homework_02-.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import nose.tools\n",
    "# Write your imports here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization and Exploratory Data Analysis Lab\n",
    "## Visualizing and exploring data. Data mining process as a whole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1. Read the dataset (1 point)\n",
    "You'll be exploring data about people's income. Your task is to understand whether there are significant differences in the lifestyle of lower- vs. higher-income groups.\n",
    "\n",
    "Read the dataset located [here](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data). The information file is [here](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names). Save it into the variable `income_data`. Change the column names to what you like. The last column is related to the income class.\n",
    "\n",
    "Get acquainted with the information file well before starting work.\n",
    "\n",
    "You don't need to clean the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "1100e6bb30ef5be00db508ca1f24d75c",
     "grade": false,
     "grade_id": "read-dataset",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "income_data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', header=None, skipinitialspace=True)\n",
    "income_data.columns = ['age', 'workclass','fnlwgt', 'education','education-num','marital-status','occupation','relationship','race','sex', 'capital-gain','capital-loss', 'hours-per-week', 'native-country','income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "6f1acd92663c630ea7be4ae6a4f4b1a3",
     "grade": true,
     "grade_id": "read-dataset-tests",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "nose.tools.assert_is_not_none(income_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2. High income (1 point)\n",
    "How many people have high income (over 50 000 USD per year)? Write a function to return the value. The function should accept the dataframe as a parameter. Work with that parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "2e3a3eb52f23b84701cddd0c71c1a443",
     "grade": false,
     "grade_id": "high-income",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def get_num_people_with_high_income(dataframe):    \n",
    "    return dataframe[dataframe['income']=='>50K'].shape[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "e6284216dc88e4a433035553084c42ab",
     "grade": true,
     "grade_id": "high-income-tests",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# This cell contains hidden tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3. Capital gain: thresholding (1 point)\n",
    "Plot a histogram of the capital gain. You can see that there are many people with relatively low gains and a few people - with very high gains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "5c1843e15f32b6f2a8ba19bd9f237a43",
     "grade": false,
     "grade_id": "high-gain",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BktQxHCRJHcNBktQxHCRJHcNBktQxHCRJHcNBktQxHCRJHcNBktQxHCRJHcNBktQxHCRJHcNBktQxHCRJHcNBktQxHCRJncXj7sBCsnr7H41t3y989hfHtm9JZ58z5swhyaYkB5NMJNk+7v5I0kJ2RoRDkkXAF4CrgXXA9UnWjbdXkrRwnSmXlS4DJqrqOYAku4DNwDNj7dU5ZFyXtLycpXPVuf536kwJhxXAi0Pzk8AHj2+UZBuwrc3+ZZKDc9zfhcArc1z3bDWWMef2073Hn+BxPvcttPGS2+c95r8zm0ZnSjhkRK26QtVdwF3z3lmyv6o2zHc7ZxPHvDAstDEvtPHC6RvzGXHPgcGZwqqh+ZXAS2PqiyQteGdKODwGrE2yJsl5wBZg95j7JEkL1hlxWamq3kxyM7AXWATsqKoDb+Eu531p6izkmBeGhTbmhTZeOE1jTlV3aV+StMCdKZeVJElnEMNBktRZUOFwtv9ER5JVSR5O8mySA0k+0eoXJNmX5FB7X9rqSXJHG++TSS4d2tbW1v5Qkq1D9Q8keaqtc0eSUY8Zn1ZJFiX5VpIH2/yaJI+0vt/bHmIgyfltfqItXz20jVta/WCSq4bqZ+RnIsmSJPcn+XY73h86l49zkn/dPtNPJ/lqkrefi8c5yY4kR5I8PVR7y4/rdPs4oapaEC8GN7q/A7wXOA/4C2DduPt1kmNYDlzapn8a+B8Mfm7k3wHbW307cHubvgb4BoPvkWwEHmn1C4Dn2vvSNr20LXsU+FBb5xvA1WfAuP8N8AfAg23+PmBLm/5d4ONt+l8Bv9umtwD3tul17XifD6xpn4NFZ/JnAtgJ/Ms2fR6w5Fw9zgy+BPs88I6h4/vPz8XjDPxD4FLg6aHaW35cp9vHCfs67r8Ep/GgfAjYOzR/C3DLuPs1zzE9APxj4CCwvNWWAwfb9JeA64faH2zLrwe+NFT/UqstB749VP+JdmMa40rgIeBy4MH2oX8FWHz8cWXwtNuH2vTi1i7HH+updmfqZwJ4d/uPZY6rn5PHmb/5hYQL2nF7ELjqXD3OwGp+Mhze8uM63T5O9FpIl5VG/UTHijH1Zd7aqfQlwCPAxVV1GKC9X9SaTTfmE9UnR9TH6XeAfwv8vzb/HuC1qnqzzQ/38a/H1Za/3tqf7J/DuL0XOAr8p3Y57T8meSfn6HGuqu8B/x74LnCYwXF7nHP/OE85Hcd1un1MayGFw6x+ouNskORdwNeAT1bV90/UdESt5lAfiyT/BDhSVY8Pl0c0rRmWnRXjHbKYwaWHO6vqEuD/MLgUMJ2zetzt+vdmBpeC/jbwTga/0Hy8c+04z2Ss41xI4XBO/ERHkrcxCIavVNXXW/nlJMvb8uXAkVafbswnqq8cUR+XDwP/NMkLwC4Gl5Z+B1iSZOoLnMN9/OtxteU/Axzj5P8cxm0SmKyqR9r8/QzC4lw9zr8APF9VR6vqr4CvA3+fc/84Tzkdx3W6fUxrIYXDWf8THe3Jg7uBZ6vqc0OLdgNTTyxsZXAvYqp+Q3vqYSPwejul3AtcmWRp+1fblQyuyR4G3kiyse3rhqFtnXZVdUtVrayq1QyO1x9X1T8DHgaua82OH+/Un8N1rX21+pb2lMsaYC2DG3dn5Geiqv4X8GKSv9dKVzD4+fpz8jgzuJy0Mcnfav2ZGu85fZyHnI7jOt0+pjeumzJjuhF0DYMnfL4D/Ma4+zOH/v8DBqeJTwJPtNc1DK63PgQcau8XtPZh8D9R+g7wFLBhaFv/Aphor48N1TcAT7d1/gPH3RQd49g/wt88rfReBn/pJ4D/DJzf6m9v8xNt+XuH1v+NNqaDDD2Zc6Z+JoD1wP52rP8Lg6dSztnjDPwW8O3Wp99n8MTROXecga8yuK/yVwz+pX/j6Tiu0+3jRC9/PkOS1FlIl5UkSbNkOEiSOoaDJKljOEiSOoaDJKljOEiSOoaDJKnz/wGs3O3c2xhfVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(income_data['capital-gain'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function which accepts a dataframe and a capital gain value (in USD) and returns how many people are there with **greater than or equal to** that threshold gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "1a4b91174558f18a2c8aa05349124a42",
     "grade": false,
     "grade_id": "high-gain-fn",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def get_num_people_with_higher_gain(dataframe, threshold_gain):\n",
    "    return dataframe[dataframe['capital-gain']>=threshold_gain].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "f95e3ca5c5cf7af82ba098cc4505060b",
     "grade": true,
     "grade_id": "high-gain-tests",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "nose.tools.assert_equal(get_num_people_with_higher_gain(income_data, 60000), 159)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a pie chart of the number of people by marital status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "3f7c991513f38ee3e685493f9196a269",
     "grade": false,
     "grade_id": "cell-1e91969a128f0bd6",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratios = income_data.groupby('marital-status')['marital-status'].count()\n",
    "plt.pie(ratios, labels = ratios.index)\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4. Marital status (2 points)\n",
    "Which type of marital status is the most prominent (i.e. has the most people)? How many are there? Write a function that **calculates and returns the two answers**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "e9966cb2920b04c4df332da313f4e508",
     "grade": false,
     "grade_id": "cell-5c9a472102a7b452",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def most_prominent_marital_status(dataframe):\n",
    "    status = \"\"\n",
    "    num_people = 0\n",
    "    most = dataframe['marital-status'].value_counts()\n",
    "    status = most.index[0];\n",
    "    num_people = most[0]\n",
    "    return (status, num_people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "a2ef87117dcbc8db2d43cf9e9883c695",
     "grade": true,
     "grade_id": "cell-0077a3c8d4339ad7",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "(status, num_people) = most_prominent_marital_status(income_data)\n",
    "nose.tools.assert_not_equal(status, \"\")\n",
    "nose.tools.assert_greater(num_people, 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 5. Age groups (1 point)\n",
    "Create a histogram of all people's ages. Use the default settings. Add the label \"Age\" on the x-axis and \"Count\" on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "60c6510ffb115c08ab7fed3b29a27465",
     "grade": false,
     "grade_id": "cell-3ba52bf669280861",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(income_data.age)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get another view of the data. Split the ages into three:\n",
    "1. Young people: $\\text{age} \\le 30$\n",
    "2. Middle-aged people: $30 < \\text{age} \\le 60$\n",
    "3. Old people: $60 < \\text{age}$\n",
    "\n",
    "Return the counts in the following function. Which age group has the most people? How many are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "03420d0fea2b98e57aa98b9b5f483b86",
     "grade": false,
     "grade_id": "cell-b8a6cda122bf0fb3",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def get_num_people_by_age_category(dataframe):\n",
    "    young, middle_aged, old = (0, 0, 0)\n",
    "    income_data['age-brackets'] = pd.cut(income_data['age'], bins=[0, 30, 60, 999], labels=['0-30', '31-60',  '60+'])\n",
    "    age_brackets = income_data['age-brackets'].value_counts()\n",
    "    young = age_brackets[\"0-30\"]\n",
    "    middle_aged = age_brackets['31-60']\n",
    "    old = age_brackets['60+'] \n",
    "    return young, middle_aged, old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "79117a39e1c6066eece95309ce45dc81",
     "grade": true,
     "grade_id": "cell-17898d5f42dd42d5",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "young, middle_aged, old = get_num_people_by_age_category(income_data)\n",
    "nose.tools.assert_greater(young, 0)\n",
    "nose.tools.assert_greater(middle_aged, 0)\n",
    "nose.tools.assert_greater(old, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a bar chart. Execute the code below to see it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Distribution of people by age groups\")\n",
    "plt.bar(range(3), [young, middle_aged, old])\n",
    "plt.xticks(range(3), [\"Young\", \"Middle-aged\", \"Old\"])\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 6. Native country (2 points)\n",
    "Have a look at the native country of the people. The highest number of people are, as expected, from the US. What country makes for **the second highest** number of people? How many are they? Write a function to **calculate** and return the answer given a dataframe. DO NOT hardcode the answer, e.g. `return \"Germany\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "f48b741de66aedff2d878c5bcaf010cf",
     "grade": false,
     "grade_id": "cell-96faf6efe52dd3d0",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def get_second_highest_num_people(dataframe):\n",
    "    countries_ordered_people = dataframe['native-country'].value_counts();\n",
    "    index_second_country = 1\n",
    "    if (countries_ordered_people.index[index_second_country] == '?'):\n",
    "        index_second_country += 1\n",
    "    return countries_ordered_people[index_second_country], countries_ordered_people.index[index_second_country]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "d5981b1108063667383480f69588bdf6",
     "grade": true,
     "grade_id": "cell-923d941301d6acc8",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "num_people, country = get_second_highest_num_people(income_data)\n",
    "nose.tools.assert_greater(num_people, 0)\n",
    "nose.tools.assert_not_equal(country, \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 7. Busiest occupations (2 points)\n",
    "Which people are most overworked? Group all data by occupation and calculate the mean hours per week for each group.\n",
    "\n",
    "Write a function that **calculates and returns** all mean hours per week as a `Series`. Sort the results in descending order (most hours to fewest hours)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "8800a118285464a703ef223c058dc869",
     "grade": false,
     "grade_id": "cell-f93bf9800cb3bc46",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def get_mean_working_hours_by_income(dataframe):\n",
    "    return dataframe.groupby('occupation')['hours-per-week'].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "95d5a2c122057fb62658d0d3602436bc",
     "grade": true,
     "grade_id": "cell-69cd7b7f6076b0ed",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "occupation\n",
      "Farming-fishing      46.989940\n",
      "Exec-managerial      44.987703\n",
      "Transport-moving     44.656230\n",
      "Protective-serv      42.870570\n",
      "Prof-specialty       42.386715\n",
      "Craft-repair         42.304221\n",
      "Sales                40.781096\n",
      "Machine-op-inspct    40.755744\n",
      "Armed-Forces         40.666667\n",
      "Tech-support         39.432112\n",
      "Handlers-cleaners    37.947445\n",
      "Adm-clerical         37.558355\n",
      "Other-service        34.701669\n",
      "Priv-house-serv      32.885906\n",
      "?                    31.906131\n",
      "Name: hours-per-week, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "hours = get_mean_working_hours_by_income(income_data)\n",
    "print(hours)\n",
    "nose.tools.assert_almost_equal(hours[\"Handlers-cleaners\"], 37.95, delta = 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's plot a bar chart. Check the values carefully. If your do not match, feel free to edit the chart generation code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.title(\"Weekly hours by occupation\")\n",
    "plt.barh(range(len(hours)), hours)\n",
    "plt.yticks(list(range(len(hours))), hours.index)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}