Raw-Analysis-IMO-99-20.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ___Raw Analysis International Meteor Organization___\n",
    "# ___1999-2020___\n",
    "---\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import plotly.express as px\n",
    "from geopy.geocoders import Nominatim\n",
    "import geopy as gp\n",
    "from datetime import datetime\n",
    "from glob import glob\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = glob('/Users/aleia/dev/Impacts /Project-Impact-2020/data/IMO/data/*.csv')\n",
    "imo9920 = pd.concat([pd.read_csv(f,  sep=';') for f in filenames], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "imo9920.to_csv('IMO9920.csv', index = False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42989, 11) \n",
      "\n",
      "Session ID                int64\n",
      "Start Date               object\n",
      "Observer ID             float64\n",
      "Submitter ID              int64\n",
      "Actual Observer Name     object\n",
      "Submitted by             object\n",
      "City                     object\n",
      "Country                  object\n",
      "Latitude                float64\n",
      "Longitude               float64\n",
      "Elevation               float64\n",
      "dtype: object \n",
      "\n",
      "<bound method NDFrame.describe of        Session ID           Start Date  Observer ID  Submitter ID  \\\n",
      "0             162  2008-12-19 09:08:00        283.0           283   \n",
      "1             163  2008-12-22 05:20:00        283.0           283   \n",
      "2             164  2008-12-31 10:47:00        283.0           283   \n",
      "3             311  2008-08-11 21:50:00        630.0           630   \n",
      "4             918  2008-08-11 23:00:00        710.0           710   \n",
      "...           ...                  ...          ...           ...   \n",
      "42984       75686  2005-01-10 13:15:00       7956.0          7956   \n",
      "42985       75687  2005-01-13 13:00:00       7956.0          7956   \n",
      "42986       75691  2005-07-29 11:10:00       7956.0          7956   \n",
      "42987       75692  2005-07-30 14:55:00       7956.0          7956   \n",
      "42988       78769  2005-03-09 19:37:00       1651.0          1651   \n",
      "\n",
      "      Actual Observer Name      Submitted by                  City    Country  \\\n",
      "0         Salvador Aguirre  Salvador Aguirre  Ej. El Carmen, Sonor     Mexico   \n",
      "1         Salvador Aguirre  Salvador Aguirre  Ej. El Carmen, Sonor     Mexico   \n",
      "2         Salvador Aguirre  Salvador Aguirre  Ej. El Carmen, Sonor     Mexico   \n",
      "3          Reyhane Akhbari   Reyhane Akhbari           Hesar Sorkh       Iran   \n",
      "4           Igor Arolovich    Igor Arolovich         Mount Tayasim     Israel   \n",
      "...                    ...               ...                   ...        ...   \n",
      "42984           Adam Marsh        Adam Marsh    Pakenham, Victoria  Australia   \n",
      "42985           Adam Marsh        Adam Marsh    Pakenham, Victoria  Australia   \n",
      "42986           Adam Marsh        Adam Marsh            Ladys Pass  Australia   \n",
      "42987           Adam Marsh        Adam Marsh            Ladys Pass  Australia   \n",
      "42988       Jaroslaw Dygos    Jaroslaw Dygos       Czernice Borowe     Poland   \n",
      "\n",
      "        Latitude   Longitude  Elevation  \n",
      "0      29.319444 -110.822778        0.0  \n",
      "1      29.319444 -110.822778        0.0  \n",
      "2      29.319444 -110.822778        0.0  \n",
      "3      36.005556   58.321111        0.0  \n",
      "4      31.766667   35.083333        0.0  \n",
      "...          ...         ...        ...  \n",
      "42984 -38.000000  145.000000      100.0  \n",
      "42985 -38.000000  145.000000      100.0  \n",
      "42986 -36.816110  144.680991      233.0  \n",
      "42987 -36.816110  144.680991      233.0  \n",
      "42988  53.033333   20.716667      150.0  \n",
      "\n",
      "[42989 rows x 11 columns]> \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'> \n",
      "\n",
      "Index(['Session ID', 'Start Date', 'Observer ID', 'Submitter ID',\n",
      "       'Actual Observer Name', 'Submitted by', 'City', 'Country', 'Latitude',\n",
      "       'Longitude', 'Elevation'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(imo9920.shape, '\\n')\n",
    "print(imo9920.dtypes, '\\n')\n",
    "print(imo9920.describe, '\\n')\n",
    "print (type(imo9920), '\\n')\n",
    "print(imo9920.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110961790>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(imo9920.corr())"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}