Investigate_Children_Out_of_School_20180108.ipynb

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    "\n",
    "\n",
    "# Project: Investigate Children Out of School\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> **Key notes**: \"Gapminder has collected a lot of information about how people live their lives in different countries, tracked across the years, and on a number of different indicators. \n",
    "\n",
    "> **Questions to explore**: \n",
    "><ul>\n",
    "><li><a href=\"#q1\"> 1. Research Question 1: What is the total numbers of children out of primary school over years, indicate the male and female numbers as well?</a></li>\n",
    "><li><a href=\"#q2\"> 2. Research Question 2: What is distribution of female children who was out of primary school from 1980 to 1995?</a></li>\n",
    "><li><a href=\"#q3\"> 3. Research Question 3: What are numbers of children out of school in total, by male and female in China, 1985?</a></li> \n",
    "><li><a href=\"#q4\"> 4. What are relationship of children out of school of female in China in russian and usa over time? Which has a better trend?</a></li>\n",
    "><li><a href=\"#q5\"> 5. Research Question 5: What is the overall trend for children out of primary school over the years?</a></li>\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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    "# Set up import statements for all of the packages that are planed to use;\n",
    "# Include a 'magic word' so that visualizations are plotted;\n",
    "# call on dataframe to display the first 5 rows.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from statistics import mode\n",
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Children out of school, primary</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
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       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Abkhazia</td>\n",
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       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8445.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>37763.0</td>\n",
       "      <td>51566.0</td>\n",
       "      <td>52014.0</td>\n",
       "      <td>53597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>806895.0</td>\n",
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       "      <td>696223.0</td>\n",
       "      <td>671496.0</td>\n",
       "      <td>646398.0</td>\n",
       "      <td>627309.0</td>\n",
       "      <td>...</td>\n",
       "      <td>190629.0</td>\n",
       "      <td>171799.0</td>\n",
       "      <td>135684.0</td>\n",
       "      <td>116195.0</td>\n",
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       "      <td>81638.0</td>\n",
       "      <td>71430.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 43 columns</p>\n",
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       "  Children out of school, primary  1970  1971  1972      1973       1974  \\\n",
       "0                        Abkhazia   NaN   NaN   NaN       NaN        NaN   \n",
       "1                     Afghanistan   NaN   NaN   NaN       NaN  1559835.0   \n",
       "2           Akrotiri and Dhekelia   NaN   NaN   NaN       NaN        NaN   \n",
       "3                         Albania   NaN   NaN   NaN       NaN        NaN   \n",
       "4                         Algeria   NaN   NaN   NaN  806895.0   731571.0   \n",
       "\n",
       "       1975      1976      1977      1978   ...         2002      2003  \\\n",
       "0       NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN   ...          NaN    8445.0   \n",
       "4  696223.0  671496.0  646398.0  627309.0   ...     190629.0  171799.0   \n",
       "\n",
       "       2004      2005      2006     2007     2008     2009     2010     2011  \n",
       "0       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "1       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "2       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "3   15754.0       NaN       NaN      NaN  37763.0  51566.0  52014.0  53597.0  \n",
       "4  135684.0  116195.0  104234.0  93561.0  74715.0  71175.0  81638.0  71430.0  \n",
       "\n",
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   "source": [
    "# Reading an Excel file in python using pandas\n",
    "# call on dataframe to display the first 5 rows\n",
    "\n",
    "xl = pd.ExcelFile('Child out of school primary.xlsx')\n",
    " \n",
    "xl.sheet_names\n",
    "[u'Data']\n",
    "\n",
    "df_tot = xl.parse(\"Data\")\n",
    "df_tot.head()\n"
   ]
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   "cell_type": "code",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
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       "  Children out of school, primary, female  1970  1971  1972      1973  \\\n",
       "0                                Abkhazia   NaN   NaN   NaN       NaN   \n",
       "1                             Afghanistan   NaN   NaN   NaN       NaN   \n",
       "2                   Akrotiri and Dhekelia   NaN   NaN   NaN       NaN   \n",
       "3                                 Albania   NaN   NaN   NaN       NaN   \n",
       "4                                 Algeria   NaN   NaN   NaN  564297.0   \n",
       "\n",
       "       1974      1975      1976      1977      1978   ...         2002  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN   ...          NaN   \n",
       "1  923588.0       NaN       NaN       NaN       NaN   ...          NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN   ...          NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN   ...          NaN   \n",
       "4  538928.0  523330.0  509066.0  491870.0  476842.0   ...     118718.0   \n",
       "\n",
       "       2003     2004     2005     2006     2007     2008     2009     2010  \\\n",
       "0       NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1       NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2       NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3    5415.0   7791.0      NaN      NaN      NaN  17469.0  24446.0  24577.0   \n",
       "4  105652.0  86044.0  75558.0  69439.0  60184.0  47538.0  47078.0  53198.0   \n",
       "\n",
       "      2011  \n",
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       "\n",
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   "source": [
    "x2 = pd.ExcelFile('Child out of school primiary female.xlsx')\n",
    " \n",
    "x2.sheet_names\n",
    "[u'Data']\n",
    "\n",
    "df_f = x2.parse(\"Data\")\n",
    "df_f.head()"
   ]
  },
  {
   "cell_type": "code",
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       "      <th>Children out of school, primary, male</th>\n",
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       "      <th>0</th>\n",
       "      <td>Abkhazia</td>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>636247.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akrotiri and Dhekelia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3030.0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20294.0</td>\n",
       "      <td>27120.0</td>\n",
       "      <td>27437.0</td>\n",
       "      <td>28053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242598.0</td>\n",
       "      <td>192643.0</td>\n",
       "      <td>172893.0</td>\n",
       "      <td>162430.0</td>\n",
       "      <td>154528.0</td>\n",
       "      <td>150467.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71911.0</td>\n",
       "      <td>66147.0</td>\n",
       "      <td>49640.0</td>\n",
       "      <td>40637.0</td>\n",
       "      <td>34795.0</td>\n",
       "      <td>33377.0</td>\n",
       "      <td>27177.0</td>\n",
       "      <td>24097.0</td>\n",
       "      <td>28440.0</td>\n",
       "      <td>25723.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Children out of school, primary, male  1970  1971  1972      1973      1974  \\\n",
       "0                              Abkhazia   NaN   NaN   NaN       NaN       NaN   \n",
       "1                           Afghanistan   NaN   NaN   NaN       NaN  636247.0   \n",
       "2                 Akrotiri and Dhekelia   NaN   NaN   NaN       NaN       NaN   \n",
       "3                               Albania   NaN   NaN   NaN       NaN       NaN   \n",
       "4                               Algeria   NaN   NaN   NaN  242598.0  192643.0   \n",
       "\n",
       "       1975      1976      1977      1978   ...        2002     2003     2004  \\\n",
       "0       NaN       NaN       NaN       NaN   ...         NaN      NaN      NaN   \n",
       "1       NaN       NaN       NaN       NaN   ...         NaN      NaN      NaN   \n",
       "2       NaN       NaN       NaN       NaN   ...         NaN      NaN      NaN   \n",
       "3       NaN       NaN       NaN       NaN   ...         NaN   3030.0   7963.0   \n",
       "4  172893.0  162430.0  154528.0  150467.0   ...     71911.0  66147.0  49640.0   \n",
       "\n",
       "      2005     2006     2007     2008     2009     2010     2011  \n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "3      NaN      NaN      NaN  20294.0  27120.0  27437.0  28053.0  \n",
       "4  40637.0  34795.0  33377.0  27177.0  24097.0  28440.0  25723.0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3 = pd.ExcelFile('Child out of school primiary male.xlsx')\n",
    " \n",
    "x3.sheet_names\n",
    "[u'Data']\n",
    "\n",
    "df_m = x3.parse(\"Data\")\n",
    "df_m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((275, 43), (275, 43), (275, 43))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the three dataframe have the same shape\n",
    "\n",
    "df_tot.shape, df_m.shape, df_f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Check if the first columns from the 3 dataframe are exactly the same\n",
    "assert (df_tot['Children out of school, primary'].tolist() == df_m['Children out of school, primary, male'].tolist()\\\n",
    "       == df_f['Children out of school, primary, female'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275, 129)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the 3 dataframe\n",
    "\n",
    "df1 = df_tot.merge(df_f, how='outer', left_index = True, right_index = True)\n",
    "\n",
    "df1 = df1.merge(df_m, how='outer', left_index = True, right_index = True)\n",
    "\n",
    "# Confirm changes\n",
    "\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> **Key notes**: In this section of the report, the following work will be done: load the data; check for cleanliness; trim and clean dataset for analysis.\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Children out of school, primary     object\n",
       "1970_x                             float64\n",
       "1971_x                             float64\n",
       "1972_x                             float64\n",
       "1973_x                             float64\n",
       "1974_x                             float64\n",
       "1975_x                             float64\n",
       "1976_x                             float64\n",
       "1977_x                             float64\n",
       "1978_x                             float64\n",
       "1979_x                             float64\n",
       "1980_x                             float64\n",
       "1981_x                             float64\n",
       "1982_x                             float64\n",
       "1983_x                             float64\n",
       "1984_x                             float64\n",
       "1985_x                             float64\n",
       "1986_x                             float64\n",
       "1987_x                             float64\n",
       "1988_x                             float64\n",
       "1989_x                             float64\n",
       "1990_x                             float64\n",
       "1991_x                             float64\n",
       "1992_x                             float64\n",
       "1993_x                             float64\n",
       "1994_x                             float64\n",
       "1995_x                             float64\n",
       "1996_x                             float64\n",
       "1997_x                             float64\n",
       "1998_x                             float64\n",
       "                                    ...   \n",
       "1982                               float64\n",
       "1983                               float64\n",
       "1984                               float64\n",
       "1985                               float64\n",
       "1986                               float64\n",
       "1987                               float64\n",
       "1988                               float64\n",
       "1989                               float64\n",
       "1990                               float64\n",
       "1991                               float64\n",
       "1992                               float64\n",
       "1993                               float64\n",
       "1994                               float64\n",
       "1995                               float64\n",
       "1996                               float64\n",
       "1997                               float64\n",
       "1998                               float64\n",
       "1999                               float64\n",
       "2000                               float64\n",
       "2001                               float64\n",
       "2002                               float64\n",
       "2003                               float64\n",
       "2004                               float64\n",
       "2005                               float64\n",
       "2006                               float64\n",
       "2007                               float64\n",
       "2008                               float64\n",
       "2009                               float64\n",
       "2010                               float64\n",
       "2011                               float64\n",
       "Length: 129, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return the datatypes of the columns.\n",
    "\n",
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicates in the data.\n",
    "\n",
    "sum(df1.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 126)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  check if any value is NaN in DataFrame and in how many columns\n",
    "\n",
    "df1.isnull().any().any(), sum(df1.isnull().any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1970_x</th>\n",
       "      <th>1971_x</th>\n",
       "      <th>1972_x</th>\n",
       "      <th>1973_x</th>\n",
       "      <th>1974_x</th>\n",
       "      <th>1975_x</th>\n",
       "      <th>1976_x</th>\n",
       "      <th>1977_x</th>\n",
       "      <th>1978_x</th>\n",
       "      <th>1979_x</th>\n",
       "      <th>...</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.030000e+02</td>\n",
       "      <td>1.060000e+02</td>\n",
       "      <td>1.070000e+02</td>\n",
       "      <td>1.180000e+02</td>\n",
       "      <td>1.130000e+02</td>\n",
       "      <td>1.220000e+02</td>\n",
       "      <td>1.160000e+02</td>\n",
       "      <td>1.130000e+02</td>\n",
       "      <td>1.130000e+02</td>\n",
       "      <td>6.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.843502e+05</td>\n",
       "      <td>1.022907e+06</td>\n",
       "      <td>3.314347e+05</td>\n",
       "      <td>3.489581e+05</td>\n",
       "      <td>4.035501e+05</td>\n",
       "      <td>4.230175e+05</td>\n",
       "      <td>3.128573e+05</td>\n",
       "      <td>3.469881e+05</td>\n",
       "      <td>3.529903e+05</td>\n",
       "      <td>2.091687e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.018418e+05</td>\n",
       "      <td>2.408376e+05</td>\n",
       "      <td>1.905158e+05</td>\n",
       "      <td>1.686734e+05</td>\n",
       "      <td>1.623407e+05</td>\n",
       "      <td>1.380507e+05</td>\n",
       "      <td>1.396097e+05</td>\n",
       "      <td>1.440379e+05</td>\n",
       "      <td>1.187534e+05</td>\n",
       "      <td>1.168063e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.321416e+06</td>\n",
       "      <td>4.052435e+06</td>\n",
       "      <td>8.888364e+05</td>\n",
       "      <td>6.248331e+05</td>\n",
       "      <td>9.659810e+05</td>\n",
       "      <td>1.098716e+06</td>\n",
       "      <td>8.575641e+05</td>\n",
       "      <td>7.929622e+05</td>\n",
       "      <td>7.108179e+05</td>\n",
       "      <td>3.436676e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>7.617249e+05</td>\n",
       "      <td>8.209809e+05</td>\n",
       "      <td>5.361852e+05</td>\n",
       "      <td>4.837907e+05</td>\n",
       "      <td>4.911265e+05</td>\n",
       "      <td>4.478736e+05</td>\n",
       "      <td>4.994961e+05</td>\n",
       "      <td>5.184244e+05</td>\n",
       "      <td>5.176267e+05</td>\n",
       "      <td>2.918273e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.610000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.160000e+03</td>\n",
       "      <td>1.860000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.250000e+02</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>1.940000e+02</td>\n",
       "      <td>2.370000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.060000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.148100e+04</td>\n",
       "      <td>2.506400e+04</td>\n",
       "      <td>2.677500e+04</td>\n",
       "      <td>3.293600e+04</td>\n",
       "      <td>2.891925e+04</td>\n",
       "      <td>2.400000e+04</td>\n",
       "      <td>1.372125e+04</td>\n",
       "      <td>1.038000e+04</td>\n",
       "      <td>1.325100e+04</td>\n",
       "      <td>1.184300e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>2.309000e+03</td>\n",
       "      <td>3.078750e+03</td>\n",
       "      <td>3.582500e+03</td>\n",
       "      <td>2.499500e+03</td>\n",
       "      <td>3.162000e+03</td>\n",
       "      <td>2.557750e+03</td>\n",
       "      <td>2.508500e+03</td>\n",
       "      <td>2.558000e+03</td>\n",
       "      <td>1.785000e+03</td>\n",
       "      <td>4.298000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.633390e+05</td>\n",
       "      <td>9.488500e+04</td>\n",
       "      <td>7.827400e+04</td>\n",
       "      <td>9.425400e+04</td>\n",
       "      <td>8.502600e+04</td>\n",
       "      <td>7.139200e+04</td>\n",
       "      <td>5.876450e+04</td>\n",
       "      <td>6.366900e+04</td>\n",
       "      <td>8.827500e+04</td>\n",
       "      <td>5.215900e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.539500e+04</td>\n",
       "      <td>1.826300e+04</td>\n",
       "      <td>1.549400e+04</td>\n",
       "      <td>1.431150e+04</td>\n",
       "      <td>1.504600e+04</td>\n",
       "      <td>1.111750e+04</td>\n",
       "      <td>1.828050e+04</td>\n",
       "      <td>1.883400e+04</td>\n",
       "      <td>1.180300e+04</td>\n",
       "      <td>1.914200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.429960e+05</td>\n",
       "      <td>4.115620e+05</td>\n",
       "      <td>1.959640e+05</td>\n",
       "      <td>4.260080e+05</td>\n",
       "      <td>3.638538e+05</td>\n",
       "      <td>2.338670e+05</td>\n",
       "      <td>1.834662e+05</td>\n",
       "      <td>3.604840e+05</td>\n",
       "      <td>3.581525e+05</td>\n",
       "      <td>2.123680e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>8.396150e+04</td>\n",
       "      <td>1.035255e+05</td>\n",
       "      <td>8.457300e+04</td>\n",
       "      <td>8.145475e+04</td>\n",
       "      <td>8.142300e+04</td>\n",
       "      <td>6.089300e+04</td>\n",
       "      <td>6.275850e+04</td>\n",
       "      <td>7.220200e+04</td>\n",
       "      <td>4.433200e+04</td>\n",
       "      <td>8.860400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.782696e+06</td>\n",
       "      <td>2.799442e+07</td>\n",
       "      <td>5.911106e+06</td>\n",
       "      <td>3.151254e+06</td>\n",
       "      <td>6.276900e+06</td>\n",
       "      <td>5.937605e+06</td>\n",
       "      <td>5.461254e+06</td>\n",
       "      <td>5.070586e+06</td>\n",
       "      <td>3.696782e+06</td>\n",
       "      <td>1.553314e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>7.096696e+06</td>\n",
       "      <td>6.637166e+06</td>\n",
       "      <td>3.128672e+06</td>\n",
       "      <td>3.155730e+06</td>\n",
       "      <td>3.221330e+06</td>\n",
       "      <td>3.631279e+06</td>\n",
       "      <td>4.558888e+06</td>\n",
       "      <td>4.884551e+06</td>\n",
       "      <td>5.054204e+06</td>\n",
       "      <td>2.090372e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1970_x        1971_x        1972_x        1973_x        1974_x  \\\n",
       "count  1.700000e+01  4.900000e+01  5.300000e+01  5.300000e+01  4.800000e+01   \n",
       "mean   6.843502e+05  1.022907e+06  3.314347e+05  3.489581e+05  4.035501e+05   \n",
       "std    1.321416e+06  4.052435e+06  8.888364e+05  6.248331e+05  9.659810e+05   \n",
       "min    4.610000e+02  0.000000e+00  1.800000e+01  1.160000e+03  1.860000e+02   \n",
       "25%    6.148100e+04  2.506400e+04  2.677500e+04  3.293600e+04  2.891925e+04   \n",
       "50%    1.633390e+05  9.488500e+04  7.827400e+04  9.425400e+04  8.502600e+04   \n",
       "75%    4.429960e+05  4.115620e+05  1.959640e+05  4.260080e+05  3.638538e+05   \n",
       "max    4.782696e+06  2.799442e+07  5.911106e+06  3.151254e+06  6.276900e+06   \n",
       "\n",
       "             1975_x        1976_x        1977_x        1978_x        1979_x  \\\n",
       "count  4.500000e+01  5.200000e+01  5.900000e+01  6.300000e+01  6.100000e+01   \n",
       "mean   4.230175e+05  3.128573e+05  3.469881e+05  3.529903e+05  2.091687e+05   \n",
       "std    1.098716e+06  8.575641e+05  7.929622e+05  7.108179e+05  3.436676e+05   \n",
       "min    0.000000e+00  2.250000e+02  8.000000e+01  1.940000e+02  2.370000e+02   \n",
       "25%    2.400000e+04  1.372125e+04  1.038000e+04  1.325100e+04  1.184300e+04   \n",
       "50%    7.139200e+04  5.876450e+04  6.366900e+04  8.827500e+04  5.215900e+04   \n",
       "75%    2.338670e+05  1.834662e+05  3.604840e+05  3.581525e+05  2.123680e+05   \n",
       "max    5.937605e+06  5.461254e+06  5.070586e+06  3.696782e+06  1.553314e+06   \n",
       "\n",
       "           ...               2002          2003          2004          2005  \\\n",
       "count      ...       1.030000e+02  1.060000e+02  1.070000e+02  1.180000e+02   \n",
       "mean       ...       2.018418e+05  2.408376e+05  1.905158e+05  1.686734e+05   \n",
       "std        ...       7.617249e+05  8.209809e+05  5.361852e+05  4.837907e+05   \n",
       "min        ...       4.800000e+01  1.000000e+00  1.100000e+01  3.500000e+01   \n",
       "25%        ...       2.309000e+03  3.078750e+03  3.582500e+03  2.499500e+03   \n",
       "50%        ...       1.539500e+04  1.826300e+04  1.549400e+04  1.431150e+04   \n",
       "75%        ...       8.396150e+04  1.035255e+05  8.457300e+04  8.145475e+04   \n",
       "max        ...       7.096696e+06  6.637166e+06  3.128672e+06  3.155730e+06   \n",
       "\n",
       "               2006          2007          2008          2009          2010  \\\n",
       "count  1.130000e+02  1.220000e+02  1.160000e+02  1.130000e+02  1.130000e+02   \n",
       "mean   1.623407e+05  1.380507e+05  1.396097e+05  1.440379e+05  1.187534e+05   \n",
       "std    4.911265e+05  4.478736e+05  4.994961e+05  5.184244e+05  5.176267e+05   \n",
       "min    2.100000e+01  5.000000e+00  9.000000e+00  7.900000e+01  1.000000e+00   \n",
       "25%    3.162000e+03  2.557750e+03  2.508500e+03  2.558000e+03  1.785000e+03   \n",
       "50%    1.504600e+04  1.111750e+04  1.828050e+04  1.883400e+04  1.180300e+04   \n",
       "75%    8.142300e+04  6.089300e+04  6.275850e+04  7.220200e+04  4.433200e+04   \n",
       "max    3.221330e+06  3.631279e+06  4.558888e+06  4.884551e+06  5.054204e+06   \n",
       "\n",
       "               2011  \n",
       "count  6.100000e+01  \n",
       "mean   1.168063e+05  \n",
       "std    2.918273e+05  \n",
       "min    3.060000e+02  \n",
       "25%    4.298000e+03  \n",
       "50%    1.914200e+04  \n",
       "75%    8.860400e+04  \n",
       "max    2.090372e+06  \n",
       "\n",
       "[8 rows x 126 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generates descriptive statistics, excluding NaN values.\n",
    "\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locate the columns whose NaN values needs to be treated\n",
    "\n",
    "col = df1.drop(['Children out of school, primary', 'Children out of school, primary, female'\\\n",
    "               , 'Children out of school, primary, male'], axis=1)\n",
    "\n",
    "# Replace NaN with mean\n",
    "\n",
    "for c in col:\n",
    "    c_mean = df1[c].mean()\n",
    "    df1[c].fillna(c_mean, inplace = True)\n",
    "    \n",
    "# Confirm changes\n",
    "\n",
    "df1.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1970_x</th>\n",
       "      <th>1971_x</th>\n",
       "      <th>1972_x</th>\n",
       "      <th>1973_x</th>\n",
       "      <th>1974_x</th>\n",
       "      <th>1975_x</th>\n",
       "      <th>1976_x</th>\n",
       "      <th>1977_x</th>\n",
       "      <th>1978_x</th>\n",
       "      <th>...</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abkhazia</td>\n",
       "      <td>684350.176471</td>\n",
       "      <td>1.022907e+06</td>\n",
       "      <td>331434.679245</td>\n",
       "      <td>348958.09434</td>\n",
       "      <td>403550.125</td>\n",
       "      <td>423017.488889</td>\n",
       "      <td>312857.269231</td>\n",
       "      <td>346988.050847</td>\n",
       "      <td>352990.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>201841.834951</td>\n",
       "      <td>240837.603774</td>\n",
       "      <td>190515.757009</td>\n",
       "      <td>168673.40678</td>\n",
       "      <td>162340.681416</td>\n",
       "      <td>138050.745902</td>\n",
       "      <td>139609.689655</td>\n",
       "      <td>144037.876106</td>\n",
       "      <td>118753.353982</td>\n",
       "      <td>116806.311475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>684350.176471</td>\n",
       "      <td>1.022907e+06</td>\n",
       "      <td>331434.679245</td>\n",
       "      <td>348958.09434</td>\n",
       "      <td>1559835.000</td>\n",
       "      <td>423017.488889</td>\n",
       "      <td>312857.269231</td>\n",
       "      <td>346988.050847</td>\n",
       "      <td>352990.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>201841.834951</td>\n",
       "      <td>240837.603774</td>\n",
       "      <td>190515.757009</td>\n",
       "      <td>168673.40678</td>\n",
       "      <td>162340.681416</td>\n",
       "      <td>138050.745902</td>\n",
       "      <td>139609.689655</td>\n",
       "      <td>144037.876106</td>\n",
       "      <td>118753.353982</td>\n",
       "      <td>116806.311475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akrotiri and Dhekelia</td>\n",
       "      <td>684350.176471</td>\n",
       "      <td>1.022907e+06</td>\n",
       "      <td>331434.679245</td>\n",
       "      <td>348958.09434</td>\n",
       "      <td>403550.125</td>\n",
       "      <td>423017.488889</td>\n",
       "      <td>312857.269231</td>\n",
       "      <td>346988.050847</td>\n",
       "      <td>352990.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>201841.834951</td>\n",
       "      <td>240837.603774</td>\n",
       "      <td>190515.757009</td>\n",
       "      <td>168673.40678</td>\n",
       "      <td>162340.681416</td>\n",
       "      <td>138050.745902</td>\n",
       "      <td>139609.689655</td>\n",
       "      <td>144037.876106</td>\n",
       "      <td>118753.353982</td>\n",
       "      <td>116806.311475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>684350.176471</td>\n",
       "      <td>1.022907e+06</td>\n",
       "      <td>331434.679245</td>\n",
       "      <td>348958.09434</td>\n",
       "      <td>403550.125</td>\n",
       "      <td>423017.488889</td>\n",
       "      <td>312857.269231</td>\n",
       "      <td>346988.050847</td>\n",
       "      <td>352990.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>201841.834951</td>\n",
       "      <td>3030.000000</td>\n",
       "      <td>7963.000000</td>\n",
       "      <td>168673.40678</td>\n",
       "      <td>162340.681416</td>\n",
       "      <td>138050.745902</td>\n",
       "      <td>20294.000000</td>\n",
       "      <td>27120.000000</td>\n",
       "      <td>27437.000000</td>\n",
       "      <td>28053.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>684350.176471</td>\n",
       "      <td>1.022907e+06</td>\n",
       "      <td>331434.679245</td>\n",
       "      <td>806895.00000</td>\n",
       "      <td>731571.000</td>\n",
       "      <td>696223.000000</td>\n",
       "      <td>671496.000000</td>\n",
       "      <td>646398.000000</td>\n",
       "      <td>627309.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>71911.000000</td>\n",
       "      <td>66147.000000</td>\n",
       "      <td>49640.000000</td>\n",
       "      <td>40637.00000</td>\n",
       "      <td>34795.000000</td>\n",
       "      <td>33377.000000</td>\n",
       "      <td>27177.000000</td>\n",
       "      <td>24097.000000</td>\n",
       "      <td>28440.000000</td>\n",
       "      <td>25723.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country         1970_x        1971_x         1972_x  \\\n",
       "0               Abkhazia  684350.176471  1.022907e+06  331434.679245   \n",
       "1            Afghanistan  684350.176471  1.022907e+06  331434.679245   \n",
       "2  Akrotiri and Dhekelia  684350.176471  1.022907e+06  331434.679245   \n",
       "3                Albania  684350.176471  1.022907e+06  331434.679245   \n",
       "4                Algeria  684350.176471  1.022907e+06  331434.679245   \n",
       "\n",
       "         1973_x       1974_x         1975_x         1976_x         1977_x  \\\n",
       "0  348958.09434   403550.125  423017.488889  312857.269231  346988.050847   \n",
       "1  348958.09434  1559835.000  423017.488889  312857.269231  346988.050847   \n",
       "2  348958.09434   403550.125  423017.488889  312857.269231  346988.050847   \n",
       "3  348958.09434   403550.125  423017.488889  312857.269231  346988.050847   \n",
       "4  806895.00000   731571.000  696223.000000  671496.000000  646398.000000   \n",
       "\n",
       "          1978_x      ...                 2002           2003           2004  \\\n",
       "0  352990.285714      ...        201841.834951  240837.603774  190515.757009   \n",
       "1  352990.285714      ...        201841.834951  240837.603774  190515.757009   \n",
       "2  352990.285714      ...        201841.834951  240837.603774  190515.757009   \n",
       "3  352990.285714      ...        201841.834951    3030.000000    7963.000000   \n",
       "4  627309.000000      ...         71911.000000   66147.000000   49640.000000   \n",
       "\n",
       "           2005           2006           2007           2008           2009  \\\n",
       "0  168673.40678  162340.681416  138050.745902  139609.689655  144037.876106   \n",
       "1  168673.40678  162340.681416  138050.745902  139609.689655  144037.876106   \n",
       "2  168673.40678  162340.681416  138050.745902  139609.689655  144037.876106   \n",
       "3  168673.40678  162340.681416  138050.745902   20294.000000   27120.000000   \n",
       "4   40637.00000   34795.000000   33377.000000   27177.000000   24097.000000   \n",
       "\n",
       "            2010           2011  \n",
       "0  118753.353982  116806.311475  \n",
       "1  118753.353982  116806.311475  \n",
       "2  118753.353982  116806.311475  \n",
       "3   27437.000000   28053.000000  \n",
       "4   28440.000000   25723.000000  \n",
       "\n",
       "[5 rows x 129 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename column for simplification\n",
    "\n",
    "df1.rename(columns = {'Children out of school, primary':'country'}, inplace = True)\n",
    "\n",
    "# check the new dataframe\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "<a id='q1'></a>\n",
    "### Research Question 1: What is the total numbers of children out of primary school over years, indicate the male and female numbers as well?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ICgqioKCAmJgYg3adnJxo1apVtY9NCPFsGzFiBPXr19e2HRwcAPDz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\nzyf87mdIsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4614e0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 364,
       "width": 581
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the sum for each group\n",
    "\n",
    "sum_tot = df1.iloc[:, 1:43]\n",
    "m_tot = df1.iloc[:, 44:86]\n",
    "f_tot = df1.iloc[:, 87:]\n",
    "\n",
    "\n",
    "tot = []\n",
    "for t in sum_tot.columns:\n",
    "    tot.append(sum_tot[t].sum())\n",
    "\n",
    "\n",
    "m = []\n",
    "for ma in m_tot.columns:\n",
    "    m.append(m_tot[ma].sum())\n",
    "\n",
    "\n",
    "f = []\n",
    "for fa in f_tot.columns:\n",
    "    f.append(f_tot[fa].sum())\n",
    "\n",
    "# Plot\n",
    "\n",
    "x = ['total number', 'male number', 'female number']\n",
    "y = [sum(tot), sum(m), sum(f)]\n",
    "\n",
    "plt.subplots(figsize=(10,6))\n",
    "sns.barplot(x,y, alpha = 0.8);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q2'></a>\n",
    "### Research Question 2: What is distribution of female children who was out of primary school from 1980 to 1995?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jQqlZkEE9GOmne+0RGHF/gsYjXuuOOu6O6+oj7u7r4i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\nxjVvfgJFJ5Bkdx9z6yp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2afc18>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 842,
       "width": 1172
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target the year and plot\n",
    "\n",
    "sum_tot1 = sum_tot.iloc[:, 10:26]\n",
    "new_col = []\n",
    "for ele in sum_tot1.columns:\n",
    "    new_col.append(ele.split('_x')[0])\n",
    "   \n",
    "sum_tot1.columns = new_col  \n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "sns.boxplot(data = sum_tot1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q3'></a>\n",
    "### Research Question 3: What are numbers of children out of school in total, by male and female in China, 1985?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The numbers of children out of school in total, by male and female in China were 284172, 177367 and 122195 in 1985, respectively.\n"
     ]
    }
   ],
   "source": [
    "china = df1.copy()\n",
    "china = china.set_index('country')\n",
    "tot_chi = china.loc['China', '1985_x']\n",
    "f_chi = china.loc['China', '1985_y']\n",
    "m_chi = china.loc['China', '1985']\n",
    "\n",
    "print('The numbers of children out of school in total, by male and female in China were \\\n",
    "{0:.0f}, {1:.0f} and {2:.0f} in 1985, respectively.'.format(tot_chi, f_chi, m_chi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q4'></a>\n",
    "### Research Question 4: What are relationship of children out of school of female in China in russian and usa over time? Which has a better trend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IbiLWCAMAACQhQnDz0REGAABIMr5ASGUuQxYhuFnoCAMAACQRQnDs0BEGAABIEt6zyyEI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ERERJLEQpiIiIiIJImFMBERERFJEgthIiIiIpIkFsJEREREJEkmgiAIPT0IIiIiIqLuxjvCRERE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e17ef0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 353,
       "width": 353
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rus_us = df1.iloc[:, 0:42].copy()\n",
    "\n",
    "\n",
    "new_col1 = []\n",
    "for ele in rus_us:\n",
    "    new_col1.append(ele.split('_x')[0])\n",
    "   \n",
    "rus_us.columns = new_col1\n",
    "\n",
    "rus_us = rus_us.set_index('country')\n",
    "rus_us_df = pd.DataFrame(columns=['USA','Russia'])\n",
    "rus_us_df['USA'] = rus_us.loc['United States'].values\n",
    "rus_us_df['Russia'] = rus_us.loc['Russia'].values\n",
    "\n",
    "\n",
    "sns.lmplot(x = 'USA', y = 'Russia', data = rus_us_df);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3eHt7a+rUlGZzU6fO4KgD7nouOW1p+PDhzWZKS0utD/7f5+9ff86V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\nRbkHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2affd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 244,
       "width": 391
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=rus_us_df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bx6O22draws/PD7t378aNGzeQmZmJ+vXrY+DAgbC1tZUexKkqMMzIyJAyB+Y1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\nvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112db7ba8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 273,
       "width": 416
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rus_us_df['year'] = rus_us.columns\n",
    "rus_us_df.index = rus_us_df.year\n",
    "rus_us_df.plot();\n",
    "plt.ylabel('Numers')\n",
    "plt.xlabel('Country')\n",
    "plt.title('Numbers of children out of primary school from 1970 to 2011');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There is a positive correlation between children droped out of primary school in Russia and USA. The estimated linear regression is shown as the blue line, the estimates varies in the light blue shade with 95% confident level. The trend of children out of school in USA is much higher than that of Russia over that past 40 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q5'></a>\n",
    "### Research Question 5: What is the overall trend for children out of primary school over the years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iPB+Lm62tLfz9/bFnzx5cuXIFmZmZqF+/PoYNGwZbW1t5Ik51gWFGRoacOTC/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112daea58>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 273,
       "width": 416
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall_df = pd.DataFrame(columns=['year','numbers'])\n",
    "overall_df['year'] = rus_us.columns \n",
    "n_list =[]\n",
    "\n",
    "for n in rus_us.columns:\n",
    "    n_list.append(rus_us[n].mean())\n",
    "\n",
    "\n",
    "overall_df['numbers'] = np.array(n_list)\n",
    "overall_df.index = overall_df.year\n",
    "\n",
    "overall_df.plot();\n",
    "plt.ylabel('Numers')\n",
    "plt.xlabel('Year')\n",
    "plt.title('Numbers of children out of primary school from 1970 to 2011');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the analysis we can conclude that the overall trend of children out of primary school had been descreasing starting between 1970 and 1975 at which point of time the numbers fell down dramatically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "> In current study, a good amount of profound analysis has been carried out. Prior to each step, deailed instructions was given and interpretions was also provided afterwards. The dataset across 41 years from 1970 to 2011. \n",
    "\n",
    "> The limitations of current study was that the structure is only 275*42 in shape, thus the analysis would not be much reliable due to small scale samples.\n",
    "\n",
    "> In addition, the parameters in the dataset is very simple, it only focus on the number of children out of school.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_Children_Out_of_School_20180108.ipynb'])"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}