demographics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from scipy.stats import ttest_ind, ttest_ind_from_stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_path = os.path.join(os.path.sep,'Users', 'qianwu', 'Documents', 'GitHub', 'gender_paper','data')\n",
    "behav_data_path = os.path.join(dir_path, 'USDA_Study_Database.xlsx')\n",
    "\n",
    "behav_data = pd.read_excel(behav_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add milk and egg experiment label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "behav_data['experiment'] = np.where(behav_data['P_No']<=8050, 'milk', 'egg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop Missing Data\n",
    "We obtained 93 observations with complete fMRI data. \n",
    "The missing 7 observation ids are 8010, 8032, 8037, 8059, 8062, 8065, 8084."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "missiong_obs = [8010, 8032, 8037, 8059, 8062, 8065, 8084]\n",
    "obs_data = behav_data[~behav_data ['P_No'].isin(missiong_obs)]\n",
    "obs_data = obs_data.rename(columns={'DEM_9':'Edu', 'DEM_10':'Income'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "47 Females in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         93\n",
       "unique         2\n",
       "top       Female\n",
       "freq          47\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data['Sex'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Age - Key Finding: No Significant difference between females and males\n",
    "### t = -0.13, p-value = 0.90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    93.000000\n",
       "mean     30.795699\n",
       "std      10.389752\n",
       "min      18.000000\n",
       "25%      23.000000\n",
       "50%      26.000000\n",
       "75%      38.000000\n",
       "max      55.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ttest_ind: t = -0.127147  p = 0.899106\n"
     ]
    }
   ],
   "source": [
    "age_female = obs_data['Age'][obs_data.Sex=='Female'].astype('float')\n",
    "age_male = obs_data['Age'][obs_data.Sex=='Male'].astype('float')\n",
    "\n",
    "t, p = ttest_ind(age_female, age_male, equal_var=False)\n",
    "print(\"ttest_ind: t = %g  p = %g\" % (t, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. BMI - Finding: No Significant difference between females and males\n",
    "### t = -0.66  p = 0.51"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    93.000000\n",
       "mean     26.308602\n",
       "std       5.453114\n",
       "min      18.400000\n",
       "25%      22.400000\n",
       "50%      24.900000\n",
       "75%      29.400000\n",
       "max      50.100000\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data['BMI'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Overweight (25-29.9)', 'Healthy Weight (18.5-24.9)',\n",
       "       'Obese (>29.9)', 'Underweight (<18.5)'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data['BMI_Status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Overweight or Obese (>25)',\n",
       "       'Underweight or Healthy Weight (<24.9)'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data['BMI_Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]), <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_gender_BMI = obs_data.groupby(['BMI_Status', 'Sex']).size()\n",
    "user_gender_BMI.unstack('Sex').plot.bar(figsize=(20,10))\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_gender_BMI = obs_data.groupby(['BMI_Category', 'Sex']).size()\n",
    "user_gender_BMI.unstack('Sex').plot.bar(figsize=(20,10))\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ttest_ind: t = -0.655351  p = 0.514172\n"
     ]
    }
   ],
   "source": [
    "bmi_female = obs_data['BMI'][obs_data.Sex=='Female']\n",
    "bmi_male = obs_data['BMI'][obs_data.Sex=='Male']\n",
    "\n",
    "t, p = ttest_ind(bmi_female, bmi_male, equal_var=False)\n",
    "print(\"ttest_ind: t = %g  p = %g\" % (t, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Income - Finding: No Significant difference between females and males by using midpoints of income intervals from survey data. \n",
    "### t = -0.66  p = 0.51"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['$40,000 to $59,999', '$80,000 to $99,999', '$20,000 to $39,999',\n",
       "       '$60,000 to $79,999', 'Less than $20,000', '$140,000 or more',\n",
       "       '$120,000 to $139,999', '$100,000 to $119,999'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data['Income'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_gender_income = obs_data.groupby(['Income', 'Sex']).size()\n",
    "user_gender_income.unstack('Sex').plot.bar(figsize=(20,10))\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_level = np.array([[0, 20000],[20000, 39999], [40000, 59999], [60000, 79999]\n",
    "                         ,[80000, 99999], [100000, 119999], [120000, 139999]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10000. ,  29999.5,  49999.5,  69999.5,  89999.5, 109999.5,\n",
       "       129999.5])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(income_level, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "midpoint = {'Less than $20,000':10000, \n",
    "            '$20,000 to $39,999':29999.5,\n",
    "            '$40,000 to $59,999':49999.5, \n",
    "            '$60,000 to $79,999':69999.5, \n",
    "            '$80,000 to $99,999':89999.5, \n",
    "            '$100,000 to $119,999':109999.5,\n",
    "            '$120,000 to $139,999':129999.5,\n",
    "            '$140,000 or more': 149999.5\n",
    "            }\n",
    "obs_data = obs_data.replace({\"Income\": midpoint})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ttest_ind: t = -0.663574  p = 0.508681\n"
     ]
    }
   ],
   "source": [
    "income_female = obs_data['Income'][obs_data.Sex=='Female'].astype('float')\n",
    "income_male = obs_data['Income'][obs_data.Sex=='Male'].astype('float')\n",
    "\n",
    "t, p = ttest_ind(income_female, income_male, equal_var=False)\n",
    "print(\"ttest_ind: t = %g  p = %g\" % (t, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Education - This distribution doesn't seem difference. Hard to conduct a numerical test for categorial educational levels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]), <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_gender_edu = obs_data.groupby(['Edu', 'Sex']).size()\n",
    "user_gender_edu.unstack('Sex').plot.bar(figsize=(20,10))\n",
    "plt.xticks(rotation=0)"
   ]
  }
 ],
 "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}