jdickinson202/gist:8cfb86a7c9ea416f2f27996a0d099cbb

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can write directly into latex using \"markdown\" by highlighting a cell and then selecting \"markdown\" from the dropdown menu: $$4+x_2^4 = 23$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typical latex commands work such as \\frac and \\x_i^2: $$\\frac{3}{4} \\quad x_i^2 $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#packages are imported like this, and sometimes given nicknames for easy recall\n",
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#lists are a list of objects demarcated with [] which could be strings or any type of value\n",
    "countries = [\"CH\",\"US\",\"FR\"]\n",
    "names = [\"Seth\",\"Tara\",\"Jeff\",\"Guillaume\"]\n",
    "evens = [2,4,6,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evens == 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names == [\"Timothy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names == [\"Seth\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Seth', 'Tara', 'Jeff', 'Guillaume']\n"
     ]
    }
   ],
   "source": [
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLkZJGAwGg8EuRkkYDAaDwS7/D+asNmKhk0dJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e205c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import wbdata   #wb api wrapper/package\n",
    "%matplotlib inline\n",
    "\n",
    "## here's an example with the wb API\n",
    "#set up the indicator, you can view the list here \n",
    "#   https://datahelpdesk.worldbank.org/knowledgebase/articles/898599-api-indicator-queries\n",
    "indicators = {'NY.GNP.PCAP.CD':'GNI per Capita'}\n",
    "countries = [\"TZ\",\"BI\",\"KE\",\"MW\"]\n",
    "countries2 = [\"US\",\"CA\",\"MX\",\"CN\"]\n",
    "#grab indicators above for countires above and load into data frame\n",
    "df = wbdata.get_dataframe(indicators, country=countries, convert_date=False)\n",
    "df2 = wbdata.get_dataframe(indicators, country=countries2, convert_date=False)\n",
    "#df needs to be \"pivoted\", pandas' unstack fucntion helps reshape it into something plottable\n",
    "dfu = df.unstack(level=0)\n",
    "dfu2 = df2.unstack(level=0)\n",
    "# a plot with legend, labels and a title\n",
    "dfu.plot(); \n",
    "plt.legend(loc='best'); \n",
    "plt.title(\"GNI Per Capita ($USD, Atlas Method)\"); \n",
    "plt.xlabel('Date'); plt.ylabel('GNI Per Capita ($USD, Atlas Method)');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEZCAYAAAC5AHPcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f60748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfu2.plot(); \n",
    "plt.legend(loc='best'); \n",
    "plt.title(\"GNI Per Capita ($USD, Atlas Method)\"); \n",
    "plt.xlabel('Date'); plt.ylabel('GNI Per Capita ($USD, Atlas Method)');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This is just an example. Use this to create an alias for a particular directory\n",
    "code_dir = \"/users/jeff/desktop/dropbox/tanzania_new_project/code/python\" #code folder for python code\n",
    "data_dir = \"/users/jeff/desktop/dropbox/tanzania_new_project/data/TZA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#to change directory you'll need to call another package called OS\n",
    "import os\n",
    "os.chdir(data_dir)\n",
    "data01 =  pd.read_stata(\"full_dataset.dta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hid</th>\n",
       "      <th>t</th>\n",
       "      <th>total_land_area</th>\n",
       "      <th>total_num_plots</th>\n",
       "      <th>num_crops_plot</th>\n",
       "      <th>plots_per_crop</th>\n",
       "      <th>improved_rice</th>\n",
       "      <th>improved_maize</th>\n",
       "      <th>maize_harv_st</th>\n",
       "      <th>rice_harv_st</th>\n",
       "      <th>...</th>\n",
       "      <th>r_avg_harv_price</th>\n",
       "      <th>nrel_1_11</th>\n",
       "      <th>nloc_1_11</th>\n",
       "      <th>nrel_2_11</th>\n",
       "      <th>nloc_2_11</th>\n",
       "      <th>nrel_1_12</th>\n",
       "      <th>nloc_1_12</th>\n",
       "      <th>nrel_2_12</th>\n",
       "      <th>nloc_2_12</th>\n",
       "      <th>ea_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.237488</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</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>101014002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.375932</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</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>101014002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.630459</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</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>101014002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100002.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.987601</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</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>101014002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100002.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.971246</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</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>101014002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        hid  t  total_land_area  total_num_plots  num_crops_plot  \\\n",
       "0  100001.0  1         3.237488              4.0        4.000000   \n",
       "1  100001.0  2         1.375932              3.0        1.666667   \n",
       "2  100001.0  3         2.630459              3.0        1.000000   \n",
       "3  100002.0  1         0.987601              2.0        2.000000   \n",
       "4  100002.0  2         0.971246              2.0        2.000000   \n",
       "\n",
       "   plots_per_crop  improved_rice  improved_maize  maize_harv_st  rice_harv_st  \\\n",
       "0        1.000000            0.0             0.0            0.0           0.0   \n",
       "1        1.666667            0.0             0.0            0.0           0.0   \n",
       "2        1.000000            1.0             0.0            0.0           0.0   \n",
       "3        1.000000            0.0             0.0            6.0           0.0   \n",
       "4        2.000000            0.0             0.0            6.0           0.0   \n",
       "\n",
       "      ...       r_avg_harv_price  nrel_1_11  nloc_1_11  nrel_2_11  nloc_2_11  \\\n",
       "0     ...                    0.0        NaN        NaN        NaN        NaN   \n",
       "1     ...                    0.0        NaN        NaN        NaN        NaN   \n",
       "2     ...                    0.0        NaN        NaN        NaN        NaN   \n",
       "3     ...                    0.0        NaN        NaN        NaN        NaN   \n",
       "4     ...                    0.0        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   nrel_1_12  nloc_1_12  nrel_2_12  nloc_2_12        ea_id  \n",
       "0        NaN        NaN        NaN        NaN  101014002.0  \n",
       "1        NaN        NaN        NaN        NaN  101014002.0  \n",
       "2        NaN        NaN        NaN        NaN  101014002.0  \n",
       "3        NaN        NaN        NaN        NaN  101014002.0  \n",
       "4        NaN        NaN        NaN        NaN  101014002.0  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         hid  t  total_land_area  total_num_plots  num_crops_plot  \\\n",
      "12  100005.0  1         2.832802              4.0        2.000000   \n",
      "13  100005.0  2         1.537807              2.0        2.000000   \n",
      "14  100005.0  3         0.607029              1.0        1.000000   \n",
      "15  100006.0  1         2.428116              3.0        1.666667   \n",
      "\n",
      "    plots_per_crop  improved_rice  improved_maize  maize_harv_st  \\\n",
      "12             1.0            0.0             0.0            0.0   \n",
      "13             2.0            0.0             0.0            0.0   \n",
      "14             1.0            0.0             0.0            0.0   \n",
      "15             1.0            0.0             0.0            0.0   \n",
      "\n",
      "    rice_harv_st     ...       r_avg_harv_price  nrel_1_11  nloc_1_11  \\\n",
      "12           0.0     ...                    0.0        NaN        NaN   \n",
      "13           0.0     ...                    0.0        NaN        NaN   \n",
      "14           0.0     ...                    0.0        NaN        NaN   \n",
      "15           0.0     ...                    0.0        NaN        NaN   \n",
      "\n",
      "    nrel_2_11  nloc_2_11  nrel_1_12  nloc_1_12  nrel_2_12  nloc_2_12  \\\n",
      "12        NaN        NaN        NaN        NaN        NaN        NaN   \n",
      "13        NaN        NaN        NaN        NaN        NaN        NaN   \n",
      "14        NaN        NaN        NaN        NaN        NaN        NaN   \n",
      "15        NaN        NaN        NaN        NaN        NaN        NaN   \n",
      "\n",
      "          ea_id  \n",
      "12  101014002.0  \n",
      "13  101014002.0  \n",
      "14  101014002.0  \n",
      "15  101014002.0  \n",
      "\n",
      "[4 rows x 92 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data01[12:16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hid',\n",
       " 't',\n",
       " 'total_land_area',\n",
       " 'total_num_plots',\n",
       " 'num_crops_plot',\n",
       " 'plots_per_crop',\n",
       " 'improved_rice',\n",
       " 'improved_maize',\n",
       " 'maize_harv_st',\n",
       " 'rice_harv_st',\n",
       " 'rice_harv_end',\n",
       " 'maize_harv_end',\n",
       " 'staple_crop_area',\n",
       " 'cash_crop_area',\n",
       " 'maize_area',\n",
       " 'maize_quantity',\n",
       " 'rice_area',\n",
       " 'rice_quantity',\n",
       " 'pct_planted_maize',\n",
       " 'pct_planted_rice',\n",
       " 'pct_planted_staple',\n",
       " 'pct_planted_cash',\n",
       " 'quant_sold_1_11',\n",
       " 'value_sold_1_11',\n",
       " 'month_sold_1_11',\n",
       " 'quant_sold_2_11',\n",
       " 'value_sold_2_11',\n",
       " 'month_sold_2_11',\n",
       " 'quant_sold_1_12',\n",
       " 'value_sold_1_12',\n",
       " 'month_sold_1_12',\n",
       " 'quant_sold_2_12',\n",
       " 'value_sold_2_12',\n",
       " 'month_sold_2_12',\n",
       " 'total_maize_sold',\n",
       " 'total_maize_value',\n",
       " 'total_rice_sold',\n",
       " 'total_rice_value',\n",
       " 'adult_age',\n",
       " 'child_age',\n",
       " 'age',\n",
       " 'age_hh_head',\n",
       " 'dep_ratio',\n",
       " 'adult',\n",
       " 'child',\n",
       " 'years_educ',\n",
       " 'head_yr_educ',\n",
       " 'years_adult_educ',\n",
       " 'total_hh_members',\n",
       " 'fem_head',\n",
       " 'rice_purch',\n",
       " 'own_rice_cons',\n",
       " 'maize_purch',\n",
       " 'own_maize_cons',\n",
       " 'mobile_phone_exp',\n",
       " 'y3_cluster_3',\n",
       " 'strataid_3',\n",
       " 'hh_a01_2_3',\n",
       " 'hh_a02_2_3',\n",
       " 'lat1',\n",
       " 'lon1',\n",
       " 'market',\n",
       " 'lat2',\n",
       " 'lon2',\n",
       " 'dist_to_mkt',\n",
       " 'dlon',\n",
       " 'dlat',\n",
       " 'maize_surplus',\n",
       " 'm_surplus',\n",
       " 'rice_surplus',\n",
       " 'r_surplus',\n",
       " 'fg_price_m1',\n",
       " 'fg_price_r1',\n",
       " 'maize_st_price',\n",
       " 'maize_end_price',\n",
       " 'maize_sale_p_1',\n",
       " 'maize_sale_p_2',\n",
       " 'm_avg_harv_price',\n",
       " 'rice_st_price',\n",
       " 'rice_end_price',\n",
       " 'rice_sale_p_1',\n",
       " 'rice_sale_p_2',\n",
       " 'r_avg_harv_price',\n",
       " 'nrel_1_11',\n",
       " 'nloc_1_11',\n",
       " 'nrel_2_11',\n",
       " 'nloc_2_11',\n",
       " 'nrel_1_12',\n",
       " 'nloc_1_12',\n",
       " 'nrel_2_12',\n",
       " 'nloc_2_12',\n",
       " 'ea_id']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_vars = ['hid','ea_id','maize_surplus','dist_to_mkt','fem_head','total_hh_members']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = data01[x_vars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hid</th>\n",
       "      <th>ea_id</th>\n",
       "      <th>maize_surplus</th>\n",
       "      <th>dist_to_mkt</th>\n",
       "      <th>fem_head</th>\n",
       "      <th>total_hh_members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>-743.923523</td>\n",
       "      <td>96.642838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100002.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>-483.492950</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100002.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>-136.961777</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        hid        ea_id  maize_surplus  dist_to_mkt  fem_head  \\\n",
       "0  100001.0  101014002.0       0.000000    96.642731       0.0   \n",
       "1  100001.0  101014002.0       0.000000    96.642731       0.0   \n",
       "2  100001.0  101014002.0    -743.923523    96.642838       0.0   \n",
       "3  100002.0  101014002.0    -483.492950    96.642731       0.0   \n",
       "4  100002.0  101014002.0    -136.961777    96.642731       0.0   \n",
       "\n",
       "   total_hh_members  \n",
       "0               5.0  \n",
       "1               5.0  \n",
       "2               4.0  \n",
       "3               3.0  \n",
       "4               4.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hid',\n",
       " 'ea_id',\n",
       " 'maize_surplus',\n",
       " 'dist_to_mkt',\n",
       " 'fem_head',\n",
       " 'total_hh_members']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hid</th>\n",
       "      <th>ea_id</th>\n",
       "      <th>maize_surplus</th>\n",
       "      <th>dist_to_mkt</th>\n",
       "      <th>fem_head</th>\n",
       "      <th>total_hh_members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>-743.923523</td>\n",
       "      <td>96.642838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100002.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>-483.492950</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100002.0</td>\n",
       "      <td>101014002.0</td>\n",
       "      <td>-136.961777</td>\n",
       "      <td>96.642731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        hid        ea_id  maize_surplus  dist_to_mkt  fem_head  \\\n",
       "0  100001.0  101014002.0       0.000000    96.642731       0.0   \n",
       "1  100001.0  101014002.0       0.000000    96.642731       0.0   \n",
       "2  100001.0  101014002.0    -743.923523    96.642838       0.0   \n",
       "3  100002.0  101014002.0    -483.492950    96.642731       0.0   \n",
       "4  100002.0  101014002.0    -136.961777    96.642731       0.0   \n",
       "\n",
       "   total_hh_members  \n",
       "0               5.0  \n",
       "1               5.0  \n",
       "2               4.0  \n",
       "3               3.0  \n",
       "4               4.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[-13245:-13240]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hid</th>\n",
       "      <th>ea_id</th>\n",
       "      <th>maize_surplus</th>\n",
       "      <th>dist_to_mkt</th>\n",
       "      <th>fem_head</th>\n",
       "      <th>total_hh_members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13239</th>\n",
       "      <td>104414.0</td>\n",
       "      <td>5.502018e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.875668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13240</th>\n",
       "      <td>104414.0</td>\n",
       "      <td>5.502018e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13241</th>\n",
       "      <td>104414.0</td>\n",
       "      <td>5.502018e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13242</th>\n",
       "      <td>104415.0</td>\n",
       "      <td>5.502018e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.875668</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13243</th>\n",
       "      <td>104415.0</td>\n",
       "      <td>5.502018e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13244</th>\n",
       "      <td>104415.0</td>\n",
       "      <td>5.502018e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            hid         ea_id  maize_surplus  dist_to_mkt  fem_head  \\\n",
       "13239  104414.0  5.502018e+09            0.0    43.875668       0.0   \n",
       "13240  104414.0  5.502018e+09            NaN          NaN       NaN   \n",
       "13241  104414.0  5.502018e+09            NaN          NaN       NaN   \n",
       "13242  104415.0  5.502018e+09            0.0    43.875668       1.0   \n",
       "13243  104415.0  5.502018e+09            NaN          NaN       NaN   \n",
       "13244  104415.0  5.502018e+09            NaN          NaN       NaN   \n",
       "\n",
       "       total_hh_members  \n",
       "13239               9.0  \n",
       "13240               NaN  \n",
       "13241               NaN  \n",
       "13242               1.0  \n",
       "13243               NaN  \n",
       "13244               NaN  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[len(x)-6:len(x)]"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}