Modules/1-Preparation/notebooks/intro_to_pandas.ipynb

{
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  {
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   "source": [
    "# author: Vladimir Osin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas\n",
    "\n",
    "Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal.\n",
    "\n",
    "**Note:** \n",
    "- Pandas is essential high level data manipulation tool, built on Numpy\n",
    "- Documentation [here](http://pandas.pydata.org/pandas-docs/stable/index.html)\n",
    "- 10 minutes tutorial [here](http://pandas.pydata.org/pandas-docs/stable/10min.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame - tabular data structure with rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# DataFrame is one of Pandas' most important data structures\n",
    "# Let's build DataFrame from a dictionary\n",
    "\n",
    "# data lists\n",
    "student_names = ['John', 'Arnold', 'Donald']\n",
    "age           = [23, 46, 31]\n",
    "salary        = [30000, 60000, 40000]\n",
    "\n",
    "# data dict\n",
    "data_dict = {'student_names': student_names, 'age': age, 'salary': salary} \n",
    "\n",
    "# and finally DataFrame\n",
    "data_frame = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_names</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>23</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>46</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Donald</td>\n",
       "      <td>31</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student_names  age  salary\n",
       "0          John   23   30000\n",
       "1        Arnold   46   60000\n",
       "2        Donald   31   40000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(pandas.core.frame.DataFrame,\n",
       " (3, 3),\n",
       " Index(['student_names', 'age', 'salary'], dtype='object'))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_frame), data_frame.shape, data_frame.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "           ...           RelationshipSatisfaction StandardHours  \\\n",
       "0          ...                                  1            80   \n",
       "1          ...                                  4            80   \n",
       "2          ...                                  2            80   \n",
       "3          ...                                  3            80   \n",
       "4          ...                                  4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# csv to DataFrame (dataset from here https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset/home)\n",
    "df_ibm = pd.read_csv('../Datasets/ibm_hr_analytics_employee_attrition_performance.csv')\n",
    "df_ibm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
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       "      <th>DailyRate</th>\n",
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       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>613</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2062</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>155</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2064</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
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       "      <th>1468</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1023</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2065</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2068</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "1466   39        No      Travel_Rarely        613  Research & Development   \n",
       "1467   27        No      Travel_Rarely        155  Research & Development   \n",
       "1468   49        No  Travel_Frequently       1023                   Sales   \n",
       "1469   34        No      Travel_Rarely        628  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
       "1466                 6          1        Medical              1   \n",
       "1467                 4          3  Life Sciences              1   \n",
       "1468                 2          3        Medical              1   \n",
       "1469                 8          3        Medical              1   \n",
       "\n",
       "      EmployeeNumber          ...           RelationshipSatisfaction  \\\n",
       "1466            2062          ...                                  1   \n",
       "1467            2064          ...                                  2   \n",
       "1468            2065          ...                                  4   \n",
       "1469            2068          ...                                  1   \n",
       "\n",
       "     StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
       "1466            80                 1                  9   \n",
       "1467            80                 1                  6   \n",
       "1468            80                 0                 17   \n",
       "1469            80                 0                  6   \n",
       "\n",
       "      TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "1466                      5               3               7   \n",
       "1467                      0               3               6   \n",
       "1468                      3               2               9   \n",
       "1469                      3               4               4   \n",
       "\n",
       "     YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "1466                  7                        1                     7  \n",
       "1467                  2                        0                     3  \n",
       "1468                  6                        0                     8  \n",
       "1469                  3                        1                     2  \n",
       "\n",
       "[4 rows x 35 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ibm.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      "Age                         1470 non-null int64\n",
      "Attrition                   1470 non-null object\n",
      "BusinessTravel              1470 non-null object\n",
      "DailyRate                   1470 non-null int64\n",
      "Department                  1470 non-null object\n",
      "DistanceFromHome            1470 non-null int64\n",
      "Education                   1470 non-null int64\n",
      "EducationField              1470 non-null object\n",
      "EmployeeCount               1470 non-null int64\n",
      "EmployeeNumber              1470 non-null int64\n",
      "EnvironmentSatisfaction     1470 non-null int64\n",
      "Gender                      1470 non-null object\n",
      "HourlyRate                  1470 non-null int64\n",
      "JobInvolvement              1470 non-null int64\n",
      "JobLevel                    1470 non-null int64\n",
      "JobRole                     1470 non-null object\n",
      "JobSatisfaction             1470 non-null int64\n",
      "MaritalStatus               1470 non-null object\n",
      "MonthlyIncome               1470 non-null int64\n",
      "MonthlyRate                 1470 non-null int64\n",
      "NumCompaniesWorked          1470 non-null int64\n",
      "Over18                      1470 non-null object\n",
      "OverTime                    1470 non-null object\n",
      "PercentSalaryHike           1470 non-null int64\n",
      "PerformanceRating           1470 non-null int64\n",
      "RelationshipSatisfaction    1470 non-null int64\n",
      "StandardHours               1470 non-null int64\n",
      "StockOptionLevel            1470 non-null int64\n",
      "TotalWorkingYears           1470 non-null int64\n",
      "TrainingTimesLastYear       1470 non-null int64\n",
      "WorkLifeBalance             1470 non-null int64\n",
      "YearsAtCompany              1470 non-null int64\n",
      "YearsInCurrentRole          1470 non-null int64\n",
      "YearsSinceLastPromotion     1470 non-null int64\n",
      "YearsWithCurrManager        1470 non-null int64\n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# get general info about data\n",
    "df_ibm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select few columns for analysis\n",
    "df_ibm_subset = df_ibm[['MonthlyIncome', 'Gender', 'Age', 'Attrition', 'EmployeeNumber']].copy()\n",
    "\n",
    "# let's make non-integer index based on employee number\n",
    "df_ibm_subset['MonthlyIncome'] = df_ibm_subset['MonthlyIncome'].astype('str')\n",
    "df_ibm_subset.set_index('MonthlyIncome', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
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       "    }\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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Age Attrition  EmployeeNumber\n",
       "MonthlyIncome                                       \n",
       "5130             Male   49        No               2\n",
       "2090             Male   37       Yes               4\n",
       "2909           Female   33        No               5\n",
       "3468             Male   27        No               7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# square brackets selection (columns selection, slicing for rows)\n",
    "df_ibm_subset[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2426</th>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Age\n",
       "MonthlyIncome             \n",
       "2426             Male   35\n",
       "2911             Male   31\n",
       "2911           Female   33"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label-based selection (row, column, row and column access)\n",
    "df_ibm_subset.loc[['2426', '2911'], ['Gender', 'Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Age Attrition  EmployeeNumber\n",
       "MonthlyIncome                                       \n",
       "5993           Female   41       Yes               1\n",
       "5130             Male   49        No               2\n",
       "2090             Male   37       Yes               4\n",
       "2909           Female   33        No               5\n",
       "3468             Male   27        No               7"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# integer-based selection \n",
    "df_ibm_subset.iloc[:5, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Age Attrition  EmployeeNumber\n",
       "MonthlyIncome                                       \n",
       "5993           Female   41       Yes               1\n",
       "5130             Male   49        No               2\n",
       "2090             Male   37       Yes               4\n",
       "2909           Female   33        No               5\n",
       "3468             Male   27        No               7\n",
       "3068             Male   32        No               8"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering pandas Data Frame\n",
    "ind = df_ibm_subset['EmployeeNumber'] < 10\n",
    "df_ibm_subset[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Age Attrition  EmployeeNumber\n",
       "MonthlyIncome                                       \n",
       "5993           Female   41       Yes               1\n",
       "5130             Male   49        No               2\n",
       "2090             Male   37       Yes               4"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Age Attrition  EmployeeNumber\n",
       "MonthlyIncome                                       \n",
       "5993           Female   41       Yes               1\n",
       "5130             Male   49        No               2\n",
       "2090             Male   37       Yes               4"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# few more ways to filter \n",
    "df_ibm_subset[np.logical_and(df_ibm_subset.Age > 35, df_ibm_subset.EmployeeNumber < 10)]\n",
    "\n",
    "df_ibm_subset[(df_ibm_subset.Age > 35) & (df_ibm_subset.EmployeeNumber < 10)] # &, |, ~  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41 Yes\n",
      "49 No\n",
      "37 Yes\n",
      "33 No\n",
      "27 No\n"
     ]
    }
   ],
   "source": [
    "# looping over Data Frame\n",
    "for ind, row in df_ibm_subset[:5].iterrows():\n",
    "    print(row['Age'], row['Attrition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EmployeeNumberSquared</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2670</th>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9526</th>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Age Attrition  EmployeeNumber  EmployeeNumberSquared\n",
       "MonthlyIncome                                                              \n",
       "5993           Female   41       Yes               1                      1\n",
       "5130             Male   49        No               2                      4\n",
       "2090             Male   37       Yes               4                     16\n",
       "2909           Female   33        No               5                     25\n",
       "3468             Male   27        No               7                     49\n",
       "3068             Male   32        No               8                     64\n",
       "2670           Female   59        No              10                    100\n",
       "2693             Male   30        No              11                    121\n",
       "9526             Male   38        No              12                    144\n",
       "5237             Male   36        No              13                    169"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ind, row in df_ibm_subset[:5].iterrows():\n",
    "    df_ibm_subset.loc[ind, 'EmployeeNumberSquared'] = row['EmployeeNumber']**2\n",
    "    \n",
    "df_ibm_subset['EmployeeNumberSquared'] = df_ibm_subset['EmployeeNumber'].apply(lambda x: your_function(x)**2)\n",
    "df_ibm_subset[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ploting with Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD8CAYAAABthzNFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFShJREFUeJzt3Xu0nXV95/H3BwiNChohB8lwYBK6uEhQAVNkBka5SEWaclHp4HIqBdrMLKnItF0aoA44a1zLrnZEuzpDm4IVHJuI4TotvTBcyoxLwQRoE4gMDEZyhMIxyk3kkvCdP/YTOcaHnJ2Ts8/eyXm/1jrrPM9vP3s/X3+y8zm/5/J7UlVIkrS5nfpdgCRpMBkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJa7dLvArbF7Nmza+7cuf0uQ5K2KytXrvxBVQ2Nt912HRBz585lxYoV/S5DkrYrSb7XzXYeYpIktTIgJEmtDAhJUqvt+hyEJE2Gl19+mZGREV544YV+lzKpZs6cyfDwMDNmzJjQ+w0ISdPeyMgIu+++O3PnziVJv8uZFFXF+vXrGRkZYd68eRP6DA8xSZr2XnjhBfbcc88dJhwAkrDnnntu06jIgJAk2KHCYZNt/d9kQEiSWnkOQpI2k89M7miiLqmutrv++uv5wAc+wJo1azj44IMntYaJMCC0Q9uWL3q3X2ppsixdupRjjjmGZcuWcemll/a7HA8xSdIgeO655/jGN77BlVdeybJlywB45ZVX+NjHPsb8+fNZuHAhJ598MsuXLwdg5cqVvOc97+Gd73wn73vf+3j88ccnvSYDQpIGwA033MBJJ53EgQceyB577ME999zDddddx9q1a1m1ahVXXHEF3/zmN4HOfRsf//jHWb58OStXruScc87h4osvnvSaPMQkSQNg6dKlXHDBBQCceeaZLF26lJdffpkzzjiDnXbaib333pvjjjsOgAcffJDVq1dz4oknArBx40bmzJkz6TUZEJLUZ+vXr+e2225j9erVJGHjxo0k4fTTT2/dvqqYP3/+T0cUveIhJknqs+XLl/PRj36U733ve6xdu5Z169Yxb948Zs+ezbXXXssrr7zCE088wR133AHAQQcdxOjo6M8ccrr//vsnva6ejSCSfAlYCDxZVYdu9trvAX8IDFXVD9K5m+OLwMnA88BvVNU9vapNkrZkqq9gW7p0KYsXL/6Ztg9+8IOsWbOG4eFhDj30UA488EDe9a538aY3vYldd92V5cuXc/755/P000+zYcMGLrjgAubPnz+pdfXyENOXgT8Brh7bmGRf4ETg0THN7wcOaH7eBVze/JakHd6mkcFY559/PtC5umm33XZj/fr1HHnkkbztbW8D4LDDDuPOO+/saV09C4iqujPJ3JaXLgM+Cdw4pu1U4OqqKuBbSWYlmVNVk3/dliRtRxYuXMhTTz3FSy+9xKc//Wn23nvvKdv3lJ6kTnIK8P2q+sfN5gjZB1g3Zn2kaTMgJE1rbaOLqTJlAZHk9cDFwC+3vdzS1noQMMkiYBHAfvvtN2n1SZreqmqHm7Cvc1Bm4qZyBPGLwDxg0+hhGLgnyZF0Rgz7jtl2GHis7UOqagmwBGDBggXOhaCB5TQf24+ZM2eyfv36HWrK703Pg5g5c+aEP2PKAqKqVgF7bVpPshZY0FzFdBPw20mW0Tk5/bTnHyRNleHhYUZGRhgdHe13KZNq0xPlJqqXl7kuBY4FZicZAS6pqitfY/Ob6Vzi+jCdy1zP7lVdkrS5GTNmTPipazuyXl7F9OFxXp87ZrmA83pViyRp63kntSSplQEhSWrlZH0aeJP9dC9J3XEEIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaeZmrpoSXqkrbH0cQkqRWjiDUNUcB0vTiCEKS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtehYQSb6U5Mkkq8e0/WGS7yT5pyTXJ5k15rULkzyc5MEk7+tVXZKk7vRyBPFl4KTN2m4BDq2qtwP/F7gQIMkhwJnA/OY9/z3Jzj2sTZI0jp4FRFXdCfxws7a/r6oNzeq3gOFm+VRgWVW9WFXfBR4GjuxVbZKk8fXzHMQ5wN80y/sA68a8NtK0SZL6pC8BkeRiYAPw1U1NLZvVa7x3UZIVSVaMjo72qkRJmvamPCCSnAUsBD5SVZtCYATYd8xmw8Bjbe+vqiVVtaCqFgwNDfW2WEmaxqY0IJKcBHwKOKWqnh/z0k3AmUl+Ick84ADg7qmsTZL0s3o23XeSpcCxwOwkI8AldK5a+gXgliQA36qq/1BV9ye5BniAzqGn86pqY69qkySNr2cBUVUfbmm+cgvbfxb4bK/qkSRtHe+kliS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1Kpnk/VJ27t8pu05VtL04QhCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLXqWUAk+VKSJ5OsHtO2R5JbkjzU/H5z054kf5zk4ST/lOSIXtUlSepOL0cQXwZO2qxtMXBrVR0A3NqsA7wfOKD5WQRc3sO6JEld6FlAVNWdwA83az4VuKpZvgo4bUz71dXxLWBWkjm9qk2SNL6pPgfxlqp6HKD5vVfTvg+wbsx2I02bJKlPBuUkdducBtW6YbIoyYokK0ZHR3tcliRNX1MdEE9sOnTU/H6yaR8B9h2z3TDwWNsHVNWSqlpQVQuGhoZ6WqwkTWdTHRA3AWc1y2cBN45p/2hzNdNRwNObDkVJkvqjZ7O5JlkKHAvMTjICXAJ8DrgmybnAo8AZzeY3AycDDwPPA2f3qi5JUnd6FhBV9eHXeOmElm0LOK9XtUiStt6gnKSWJA0YHxg0jfgAHElbwxGEJKlVVwGR5NBeFyJJGizdjiD+NMndST6WZFZPK5IkDYSuAqKqjgE+QudmthVJ/jLJiT2tTJLUV12fg6iqh4DfBz4FvAf44yTfSfKBXhUnSeqfbs9BvD3JZcAa4HjgV6vqrc3yZT2sT5LUJ91e5vonwJ8DF1XVTzY1VtVjSX6/J5VJkvqq24A4GfhJVW0ESLITMLOqnq+qr/SsOklS33R7DuJ/Aa8bs/76pk2StIPqNiBmVtVzm1aa5df3piRJ0iDoNiB+nOSITStJ3gn8ZAvbS5K2c92eg7gA+HqSTQ/xmQP8296UJEkaBF0FRFV9O8nBwEF0Hg/6nap6uaeVSZL6amtmc/0lYG7znsOTUFVX96QqSVLfdRUQSb4C/CJwH7CxaS7AgJCkHVS3I4gFwCHNk98kSdNAt1cxrQb27mUhkqTB0u0IYjbwQJK7gRc3NVbVKRPZaZL/CPwmncNUq4Cz6VwZtQzYA7gH+PWqemkiny9J2nbdBsSlk7XDJPsA59M5ZPWTJNcAZ9KZzuOyqlqW5E+Bc4HLJ2u/kqSt0+3zIP4BWAvMaJa/Teev/InaBXhdkl3o3JH9OJ2ZYZc3r18FnLYNny9J2kbdTvf9W3T+8f6zpmkf4IaJ7LCqvg/8EfAonWB4GlgJPFVVG5rNRpp9SJL6pNuT1OcBRwPPwE8fHrTXRHaY5M3AqcA84F8AbwDe37Jp6xVTSRYlWZFkxejo6ERKkCR1oduAeHHsCePm0NBEL3l9L/Ddqhpt7sa+DvjXwKzmcwGGgcfa3lxVS6pqQVUtGBoammAJkqTxdBsQ/5DkIjrnDU4Evg78zwnu81HgqCSvTxLgBOAB4HbgQ802ZwE3TvDzJUmToNurmBbTuapoFfDvgZuBKyayw6q6K8lyOie5NwD3AkuAvwaWJfkvTduVE/l8aUeQz2TC761LvJ9Vk6PbyfpeofPI0T+fjJ1W1SXAJZs1PwIcORmfL0nadt3OxfRdWs45VNX+k16RJGkgbM1cTJvMBM6gc8ezJGkH1e2NcuvH/Hy/qr5A58Y2SdIOqttDTEeMWd2Jzohi955UJEkaCN0eYvqvY5Y30Jl249cmvRpJ28wroDRZur2K6bheFyJJGizdHmL6nS29XlWfn5xyJEmDYmuuYvol4KZm/VeBO4F1vShKktR/W/PAoCOq6lmAJJcCX6+q3+xVYZKk/up2Lqb9gLFPd3sJmDvp1UiSBka3I4ivAHcnuZ7OHdWnA1f3rCpJUt91exXTZ5P8DfBvmqazq+re3pUlSeq3bg8xQefRoM9U1ReBkSTzelSTJGkAdPvI0UuATwEXNk0zgP/Rq6IkSf3X7QjidOAU4McAVfUYTrUhSTu0bgPipaoqmim/k7yhdyVJkgZBt1cxXZPkz+g8N/q3gHOYpIcHaetsyzw7krQ1ur2K6Y+aZ1E/AxwE/KequqWnlUmS+mrcgEiyM/B3VfVewFCQpGli3HMQVbUReD7Jm6agHknSgOj2HMQLwKokt9BcyQRQVedPZKdJZgFXAIfSOfF9DvAg8DU6U3isBX6tqn40kc+XJG27bgPir5ufyfJF4G+r6kNJdqVzE95FwK1V9bkki4HFdO69kCT1wRYDIsl+VfVoVV01WTtM8kbg3cBvAFTVS8BLSU4Fjm02uwq4AwNCkvpmvHMQN2xaSHLtJO1zf2AU+Isk9ya5ormv4i1V9ThA83uvtjcnWZRkRZIVo6Ojk1SSJGlz4wXE2Ivu95+kfe4CHAFcXlWH0zmnsbjbN1fVkqpaUFULhoaGJqkkSdLmxguIeo3lbTECjFTVXc36cjqB8USSOQDN7ycnaX+SpAkYLyDekeSZJM8Cb2+Wn0nybJJnJrLDqvpnYF2Sg5qmE4AH6DzO9Kym7Szgxol8viRpcmzxJHVV7dyj/X4c+GpzBdMjwNl0wuqaJOcCjwJn9GjfkqQudHuZ66SqqvuABS0vnTDVtUh61bbM9VWXTNZRaA2KrXlgkCRpGjEgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1MqAkCS1MiAkSa36MtWGJG3OaT4GjyMISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktfI+CEmTYlvuY9Bg6tsIIsnOSe5N8lfN+rwkdyV5KMnXkuzar9okSf09xPQJYM2Y9T8ALquqA4AfAef2pSpJEtCngEgyDPwKcEWzHuB4YHmzyVXAaf2oTZLU0a9zEF8APgns3qzvCTxVVRua9RFgn7Y3JlkELALYb7/9elxmb3isVhoczgH12qZ8BJFkIfBkVa0c29yyaWvPV9WSqlpQVQuGhoZ6UqMkqT8jiKOBU5KcDMwE3khnRDEryS7NKGIYeKwPtUmSGlM+gqiqC6tquKrmAmcCt1XVR4DbgQ81m50F3DjVtUmSXjVIN8p9CvidJA/TOSdxZZ/rkaRpra83ylXVHcAdzfIjwJH9rEfS9skLP3pjkEYQkqQBYkBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWvlM6gny1n5J22rQn0UxbQPCf+Alacs8xCRJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWUx4QSfZNcnuSNUnuT/KJpn2PJLckeaj5/eaprk2S9Kp+jCA2AL9bVW8FjgLOS3IIsBi4taoOAG5t1iVJfTLlAVFVj1fVPc3ys8AaYB/gVOCqZrOrgNOmujZJ0qv6eg4iyVzgcOAu4C1V9Th0QgTYq3+VSZL6FhBJdgOuBS6oqme24n2LkqxIsmJ0dLR3BUrSNNeXgEgyg044fLWqrmuan0gyp3l9DvBk23uraklVLaiqBUNDQ1NTsCRNQ1M+m2uSAFcCa6rq82Neugk4C/hc8/vGqa5NkrbGjj4rdD+m+z4a+HVgVZL7mraL6ATDNUnOBR4FzuhDbZKkxpQHRFX9H+C1YveEqaxFkvTavJNaktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQYuIJKclOTBJA8nWdzveiRpuhqogEiyM/DfgPcDhwAfTnJIf6uSpOlpoAICOBJ4uKoeqaqXgGXAqX2uSZKmpUELiH2AdWPWR5o2SdIU26XfBWwmLW31Mxski4BFzepzSR7seVU/bzbwgz7sd3th/4zPPtoy+2ccuTTb0kf/spuNBi0gRoB9x6wPA4+N3aCqlgBLprKozSVZUVUL+lnDILN/xmcfbZn9M76p6KNBO8T0beCAJPOS7AqcCdzU55okaVoaqBFEVW1I8tvA3wE7A1+qqvv7XJYkTUsDFRAAVXUzcHO/6xhHXw9xbQfsn/HZR1tm/4yv532Uqhp/K0nStDNo5yAkSQPCgNiCJPsmuT3JmiT3J/lE075HkluSPNT8fnO/a+2XJDOT3J3kH5s++kzTPi/JXU0ffa256GDaSrJzknuT/FWzbv+MkWRtklVJ7kuyomnze9ZIMivJ8iTfaf49+ldT0T8GxJZtAH63qt4KHAWc10z9sRi4taoOAG5t1qerF4Hjq+odwGHASUmOAv4AuKzpox8B5/axxkHwCWDNmHX75+cdV1WHjbl00+/Zq74I/G1VHQy8g85/Sz3vHwNiC6rq8aq6p1l+ls7/KfvQmf7jqmazq4DT+lNh/1XHc83qjOangOOB5U37tO6jJMPArwBXNOvB/umG3zMgyRuBdwNXAlTVS1X1FFPQPwZEl5LMBQ4H7gLeUlWPQydEgL36V1n/NYdP7gOeBG4B/h/wVFVtaDaZ7lOmfAH4JPBKs74n9s/mCvj7JCub2RLA79km+wOjwF80hymvSPIGpqB/DIguJNkNuBa4oKqe6Xc9g6aqNlbVYXTufD8SeGvbZlNb1WBIshB4sqpWjm1u2XRa9s8YR1fVEXRmcj4vybv7XdAA2QU4Ari8qg4HfswUHW4zIMaRZAadcPhqVV3XND+RZE7z+hw6fzlPe82w9w4652tmJdl0n83PTZkyjRwNnJJkLZ3ZiY+nM6Kwf8aoqsea308C19P5Q8PvWccIMFJVdzXry+kERs/7x4DYguZY8ZXAmqr6/JiXbgLOapbPAm6c6toGRZKhJLOa5dcB76VzruZ24EPNZtO2j6rqwqoarqq5dKaOua2qPoL981NJ3pBk903LwC8Dq/F7BkBV/TOwLslBTdMJwANMQf94o9wWJDkG+N/AKl49fnwRnfMQ1wD7AY8CZ1TVD/tSZJ8leTudE2Q70/mD45qq+s9J9qfzF/MewL3Av6uqF/tXaf8lORb4vapaaP+8qumL65vVXYC/rKrPJtkTv2cAJDmMzkUOuwKPAGfTfN/oYf8YEJKkVh5ikiS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLU6v8D4PcGLSij7eMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1 = df_ibm.Age.plot(kind='hist', bins=20, color='g', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_2 = df_ibm.HourlyRate.plot(kind='hist', bins=20, color='r', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3 = df_ibm.JobRole.value_counts().plot(kind='barh', color='m', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save figure\n",
    "fig = plot_1.get_figure()\n",
    "fig.savefig(\"age_histogram.png\")"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}