dm_final_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <th>country-year</th>\n",
       "      <th>HDI for year</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>21</td>\n",
       "      <td>312900</td>\n",
       "      <td>6.71</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>16</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>1</td>\n",
       "      <td>21800</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>9</td>\n",
       "      <td>274300</td>\n",
       "      <td>3.28</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population  \\\n",
       "0  Albania  1987    male  15-24 years           21      312900   \n",
       "1  Albania  1987    male  35-54 years           16      308000   \n",
       "2  Albania  1987  female  15-24 years           14      289700   \n",
       "3  Albania  1987    male    75+ years            1       21800   \n",
       "4  Albania  1987    male  25-34 years            9      274300   \n",
       "\n",
       "   suicides/100k pop country-year  HDI for year  gdp_for_year ($)   \\\n",
       "0               6.71  Albania1987           NaN      2,156,624,900   \n",
       "1               5.19  Albania1987           NaN      2,156,624,900   \n",
       "2               4.83  Albania1987           NaN      2,156,624,900   \n",
       "3               4.59  Albania1987           NaN      2,156,624,900   \n",
       "4               3.28  Albania1987           NaN      2,156,624,900   \n",
       "\n",
       "   gdp_per_capita ($)       generation  \n",
       "0                 796     Generation X  \n",
       "1                 796           Silent  \n",
       "2                 796     Generation X  \n",
       "3                 796  G.I. Generation  \n",
       "4                 796          Boomers  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"master.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Albania</td>\n",
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       "      <td>308000</td>\n",
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       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
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       "      <td>21800</td>\n",
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       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
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       "      <td>9</td>\n",
       "      <td>274300</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population\n",
       "0  Albania  1987    male  15-24 years           21      312900\n",
       "1  Albania  1987    male  35-54 years           16      308000\n",
       "2  Albania  1987  female  15-24 years           14      289700\n",
       "3  Albania  1987    male    75+ years            1       21800\n",
       "4  Albania  1987    male  25-34 years            9      274300"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['country-year']\n",
    "del df['HDI for year']\n",
    "del df[' gdp_for_year ($) ']\n",
    "del df['generation']\n",
    "del df['suicides/100k pop']\n",
    "del df['gdp_per_capita ($)']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Russian Federation': 0, 'Armenia': 1, 'Italy': 2, 'Seychelles': 3, 'Germany': 4, 'Bahrain': 5, 'Israel': 6, 'Mauritius': 7, 'Kyrgyzstan': 8, 'Belize': 9, 'Suriname': 10, 'Serbia': 11, 'Singapore': 12, 'Aruba': 13, 'Cyprus': 14, 'Finland': 15, 'Kazakhstan': 16, 'Sri Lanka': 17, 'Hungary': 18, 'Lithuania': 19, 'Jamaica': 20, 'Kuwait': 21, 'Japan': 22, 'United States': 23, 'France': 24, 'Mexico': 25, 'Belgium': 26, 'Australia': 27, 'Austria': 28, 'Uzbekistan': 29, 'Malta': 30, 'Spain': 31, 'Dominica': 32, 'Oman': 33, 'Slovakia': 34, 'Grenada': 35, 'Saint Vincent and Grenadines': 36, 'United Kingdom': 37, 'Denmark': 38, 'Kiribati': 39, 'Trinidad and Tobago': 40, 'Ecuador': 41, 'Qatar': 42, 'New Zealand': 43, 'San Marino': 44, 'Slovenia': 45, 'Colombia': 46, 'Latvia': 47, 'Albania': 48, 'Saint Lucia': 49, 'Panama': 50, 'Ireland': 51, 'Bulgaria': 52, 'United Arab Emirates': 53, 'Thailand': 54, 'Romania': 55, 'Belarus': 56, 'Maldives': 57, 'Portugal': 58, 'Sweden': 59, 'Cabo Verde': 60, 'Chile': 61, 'Mongolia': 62, 'Cuba': 63, 'Iceland': 64, 'Luxembourg': 65, 'Bosnia and Herzegovina': 66, 'Guyana': 67, 'Bahamas': 68, 'El Salvador': 69, 'Azerbaijan': 70, 'Norway': 71, 'Ukraine': 72, 'Nicaragua': 73, 'Switzerland': 74, 'Fiji': 75, 'Macau': 76, 'Barbados': 77, 'Brazil': 78, 'South Africa': 79, 'Netherlands': 80, 'Greece': 81, 'Turkmenistan': 82, 'Czech Republic': 83, 'Poland': 84, 'Canada': 85, 'Paraguay': 86, 'Montenegro': 87, 'Puerto Rico': 88, 'Costa Rica': 89, 'Uruguay': 90, 'Argentina': 91, 'Croatia': 92, 'Turkey': 93, 'Georgia': 94, 'Republic of Korea': 95, 'Philippines': 96, 'Saint Kitts and Nevis': 97, 'Antigua and Barbuda': 98, 'Guatemala': 99, 'Estonia': 100}\n"
     ]
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       "      <td>Albania</td>\n",
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       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
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       "      <td>16</td>\n",
       "      <td>308000</td>\n",
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       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14</td>\n",
       "      <td>289700</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
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       "      <td>75+ years</td>\n",
       "      <td>1</td>\n",
       "      <td>21800</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
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      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population\n",
       "0  Albania  1987    male  15-24 years           21      312900\n",
       "1  Albania  1987    male  35-54 years           16      308000\n",
       "2  Albania  1987  female  15-24 years           14      289700\n",
       "3  Albania  1987    male    75+ years            1       21800\n",
       "4  Albania  1987    male  25-34 years            9      274300"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst=set(df['country'])\n",
    "#print(lst)\n",
    "con=dict()\n",
    "i=0\n",
    "for country in lst:\n",
    "    con[country]=i\n",
    "    i=i+1\n",
    "print(con)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({\"country\":con},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
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       "      <td>48</td>\n",
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       "      <td>48</td>\n",
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       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>1987</td>\n",
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       "      <td>9</td>\n",
       "      <td>274300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex age  suicides_no  population\n",
       "0       48  1987    male   2           21      312900\n",
       "1       48  1987    male   4           16      308000\n",
       "2       48  1987  female   2           14      289700\n",
       "3       48  1987    male   6            1       21800\n",
       "4       48  1987    male   3            9      274300"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age[df.age=='5-14 years']=1\n",
    "df.age[df.age=='15-24 years']=2\n",
    "df.age[df.age=='25-34 years']=3\n",
    "df.age[df.age=='35-54 years']=4\n",
    "df.age[df.age=='55-74 years']=5\n",
    "df.age[df.age=='75+ years']=6\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
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       "    </tr>\n",
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      ],
      "text/plain": [
       "   country  year sex age  suicides_no  population\n",
       "0       48  1987   1   2           21      312900\n",
       "1       48  1987   1   4           16      308000\n",
       "2       48  1987   2   2           14      289700\n",
       "3       48  1987   1   6            1       21800\n",
       "4       48  1987   1   3            9      274300"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex[df.sex=='male']=1\n",
    "df.sex[df.sex=='female']=2\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('final.csv',encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>21800</td>\n",
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       "      <th>4</th>\n",
       "      <td>48</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year sex age  suicides_no  population\n",
       "0       48  1987   1   2           21      312900\n",
       "1       48  1987   1   4           16      308000\n",
       "2       48  1987   2   2           14      289700\n",
       "3       48  1987   1   6            1       21800\n",
       "4       48  1987   1   3            9      274300"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27820, 7)\n"
     ]
    },
    {
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       "</div>"
      ],
      "text/plain": [
       "   country  year  sex  age  suicides_no  population\n",
       "0       48  1987    1    2           21      312900\n",
       "1       48  1987    1    4           16      308000\n",
       "2       48  1987    2    2           14      289700\n",
       "3       48  1987    1    6            1       21800\n",
       "4       48  1987    1    3            9      274300"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('final.csv')\n",
    "print(data.shape)\n",
    "data.head()\n",
    "del data['Unnamed: 0']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>48</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>274300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  sex  age  rate  population\n",
       "0       48  1987    1    2    21      312900\n",
       "1       48  1987    1    4    16      308000\n",
       "2       48  1987    2    2    14      289700\n",
       "3       48  1987    1    6     1       21800\n",
       "4       48  1987    1    3     9      274300"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colnames = ['country', 'year', 'sex', 'age', 'rate', 'population']\n",
    "data.columns = colnames\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1128b2ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotFeatures(col_list,title):\n",
    "    plt.figure(figsize=(10, 14))\n",
    "    i = 0\n",
    "    print(len(col_list))\n",
    "    for col in col_list:\n",
    "        i+=1\n",
    "        plt.subplot(7,2,i)\n",
    "        plt.plot(data[col],data[\"rate\"],marker='.',linestyle='none')\n",
    "        plt.title(title % (col))   \n",
    "        plt.tight_layout()\n",
    "#in [11]:\n",
    "colnames = ['country', 'year', 'sex', 'age', 'population']\n",
    "plotFeatures(colnames,\"Relationship bw %s and sucide rate\")"
   ]
  }
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