1.9_significant_feature_graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------\n",
    "# stunting |\n",
    "# ---------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "less_three_Significant = pd.DataFrame({\n",
    "    'top 3 less significant feature for stunting' : ['residence', 'breastfeeding', 'ari'],\n",
    "    'p value':[0.911, 0.623, 0.591]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>top 3 less significant feature for stunting</th>\n",
       "      <th>p value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>residence</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>breastfeeding</td>\n",
       "      <td>0.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ari</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  top 3 less significant feature for stunting  p value\n",
       "0                                   residence    0.911\n",
       "1                               breastfeeding    0.623\n",
       "2                                         ari    0.591"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_three_Significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'non significant feature for stunting according to \"P\" value(>5%):\\n--------------------------------------------\\n')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "barplot = sns.barplot(x = 'top 3 less significant feature for stunting', y = 'p value', data = less_three_Significant)\n",
    "plt.title('non significant feature for stunting according to \"P\" value(>5%):\\n--------------------------------------------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "less_five_Significant = pd.DataFrame({\n",
    "    'top 5 less significant feature for stunting' : ['residence', 'breastfeeding', 'ari', 'currently working mother', 'mother education'],\n",
    "    'p value':[0.911, 0.623, 0.591, 0.971, 0.581]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>top 5 less significant feature for stunting</th>\n",
       "      <th>p value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>residence</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>breastfeeding</td>\n",
       "      <td>0.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ari</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>currently working mother</td>\n",
       "      <td>0.971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mother education</td>\n",
       "      <td>0.581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  top 5 less significant feature for stunting  p value\n",
       "0                                   residence    0.911\n",
       "1                               breastfeeding    0.623\n",
       "2                                         ari    0.591\n",
       "3                    currently working mother    0.971\n",
       "4                            mother education    0.581"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_five_Significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'top 5 non significant features for stunting according \"P\" value(>5%):\\n--------------------------------------------\\n')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "barplot = sns.barplot(x = 'top 5 less significant feature for stunting', y = 'p value', data = less_five_Significant)\n",
    "plt.title('top 5 non significant features for stunting according \"P\" value(>5%):\\n--------------------------------------------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------\n",
    "# underweight |\n",
    "# ------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "less_Significant_underweight = pd.DataFrame({\n",
    "    'non significant features for underweight' : ['mother bmi', 'mother education'],\n",
    "    'p value':[0.652, 0.674]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>non significant features for underweight</th>\n",
       "      <th>p value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mother bmi</td>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mother education</td>\n",
       "      <td>0.674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  non significant features for underweight  p value\n",
       "0                               mother bmi    0.652\n",
       "1                         mother education    0.674"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_Significant_underweight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'non significant feature for underweight according to \"P\" value(>5%):\\n--------------------------------------------\\n')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "barplot = sns.barplot(x = 'non significant features for underweight', y = 'p value', data = less_Significant_underweight)\n",
    "plt.title('non significant feature for underweight according to \"P\" value(>5%):\\n--------------------------------------------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------\n",
    "# wasting |\n",
    "# ------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "less_Significant_wasting = pd.DataFrame({\n",
    "    'non significant features for wasting' : ['mother bmi', 'birth order', 'mother education', 'walth_index', 'father education', 'householding', 'mother_working', 'residence', 'sex'],\n",
    "    'p value':[0.976, 0.963, 0.965, 0.966, 0.998, 0.977, 0.616, 0.793, 0.791]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>non significant features for wasting</th>\n",
       "      <th>p value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mother bmi</td>\n",
       "      <td>0.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>birth order</td>\n",
       "      <td>0.963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mother education</td>\n",
       "      <td>0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>walth_index</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>father education</td>\n",
       "      <td>0.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>householding</td>\n",
       "      <td>0.977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mother_working</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>residence</td>\n",
       "      <td>0.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sex</td>\n",
       "      <td>0.791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  non significant features for wasting  p value\n",
       "0                           mother bmi    0.976\n",
       "1                          birth order    0.963\n",
       "2                     mother education    0.965\n",
       "3                          walth_index    0.966\n",
       "4                     father education    0.998\n",
       "5                         householding    0.977\n",
       "6                       mother_working    0.616\n",
       "7                            residence    0.793\n",
       "8                                  sex    0.791"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_Significant_wasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'non significant feature for wasting according to \"P\" value(>5%):\\n--------------------------------------------\\n')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,5))\n",
    "barplot = sns.barplot(x = 'non significant features for wasting', y = 'p value', data = less_Significant_wasting)\n",
    "plt.title('non significant feature for wasting according to \"P\" value(>5%):\\n--------------------------------------------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}