weather-in-poland/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analiza temperatury w Polsce na wybranych przykładach (dane z okresu 1901-2016)\n",
    "\n",
    "Dane pobrane z: [worldbank.org](worldbank.org).\n",
    "\n",
    "Plik CSV: https://climateknowledgeportal.worldbank.org/api/data/get-download-data/historical/tas/1901-2016/POL/Poland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data = {}\n",
    "\n",
    "with open(\"tas_1901_2016_POL.csv\") as f:\n",
    "\n",
    "    # przeczytaj plik, iteruj po liniach...\n",
    "    for line in f.readlines():\n",
    "\n",
    "        # wyłuskaj interesujące nas wartości\n",
    "        t_str, y_str, m_str = line.split(\",\")\n",
    "\n",
    "        # konwertuj odczytane wartości na odpowiednie typy\n",
    "        t, y, m = float(t_str), int(y_str), m_str.strip().lower()\n",
    "\n",
    "        # zapisz do zmiennej\n",
    "        data[y] = data.get(y, {})\n",
    "        data[y][m] = t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przykład 1. Najzimniejszy oraz najcieplejszy miesiąc w Polsce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Najniższa średnia temperatura wystąpiła jan 1942: -10.882°C\n",
      "Najwyższa średnia temperatura wystąpiła jul 1994: 21.1785°C\n"
     ]
    }
   ],
   "source": [
    "# dane początkowe\n",
    "top_y, top_m, top_t = 0, 0, 0\n",
    "bot_y, bot_m, bot_t = 0, 0, 0\n",
    "\n",
    "# przez każdy rok w zestawie danych...\n",
    "for year in data:\n",
    "\n",
    "    # przygotuj słowniki po wartościach temperatur i nazwach miesięcy\n",
    "    months = data[year].values()\n",
    "    temperatures = {v: k for k, v in data[year].items()}\n",
    "\n",
    "    # znajdź minima i maksima\n",
    "    max_months, min_months = max(months), min(months)\n",
    "\n",
    "    # dokonaj porównania z poprzednimi wartościami\n",
    "    if top_t < max_months:\n",
    "        top_y = year\n",
    "        top_t = max_months\n",
    "        top_m = temperatures[top_t]\n",
    "\n",
    "    if bot_t > min_months:\n",
    "        bot_y = year\n",
    "        bot_t = min_months\n",
    "        bot_m = temperatures[bot_t]\n",
    "\n",
    "# wypisz wyniki\n",
    "print(f\"Najniższa średnia temperatura wystąpiła {bot_m} {bot_y}: {bot_t}°C\")\n",
    "print(f\"Najwyższa średnia temperatura wystąpiła {top_m} {top_y}: {top_t}°C\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przygotuj notes do wyświetlania wykresów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# zaimportuj bibliotekę do obsługi wykresów\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# rysuj wykres w notatniku\n",
    "%matplotlib inline\n",
    "\n",
    "# ustaw rozmiar wykresu\n",
    "plt.rcParams[\"figure.figsize\"] = (20.0, 10.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stwórz funkcję pomocniczą do rysowania wykresów w wygodny sposób."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def graph(ox, oy, margin_x=5, step_x=10, margin_y=2, step_y=2):\n",
    "    plt.grid()\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"Rok\")\n",
    "    plt.ylabel(\"Temperatura\")\n",
    "    plt.title(\"Wykres średniej temperatury\")\n",
    "\n",
    "    min_x, max_x = round(min(ox)) - margin_x, round(max(ox)) + margin_x\n",
    "    min_y, max_y = round(min(oy)) - margin_y, round(max(oy)) + margin_y\n",
    "    plt.xticks(range(int(min_x), int(max_x), step_x))\n",
    "    plt.yticks(range(int(min_y), int(max_y), step_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przykład 2. Zaprezentuj odkryte minimum i maksimum temperatur za pomocą wykresów"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Najcieplejszy rok na przestrzeni miesięcy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# przygotuj dane, oś OX\n",
    "x = range(1, len(data[top_y].keys())+1)\n",
    "y = list(data[top_y].values())\n",
    "\n",
    "# narysuj dane\n",
    "plt.bar(x, y, label=f\"Temperatury w {top_y}\")\n",
    "graph(x, y, step_x=1, margin_x=1)\n",
    "\n",
    "# popraw labelki\n",
    "plt.title(f\"Wykres średniej temperatury roku {top_y} z najgorętszym miesiącem\")\n",
    "plt.xlabel(\"Miesiąc\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Najzimniejszy rok na przestrzeni miesięcy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# przygotuj dane, oś OX\n",
    "x = range(1, len(data[bot_y].keys())+1)\n",
    "y = list(data[bot_y].values())\n",
    "\n",
    "# narysuj dane\n",
    "plt.bar(x, y, label=f\"Temperatury w {bot_y}\")\n",
    "graph(x, y, step_x=1, margin_x=1)\n",
    "\n",
    "# popraw labelki\n",
    "plt.title(f\"Wykres średniej temperatury roku {bot_y} z najzimniejszym miesiącem\")\n",
    "plt.xlabel(\"Miesiąc\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Przykład 3. Na podstawie informacji o najgorętszym miesiącu roku przygotuj wykres wzrostu średniej temperatury w okresie wakacyjnym na przestrzeni lat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Najgorętszym miesiącem był lipiec więc to głównie na nim oprzemy naszą analizę."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# przygotuj dane\n",
    "x = list(data.keys())\n",
    "jun_y = [data[y][\"jun\"] for y in data.keys()]\n",
    "jul_y = [data[y][\"jul\"] for y in data.keys()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tu następuje faktyczne rysowanie wykresu dla miesięcy wakacyjnych."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pierwszy zestaw danych\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x, jun_y, \"b\", label=\"Czerwiec\", linewidth=2)\n",
    "graph(x, jun_y)\n",
    "\n",
    "# drugi zestaw danych\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, jul_y, \"c\", label=\"Lipiec\", linewidth=2)\n",
    "graph(x, jul_y)\n",
    "\n",
    "# pokaż wykres\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przykład 4: Hipoteza: Dane sugerują stabilny poziom średniej miesięcznej temperatury na przestrzeni prawie stu lat, ale ostatnie dwadzieścia lat zdają się mieć znaczący trend wzrostowy.\n",
    "\n",
    "Aby się temu lepiej przyjrzeć, wygładzę dane korzystając z filtra Gaussa\n",
    "oraz nałożę na siebie wykresy by móc wygodniej porównać trend\n",
    "sąsiadujących ze sobą miesięcy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIwAAAJcCAYAAACbuD+6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAADRXUlEQVR4nOzdd3ib1dnH8e+RLe+9HduxsxNnk4TVsjel7L1HSyllb14ou2VTZksplD0LZZRdRtgECGQ404njvfde0nn/kJImIcNJLEu2f5/r0mX7kfScWyeOLd/Pfe5jrLWIiIiIiIiIiIis5fB3ACIiIiIiIiIiEliUMBIRERERERERkQ0oYSQiIiIiIiIiIhtQwkhERERERERERDaghJGIiIiIiIiIiGxACSMREREREREREdmAEkYiIiKy3YwxOcYYa4wJ9uP4ecaYSf14TmuMGdvHx7YaY0b34XF7GGNW7Hh0IiIiIgNDCSMREZFhxhhzrTHmvY2O5W/m2IkDG13fGWOigaeAk621y/wRg7U2ylpb0IfHfWGtnbC5+7clSRXojDFPGWNu83ccIiIismOUMBIRERl+Pgd2N8YEARhj0gEnMHOjY2O9j+0X/V2FZK1tsdbuba1dNFBjDncDMZ/6NxMREQkMShiJiIgMP9/jSRDN8H69B/ApsGKjY6utteXeJV+/XvtkY4zTGFNrjJm58YmNMccYYwqNMVPWW652jjGmGPjE+5izjTHLjDENxpgPjDHZ3uPGGPMXY0y1MabZGLPYGDNlUy/AGHOmMabAGNNijFljjDllveNfec9TB9xkjAk1xtxjjCk2xlQZYx41xoSvd64rjTEVxphyY8zZG43zlDHmEWPMO96x5hljxqx3/7rKoC2NY4zZ2xhTupnXsjYpt9C7xO0E7/HDjDELjDGNxpivjTHT1ntOoTfuRcaYNmPME8aYVGPMe944PzLGxHsfu/bf4Vzva6wwxlyx3rkcxphrjDGrjTF1xphXjDEJGz1343/DfxljKo0xTcaYz40xk73HzwVOAa7yvpb/bDxP683rbevPjTHmamNMJfDktnzPiYiIiG8oYSQiIjLMWGu7gXnAnt5DewJfAF9udGxtIuMZ4NT1TnEoUGGt/Wn98xpjzgLuBPa31uatd9dewCTgIGPMEcD/AUcDyd5xX/Q+7kDvuOOBWOB4oG7j+I0xkcCDwCHW2mhgd2DBeg/ZBSgAUoE/AXd4zzkDT9VUBnCD91wHA1cABwDjgP03Hg84EbgZiAdWec+5KZsdZ0ustWvnfLp3idvL3sTIP4HfAYnA34G3jDGh6z31GG/c44FfA+/hmdtkPO/xLtpoqH28r/FA4GpjzNrXeiFwJJ5/pxFAA/DIRs9d92/o/fo977lSgB+B572v5THv53d5X8uv6Zs0IAHIBs6lj99zIiIi4jtKGImIiAxPn/G/5NAeeBI3X2x07DPv588BhxpjYrxfnwY8u9H5LgGuBPa21q7a6L6brLVt1toO4DzgdmvtMmttL/BnYIa3yqgHiAYmAsb7mIrNxO8Gphhjwq21FdbaJevdV26tfch7/k48CYhLrbX11toW75hrezMdDzxprc2z1rYBN21irNettd95z/c8/6vCWscYY7YyzrY6F/i7tXaetdZlrX0a6AJ2Xe8xD1lrq6y1ZXj+7eZZa3+y1nYCrwMbV+Pc7P13WAw8CZzkPX4ecJ21ttRa24VnDo41Gy4NW//fEGvtP71LAtc+froxJnY7Xyt4/j1vtNZ2ecfoy/eciIiI+JASRiIiIsPT58AvvUuPkq21+cDXeHobJQBTvI/BWlsOfAUcY4yJAw7BW1GyniuBR6y1m1p2VbLe59nAA95lVo1APWCADGvtJ8DDeKpbqo0xj62XMFjHm9g5AU+io8K7XGziZsZLBiKA+euN+b73OHgqatZ/fNEm4q9c7/N2IGoTj9naONsqG7h87bm858vyxrtW1Xqfd2zi643j3Ph1rj1XNvD6euMsA1x4KrR+9lxjTJAx5g7vErZmoNB7V1LfX97P1HgTXUCfv+dERETEh5QwEhERGZ6+wbPs67d4/jDHWtsMlHuPlVtr16z3+KfxLBE6DvjGW9WyvgOB640xx2xiLLve5yXA76y1cevdwq21X3tjeNBaOwvIxbPU6spNBW+t/cBaewCQDiwH/rGZ8WrxJE8mrzderLV2bTKlAk8iZq2RmxqvD7Y2zrYqAf600TxFWGtf3OozN2/j11m+3liHbDRW2Eb/xuvP6cnAEXiW78UCOd7jZhOPXasdT0JtrbSN7t/Uc7b2PSciIiI+pISRiIjIMORd9vMDcBme5Uxrfek9tvHuaG8AOwEX4+kvs7ElwMHAI8aYw7cw9KPAtes1SY41xhzn/XyOMWYXY4wTaMOznMy98Qm8zZ2P8PYy6gJaN/U47+t040km/cUYk+J9foYxZm0vnleAM40xucaYCODGLcS+WX0YZ2uqgNHrff0P4DzvfBhjTKQx5lfGmOjtic/rj8aYCO/cnwW87D3+KPAn87/m48neXlObE41n3uvwJIH+vJXXAp4eUyd7q5MOxtMTaWveYMvfcyIiIuJDShiJiIgMX5/haVr85XrHvvAe2yBh5E0wvQaMAv69qZNZaxcChwH/MMYcspnHvI6nMfZL3uVMeXiWGwHE4EmUNOBZMlUH3L2J0zjwJLXK8Sxp2wv4/RZe59V4mlV/6x3zI2CCN573gPvx7P61yvtxe212nD64CXjauyzseGvtD3gqvR7GMx+rgDN3IDbw/HuvAj4G7rHWfug9/gDwFvChMaYF+BZP4/DNeQbPv08ZsNT7+PU9AeR6X8sb3mMX42nM3YhnF7U32Iq+fM+JiIiI7xhrN1UBLCIiIrIhY8wNwHhr7albfbBswBizL/C4tXbjypuBGDsHWAM4vY27Bw19z4mIiPhP8NYfIiIiIsOdtxH2OXh2q5JtNwVP0kb6SN9zIiIi/uWzJWnGmCxjzKfGmKXGmCXGmIu9xxOMMf81xuR7P8b7KgYRERHZccaY3+JpjPyetXbj3kayFcaYB4BLgZv9Hctgoe85ERER//PZkjRjTDqQbq390dugcT5wJJ719/XW2juMMdcA8dbaq30ShIiIiIiIiIiIbDOfVRhZayustT96P28BlgEZeLZhfdr7sKfxJJFERERERERERCRADEjTa2+zxc/xrN8vttbGeY8boGHt1xs951zgXIDw8PBZWVlZmzy32+3G4dBmb76i+fUtza9vaX59S/Pre5pj39L8+pbm17c0v76nOfYtza9vaX59ayjN78qVK2uttcmbus/nCSNjTBSebVz/ZK39tzGmcf0EkTGmwVq7xT5Gs2fPtj/88MMm75s7dy577713P0Ys69P8+pbm17c0v76l+fU9zbFvaX59S/PrW5pf39Mc+5bm17c0v741lObXGDPfWjt7U/f5NCVmjHECrwHPW2v/7T1c5e1vtLbPUbUvYxARERERERERkW3jy13SDPAEsMxae996d70FnOH9/AzgTV/FICIiIiIiIiIi2y7Yh+f+BXAasNgYs8B77P+AO4BXjDHnAEXA8T6MQUREREREREREtpHPEkbW2i8Bs5m799vR8/f09FBaWkpsbCzLli3b0dMNO2FhYWRmZuJ0Ov0dioiIiIiIiIgEGF9WGPlUaWkp0dHRJCYmEhMT4+9wBhVrLXV1dZSWljJq1Ch/hyMiIiIiIiIiAWbQ7gPX2dlJYmIinlZJsi2MMSQmJtLZ2envUEREREREREQkAA3ahBGgZNEO0NyJiIiIiIiIyOYM6oSRiIiIiIiIiIj0PyWMdkBlZSUnnngiY8aMYdasWRx66KGsXLnSp2PecMMNfPTRRz4dQ0RERERERESGt0Hb9NrfrLUcddRRnHHGGbz00ksALFy4kKqqKsaPH79d53S5XAQFBW3xMbfccst2nVtEREREREREpK9UYbSdPv30U5xOJ+edd966Y9OnT+e///0vM2bMYMaMGWRkZHDWWWcB8Nxzz7HzzjszY8YMfve73+FyuQCIiori8ssvZ/r06Xz99dccffTRALz55puEh4f
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# użyj filtra Gaussa oraz wygładź dane\n",
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "\n",
    "jul_y = gaussian_filter1d(jul_y, sigma=2)\n",
    "jun_y = gaussian_filter1d(jun_y, sigma=2)\n",
    "\n",
    "# poinformuj plt o chęci nałożenia wykresów\n",
    "plt.plot(x, jun_y, \"b\", label=\"Czerwiec\", linewidth=2)\n",
    "plt.plot(x, jul_y, \"c\", label=\"Lipiec\", linewidth=2)\n",
    "\n",
    "# przygotuj wspólną listę osi OY dla funkcji rysującej\n",
    "import numpy as np\n",
    "y = np.concatenate((jul_y, jun_y))\n",
    "graph(x, y, margin_y=1, step_y=1)\n",
    "\n",
    "# pokaż wykres\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Wniosek: tendencja wzrostowa średnich temperatur w okresie wakacyjnym na przestrzeni lat jest wyrażnie widoczna w ostatnim dwudziestoleciu."
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}