Data Visualization and EDA Lab.ipynb

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   "source": [
    "%matplotlib inline"
   ]
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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import nose.tools"
   ]
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   "source": [
    "# Data Visualization and Exploratory Data Analysis Lab\n",
    "## Visualizing and exploring data. Data mining process as a whole"
   ]
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   "source": [
    "### Income information\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."
   ]
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      "/Users/stanislavdimitrov/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   age         workclass  fnlwgt  education  education_num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital_status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week native_country  label  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
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   "source": [
    "income_data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", header = None, sep = \", \")\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\", \"label\"\n",
    "]\n",
    "income_data.head()"
   ]
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   "source": [
    "nose.tools.assert_is_not_none(income_data)"
   ]
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   "source": [
    "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."
   ]
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    {
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       "array(['<=50K', '>50K'], dtype=object)"
      ]
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   ],
   "source": [
    "income_data.label.unique()"
   ]
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   "source": [
    "def get_num_people_with_high_income(dataframe):\n",
    "    return dataframe[dataframe['label'] == '>50K'].shape[0]"
   ]
  },
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     "text": [
      "7841\n"
     ]
    }
   ],
   "source": [
    "print(get_num_people_with_high_income(income_data))"
   ]
  },
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    "# This cell contains hidden tests"
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   "source": [
    "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."
   ]
  },
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     "data": {
      "image/png": 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Ad4GPt/a7gCuBMeD7wCcAqupIks8Cj7Z2n6mqI7PolyRplmYcDlX1HPDzE9RfAS6doF7A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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e68cf8>"
      ]
     },
     "metadata": {},
     "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": 10,
   "metadata": {
    "collapsed": true,
    "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159\n"
     ]
    }
   ],
   "source": [
    "print(get_num_people_with_higher_gain(income_data, 60000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Never-married', 'Married-civ-spouse', 'Divorced',\n",
       "       'Married-spouse-absent', 'Separated', 'Married-AF-spouse',\n",
       "       'Widowed'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_data.marital_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       14976\n",
       "Never-married            10683\n",
       "Divorced                  4443\n",
       "Separated                 1025\n",
       "Widowed                    993\n",
       "Married-spouse-absent      418\n",
       "Married-AF-spouse           23\n",
       "Name: marital_status, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_data.marital_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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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JYzHujlNXvEW4eMwzsxWEJgcAJH0OWAtsjOf6JiFhrQbuACZKeoJQ83w5XlwK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113fb6c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income_data.marital_status.value_counts().plot(kind='pie')\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "plt.title('Number of people by marital status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 16,
   "metadata": {
    "collapsed": true,
    "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 = dataframe['marital_status'].value_counts().idxmax()\n",
    "    num_people = dataframe['marital_status'].value_counts().max()\n",
    "\n",
    "    return (status, num_people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Married-civ-spouse', 14976)\n"
     ]
    }
   ],
   "source": [
    "(status, num_people) = most_prominent_marital_status(income_data)\n",
    "print((status, num_people))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "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": [
    "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": 19,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "60c6510ffb115c08ab7fed3b29a27465",
     "grade": false,
     "grade_id": "cell-3ba52bf669280861",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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auqABrgTeDJwOXDkZNpKk0RhacFTV/wT27lNeB1zXpq8DLhioX1+dbwILkywBzgG2VNXe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\nf7mPFj7c7uHOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b62828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(income_data.age)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get another view of the data. Split the ages into three:\n",
    "1. Young people: $\\text{age} <= 30$\n",
    "2. Middle-aged people: $30 < \\text{age} <= 60$\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": 20,
   "metadata": {
    "collapsed": true,
    "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",
    "    \n",
    "    young = dataframe[dataframe['age'] <= 30].shape[0]\n",
    "    middle_aged = dataframe[(dataframe['age'] > 30) & (dataframe['age'] <= 60)].shape[0]\n",
    "    old = dataframe[dataframe['age'] > 60].shape[0]\n",
    "    \n",
    "    return young, middle_aged, old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10572, 19657, 2332)\n"
     ]
    }
   ],
   "source": [
    "print(get_num_people_by_age_category(income_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1QLMnlTnAUElDJPUBxgDTG9wnM7Meq6mHvyJilaQzgFuAXsCUiFjQ4G5tTHrc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\nOKmYmVlhnFTMzKwwTipmZlaY/w+6Vx1aenPbmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149ce160>"
      ]
     },
     "metadata": {},
     "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": [
    "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": 24,
   "metadata": {
    "collapsed": true,
    "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",
    "    num_people = dataframe['native_country'].value_counts()[1]\n",
    "    country = dataframe['native_country'].value_counts().index[1]\n",
    "    \n",
    "    return num_people, country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(643, 'Mexico')\n"
     ]
    }
   ],
   "source": [
    "print(get_second_highest_num_people(income_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "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": [
    "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 fewestt hours)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Adm-clerical', 'Exec-managerial', 'Handlers-cleaners',\n",
       "       'Prof-specialty', 'Other-service', 'Sales', 'Craft-repair',\n",
       "       'Transport-moving', 'Farming-fishing', 'Machine-op-inspct',\n",
       "       'Tech-support', '?', 'Protective-serv', 'Armed-Forces',\n",
       "       'Priv-house-serv'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_data.occupation.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "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",
    "    grouped = dataframe.groupby('occupation')['hours_per_week'].mean()\n",
    "    \n",
    "    return grouped.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rfj9Jd0q6RtJISXsszXi7iOGtks7r63EjIiIi+qOWSEQlCbgQmGT7XeXnMb8G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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114bbf860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.title(\"Weekly hours by occupation\")\n",
    "plt.barh(range(len(hours.values)), hours.values)\n",
    "plt.yticks(range(len(hours.index)), hours.index)\n",
    "plt.gca().invert_yaxis()\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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}