
    "7. Make a line plot showing the changes of number of people in vesterbro and østerbro from 1992 to 2015\n",
    "\n",
    "#### Exercise 2  A bit harder (Extra only if you have the time)\n",
    "\n",
    "1. From \"Danmarks Statistik\" download demographic data here:\n",
    "https://api.statbank.dk/v1/data/FOLK1A/CSV?valuePresentation=Code&delimiter=Semicolon&OMR%C3%85DE=000%2C084%2C147%2C400%2C085%2C083%2C082%2C081%2C851%2C461%2C561%2C751&K%C3%98N=1%2C2&ALDER=0%2C1%2C2%2C3%2C4%2C5%2C6%2C7%2C8%2C9%2C10%2C11%2C12%2C13%2C14%2C15%2C16%2C17%2C18%2C19%2C20%2C21%2C22%2C23%2C24%2C25%2C26%2C27%2C28%2C29%2C30%2C31%2C32%2C33%2C34%2C35%2C36%2C37%2C3%2C39%2C40%2C41%2C42%2C43%2C44%2C45%2C46%2C47%2C48%2C49%2C50%2C51%2C52%2C53%2C54%2C55%2C56%2C57%2C58%2C59%2C60%2C61%2C62%2C63%2C64%2C65%2C66%2C67%2C68%2C69%2C70%2C71%2C72%2C73%2C74%2C75%2C76%2C77%2C78%2C79%2C80%2C81%2C82%2C83%2C84%2C85%2C86%2C87%2C88%2C89%2C90%2C91%2C92%2C93%2C94%2C95%2C96%2C97%2C98%2C99%2C100&Tid=2008K1%2C2009K1%2C2010K1%2C2011K1%2C2012K1%2C2013K1%2C2014K1%2C2015K1%2C2016K1%2C2017K1%2C2018K1%2C2019K1%2C2020K1\n",
    "\n",
    "\n",
    "2. clean up the data so it only contains numbers. (If you find this hard to do then Pandas can help (we will cover it next week)\n",
    "```python\n",
    "import pandas as pd \n",
    "url = 'url from above here'\n",
    "df = pd.read_csv(url,sep=';')\n",
    "df['TID'] = df['TID'].map(lambda x:x[:-2]) #cut the last 2 characters\n",
    "df.to_csv('demografic_cleaned.csv',header=False, index=False)\n",
    "```\n",
    "Now you have csv data with columns: OMRÅDE;KØN;ALDER;TID;INDHOLD\n",
    "3. Now load your demographic data with numpy. (Hint: use np.loadtxt() with dtype and delimiter).\n",
    "4. Use file: BY_KODER_demographics.csv to get cities from the city codes\n",
    "5. Now find out which city in 2020 has the highest percentage of men (gender code 1),(Hint: create a function to return summed up data by city code ala: def summed_by_city(city_code): returning pct_male \n",
    "6. Create a line chart with 5 lines showing population changes in the 5 largest cities from 2008 to 2020\n",
    "\n"
   ]
  },
  {
   "source": [
    "## 02 Status\n",
    "Completed all EX1 tasks"
   ],
   "cell_type": "markdown",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1"
   ]
  },
  {
   "source": [
    "### Exercise 1-3 setup\n",
    "1. Open the file './data/befkbhalderstatkode.csv'\n",
    "2. Turn the csv file into a numpy ndarray with `np.genfromtxt(filename, delimiter=',', dtype=np.uint, skip_header=1)`\n",
    "3. Using this data:\n",
    "neighb = {1: 'Indre By', 2: 'Østerbro', 3: 'Nørrebro', 4: 'Vesterbro/Kgs. Enghave', \n",
    "          5: 'Valby', 6: 'Vanløse', 7: 'Brønshøj-Husum', 8: 'Bispebjerg', 9: 'Amager Øst', \n",
    "          10: 'Amager Vest', 99: 'Udenfor'}\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "filename = \"week4modules/befkbhalderstatkode.csv\"\n",
    "\n",
    "inputpop = np.genfromtxt(filename, delimiter=',', dtype=np.uint, skip_header=1)\n",
    "dataset = inputpop\n",
    "\n",
    "neighb = {1: 'Indre By', 2: 'Østerbro', 3: 'Nørrebro', 4: 'Vesterbro/Kgs. Enghave', \n",
    "          5: 'Valby', 6: 'Vanløse', 7: 'Brønshøj-Husum', 8: 'Bispebjerg', 9: 'Amager Øst', \n",
    "          10: 'Amager Vest', 99: 'Udenfor'}\n",
    "\n",
    "\n"
   ]
  },
  {
   "source": [
    "### Exercise 4\n",
    "Find out how many people lived in each of the 11 areas in 2015,                                                                                                              \n",
    " Make a bar plot to show the size of each city area from the smallest to the largest"
   ],
   "cell_type": "markdown",
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  },
  {
   "cell_type": "code",
   "execution_count": 44,
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   "outputs": [
    {
     "output_type": "stream",
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     "text": [
      "Indre By has a population of 51937 people\nØsterbro has a population of 75113 people\nNørrebro has a population of 78802 people\nVesterbro/Kgs. Enghave has a population of 61623 people\nValby has a population of 51727 people\nVanløse has a population of 39537 people\nBrønshøj-Husum has a population of 43908 people\nBispebjerg has a population of 53604 people\nAmager Øst has a population of 55205 people\nAmager Vest has a population of 64967 people\nUdenfor has a population of 3872 people\n---Bar chart---\n"
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def get_amount_of_people():\n",
    "    set_of_people = {}\n",
    "    for neighbh in neighb.keys():\n",
    "        amount = dataset[(dataset[:,0] == 2015) & (dataset[:,1] == neighbh)]\n",
    "        set_of_people[neighb.get(neighbh)] = amount[:,4].sum()\n",
    "    return set_of_people\n",
    "\n",
    "for key, value in get_amount_of_people().items():\n",
    "        print(key, \"has a population of\", value, \"people\")\n",
    "\n",
    "\n",
    "print(\"---Bar chart---\")\n",
    "tabledata = get_amount_of_people()\n",
    "amount = sorted(tabledata.values())\n",
    "area = sorted(tabledata, key=tabledata.get)\n",
    "\n",
    "plt.figure()\n",
    "plt.bar(area, amount)\n",
    "plt.title(\"Population In Copenhagen\")\n",
    "plt.xlabel(\"Area\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Population\")\n",
    "plt.show(block=True)\n",
    "\n"
   ]
  },
  {
   "source": [
    "### Excercise 5 & 6\n",
    "5. Create a boolean mask to find out how many people above 65 years lived in Copenhagen in 2015\n",
    "6. How many of those were from the other nordic countries (not dk) Disclaimer, could not figure out which countrycodes the nordic country has so i just checked for not dk in general)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Copenhagen had a population of 65+ citizen in 2015 of:  55750\nEach area has the following amount of 65+:\nIndre By  Has  6818 people of 65+ age\n332  of those people is not DK nationality which is  4.8694631856849515 %\nØsterbro  Has  8370 people of 65+ age\n430  of those people is not DK nationality which is  5.137395459976105 %\nNørrebro  Has  5059 people of 65+ age\n582  of those people is not DK nationality which is  11.504249851749359 %\nVesterbro/Kgs. Enghave  Has  3984 people of 65+ age\n405  of those people is not DK nationality which is  10.16566265060241 %\nValby  Has  5397 people of 65+ age\n263  of those people is not DK nationality which is  4.873077635723551 %\nVanløse  Has  4668 people of 65+ age\n149  of those people is not DK nationality which is  3.1919451585261354 %\nBrønshøj-Husum  Has  5461 people of 65+ age\n337  of those people is not DK nationality which is  6.171030946713056 %\nBispebjerg  Has  4945 people of 65+ age\n395  of those people is not DK nationality which is  7.987866531850354 %\nAmager Øst  Has  5027 people of 65+ age\n213  of those people is not DK nationality which is  4.237119554406207 %\nAmager Vest  Has  5971 people of 65+ age\n331  of those people is not DK nationality which is  5.543460056941886 %\nUdenfor  Has  50 people of 65+ age\n14  of those people is not DK nationality which is  28.000000000000004 %\n"
     ]
    }
   ],
   "source": [
    "amount = dataset[(dataset[:,0] == 2015) & (dataset[:,2] > 65)]\n",
    "print(\"Copenhagen had a population of 65+ citizen in 2015 of: \", amount[:,4].sum())\n",
    "print(\"Each area has the following amount of 65+:\")\n",
    "for neighbh in neighb.keys():\n",
    "        pop_in_area = amount[amount[:,1] == neighbh]\n",
    "        pop_in_area_not_dk = pop_in_area[pop_in_area[:,3] != 5100]\n",
    "        print(neighb.get(neighbh), \" Has \", pop_in_area[:,4].sum() , \"people of 65+ age\")\n",
    "        print(pop_in_area_not_dk[:,4].sum(), \" of those people is not DK nationality which is \",(pop_in_area_not_dk[:,4].sum()/pop_in_area[:,4].sum())*100 , \"%\")\n",
    "        \n"
   ]
  },
  {
   "source": [
    "# Exercise 7\n",
    "7. Make a line plot showing the changes of number of people in vesterbro and østerbro from 1992 to 2015"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[62221. 62583. 63050. 63572. 64503. 65856. 66148. 66295. 66673. 66877.\n 67020. 67550. 67516. 67671. 67351. 67330. 67964. 68769. 69951. 70938.\n 72400. 73388. 74521. 75113.]\n[47854. 47589. 47538. 47633. 47734. 48275. 49109. 49520. 50312. 50756.\n 51039. 51045. 50869. 50643. 50303. 50964. 51466. 53351. 55315. 57150.\n 58411. 59211. 60328. 61623.]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "østerbro = dataset[dataset[:,1] == 2] \n",
    "vesterbro = dataset[dataset[:,1] == 4]\n",
    "set_of_years = np.unique(dataset[:,0])\n",
    "\n",
    "østerbro_pop = np.array([], dtype='int16')\n",
    "vesterbro_pop = np.array([], dtype='int16')\n",
    "for year in set_of_years:\n",
    "    østerbro_pop = np.append(østerbro_pop, np.sum(østerbro[østerbro[:,0] == year][:,4]))\n",
    "    vesterbro_pop = np.append(vesterbro_pop, np.sum(vesterbro[vesterbro[:,0] == year][:,4]))\n",
    "\n",
    "print(østerbro_pop)\n",
    "print(vesterbro_pop)\n",
    "\n",
    "plt.title(\"Population Growth\", fontsize=20)\n",
    "plt.plot(set_of_years, østerbro_pop, label=\"Østerbro\")\n",
    "plt.plot(set_of_years, vesterbro_pop, label=\"Vesterbrobro\")\n",
    "plt.xlabel(\"Year\", fontsize=14)\n",
    "plt.ylabel(\"Amount of people\", fontsize=14)\n",
    "plt.legend(loc=2)\n",
    "plt.show(block=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}