scrutinizing-european-countries (1).ipynb

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   "source": [
    "## Analyzing Europe's Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"http://wallpoper.com/images/00/40/48/35/europe-boats_00404835.jpg\" width=\"800px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Datasets Used for Analysis**"
   ]
  },
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       "        require.undef(\"plotly\");\n",
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     "text": [
      "['pollution_2016.csv', 'env_satisfaction_2013.csv', 'life_satisfaction_2013.csv', 'trust_in_politics_2013.csv', 'median_income_2016.csv', 'trust_in_legal_2013.csv', 'job_satisfaction_2013.csv', 'make_ends_meet_2016.csv', 'crime_2016.csv', 'leisure_satisfaction_2013.csv', 'underemployment_2016.csv', 'close_relations_2015.csv', 'low_savings_2016.csv', 'weather.csv', 'life_expectancy_2016.csv', 'unemployment_2016.csv', 'trust_in_police_2013.csv', 'gdp_2016.csv', 'perceived_health_2016.csv', 'population_2011.csv', 'work_hours_2016.csv']\n"
     ]
    }
   ],
   "source": [
    "# for basic operations\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "# for visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# for advanced visualizations\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import folium\n",
    "\n",
    "# for providing path\n",
    "import os\n",
    "print(os.listdir(\"../input\"))"
   ]
  },
  {
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   "source": [
    "pollution = pd.read_csv('../input/pollution_2016.csv')\n",
    "env_satisfaction = pd.read_csv('../input/env_satisfaction_2013.csv')\n",
    "life_satisfaction = pd.read_csv('../input/life_satisfaction_2013.csv')\n",
    "politics = pd.read_csv('../input/trust_in_politics_2013.csv')\n",
    "income = pd.read_csv('../input/median_income_2016.csv')\n",
    "legal = pd.read_csv('../input/trust_in_legal_2013.csv')\n",
    "job_satisfaction = pd.read_csv('../input/job_satisfaction_2013.csv')\n",
    "budget = pd.read_csv('../input/make_ends_meet_2016.csv')\n",
    "crime = pd.read_csv('../input/crime_2016.csv')\n",
    "leisure_satisfaction = pd.read_csv('../input/leisure_satisfaction_2013.csv')\n",
    "underemployment = pd.read_csv('../input/underemployment_2016.csv')\n",
    "close_relations = pd.read_csv('../input/close_relations_2015.csv')\n",
    "low_savings = pd.read_csv('../input/low_savings_2016.csv')\n",
    "weather = pd.read_csv('../input/weather.csv')\n",
    "life_expectancy = pd.read_csv('../input/life_expectancy_2016.csv')\n",
    "unemployment = pd.read_csv('../input/unemployment_2016.csv')\n",
    "police = pd.read_csv('../input/trust_in_police_2013.csv')\n",
    "gdp = pd.read_csv('../input/gdp_2016.csv')\n",
    "health = pd.read_csv('../input/perceived_health_2016.csv')\n",
    "population = pd.read_csv('../input/population_2011.csv')\n",
    "work_hours = pd.read_csv('../input/work_hours_2016.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://images.adsttc.com/media/images/563c/b089/e58e/ce8c/e300/0095/original/Metropolis_Madrid.gif?1446817925\" width=\"800px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://images.adsttc.com/media/images/563c/ac36/e58e/ce14/7c00/0095/original/San_Pedro_Vaticano.gif?1446816819\" width=\"800px\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geospatial Visualization"
   ]
  },
  {
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   "execution_count": 3,
   "metadata": {
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    "trace = [go.Choropleth(\n",
    "               colorscale = 'YlOrRd',\n",
    "               locationmode = 'country names',\n",
    "               locations = underemployment['country'],\n",
    "               text = underemployment['country'], \n",
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    "             x = underemployment['country'],\n",
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    "layout = go.Layout(title = 'Unemployment Rate in European Countries',\n",
    "                  geo = go.layout.Geo(\n",
    "                       scope = 'europe',\n",
    "                       showcountries = True,))\n",
    "\n",
    "fig = go.Figure(data = trace, layout = layout)\n",
    "iplot(fig)"
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    "             name = 'Unemployment',\n",
    "             marker = dict(color = 'rgb(4, 17, 54)'),\n",
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    "trace = [go.Bar(\n",
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    "trace = go.Bar(\n",
    "             x = health['country'],\n",
    "             y = health['prct_health_verygood'],\n",
    "             name = 'Very Good Health',\n",
    "             marker = dict(color = 'rgb(10, 15, 20)'),\n",
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    "             name = 'Very Bad Health',\n",
    "             marker = dict(color = 'rgb(5, 10, 5)'),\n",
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    "data = [trace, trace2, trace3, trace4, trace5]\n",
    "layout = go.Layout(title = 'Health in Europe',\n",
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    "fig = go.Figure(data = data, layout = layout)\n",
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    "               colorscale = 'Picnic',\n",
    "               locationmode = 'country names',\n",
    "               locations = pollution['country'],\n",
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       "                        {\"title\": {\"text\": \"Pollution in Europe\"}},\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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   "source": [
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    "             x = pollution['country'],\n",
    "             y = pollution['prct_rpt_pollution'],\n",
    "             name = 'Pollution',\n",
    "             marker = dict(color = 'rgb(220, 220, 220)'),\n",
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    "layout = go.Layout(title = 'Pollution in Europe')\n",
    "fig = go.Figure(data = trace, layout = layout)\n",
    "iplot(fig)"
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   ],
   "source": [
    "trace = go.Bar(\n",
    "             x = weather['country'],\n",
    "             y = weather['avg_temp'],\n",
    "             name = 'Average Temp.',\n",
    "             marker = dict(color = 'rgb(250, 250, 200)'),\n",
    ")\n",
    "trace2 = go.Bar(\n",
    "             x = weather['country'],\n",
    "             y = weather['avg_high_temp'],\n",
    "             name = 'Avg. High Temp',\n",
    "             marker = dict(color = 'rgb(20, 20, 20)'),\n",
    ")\n",
    "trace3 = go.Bar(\n",
    "             x = weather['country'],\n",
    "             y = weather['avg_low_temp'],\n",
    "             name = 'Avg. Low Temp',\n",
    "             marker = dict(color = 'rgb(186, 200, 250)'),\n",
    ")\n",
    "trace4 = go.Bar(\n",
    "             x = weather['country'],\n",
    "             y = weather['avg_precipitation'],\n",
    "             name = 'Avg. Precipitation',\n",
    "             marker = dict(color = 'rgb(220, 190, 220)'),\n",
    ")\n",
    "df = [trace, trace2, trace3, trace4]\n",
    "layout = go.Layout(title = 'Weather in Europe',\n",
    "                  barmode = 'relative')\n",
    "fig = go.Figure(data = df, layout = layout)\n",
    "iplot(fig)"
   ]
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    "               locations = low_savings['country'],\n",
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    "\n",
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    "trace = [go.Bar(\n",
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    "trace = go.Bar(\n",
    "             x = job_satisfaction['country'],\n",
    "             y = job_satisfaction['prct_job_satis_high'],\n",
    "             name = 'job_satisfaction',\n",
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    "             name = 'job_satisfaction',\n",
    "             marker = dict(color = 'lightgreen'),\n",
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    "             name = 'job_satisfaction',\n",
    "             marker = dict(color = 'pink'),\n",
    ")\n",
    "df = [trace, trace2, trace3]\n",
    "layout = go.Layout(title = 'Job Satisfaction in Europe',\n",
    "                   barmode = 'relative')\n",
    "fig = go.Figure(data = df, layout = layout)\n",
    "iplot(fig)"
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   "metadata": {},
   "source": [
    "### Thanks for reading the kernel, please, stay tuned there is more to come."
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}