pj02_analysis.ipynb

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  {
   "source": [
    "# Hypothesis:\n",
    "## I think that there is a strong correlation between the percent of females enrolled in primary school and the percent of females in the labor force.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### I'm going to read in the CSV file into a DataFrame and then I initialized two named constants for each of the indicator code strings I'm going to explore."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "In [1]: import pandas as pd\n",
    "In [2]: df = pd.read_csv(\"../../data/databank_education_2018.csv\")\n",
    "In [3]: SCHOOL = \"SE.PRM.ENRR.FE\"\n",
    "In [4]: LABOR_FORCE = \"SL.TLF.TOTL.FE.ZS\""
   ]
  },
  {
   "source": [
    "### Next, I printed the amount of rows and columns the DataFrame has and then I used the head method to display the first 10 rows."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(244, 94)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               Country  SE.COM.DURS  SE.ENR.PRIM.FM.ZS  SE.ENR.PRSC.FM.ZS  \\\n",
       "0          Afghanistan          9.0            0.66691            0.63602   \n",
       "1              Albania          9.0            1.03765            1.01730   \n",
       "2              Algeria         10.0            0.95499                NaN   \n",
       "3              Andorra         10.0                NaN                NaN   \n",
       "4               Angola          6.0                NaN                NaN   \n",
       "5  Antigua and Barbuda         11.0            0.99208            0.97924   \n",
       "6           Arab World          9.0            0.94617            0.94833   \n",
       "7            Argentina         14.0                NaN                NaN   \n",
       "8              Armenia         12.0            0.99961            1.02283   \n",
       "9                Aruba         13.0                NaN                NaN   \n",
       "\n",
       "   SE.ENR.SECO.FM.ZS  SE.ENR.TERT.FM.ZS  SE.PRE.DURS  SE.PRE.ENRL.TC.ZS  \\\n",
       "0            0.57098            0.34523          1.0                NaN   \n",
       "1            1.00539            1.36299          5.0           17.09409   \n",
       "2                NaN            1.39722          1.0                NaN   \n",
       "3                NaN                NaN          3.0           13.27711   \n",
       "4                NaN                NaN          5.0                NaN   \n",
       "5            0.96286                NaN          5.0                NaN   \n",
       "6            0.95186            1.09935          3.0           21.16757   \n",
       "7                NaN                NaN          6.0                NaN   \n",
       "8            1.03762            1.24788          3.0            6.27090   \n",
       "9                NaN                NaN          2.0                NaN   \n",
       "\n",
       "   SE.PRE.ENRR  SE.PRE.ENRR.FE  ...  SH.DYN.1014  SH.DYN.1519  SH.DYN.2024  \\\n",
       "0          NaN             NaN  ...     2.000000    13.800000    17.600000   \n",
       "1    80.498734       80.790977  ...     1.100000     1.800000     1.900000   \n",
       "2          NaN             NaN  ...     1.700000     2.700000     3.300000   \n",
       "3          NaN             NaN  ...     0.500000     1.400000     2.100000   \n",
       "4          NaN             NaN  ...     6.400000    13.100000    19.900000   \n",
       "5    70.253601       74.025070  ...     1.000000     2.500000     3.200000   \n",
       "6    27.001801       26.855440  ...     2.923226     6.268086     8.017785   \n",
       "7          NaN             NaN  ...     1.100000     3.500000     4.900000   \n",
       "8    38.021290       39.659691  ...     1.000000     2.300000     2.700000   \n",
       "9          NaN             NaN  ...          NaN          NaN          NaN   \n",
       "\n",
       "   SL.TLF.TOTL.FE.ZS  SL.TLF.TOTL.IN  SL.UEM.TOTL.FE.ZS  SL.UEM.TOTL.MA.ZS  \\\n",
       "0          21.247021      10319282.0          13.920000          10.290000   \n",
       "1          42.190687       1400072.0          11.870000          12.620000   \n",
       "2          20.126974      12589369.0          20.490000           9.720000   \n",
       "3                NaN             NaN                NaN                NaN   \n",
       "4          50.202910      12705650.0           7.280000           6.720000   \n",
       "5                NaN             NaN                NaN                NaN   \n",
       "6          20.368759     134761988.0          19.903642           7.788688   \n",
       "7          43.001857      20551682.0          10.540000           8.230000   \n",
       "8          44.231575       1245626.0          20.400000          17.830000   \n",
       "9                NaN             NaN                NaN                NaN   \n",
       "\n",
       "   SL.UEM.TOTL.ZS  SP.POP.0014.TO.ZS  SP.POP.1564.TO.ZS  \n",
       "0       11.060000          43.090176          54.324898  \n",
       "1       12.300000          17.672874          68.582390  \n",
       "2       11.890000          30.148679          63.488825  \n",
       "3             NaN                NaN                NaN  \n",
       "4        7.000000          46.808925          50.974702  \n",
       "5             NaN          22.081092          69.119083  \n",
       "6       10.256798          32.780436          62.661687  \n",
       "7        9.220000          24.760934          64.121277  \n",
       "8       18.969999          20.633422          68.112760  \n",
       "9             NaN          17.802447          68.646606  \n",
       "\n",
       "[10 rows x 94 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\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>SE.COM.DURS</th>\n      <th>SE.ENR.PRIM.FM.ZS</th>\n      <th>SE.ENR.PRSC.FM.ZS</th>\n      <th>SE.ENR.SECO.FM.ZS</th>\n      <th>SE.ENR.TERT.FM.ZS</th>\n      <th>SE.PRE.DURS</th>\n      <th>SE.PRE.ENRL.TC.ZS</th>\n      <th>SE.PRE.ENRR</th>\n      <th>SE.PRE.ENRR.FE</th>\n      <th>...</th>\n      <th>SH.DYN.1014</th>\n      <th>SH.DYN.1519</th>\n      <th>SH.DYN.2024</th>\n      <th>SL.TLF.TOTL.FE.ZS</th>\n      <th>SL.TLF.TOTL.IN</th>\n      <th>SL.UEM.TOTL.FE.ZS</th>\n      <th>SL.UEM.TOTL.MA.ZS</th>\n      <th>SL.UEM.TOTL.ZS</th>\n      <th>SP.POP.0014.TO.ZS</th>\n      <th>SP.POP.1564.TO.ZS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Afghanistan</td>\n      <td>9.0</td>\n      <td>0.66691</td>\n      <td>0.63602</td>\n      <td>0.57098</td>\n      <td>0.34523</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>2.000000</td>\n      <td>13.800000</td>\n      <td>17.600000</td>\n      <td>21.247021</td>\n      <td>10319282.0</td>\n      <td>13.920000</td>\n      <td>10.290000</td>\n      <td>11.060000</td>\n      <td>43.090176</td>\n      <td>54.324898</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>9.0</td>\n      <td>1.03765</td>\n      <td>1.01730</td>\n      <td>1.00539</td>\n      <td>1.36299</td>\n      <td>5.0</td>\n      <td>17.09409</td>\n      <td>80.498734</td>\n      <td>80.790977</td>\n      <td>...</td>\n      <td>1.100000</td>\n      <td>1.800000</td>\n      <td>1.900000</td>\n      <td>42.190687</td>\n      <td>1400072.0</td>\n      <td>11.870000</td>\n      <td>12.620000</td>\n      <td>12.300000</td>\n      <td>17.672874</td>\n      <td>68.582390</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Algeria</td>\n      <td>10.0</td>\n      <td>0.95499</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.39722</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>1.700000</td>\n      <td>2.700000</td>\n      <td>3.300000</td>\n      <td>20.126974</td>\n      <td>12589369.0</td>\n      <td>20.490000</td>\n      <td>9.720000</td>\n      <td>11.890000</td>\n      <td>30.148679</td>\n      <td>63.488825</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Andorra</td>\n      <td>10.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>13.27711</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>0.500000</td>\n      <td>1.400000</td>\n      <td>2.100000</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>4</th>\n      <td>Angola</td>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>6.400000</td>\n      <td>13.100000</td>\n      <td>19.900000</td>\n      <td>50.202910</td>\n      <td>12705650.0</td>\n      <td>7.280000</td>\n      <td>6.720000</td>\n      <td>7.000000</td>\n      <td>46.808925</td>\n      <td>50.974702</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Antigua and Barbuda</td>\n      <td>11.0</td>\n      <td>0.99208</td>\n      <td>0.97924</td>\n      <td>0.96286</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>70.253601</td>\n      <td>74.025070</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>2.500000</td>\n      <td>3.200000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>22.081092</td>\n      <td>69.119083</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Arab World</td>\n      <td>9.0</td>\n      <td>0.94617</td>\n      <td>0.94833</td>\n      <td>0.95186</td>\n      <td>1.09935</td>\n      <td>3.0</td>\n      <td>21.16757</td>\n      <td>27.001801</td>\n      <td>26.855440</td>\n      <td>...</td>\n      <td>2.923226</td>\n      <td>6.268086</td>\n      <td>8.017785</td>\n      <td>20.368759</td>\n      <td>134761988.0</td>\n      <td>19.903642</td>\n      <td>7.788688</td>\n      <td>10.256798</td>\n      <td>32.780436</td>\n      <td>62.661687</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Argentina</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>1.100000</td>\n      <td>3.500000</td>\n      <td>4.900000</td>\n      <td>43.001857</td>\n      <td>20551682.0</td>\n      <td>10.540000</td>\n      <td>8.230000</td>\n      <td>9.220000</td>\n      <td>24.760934</td>\n      <td>64.121277</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Armenia</td>\n      <td>12.0</td>\n      <td>0.99961</td>\n      <td>1.02283</td>\n      <td>1.03762</td>\n      <td>1.24788</td>\n      <td>3.0</td>\n      <td>6.27090</td>\n      <td>38.021290</td>\n      <td>39.659691</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>2.300000</td>\n      <td>2.700000</td>\n      <td>44.231575</td>\n      <td>1245626.0</td>\n      <td>20.400000</td>\n      <td>17.830000</td>\n      <td>18.969999</td>\n      <td>20.633422</td>\n      <td>68.112760</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Aruba</td>\n      <td>13.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</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>17.802447</td>\n      <td>68.646606</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 94 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "In [5]: print(df.shape)\n",
    "In [6]: df.head(10)"
   ]
  },
  {
   "source": [
    "### Next, I selected the \"Country\" column and the two indicator columns I chose. I then printed out the first 10 rows."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               Country  SE.PRM.ENRR.FE  SL.TLF.TOTL.FE.ZS\n",
       "0          Afghanistan       82.850250          21.247021\n",
       "1              Albania      109.111229          42.190687\n",
       "2              Algeria      107.302292          20.126974\n",
       "3              Andorra             NaN                NaN\n",
       "4               Angola             NaN          50.202910\n",
       "5  Antigua and Barbuda      104.598907                NaN\n",
       "6           Arab World       92.840490          20.368759\n",
       "7            Argentina             NaN          43.001857\n",
       "8              Armenia       92.695900          44.231575\n",
       "9                Aruba             NaN                NaN"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\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>SE.PRM.ENRR.FE</th>\n      <th>SL.TLF.TOTL.FE.ZS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Afghanistan</td>\n      <td>82.850250</td>\n      <td>21.247021</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>109.111229</td>\n      <td>42.190687</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Algeria</td>\n      <td>107.302292</td>\n      <td>20.126974</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Andorra</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Angola</td>\n      <td>NaN</td>\n      <td>50.202910</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Antigua and Barbuda</td>\n      <td>104.598907</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Arab World</td>\n      <td>92.840490</td>\n      <td>20.368759</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Argentina</td>\n      <td>NaN</td>\n      <td>43.001857</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Armenia</td>\n      <td>92.695900</td>\n      <td>44.231575</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Aruba</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "In [7]: columns = df[[\"Country\", SCHOOL, LABOR_FORCE]]\n",
    "In [8]: columns.head(10)"
   ]
  },
  {
   "source": [
    "### Next, I only selected the rows that had values that were numbers. I then printed out the number of rows and columns this data set has. Finally, I printed out the first 10 rows of the data set."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(175, 3)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         Country  SE.PRM.ENRR.FE  SL.TLF.TOTL.FE.ZS\n",
       "0    Afghanistan       82.850250          21.247021\n",
       "1        Albania      109.111229          42.190687\n",
       "2        Algeria      107.302292          20.126974\n",
       "6     Arab World       92.840490          20.368759\n",
       "8        Armenia       92.695900          44.231575\n",
       "10     Australia      100.104599          46.500400\n",
       "11       Austria      103.043426          46.428909\n",
       "12    Azerbaijan      100.014618          48.760118\n",
       "13  Bahamas, The       81.158783          48.176994\n",
       "14       Bahrain       98.753159          20.493052"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\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>SE.PRM.ENRR.FE</th>\n      <th>SL.TLF.TOTL.FE.ZS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Afghanistan</td>\n      <td>82.850250</td>\n      <td>21.247021</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>109.111229</td>\n      <td>42.190687</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Algeria</td>\n      <td>107.302292</td>\n      <td>20.126974</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Arab World</td>\n      <td>92.840490</td>\n      <td>20.368759</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Armenia</td>\n      <td>92.695900</td>\n      <td>44.231575</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Australia</td>\n      <td>100.104599</td>\n      <td>46.500400</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Austria</td>\n      <td>103.043426</td>\n      <td>46.428909</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Azerbaijan</td>\n      <td>100.014618</td>\n      <td>48.760118</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Bahamas, The</td>\n      <td>81.158783</td>\n      <td>48.176994</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Bahrain</td>\n      <td>98.753159</td>\n      <td>20.493052</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "In [9]: data = columns[columns[SCHOOL].notna() & columns[LABOR_FORCE].notna()]\n",
    "In [10]: print(data.shape)\n",
    "In [11]: data.head(10)"
   ]
  },
  {
   "source": [
    "### Next, I sorted the data by the percent of the labor force that is female. I then printed out the first 10 rows."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                  Country  SE.PRM.ENRR.FE  SL.TLF.TOTL.FE.ZS\n",
       "154            Mozambique      108.413452          52.247607\n",
       "31                Burundi      122.178421          51.893539\n",
       "185                Rwanda      132.125259          51.650102\n",
       "130             Lithuania      104.282204          50.230965\n",
       "123                Latvia       99.860649          49.946459\n",
       "193          Sierra Leone      114.686569          49.737194\n",
       "16               Barbados       97.466080          49.351385\n",
       "156               Namibia      122.135742          49.308642\n",
       "92   Hong Kong SAR, China      111.156753          49.239928\n",
       "20                  Benin      117.958908          49.173911"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\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>SE.PRM.ENRR.FE</th>\n      <th>SL.TLF.TOTL.FE.ZS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>154</th>\n      <td>Mozambique</td>\n      <td>108.413452</td>\n      <td>52.247607</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Burundi</td>\n      <td>122.178421</td>\n      <td>51.893539</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>Rwanda</td>\n      <td>132.125259</td>\n      <td>51.650102</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>Lithuania</td>\n      <td>104.282204</td>\n      <td>50.230965</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>Latvia</td>\n      <td>99.860649</td>\n      <td>49.946459</td>\n    </tr>\n    <tr>\n      <th>193</th>\n      <td>Sierra Leone</td>\n      <td>114.686569</td>\n      <td>49.737194</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Barbados</td>\n      <td>97.466080</td>\n      <td>49.351385</td>\n    </tr>\n    <tr>\n      <th>156</th>\n      <td>Namibia</td>\n      <td>122.135742</td>\n      <td>49.308642</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>Hong Kong SAR, China</td>\n      <td>111.156753</td>\n      <td>49.239928</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Benin</td>\n      <td>117.958908</td>\n      <td>49.173911</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "In [12]: data.sort_values(by=LABOR_FORCE, ascending=False).head(10)"
   ]
  },
  {
   "source": [
    "### Next, I created a scatter plot with a regression line. I made the percent of females enrolled in primary school be the x-axis and I made the percent of females in the labor force be the y-axis."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb24f78fe20>]"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "In [13]: import matplotlib.pyplot as plt\n",
    "In [14]: import numpy as np\n",
    "In [15]: data.plot.scatter(x=SCHOOL, y=LABOR_FORCE, alpha=0.5, xlabel=\"percent of females enrolled in primary school\", ylabel=\"percent of females in the labor force\", title=\"females enrolled in primary school vs. females in the labor force\")\n",
    "m, b = np.polyfit(data[SCHOOL], data[LABOR_FORCE], 1)\n",
    "plt.plot(data[SCHOOL], m*data[SCHOOL] + b)\n"
   ]
  },
  {
   "source": [
    "### Next, I found the correlation between the percent of females enrolled in primary school and the percent of females in the labor force."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.14085781673748657"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "data[SCHOOL].corr(data[LABOR_FORCE], method=\"pearson\")"
   ]
  },
  {
   "source": [
    "# Conclusion:\n",
    "I was not able to prove that there is a strong correlation between the percent of females enrolled in primary school and the percent of females in the labor force. The correlation that I found is potitive, however since it's only about 0.141, it isn't very strong."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}