template-analysis-population.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PaNOSC EOSC Data analysis (of EU population data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instructions:\n",
    "\n",
    "- This notebook contains a basic analysis of the data set. You can browse the outputs below.\n",
    "\n",
    "- You can also re-execute the notebook (either use the menu entries, or press SHIFT+RETURN to execute one cell at the time).\n",
    "\n",
    "- You can also modify all lines and re-execute to extend the given study for your research needs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: fetch the data [to be resolved - WP3 / WP4 / WP6 / EGI]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-19 19:14:12--  https://fangohr.github.io/data/eurostat/population2017/eu-pop-2017.csv\n",
      "Resolving fangohr.github.io... 185.199.111.153, 185.199.108.153, 185.199.110.153, ...\n",
      "Connecting to fangohr.github.io|185.199.111.153|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1087 (1.1K) [text/csv]\n",
      "Saving to: ‘eu-pop-2017.csv.1’\n",
      "\n",
      "eu-pop-2017.csv.1   100%[===================>]   1.06K  --.-KB/s    in 0s      \n",
      "\n",
      "2019-06-19 19:14:12 (74.0 MB/s) - ‘eu-pop-2017.csv.1’ saved [1087/1087]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://fangohr.github.io/data/eurostat/population2017/eu-pop-2017.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: offer some analysis. Here: summary of data, and plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"eu-pop-2017.csv\", index_col=['geo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Present first summary as text\n",
    "\n",
    "Raw data is imported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop17</th>\n",
       "      <th>pop18</th>\n",
       "      <th>births</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>11351727</td>\n",
       "      <td>11413058</td>\n",
       "      <td>119690</td>\n",
       "      <td>109666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>7101859</td>\n",
       "      <td>7050034</td>\n",
       "      <td>63955</td>\n",
       "      <td>109791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>10578820</td>\n",
       "      <td>10610055</td>\n",
       "      <td>114405</td>\n",
       "      <td>111443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>5748769</td>\n",
       "      <td>5781190</td>\n",
       "      <td>61397</td>\n",
       "      <td>53261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>82521653</td>\n",
       "      <td>82850000</td>\n",
       "      <td>785000</td>\n",
       "      <td>933000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>1315634</td>\n",
       "      <td>1319133</td>\n",
       "      <td>13784</td>\n",
       "      <td>15543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>4784383</td>\n",
       "      <td>4838259</td>\n",
       "      <td>62084</td>\n",
       "      <td>30324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>10768193</td>\n",
       "      <td>10738868</td>\n",
       "      <td>88523</td>\n",
       "      <td>124530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>46527039</td>\n",
       "      <td>46659302</td>\n",
       "      <td>390024</td>\n",
       "      <td>421269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>66989083</td>\n",
       "      <td>67221943</td>\n",
       "      <td>767691</td>\n",
       "      <td>603141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>4154212</td>\n",
       "      <td>4105493</td>\n",
       "      <td>36556</td>\n",
       "      <td>53477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60589445</td>\n",
       "      <td>60483973</td>\n",
       "      <td>458151</td>\n",
       "      <td>649061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>854802</td>\n",
       "      <td>864236</td>\n",
       "      <td>9229</td>\n",
       "      <td>5997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>1950116</td>\n",
       "      <td>1934379</td>\n",
       "      <td>20828</td>\n",
       "      <td>28757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>2847904</td>\n",
       "      <td>2808901</td>\n",
       "      <td>28696</td>\n",
       "      <td>40142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>590667</td>\n",
       "      <td>602005</td>\n",
       "      <td>6174</td>\n",
       "      <td>4263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>9797561</td>\n",
       "      <td>9778371</td>\n",
       "      <td>94646</td>\n",
       "      <td>131877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>460297</td>\n",
       "      <td>475701</td>\n",
       "      <td>4319</td>\n",
       "      <td>3571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>17081507</td>\n",
       "      <td>17181084</td>\n",
       "      <td>169200</td>\n",
       "      <td>150027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>8772865</td>\n",
       "      <td>8822267</td>\n",
       "      <td>87633</td>\n",
       "      <td>83270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>37972964</td>\n",
       "      <td>37976687</td>\n",
       "      <td>401982</td>\n",
       "      <td>402852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>10309573</td>\n",
       "      <td>10291027</td>\n",
       "      <td>86154</td>\n",
       "      <td>109586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>19644350</td>\n",
       "      <td>19523621</td>\n",
       "      <td>189474</td>\n",
       "      <td>260599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>2065895</td>\n",
       "      <td>2066880</td>\n",
       "      <td>20241</td>\n",
       "      <td>20509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovakia</th>\n",
       "      <td>5435343</td>\n",
       "      <td>5443120</td>\n",
       "      <td>57969</td>\n",
       "      <td>53914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>5503297</td>\n",
       "      <td>5513130</td>\n",
       "      <td>50321</td>\n",
       "      <td>53722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>9995153</td>\n",
       "      <td>10120242</td>\n",
       "      <td>115416</td>\n",
       "      <td>91972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>65808573</td>\n",
       "      <td>66238007</td>\n",
       "      <td>755043</td>\n",
       "      <td>607172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   pop17     pop18  births  deaths\n",
       "geo                                               \n",
       "Belgium         11351727  11413058  119690  109666\n",
       "Bulgaria         7101859   7050034   63955  109791\n",
       "Czechia         10578820  10610055  114405  111443\n",
       "Denmark          5748769   5781190   61397   53261\n",
       "Germany         82521653  82850000  785000  933000\n",
       "Estonia          1315634   1319133   13784   15543\n",
       "Ireland          4784383   4838259   62084   30324\n",
       "Greece          10768193  10738868   88523  124530\n",
       "Spain           46527039  46659302  390024  421269\n",
       "France          66989083  67221943  767691  603141\n",
       "Croatia          4154212   4105493   36556   53477\n",
       "Italy           60589445  60483973  458151  649061\n",
       "Cyprus            854802    864236    9229    5997\n",
       "Latvia           1950116   1934379   20828   28757\n",
       "Lithuania        2847904   2808901   28696   40142\n",
       "Luxembourg        590667    602005    6174    4263\n",
       "Hungary          9797561   9778371   94646  131877\n",
       "Malta             460297    475701    4319    3571\n",
       "Netherlands     17081507  17181084  169200  150027\n",
       "Austria          8772865   8822267   87633   83270\n",
       "Poland          37972964  37976687  401982  402852\n",
       "Portugal        10309573  10291027   86154  109586\n",
       "Romania         19644350  19523621  189474  260599\n",
       "Slovenia         2065895   2066880   20241   20509\n",
       "Slovakia         5435343   5443120   57969   53914\n",
       "Finland          5503297   5513130   50321   53722\n",
       "Sweden           9995153  10120242  115416   91972\n",
       "United Kingdom  65808573  66238007  755043  607172"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population in 28 EU countries as of 1 January 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['pop17'].sort_values().plot(kind='bar')\n",
    "ax.set_title(\"Population 2017\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population in 28 EU countries as of 1 January 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.sort_values(by='pop17')['pop18'].plot(kind='bar')\n",
    "ax.set_title(\"Population 2018\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population changes from 1/1/2017 to 1/1/2018 in 28 EU countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['rel-change'] = (df['pop18'] - df['pop17']) /  (df['pop18'] + df['pop17']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.sort_values(by='pop17')['rel-change'].plot(kind='bar')\n",
    "ax.set_ylabel(\"Relative change of population in 2017 (percentage)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Download (or make persistent) the modified analysis\n",
    "\n",
    "If the user has modified the enough analysis, then it may be worth preserving this. This can be done by\n",
    "\n",
    "- downloading the notebook (go to `File -> Download as -> `)\n",
    "  - select `Notebook (.ipynb)` to obtain this document, which can be re-executed in the future\n",
    "  - select `HTML` or `pdf` for a read-only version of this document.\n",
    "  \n",
    "In the future, it may be possible to \n",
    "\n",
    "- save this file into your personal space in the EOSC\n",
    "\n",
    "- make this file available together with the data set\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------\n",
    "\n",
    "**Book keeping: which software do we use?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.7 | packaged by conda-forge | (default, Feb 28 2019, 02:16:08) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "pandas: 0.23.1\n",
      "matplotlib: 2.2.2\n",
      "numpy: 1.15.1\n"
     ]
    }
   ],
   "source": [
    "import sys; print(sys.version)\n",
    "import pandas as pd; print(f\"pandas: {pd.__version__}\")\n",
    "import matplotlib as mpl; print(f\"matplotlib: {mpl.__version__}\")\n",
    "import numpy as np; print(f\"numpy: {np.__version__}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}