Libraries (Python).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries\n",
    "\n",
    "Instead of inventing everything from ground up, developers use software libraries (or packages) to utilise commonly used functionalities.\n",
    "Libraries have functions (in some programming languages, methods) which take an input and conduct operations to achieve the expected output.\n",
    "The input is series of parameters described in the method documentation, a written text which spesifies how the library should be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing libraries\n",
    "\n",
    "Before you can use a library, it needs to be installed into your system.\n",
    "We now install [seaborn](https://seaborn.pydata.org/), a popular library for plotting data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking the library into use\n",
    "\n",
    "After an library has been installed, you need to take it into use: state that you want to use this library.\n",
    "After that you can use the functions spesified in [the documentation](https://seaborn.pydata.org/api.html).\n",
    "Often documentation is supplemented by [examples](https://seaborn.pydata.org/examples/index.html) and for often used libraries, Google will also prove answers to many more spesific questions.\n",
    "\n",
    "The library includes a dataset `mpg` including details on the fuel econonomy of various cars ([see more](https://github.com/mwaskom/seaborn-data/blob/master/mpg.csv)).\n",
    "We use it in the next examples to have an easy to use dataset available for these exercises."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "mpg = sns.load_dataset('mpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='displacement', ylabel='horsepower'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=mpg, x=\"displacement\", y=\"horsepower\", hue=\"origin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "\n",
       "   model_year origin                       name  \n",
       "0          70    usa  chevrolet chevelle malibu  \n",
       "1          70    usa          buick skylark 320  \n",
       "2          70    usa         plymouth satellite  \n",
       "3          70    usa              amc rebel sst  \n",
       "4          70    usa                ford torino  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.head() ## this is how the data looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='origin', ylabel='count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bt+5gn09U1eYkDwYeUlVf7dpXAZ8Y6vdxgKq6JMmDkjwEeDrwnCSndH32Ah4GrN0Z38y4GQqTtYm7n63t1b0+C3gy8BzgDUkOB94CXFxVxyVZBHxljHVqqxcBC4DHVNXPkqxn63G75wBdJdkLOBOYqqobk5w+1B/gru51M/5/3FkCrKqq0+7WmLx2aHWvu+/CT0d873sd4+7Pe15VzYnJOb18NFm3APsneWiSPRlcVtgNWFhVFwOvAx4C7M3gTOEH3X5/NP5S1XkwcGsXCE8FHj607WFJfrdbPgH4Glt/+NyWZG/g+PGVOm+tBo5Psj9Akn2TPBy4JcmjkuwGHDfTjlX1Y+D2ofGdlwBfHerywu49nwj8uOv/ReCVQ2NFR/bxTY2Lv5lMUPeD5c3At4DrGYwb7A58tDuNDfCeqvqvJH/D4PLRa4AvT6zo+a2AjwGfSzINXMngmG2xFlie5IPAd4Gzquq/k3yIwbXp9Qzm91KPquraJH8FfKkLgJ8BJzIYq/s8cCOwhsEvWzNZDvxdd6PA94CXDm27vbs1+UHAy7q2twDvBa7qgmE9dx832qV4S6o0giQPBa6oqodvY/si4PPdYLLmoCRfAU6pqulJ19InLx9JO5DkIODfGAwaS3OaZwqSpMYzBUlSYyhIkhpDQZLUGArSTpLkC90Trtvr8+YkTxtTSdIvzIFm6VfU3Zueqvr5pGuRflWeKUgjSPKabmbMNUlO7mZFXZvkTOAKYGGS9Un26/q/oZs19aIkH98yL043I+fx3fL6JH89NOPqb0zuO5QGDAVpB5I8hsFTrY8DHg/8CbAPg1lRz62qI6vqhqH+U8DzgCOB5wJT23n727oZV88CTtlOP2ksDAVpx54IXFBVP62qOxnMbPok4Iaq+uY2+n+2qv6nqu5g64yoM3GWVM0qhoK0Y9lG+7Zm1txW/5k4S6pmFUNB2rFLgGOT3L/78JTjgH/dTv+vAc9Oslc3M+qzxlGktDP4m4m0A1V1RffRjZd2TX8P3L6d/pcluRD4d+AGYBr4cd91SjuDt6RKPUiyd1Xd2U2/fAmwoqqumHRd0o54piD1Y2WSwxh8yM4qA0G7Cs8UJEmNA82SpMZQkCQ1hoIkqTEUJEmNoSBJav4fI+9awtEuEaAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## bar chart that automatically counts different cases\n",
    "sns.countplot( data = mpg, x=\"origin\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "\n",
    "* Visualise the various cycle models of cars in `mpg` using a bar chart.\n",
    "* Visualise the production years of the cars in `mpg` using [a histogram](https://seaborn.pydata.org/generated/seaborn.histplot.html#seaborn-histplot).\n",
    "* Visualise the production years cylinder count of the cars in `mpg` using a frequency polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cylinders', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot( data = mpg, x=\"cylinders\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='model_year', ylabel='Count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=mpg, x='model_year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='model_year', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=mpg, x=\"model_year\", hue=\"cylinders\", element=\"poly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 * 1\n",
      "1\n",
      "1 * 2\n",
      "2\n",
      "2 * 3\n",
      "6\n",
      "6 * 4\n",
      "24\n",
      "24 * 5\n",
      "120\n",
      "120 * 6\n",
      "720\n",
      "720 * 7\n",
      "5040\n",
      "5040 * 8\n",
      "40320\n",
      "40320 * 9\n",
      "362880\n",
      "362880 * 10\n",
      "3628800\n"
     ]
    }
   ],
   "source": [
    "# Exercise 12.10\n",
    "factorial = 1 # bug = initial value was 0 -> all multiplication ends up being 0\n",
    "for current in range(1, 11):\n",
    "    print(f'{factorial} * {current}')\n",
    "    factorial = factorial * current\n",
    "    print(factorial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n"
     ]
    }
   ],
   "source": [
    "# Exercise 12.11\n",
    "sum_val = 0\n",
    "for current in range(1, 1001): # loop is not designed to do what is intended\n",
    "    sum_val = current\n",
    "    current = current + 1\n",
    "print(current) # value from the last value of current + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500500\n"
     ]
    }
   ],
   "source": [
    "sum_val = 0\n",
    "for current in range(1, 1001):\n",
    "    sum_val = current + sum_val\n",
    "print(sum_val)"
   ]
  },
  {
   "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
}