Assignment2_Anurag.ipynb

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      "name": "Assignment2_Anurag.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCR_aMoZAwQB",
        "colab_type": "text"
      },
      "source": [
        "# **ANURAG**\n",
        "\n",
        "*DATA DESCRIPTION*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4ts1J6x8Orr",
        "colab_type": "code",
        "outputId": "648b8953-5585-456d-d571-3256c808bd14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/employee.csv')\n",
        "df.head(10)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>employment_type</th>\n",
              "      <th>salary</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>relationship</th>\n",
              "      <th>Gender</th>\n",
              "      <th>hours_per_week</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>Never-married</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</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>Married-civ-spouse</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Male</td>\n",
              "      <td>13</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>Divorced</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</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>Married-civ-spouse</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</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>Married-civ-spouse</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>37</td>\n",
              "      <td>Private</td>\n",
              "      <td>284582</td>\n",
              "      <td>Masters</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>49</td>\n",
              "      <td>Private</td>\n",
              "      <td>160187</td>\n",
              "      <td>9th</td>\n",
              "      <td>Married-spouse-absent</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Female</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>52</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>209642</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Male</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>31</td>\n",
              "      <td>Private</td>\n",
              "      <td>45781</td>\n",
              "      <td>Masters</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Female</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>42</td>\n",
              "      <td>Private</td>\n",
              "      <td>159449</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age   employment_type  salary  ...   relationship  Gender hours_per_week\n",
              "0   39         State-gov   77516  ...  Not-in-family    Male             40\n",
              "1   50  Self-emp-not-inc   83311  ...        Husband    Male             13\n",
              "2   38           Private  215646  ...  Not-in-family    Male             40\n",
              "3   53           Private  234721  ...        Husband    Male             40\n",
              "4   28           Private  338409  ...           Wife  Female             40\n",
              "5   37           Private  284582  ...           Wife  Female             40\n",
              "6   49           Private  160187  ...  Not-in-family  Female             16\n",
              "7   52  Self-emp-not-inc  209642  ...        Husband    Male             45\n",
              "8   31           Private   45781  ...  Not-in-family  Female             50\n",
              "9   42           Private  159449  ...        Husband    Male             40\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ba1ZdqmK9iro",
        "colab_type": "code",
        "outputId": "c5b0ef5d-fa74-4ec5-b9ab-01f57702d7a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "df.columns\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'employment_type', 'salary', 'education', 'marital-status',\n",
              "       'relationship', 'Gender', 'hours_per_week'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LopJTp9Q2Px",
        "colab_type": "text"
      },
      "source": [
        "***PROBLRM #1 - Identify Categorical variables & Continuous variables***\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvzYfSVuDDoV",
        "colab_type": "code",
        "outputId": "921e7153-99bd-4f15-a803-93e21637ec76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "b=[]\n",
        "a=df.columns\n",
        "for i in a:\n",
        "  b.append(i)\n",
        "  for k in b:\n",
        "   \n",
        "    d=df[k].unique()\n",
        "  print('\\n','Variable Name : ',k)\n",
        "  print('Total values in' ,k,'is = ',len(d))\n",
        "  print(d)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Variable Name :  age\n",
            "Total values in age is =  65\n",
            "[39 50 38 53 28 37 49 52 31 42 30 23 32 40 34 25 43 54 35 59 56 19 20 45\n",
            " 22 48 21 24 57 44 41 29 18 47 46 36 79 27 67 33 76 17 55 61 70 64 71 68\n",
            " 66 51 58 26 60 90 75 65 77 62 63 80 72 74 69 73 81]\n",
            "\n",
            " Variable Name :  employment_type\n",
            "Total values in employment_type is =  7\n",
            "['State-gov' 'Self-emp-not-inc' 'Private' 'Federal-gov' 'Local-gov' '?'\n",
            " 'Self-emp-inc']\n",
            "\n",
            " Variable Name :  salary\n",
            "Total values in salary is =  966\n",
            "[  77516   83311  215646  234721  338409  284582  160187  209642   45781\n",
            "  159449  280464  141297  122272  205019  121772  245487  176756  186824\n",
            "   28887  292175  193524  302146   76845  117037  109015  216851  168294\n",
            "  180211  367260  193366  190709  266015  386940   59951  311512  242406\n",
            "  197200  544091   84154  265477  507875   88506  172987   94638  289980\n",
            "  337895  144361  128354  101603  271466   32275  226956   51835  251585\n",
            "  109832  237993  216666   56352  147372  188146   59496  293936  149640\n",
            "  116632  105598  155537  183175  169846  191681  200681  101509  309974\n",
            "  162298  211678  124744  213921   32214  212759  309634  125927  446839\n",
            "  276515   51618  159937  343591  346253  268234  202051   54334  410867\n",
            "  249977  286730  212563  117747  226296  115585  191277  202683  171095\n",
            "  249409  124191  198282  149116  188300  103432  317660  304873  194901\n",
            "  189265  124692  432376   65324  335605  377869  102864   95647  303090\n",
            "  197371  247552  102632  199915  118853   77143  267989  301606  287828\n",
            "  111697  114937  129305  365739   69621   43323  120985  254202  146195\n",
            "  125933   56920  163127   34310   81973   66614  232782  316868  196584\n",
            "  105376  185814  175374  108293  181232  174662  186009  198183  163003\n",
            "  296158  252903  187715  214542  494223  191535  228456   38317  252752\n",
            "   78374   88419  201080  207157  235485  102628   25828   54826  124953\n",
            "  175325   96062  428030  149624  253814  312956  483777  183930   37274\n",
            "  181344  114580  633742  286370   29054  304030  143129  135105   99928\n",
            "  109567  155222  159567  523910  120939  130760  197387   99374   56795\n",
            "  138992   32921  397317  170653  259323  254817   48211  140164  128757\n",
            "   36270  210563   65368  160943  208358  153790   85815  125417  635913\n",
            "  313321  182609  109434  255004  197860  187656   51744  176681  140359\n",
            "  243313   24215  167687  314209  176796  538583  130408  159732  110978\n",
            "   76714  268700  170525  180138  115076  115458  347890  196001  273905\n",
            "  119156  179488  203580  236596  183916  207578  153141  112763  390781\n",
            "  171328   27382  259014  303044  117789  172579  187666  204518  150042\n",
            "   98092  245918  146013  378322  257295  218956   21174  185480  222205\n",
            "   69867  191260   30653  209109   70377  477983  170924  190174  193787\n",
            "  279472   34918   97688  175413  173960  205759  425161  220531  176609\n",
            "  371987  193884  200352  127595  220419  231931  248402  111095   57424\n",
            "  157443  278130  169469  146268  153718  217460  238638  303296  173321\n",
            "  193945   83082  193815   34987   59306  142897  860348  205607  199698\n",
            "  191954  138714  399087  423158  159841  174308   50356  186110  200381\n",
            "  174309   78383  211601  187728  321171  127921  206565  224563  178686\n",
            "   98545  242606  270942   94235   71195  104112  261192   94936  296478\n",
            "  119272   85043  293364  241895   36135  151989  101128  156464  117963\n",
            "  192262  111363  329752  372020   95432  161400   96129  111949  117125\n",
            "  348022  270092  180609  174575  410439   92262  183081  362589  212448\n",
            "  481060  185885   89821  184018  256649  160323  350845  267404   35633\n",
            "   80914  172927  174319  214955  344991  108699  117312  396099  134152\n",
            "  162028   25429  232392  220098  301302  277946   98101  196164  115562\n",
            "   96975  137300   86872  132178  416103  108574  288353  227689  166481\n",
            "  445382  110145  317253  123147  364657   42346  241951  118500  188386\n",
            " 1033222   92440  190762  426017  243867  240283   61777  175024   92003\n",
            "  188401  228528  133373  255191  204653  222289  287480  107762  202521\n",
            "  204116   29662  116358  208405  284843  117018   81281  340148  363425\n",
            "   45857  191073  405855  298227  290521   56915  146538  258872  206399\n",
            "  197332  245062  197583  234885   72887  180374  351299   54012  115745\n",
            "  288825  132601  193374  170070  126708   35598   33983  192776  118551\n",
            "  201965  139883  285020  303990   49401  279196  211870  281432  161155\n",
            "  197904  111746  170721   70100  193626  271749  189775  401531  286967\n",
            "  164427   91039  347934  371373   32220  187251  178107  343121  262749\n",
            "  403107   64293  303588  324960  114060   48925  180980  181054  388093\n",
            "  249609  112131  543162   91996  141944  251804   37070  337587  189346\n",
            "  222216  267044  214635  204226  108116   99146  196232  248344  186035\n",
            "  177905   85812  221172   99183  190387  202692  109339  108658  197202\n",
            "  101739  231559  207853  190942  102345   41493  190027  210525  133937\n",
            "  237903  163862  201872   84179   51662  233327  259510  184831  245724\n",
            "   27053  205343  229328  319560  136218   54576  323069  148291  152453\n",
            "  114053  212960  264052   82804  334273   27337  188436  433665  110663\n",
            "   87490  354351   95469  242718   22463  158156  350162  165532   28738\n",
            "  283635   86646  195733   69884  199713  181659  340939  197747   34292\n",
            "  156764   25826  103948  137390  105138   39352  168387  267147   99399\n",
            "  214242  200408  136455  239824  217039   51290  175674  194404   45612\n",
            "  410114  182521  339772  169658  200853  247564  249909  208122  109881\n",
            "  207824  369027  114117   51048  102388  190483  462440  109351   34383\n",
            "  241832  124187  153614  267556  205469  268090  165039  120451  154374\n",
            "  103649   35723  262601  226181  175697  248145  289436   75654  199378\n",
            "  160968  188563   55849  195322  402089   78277  158611  169496  130959\n",
            "  556660  292472  143774  288341   71592  167358  106742  219288  174524\n",
            "  335183  261293  111900  194360   81145  341204  249362  247019  114746\n",
            "  172146  110457   80077  368700  182556  219420  240817  102726  226267\n",
            "  125457  204021  161141  190290  430828   59342  136721  149422   86644\n",
            "  195124  167350  113000  140027  262425  316702  335453  202480  203628\n",
            "  118710  189620  475028  110866  243605  163870   80145  295566   63042\n",
            "  229148  242552  177665  208103  296450   70282  271767  144995  382635\n",
            "  295697  194141  378418  214399  125831  271328   50459  162140  177937\n",
            "  111502  299047  223212  118474  352139  173093  181655  332702   51164\n",
            "  234901  131414  260960  156052  279914  192453  200939  151408  112847\n",
            "  316929  126319  197422  267736  267034  193047  356089  223515   87510\n",
            "  145111   48093   31757  285854  120064  167381  103408  101460  420537\n",
            "  119411  128272  386773  283268  301526  151790  106252  188557  171114\n",
            "  327323  244147  280282  116442  282579   51838   73585  226902  279129\n",
            "  146908  196690   49572  237601  169628   36671  231193  192130  149704\n",
            "  102102   32185  196061  211046   31577  162343  128831  316688   90758\n",
            "  274363  154538  106085  315859   51471  193830  231043   23780  169879\n",
            "  270333  138768  191571  219941   94113  137510   32607   93208  254440\n",
            "  186556  169871  167159  171871  154411  129227  110331   34269  174355\n",
            "  680390  233130  165474  257780  194259  280093  177387   28929  105304\n",
            "  499233  180572  321435   86108  198124  135162  146813  291175  387569\n",
            "  102895   33274   86551  138192  118966   99784   90980  177407   96467\n",
            "  327886  111567  166545  142182  188798   38563  216284  191547  285335\n",
            "  142712   80945  309055   62339  176186  266855   48087  121313  143437\n",
            "  160724  282753  194636  153044  411797  117683  376540   72393  270335\n",
            "   96226   95336  258498  149698  205865  155781  406468  177119  144397\n",
            "  372525  164170  183800  177307  170108  341995  226508   87418  109165\n",
            "   28856  175897   99697   90270  152375  171550  211154  202570  168496\n",
            "   68898   93235  278924  311020  175878  543028  202027  158926   76860\n",
            "  136063  186648  257509   98155  274198   97083   29825  262153  214738\n",
            "  138022   91842  373662  162003   52114  241843  375871  186934  176900\n",
            "   21906  132222  143653   78602  465507  196373  293227  241752  166398\n",
            "  184682  250802  325159  174675  227065  269080  177722  133461  239683\n",
            "  398473  298785  123424  176286  150062  169240  288273  526968   57066\n",
            "  323573  368825  189721  164966   94954  202046  161538  105252  200153\n",
            "  178326  255957  188693]\n",
            "\n",
            " Variable Name :  education\n",
            "Total values in education is =  16\n",
            "['Bachelors' 'HS-grad' '11th' 'Masters' '9th' 'Some-college' 'Assoc-acdm'\n",
            " 'Assoc-voc' '7th-8th' 'Doctorate' 'Prof-school' '5th-6th' '10th'\n",
            " '1st-4th' 'Preschool' '12th']\n",
            "\n",
            " Variable Name :  marital-status\n",
            "Total values in marital-status is =  7\n",
            "['Never-married' 'Married-civ-spouse' 'Divorced' 'Married-spouse-absent'\n",
            " 'Separated' 'Married-AF-spouse' 'Widowed']\n",
            "\n",
            " Variable Name :  relationship\n",
            "Total values in relationship is =  6\n",
            "['Not-in-family' 'Husband' 'Wife' 'Own-child' 'Unmarried' 'Other-relative']\n",
            "\n",
            " Variable Name :  Gender\n",
            "Total values in Gender is =  2\n",
            "['Male' 'Female']\n",
            "\n",
            " Variable Name :  hours_per_week\n",
            "Total values in hours_per_week is =  56\n",
            "[40 13 16 45 50 80 30 35 60 20 52 44 15 25 38 43 55 48 58 32 70  2 22 56\n",
            " 41 28 36 24 46 42 12 65  1 10 34 75 98 33 54  8  6 64 19 18 72  5  9 47\n",
            " 37 21 26 14  4 59  7 99]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDgxu9RP3qGL",
        "colab_type": "text"
      },
      "source": [
        "*Inference from above code:\n",
        "Categorical variables are: employment_type, education, marital-status, \n",
        "       relationship and Gender,\n",
        "Continuous variables are: hours_per_week , age and salary*\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enBb4IeiSbMw",
        "colab_type": "text"
      },
      "source": [
        "***PROBLEM #2 - Carry on a Univariate analysis on all the Variables/Fields. User bar plots for Categorical Variables & histogram or line plot for Continuous ones. Label the plot and axis clearly for intuitive understanding. Mention observations drawn from each Univariate plot***\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SW5_tQLiVAwj",
        "colab_type": "code",
        "outputId": "1dba47e7-f1a3-43ab-ea31-f99d31524ce5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        }
      },
      "source": [
        "df.hist(figsize=[15,8]) # to show histogram for Continuous variables"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f0c714d0278>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0c703c1e48>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0c706574e0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0c70695860>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fI4wY7c26HWj",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "***\n",
        "\n",
        "*   in dataset there is maximum of employee worked whoese age between 20 to 40\n",
        "\n",
        "*   more then 500 employee work average 40 hours\n",
        "\n",
        "*   most of employee salary is < 0.3 million\n",
        "\n",
        "***\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2xQxqKdV23F",
        "colab_type": "code",
        "outputId": "eb822eb0-2935-4822-b273-cd62a3fddd33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "e1=df.employment_type.value_counts().plot.bar()\n",
        "e1.set_xlabel('Employment_type')\n",
        "e1.set_ylabel('Number of Employees')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Employees')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsj6qO_j_U7Q",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "1.   there is most of(>600) employee from private sector (employee type)\n",
        "2.   very less employee from federal-gov\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oNwYhKSV3sq",
        "colab_type": "code",
        "outputId": "1612a2e5-c904-40a0-86ff-3368b9f03166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "e=df.education.value_counts().plot.bar()\n",
        "e.set_xlabel('Education of the employee')\n",
        "e.set_ylabel('No. of Employees')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No. of Employees')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCuem7OMAEMh",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   more the 300 employee education is HS- grade\n",
        "\n",
        "*  there very few employee preschool educated\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQqPLzl0V3ww",
        "colab_type": "code",
        "outputId": "55b49016-d5f6-4bbb-a912-8bbbcc433a24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "e2=df['marital-status'].value_counts().plot.bar()\n",
        "e2.set_xlabel('Marital-status')\n",
        "e2.set_ylabel('No. of employees')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No. of employees')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKBt6bLeBG0-",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   most of employee marital-status is Never-married and Married-civ-spouse\n",
        " \n",
        "*   no any employee in Married -AF-spouse(Marital-status)\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V33yB3gxV4yX",
        "colab_type": "code",
        "outputId": "422b94b1-f11d-4740-ef8e-9b10f8af9566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "e3=df.relationship.value_counts().plot.bar()\n",
        "e3.set_xlabel('Relationship Status')\n",
        "e3.set_ylabel('No. of Employees')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No. of Employees')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qi6EIVKcEF40",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   more then 300 employee relationship status is Husband\n",
        "*   there is very employee whose relationship status is Other relative\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJVm9fm5V47M",
        "colab_type": "code",
        "outputId": "8ad22945-b8fb-4b46-d542-4e06740a66c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "e4=df.Gender.value_counts().plot.bar()\n",
        "e4.set_xlabel('Gender')\n",
        "e4.set_ylabel('No. of Employees')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No. of Employees')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hddTRM9tFUbT",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   there is most of male employee\n",
        "*   approximate 33% female employee are there\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvXCDop9WNAF",
        "colab_type": "code",
        "outputId": "6d5206e2-3d9d-4897-8a67-1c6a77fb173b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "e5=df.age.plot.hist()\n",
        "e5.set_xlabel('Age')\n",
        "e5.set_ylabel('No. of Employees')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No. of Employees')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rS6zud37GTgu",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   approximately 80% employee lie in 20-45 age group\n",
        "*   very less employee(approx 8%) whose age is more then 50 \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNh9Ldo6SsHW",
        "colab_type": "text"
      },
      "source": [
        "***PROBLEM #3 -\tShow a bivariate analysis between Salary and hour_per_week.Choose a plot of your choice. Mention the inferences drawn***  \n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWyBnJa0HgoQ",
        "colab_type": "code",
        "outputId": "79085dc7-9cef-4f7c-cb1f-24037b2c1841",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "df.plot.scatter(x='hours_per_week', y='salary') #plot scatter between Salary and hour_per_week\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c7115d4e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wx/NYfMNTWNuxs+QylDJYz7m4uf71PXAEFiPFRnspGAvXPx+qPcBS8I7nXEPC2GQs2XlLYZZSLW6+8s6Actutbw/gnAX+mofG0vXPh2oOsBS8IzOCCsey89qx7LzlINYYxYLpk3ybCahSaBDUsRINteGiy+CkWNe/nC6cft4zoTKQGUGFEyQ7r25xc3pTg3L7U2bFfJepGNHc4sIplBtfZwREdBYRvUpEXUR0lct25xMRE1Gbn/JUI9Vm53UbGeuUni52+p2B0oyuC4nmroZEgRKQVvn4NiMgojCAW2DEHnQD2EhEa5l5q2O78QAuB/C8X7JUO8W285bLAybbyFgXJ9HxVp/y91588z2cs2CqrzJb0dz74iOjbVY0t5drV+kunDKbqQ78NA2dDKCLmbcBABHdB+A8AFsd2/0AwA2QuISCKJbLaLlebK+Lriql9/tOtSdzz76DyvZiUqhprpJNe5W+UC4cwk/T0DQAO2yfu822UYjoBADTmfm3bj9ERJcSUTsRtff09BRfUgFAec0UuSy6Ohc3m8fXZWzj1l5MCjXNWftHawjjImFEa6hiTHtjzVFByJ+yLRYTUQjAzQC+lG1bZr4NwG0A0NbWVv3pNMtEOc0ULU31GEqMpLUNJUY8jYwn1kdyai82hZrmjAeaAALA5IOE/lDJsxkhHT9nBDsBTLd9bjHbLMYDOA7AH4noDQCnAlgrC8bloxgeMIUsHBKR62cdewaHc2r3g3xdMK1ZWHwkhQPDScRHKmexuNocFYKMnzOCjQBmE9EsGArgcwAusr5k5n4AzdZnIvojgO8wc7uPMpWMSkw5AHj3gFGdXyHrC919Q6irCSORPDQrqKsJe5qNFKtCWTnuWbFmYeV63sodkFap79lYwzdFwMwjRHQZgCcAhAHcycydRLQCQDszr/Xr2OWmGAuu5eqUvHjAqM5vcWtzQQuHhZgZ3uzdn1O7ikLvWb73qxjmlXJ77pSr4l+5z7ua8DWOgJkfY+ZjmfkYZv43s+0alRJg5g9Vw2ygGAuu5cpd46VT0p1f567+ghYOCzEz7HjvQE7tTgq9Z4Xcr0LNK9UQh5APQT1vv5DI4iJT6FS/XC551oj26rPn4ge/3aqtZ6A7P4AKHtnma2aYe+REtL/Vr2z3QiH3rBj3qxDzSqXHIeRLUM/bL0QRFJlCp/rleMAf7tiJK1dvRjhESKYY15w7F8dNnajslHTnN2/qhLIVxTl6SmNO7U4KuWfFul/5mleC6rkT1PP2C0k6V2RijVEsa2tJa1vW1uL5JS/1A947GMd3HtyU5rVy3dpO7cjUzZSxdOE0rFu+BPd85RSsW74kZ3vtwx07cdr1T+HC2zfgtOufwi83vOnJA2m65tro2nM5p2wU4vZqJ19vq6B67gT1vP1CZgRFpncwjgfau9PaHmjvxuUfPtbTQ1rqcpOdu/qRSKZ7BiWSjM5d/Tjj2CnKfdxMGfmObC2FZJflew9tQWM0jJEUuy4EvvLOPm37h+ce4en4hZhnDDdXdnz2TqGLnuX23CkXQT1vPxBFUGSKYSootP5ubh4suk7LvTMrtqeISiEBwGDciGtws7vv0Yyide068jmn7r6hjGl1yGwfy2tC1UK5PJaqDVEERabc7oC57jtv6gTUhIARm8g1IaM9H9yUkLuCclc8bsr0rffUbqK69mLSUBtG3KHA4kn2XAuhGAMHcaMUCkXWCIpMOd0B89k31hjFzcsWpuW6uXnZwrxGWW5ulNlcLC2FpMNNme7aq04up2svJrv61e6xunYnhQ4cxI1SKAYyI/CBcrkDdvcNYSSZ3qmMJFNZ93XKCwCbduzNSXY3EweArOYPSyFdsXoTwhRCfCSJUIiMaOMs6yQfPHYyXn5nUNnuNwNDIzm1Oyl0TUjcKIViIIrAJ8rhDpgYSaaZeADD5JNw5A9SYcmbr5lBFzhmtXvprFQKyYsyPWnmYbj1me3K9kqgkIGDuFEKxUBMQ2OMQtxPn3r13aztbm6KhZgZGmrDOJhI75AOJlJoqA3n1FnZk7d5TeT231vU9Qh07cVkgibDqa5dR75J62KNUSw7MX93ZUEARBGMOXoH4/jV82+ltf3q+bc8dcbvakozWu3Z7PSF5JffP5xENJy+4BsNE/YPJ20590MYVxtGtEa/bpKPP32/xgyjay8mqrWNQhbbc6V3MI4HXsh0V5Y1AiEXRBGMMTp3DSjNO527BrLue+KMJm27l9F+IWaGlqZ6UMiRRjpEo/uy9V8+9MnJwx07seiHf8CyW9dj0Q//4DlnT6EBZYUQa4xi0dGxtLZFR8dKmigwl3YVUnNYEEUw5tDV3clej2e2JqXC7CmNnkb7hXg8ue17KOc+40AiifgIZyih3sE4/um+DgwngXgyheEkcPl9HZ46p5qw+jHWtevo2r0Pq9t3oGu3OkDNktPeaXbt3odnu3rTtnm2q9f1N4qJm0nOC+VKcCiMLWSxeIwxb+pERMKUFlwVCRPmTc2eQG2LZtawZdcAzl0w1dNov5CFS92+Xjxb1r/em6Hq2GzPVoB+0w518Xpdu4prHnoJqzYcMsldvOgorDjv+LRtVAvpw87pm0nHjr05BwLmg2WSs8cyWCa5bBQrmE1qAlQ+MiMYY8Qao/jRZxak2dN/9JkFnl6wZs02zebCq9to30/zgJd8PHsG1T7/unY7Awc1Lpy2drfz69q9L00JAMCq9W+ljep1prWZsXHKYy+cPimr3DpyuRctTfVIOooHJZlzSphnJ9eaw+WeUYhZqzjIjGAMku+ofNExsdEMohbhEGHRMTHX37WPdIcSIyBK998vRlRztnw8x2lmPLp2OwMHE67tbnL1DsZxf/sO5f7Pde0ZHdXrZjUHEinlNW9qyK06mkU+7ruM9GvLWaK0LYoZzFaO9BgSUV08ZEYwRsnHnTDWGMWPlxmzCevfj5elzyacv+sc6Y6kjKRzxYxqtspQ2rHKUFpEasJwOB0hTEa76lj2UeC+IbUi2DeUUMp1xerNeOa1Hvxyw5tYfMNTuHv9G8r97aUudfWcAca4SLqM4yLhnEbV9vPK1X23c1d/mhICgGTKSBqYjUKj4Isxo8gXiaguLjIjqDJyTVinGunaCYcIT7/yLs6cM8W1g3BbB/Ay8mxpqkekJoSkbeEzUhPKGJ2qRoGN0QgG4pkdQGM0opQrPpLC11a1Y0hj3weAEAGLjmlOaxtx5BQaSTKmTlQriHwCuvKLEs4vaaCloBe3NmPd8iUVF8wmEdXFRWYEY4hi2Dsf7tiJc/7rOXz/ka0457+ey2qzVb3MdvbHk7h2bWdW+69bp+Bl5Kna5uqz56K7b2j0euhGgYmkemE0aR5fdX46JRAJE2rDhH//bHq+pc5dA8rF7FfeGVCOyPMhn441nzgGp11/XdeevIPZylUTQCKqi4vMCFwopTeEc6R79dlzcdw0dZUwN3lztdk6c91Yi7o14dCoW6LlgeL2W9ly5nhZ97Bvs2Vnf0bJzBmxBuUoULdYvC8+kiZXiAgHsnjT/HjZAiw6RnWO6s79zd4DcPb7KYZrPQcd+eQdijVGcdHJR6Utdl90ylHafYpt1y+0JkC+71ip63ZUO6IINHhdiCqGslC9nF6LsthxCy7ymnRuy85+rHh0K1KKUW22qXc2s5TX/Ev9Q8NY8Wgn4iOc1lk9etnpylGgDuscrPPr3NWPr65qR3xE3anXhinDJ99C59bbr1mfePHNvpwUQb6mmt7BOO52eDzdvf4tbSEkP0wq+ebVkoI8YwcxDSnwuhCVj+ucyvyjWnQDjKIsxcr3k41YYxQtTfW47pFOxEdSGFYUick29c7VLKXaf/ENT+Hr97yY0VlHQiHsH04qTRGHT6hT/t4UW3usMYozjp2CGy9YgLpICA3RzGsynGRc/XAnTrs+817q3Hrfeu+A8ti6qmm68z7t+idx4W0bcNr1T+LxLe943tct/kLFWDGpFGuxN98cTUI6MiNQ4GXUlM8U2ygSb6RZTnIKN16wAEsXTstqp/c6YiskuAjQVwmrixhKym3qXajJwb6/CquzWjB9UsYo8D/+8Jpyn4gtslg14rbMT2E6dI2GEsb/v/3gpgzZVSPQbT2DeLxzd8ax5xzhLZisdzCObz/QYaYVMY6dy2ww1/iLsWJSkcXesYUoAgVeRk25PsiqF/5bD3SMdjbWy2nvlHTHdpObQgTYOnN7vp/sqD1NrjprDs5dMDWr1xA7zEmcYs8vts57aVwkjBQ4rbNymiJCmhrBVruhgDeP+vvfeIHRuS6YPglnHXcEHtm0E9c98nLavrq6zc5jN0bVr5Cu3YkqtxTgrUQnAJzeOhnAy5p2NWPBpDJWZiaCgZiGFHjxhsj1Qc6WTG7pwmlYt3wJfvXVU/Fvnzqu6Pl+vKDzQMmmBIDCSzaqrme0JoRbv3gi1i1f4joq7hlUr430DBoeR995cBPiIykcGE4iPpLCtx/cNGqCiDVGcfRk3eg9U8E48xHt3qc2ZejaM3H3MMrml996+HhcvOiotLaLFx2V1W243CaVcnocCZnIjEBDtlFT7lPs7Mnk7Dn4z5p3RN75fvItfJ+rB4qd/cNJ5WKqV7OUdT2vcIzcz/BQZWxgSG1OGhhKKc1diSTjkU07ce6CaYg1RjF1onqNwdmuyke0dP5U3P5sZlGcv517eFa5jWO4j4C9jJJXnHc8Lj51Zk73fCzkBxoLMxPBQBSBC9m8IXJ5kHNNJldqT4zewTg6dw3g/vbM3PY6DxQ7DbVhZYfrdUYAGCqROYVkMgTWBLhZstqveW0YOKjQN8ah1WajGx5/Ddc//ipWnj8fk8ap00Hs6j842qnq8hFdfOpMTKwLo98mwMS6MNpmpaem1rF/OIm6SChjbSQaDgHEnkfJTQ21mH34eE+pLcZSaoZ8n3OhuIhpqEC8TrFzTSaXT3BZvp4YltfKV3/RjrjDfqUzTTjlK7SIu7WGYk9D/a0HMtNQqzy1DtN0foc11CrNXYCxKGxdn4GhYY1UhxRbx469yi1+85fuNCUAAP0Hk2jfrvbacaIb7RtOZN5yBuXivSapGQQVgZwR5DMtLsZU2usMQre4mY18FmzTF7G9uYyqRpS6UbXXzsxtDcUyD+k8k5o1iqCmJoRYYxQ3L1uIK1ZvApgQT2YqOl1ZSftsTZdNdPeA2jvnia27R2cFbs9OmqNAiLA/bnkvefO8ytVbS7x1BBWBUwT5TIuLOZXONhW2FjftZhaVK6OKfBZsdV4rFifNaEo7rq7jefSy01ETQtpv5VayMfsaiq4TG9REFg+ZneqhgLIBM6Ds0P6JVAoJzQXY3jM4eu7Wouyq9elrBNObxgHYlbHvlPGZmV11z44l39OvvItr13amratk66Rz7dhL6a0zFtYhBG/4ahoiorOI6FUi6iKiqxTff4uIthLRZiJ6kohm+ClPPtPiUk+ldYubXrJJWvZmO3WRUJYFW3evFWe1Ld2sY/9wEjcvW4hoDWFcJIxoDeHmZQs9dwC6ztjeruvEWHMOI6n0hfgzjp2MGy/I9FR5+tUe5f4PdaR38CvOOx6rv3Yq/nFJK1Z/7VSsOO94HKlZaD5yYl1Oz06sMYoz50zJqC2QrZNuaarHvni6ItwXH9HuUypvHWeQnFQ+G9v4NiMgojCAWwB8FEA3gI1EtJaZt9o2+wuANmY+QET/AGAlgM/6JVM+0+LST6XzyyYJmEVKFAnQ3JOWZS5iO7FX23KbdeTiseQcLeo646df7cGH5x4xuv3VZ8/Fikc7R4PyVp4/H9eufUl9EDokp1sKh4631JXMah15se2j+9ue3YaV58/Xmobe7j+Y07NjPz9njiW352x7z6C23S0ViJ/eOtliZoSxh5+moZMBdDHzNgAgovsAnAdgVBEw89O27TcA+IKP8uQ1LS514Mu4iHqStrPvAHoH41lfJFWqZDdijVFceNL0DI8YO3b7uMrLxZp1eF3bUJlLkprI6t0DB/HLDW+Odo4HR5JIpRjhCAA2Oup9B9QzHqs9m3lGt0YwlEiOXnOdSewjc9T5hF59ZwCfPqHF07OTkXDwnLk4bqq3hIPOWYu93c1zyU9vHS/rPcLYwk/T0DQA9tJP3WabjksA/LfqCyK6lIjaiS041QcAABz7SURBVKi9p0c9cvRCPtPiYk+ls3kDvdGrzl1z3SNbs3qE5Jp3xpLn3o3qCl0A8IHWWNrIXqcAG2rDroFb9uOpCsX0Dqo9d55+9V1876Eto9snkowkY/QYV67ZDPUKATCiOZ7dPNM7GMembrVH0AMbu0evua4IC2nMUuPrIog1RrGsrSWtfVlbi3bNxZLvB49u9TxS1ylQXXtpyL7eI4wtxsRiMRF9AUAbgA+qvmfm2wDcBgBtbW0FPU35TIuLNZX2snCo806xFjjdPEJefPM95b4vvvmetgC8Lr+QxYbt76XNRHSBdLv6h7RrG/Y0DbpCMU++/K7y+G4L2UD23P9u5pnnuvZg+ZrN2llTEkDSVBy6zKf1EfUrlEwyegfjeCBLXEY+5iP7MzgWu9xcY2aE8uOnItgJYLrtc4vZlgYRfQTA9wB8kJlL4sycz7Q4132cL61XN7+mhlqECBk57i3snYTzGNlq96pxX3tQdeYqxfjoJvVMZcCRplmXYM9b/LFaPjd0JSYbasOuSe7s2DOfOhXgz5/dptxnU/deT528V9OjbhBRE1LfP127imJ791gxM84ocVkfKAw/vbD8VAQbAcwmolkwFMDnAFxk34CI3g/gZwDOYmb1kLACUWUZ1RVVcY78uvuG0FBbk+EJYmF1EqqOwXBlzCQSDmnXF+ZNneCqeAwyOxWnYpxQr/bld7bbZxReCsXYqQ2TMj12NlQL6Lv63Ut02hlOJtE/NKxcaF61PjO9BABEashTJ+8lVYnbIOLM903BPc9nmvbOfJ+3Wgh+RRmXKn1EUFxU/Y4G900RMPMIEV0G4AkAYQB3MnMnEa0A0M7MawHcCKARwINkZIp8i5mX+iVTIXh94HQeE49ffoankV9LU/1olTA7DdEwkmZaYgDKjuHCk6Zn7AcAq1/oxm86dmkfHjdF4DUWwGu+HsB7oRgAqI+EwACuPmcu9u4fxo2/U6ec1tG5q19ZQWxgKOGa+ttOIsn4xi//onwBG2rVC80NtRHP+aiydZpuM4tIjTpGRNdup1jVynTvht/pI8ZSqgw/KXZVORW+rhEw82MAHnO0XWP7+yN+Hr9Y5PLA6TwmdvUf9JykzlCKh3qvmhBwxcfeh9Nbm9F6+Hhs2rFX2THs2ad2ZUykDKWjeni6+4ZQH9HPQLwmnXvlnQFtu8qN1PDrNwrFXLlmMwiHomntLLelwL7nT+rRtztqE8mE+trR+5FIsutaQ4oxen2uWL0Zk8YZqStijVFMqFO/QlZ7MUbGbjOLfg/pMXQUwzVaV2PDb0rROY4VSuHCPiYWi8cyqgfuitWbMGlcBPOmTlTcCP3ynZdOobtvCHU1YSSShzrmkRSw8vFXcT1ewcrz52Nxa7OyY5g8Xj0qt1A9PLoZiIUu6ZxzFPimxttJ125hXZPfdb6D7/5mS8b3x5kdLgD88a97XH9Lhcr0FSKMduSLW5tx0xOv4N6N3fofsREfSeHrd78wWiPhtNZmPPJSZkWx01qbR//ONjLONtBwm1kUsjBbqGt0OeMFgpQqoxQu7JJ0zoHTvVPlNhgfYXz9nheV7pzWi2nH/mJmS1KnW0w9YEuSBkDp0nrCjMNcz0338JCmsAtg2NOdSedUSc5mxBqU+9vbda6zscZoRrI7iy27Ds00zjn+CK2cKqyzcq6b2j/HGqP4+PFqjyod9nthpZJwMntKo6ff8hp9bNWruOcrp6TVZ4g1RnHKrPT7fuqswzx1hoW6RmerseEnQSpsU4pocJkR2FCNzFSjbwCji5zO6WihHhOxxiiWndiiDfCyRj2q2YU9FYSdcZEQUlCXmlTNQOwkkoyEzetGNyW/5+9PVu5vlWzMNupt1lyf/fGR0YXuT54wHTc8/greHjhkDjlyQi3eGRjWzsNUpq/6SE3ayFEXxFcfCWEkxWBm1NaElQvbm7rVqT/e6D3gKRV1LiNb1cyia/c+PNeVHidipQXxUpegMNNV+ZxXva6/VAt+L76LIjDRdXDrli855OUCwoFEemegemkLuWm9g3E88ILeTGEf9Tg7BlXN4towYcV5x+HMOVM8JyFzYu/UdB3XG70HkL6yYYzI9w8nPdlzFx0TUy5a/+SPr+P/Pt01qjiu+sRcfOu+DrD5+9/9xFx8+/4OJBR9Tw2pTV9DifRcPFs0I9jPnzID//ChYwAYQXmX3fuXtO8PJlKY0qj2lmoap15EdlLoyFaXHtueFiQb+S7qzps6UWN2K028QNAK2/i5+C6mIRNd5Kg1+l63fAlu/eKJiDqS2+te2mwmoFzkAIzavdmmhKM1i22EQqRVApac1rRzXK36cbAHuek6rqZxEWVUc2Ik6Xpt7ajWa/cPHzLDdO3eh+VrNiMJIAXDKn3lms2o1QxnamuMa+E0fTk/16kKFgBondww+vJNP2wcog6TXzRMeOM9db2FHS7lJe0UOu3XBSDq2otN2PG8OT/7Tb7vmZCOzAhMso3MDC8XI3uln9NRt9q982wLpyqsTsVulvIinz1Z3LNdPXi44+3R75z1b3VT8h196kXhLbv6ce6CaVlHvetfd18I5hSjQ+MtpVtfSDIrTV91NeG0WZwXA8eokrXNtihEaJ2sXhvRmbpUFFJeVBWAGCJ4qlRWKF6urVAZiCIwKZbPt19yeE3WZZR7ZCRTxv+94LTf/8vH5+Cwhlptp6S6Bvc+/6byt6M1YU/Xdo8m15BFPMmYGRunNPOENKfJSW+mF50isbfrziGlMauNJL0HyhXiD6+Lk3BGg/uBLmq7Ghdsqx1RBDa8dvJ+B8o45QCATTv2ZsikSmNxqKiN0TtkK2qjst/f/IfXsG75kqyzD/v30zQvv9We7dqebnO31PHOQDwjxoKIUB8JYTie2fHWR70pId2xne2qc7jcsW5g8fSre/DJE9QBfnby9Ye37v1OjQlqYEjvElxMnIMNr4MPYWwhisCB3518rnLoUjury0VGPCV+s5OvP7ZTCU2dqFYEunYnrYePx/sOb8Cru/drt9kzGM9Y1AoBSGhG30lOr1CmU0I6M4qq3fl8HN2sTuuha3eSz/W333vniNxin2t+qeLgxSNLqAwCowgqMSeJrmzl3CMnKEeR154zV/k7bqPDfLxWVEpoRqwhw2MpGqbR6mjZzB9du/e5KoGakBFcpiqKo2OfzRXfTcH/rjMzIAwA7nxuG/7+9KNdn5f3z2jKqd2JF68mO6oZhIr39rub2oqBmIaqh0B4DakCoCoBXdnK57p6lF44cc3ocEK9Xt/HGqM46jBHvqNJdejuG1LWTNAFQDXUhjPKLCbZqI7mJWjquS71YnFNCKNlLyM1YWUpTjfcaj9Y6AKgfvbMtqzPy7iIOqePrl0FO9JgOD9b9A7G8fQr73rKLKoL8Cs25TYNZavvIXij6mcEpcxJUvxZh/qFb26sU47EjtP4b7uZZ9q39+I1x0i8q+cAlt26DiDKyB2jM2Xs6j9odmCHOgKrQ3O6idp/y7pOTrdci6+dcfToqDyfl/0LdzyfdQG2WRMLMJICRjQ5miye366uAfH89vc8BZR17upXZkd1mvOsGVWYKEsNaoMjJvg/6y23achrRTwhO1U/I/Dqw14ofsw6dBk9j5gQVY7EDiRSORevf0aTvyeeNFJpfOuBjrQOWGdKGhhKaDu0htpwRt7/g4kUGmoPjZp1i82nHB1Li9pW+dy7cagK2iY889q7SmUSCbu/BvbnxTkC3aNRTrr2TLLXqLYPZuz3siEaRk1Yvb+u0l0xydWspSLfEb1lNs1WEU/wRtXPCIqVk8RttK9OTJeepTIfdPWB3+g9oByJAYyRZPq5jiTdz/WM2c34z6e6tN87a83qvHB0dX8BUkY829cPAO9RqqqF33+8r0Mrv0V8hHHpqhfAQMbI8bAsPvduNSCyd+PuzJs6QRmRbU/9rZqFNdSG8f1z56FpXASXrHoh43dnxrwtVheKyovLK4W6zebqGCHoqXpFUIycJNkeWF35RXuWSt0D7qZgdB34wumTlMpt6sT6nF/MtlkxfKA1hme79HWNnSFXueQ5mjqxDk0NtRnBWCCgf2g4rWBOOERI2bYpdpTqQTMuwOlSO75OrcTqagggcq0B8amFuSWsU0EE2Cd4zlumGswkmXHmnCna9Y0DHiqvFUohAWWFm2wLVcGCnapXBEDhuX+yPbBuGUNV21u4KRhLQVx9zlz84NGtadu0Hj5eqdz2DycRDqWnJA6HKOuLefdXTkX79l48sXU37nh2e1q3r8sdo8pzpEqHvH84idbD05XxUGIEKUZasZcZsQZPnYrqmkUA6Jwl62pCowrAwjlyfLtfbSa86OSj8I0lsxFrjGprQIQV6UCs6+OF9a/3KgPC1r/eO1pn2n0wU77Eb4XMtgtNIz1v6gTUhNJrWnstoiRkEghFAOQfH+DlgbW/qKryi6oH3E3BWEXVrZf+6rPn4rhpE9OUmG5Uns0Wr6NtVgyzJjfirj+9kaFIvNBQG1ZO1a1jH6pKNmBWJUuNdvpuxeHtnYrumo2LhtAfz1TEE6Mh/PPH5uC6R7YqJD50XrqaCcNJdlX2iVQKn1w4Ffc8n5kp9m/neUuZ7XWNQTeYKWeh+EJm24WabGONUdy8bCGucBTFqRTX8LFGYBRBvnh9YN3KL9q3t0b6/UMJpYLp3DWQ0dn94LdblZG+TuW2q19doWxX/0FP+WsKmep7WQeINUYxsT6C2nB6fiC34vD24+qUMoXUZpDamjDOXTAV//rbra4jxxkae7q9Xdfptc2K4eJFR2HV+kPKwJmfyY3pmo5P1a4azMQao7jw5Olpx7/w5Okl6xDznW0Xw2QbtOyjfiKKIAu5PLBGYrpD5Red29vNGsPJFJIKBQNw3lPmgSG1gUTX7qSQUZouKZuqJrPuGAumT3JNvqbbV2cFGR5JeRo5vu8ItTnB2a7reFacdzyWzp+KZ/66B2fMbvbkNmrRd0B9b3TtTnoH47j3z+nF6+/98w5lVTm/yHe2XYyOfKxkAqh0RBG4YI3eF7c2Y93yJZ4fWNUDrjJrRMKEaA1QGw6PKox5Uyfm3RnrPHf0Hj3pFDJKizVmFtRZ1taiHMHqjpFvycZ/eWiTUqYkGdcxW4eTi71Z1fHY5b7t2W05eb8Umka60r1npCMfG4gi0ODVtc1rEJnKrFFXE8Ytn38/JtbXpu2fb2fsVp83G/kqPfv+zoI6unrHXhWlapFdlbL5Z890oXPXYIZMMw7zFl0ba4ziopOPSlNiF51ylKdzL9T7pfXw8RleWx9ojeWQilq8Z4TCEUWgwOvLrVMWXkteJlIpzJs60VNH6RWVIshGPv7cTgWYqxeIcySYbX/reFt29uMHv033omqqjwLIVARGe/bzy0WJOfESNe1G72Ac67elu+6u39ab5lbrhq7Mpq5dEFTI06LASzSyLn+OVUXL2Q6oC87rXvZYY+6Vl6yQfztWyL8Or8XT7aiiqAv1AnFLYGYd76LbN+B7D23JkPWvmhiGbT2Dns4vl+hzZySsl6hpNwotAP/0q7tzahcEFTIjUOClU9ONYHVVtHQF53Xkk7con8441xGtl9rO+XqBqNJm9O0fHj2eCmPhXZ1CYySZ8jRT8ZpFU5f6W8Wu/qGs5p3ewTi29eiU2D5PUen9Q+pz17Xr5BDPm2AjikCBtShp9zRxdmq6zkMX9asrOK8i39D7WGMUJ81oSrM3nzSjyfV4uhHtjvf2KzsGt461EJOWLoGZSrHaSaRSmHPEBGx8M7OIe+vh4z3nw1HlSbKjU4A3XbBAc0buNjl7EjkVN/3uNVz/+KtZ770ujuGTHiOeC0nzYCGKxD9KdW3FNKTB6AbIfJ/VL6tqBNvUUIuV589HtCaEcbVhRGtyK0auMmV858FNuGvddm0aB4uu3fsyUkU829Xrut/+4SRUecuuXPOSMnmel9rO+RQT1/2uSrHaWdbWgsWaCmMnzzoMQPY0z27lHgF9+mfDnKT2XdUlDLR+T5VEzs5gPOnJTFcI+ZgFnVRqivdKoJTXVhSBAusFsWc2VNmVdfZ4o2tgs4/ILdRfZa8eTjKue2QrPvLjZ3DNwy9p99Xl9Ne1AzDrCGS2HxhWd0S6DKCFjlYs91M7y9paRtNp1EVCSrv7A+3deG232p7+Zu9+1zTPh9B73lgv47VrOzEYz5wB6tAF9wHqe6wjW6bchzp25dSeTY5cMvMWQ5EIakp9bUURKPDyguhGsA21YVOJMA4kkoiPcE43UJe3yGLV+re0I/xmTWesawcOZTjVwSnO6BiWLpyGdcuX4J6vnIJ1y5cUJQe8znOndzA+erzvL52Hxmi6MoiEQhn1FCxe7zkAL+6VVhyBnZqQMap3G7kzMwYPqqu/uQXxZbvHdrKt8eiKDrkVI3KTI5cF/lKleA8ipb62oggUeHlBdCPj/cPJgm6g/Xd1xVo6dmTawwFg0TGxjNxA4RBh0TH6SNdsL33cli/IKWc+JiAd2R78WGMUZ86ZghHH6D6RSuHIiWoZJo+PaDt5e2yFFX0crSGMi4RHK6Kp7qWd+khNRkI7C7cgPvs9dqtw5mW29en3t+TUrpMjn9ldsVK8C5mU+trKYrECrxG2usCoQm+g9bvrX9+Dy+7NzLWvizqNNUbx42ULcMXqzaNtN17g/mKnJcwDjWZMtchW2KZY5KJ8nfflz9vfw7NdmZXCjpw4znNyMq/30inf6a3NeWXBVCXhs4jWhHDrF0/05DXUevj4gnIdFbLAX4x8QYKaUl9b8rPGKBGdBeA/AIQB3MHM1zu+jwJYBeBEAL0APsvMb7j9ZltbG7e3t/sjsIN8V+zXduzMuIH5mk+uefiljJd8xXnHF13u3sG4slOqi4SUCe/8wOt1c55f+/ZeXPCzDRnbrf7aqaN5f4pxLw+OJMHMqI/UpMm3tmNnhqLJ5X4X43np2r1Pm6PJb8RryD+KeW2J6AVmblN+55ciIKIwgNcAfBRAN4CNAC5k5q22bf4XgPnM/HUi+hyATzHzZ91+t5SKoBCKeQNL+ZIXU4nlQ77X7Yt3bMhI03D3V04tukwAlPIVer+lMxX8plyKYBGA65j5b83P3wUAZv6hbZsnzG3WE1ENgHcATGYXoSpFEVQyldoptW/vzSsDqCAEATdF4OcawTQA9vy43QBO0W3DzCNE1A8gBiDN35GILgVwKQAcddRRfskrmFRqRsi2WTFRAIKQBxXhNcTMtzFzGzO3TZ48udziCIIgVBV+KoKdAKbbPreYbcptTNPQRBiLxoIgCEKJ8FMRbAQwm4hmEVEtgM8BWOvYZi2AvzP/vgDAU27rA4IgCELx8W2NwLT5XwbgCRjuo3cycycRrQDQzsxrAfwcwN1E1AXgPRjKQhAEQSghvgaUMfNjAB5ztF1j+/sggM/4KYMgCILgjq8BZX5ARD0A3sxhl2Y4vJACgpx38Ajquct5e2MGMyu9bSpOEeQKEbXrfGerGTnv4BHUc5fzLpyKcB8VBEEQ/EMUgSAIQsAJgiK4rdwClAk57+AR1HOX8y6Qql8jEARBENwJwoxAEARBcEEUgSAIQsCpakVARGcR0atE1EVEV5VbHr8goulE9DQRbSWiTiK63Gw/jIh+T0R/Nf/fVG5Z/YCIwkT0FyJ61Pw8i4ieN+/7/WaKk6qCiCYR0WoieoWIXiaiRUG430T0z+YzvoWI7iWiumq830R0JxG9S0RbbG3K+0sG/2me/2YiOiHX41WtIjAL49wC4OMA5gK4kIjmllcq3xgB8G1mngvgVADfMM/1KgBPMvNsAE+an6uRywG8bPt8A4AfM3MrgD4Al5RFKn/5DwCPM/McAAtgnH9V328imgbgHwG0MfNxMFLXfA7Veb/vAnCWo013fz8OYLb571IAP831YFWrCACcDKCLmbcx8zCA+wCcV2aZfIGZ32bmF82/98HoFKbBON9fmJv9AsAnyyOhfxBRC4CzAdxhfiYASwCsNjepuvMmookAzoCRqwvMPMzMexGA+w0jLU69ma14HIC3UYX3m5mfgZF/zY7u/p4HYBUbbAAwiYiOzOV41awIVIVxSldzsUwQ0UwA7wfwPIDDmflt86t3ABxeJrH85N8BXAnAKrQcA7CXmUfMz9V432cB6AHw/0yT2B1E1IAqv9/MvBPATQDegqEA+gG8gOq/3xa6+1twX1fNiiBwEFEjgDUA/omZB+zfmem9q8pXmIjOAfAuM79QbllKTA2AEwD8lJnfD2A/HGagKr3fTTBGv7MATAXQgEzzSSAo9v2tZkXgpTBO1UBEERhK4JfM/Guzebc1RTT//2655POJxQCWEtEbMEx/S2DYzieZpgOgOu97N4BuZn7e/LwahmKo9vv9EQDbmbmHmRMAfg3jGaj2+22hu78F93XVrAi8FMapCky7+M8BvMzMN9u+shf++TsAD5daNj9h5u8ycwszz4Rxf59i5s8DeBpGoSOgOs/7HQA7iOh9ZtOHAWxFld9vGCahU4lonPnMW+dd1ffbhu7+rgVwsek9dCqAfpsJyRvMXLX/AHwCwGsAXgfwvXLL4+N5ng5jmrgZQIf57xMw7OVPAvgrgD8AOKzcsvp4DT4E4FHz76MB/BlAF4AHAUTLLZ8P57sQQLt5zx8C0BSE+w3g+wBeAbAFwN0AotV4vwHcC2MdJAFjBniJ7v4CIBgekq8DeAmGV1VOx5MUE4IgCAGnmk1DgiAIggdEEQiCIAQcUQSCIAgBRxSBIAhCwBFFIAiCEHBEEQiCIAQcUQRCRUFEM+2peQU1cp2EXBBFIAQeW3qCqjqWIHhFFIFQiYSJ6HazQMnviKieiBYS0QazMMdvbEU7/khEbebfzWZeIhDRl4hoLRE9BeBJIjqSiJ4hog6z6MkHdAcnokEi+rF5/CeJaLLZfgwRPU5ELxDRs0Q0x2y/i4huJaLnAazU/OZLZrEZIqJeIrrYbF9FRB8lo/jOjUS00TzHr9n2vcLW/n3Fbx9tZik9Kc/rLVQ5ogiESmQ2gFuYeR6AvQDOB7AKwHJmng8jzP5aD79zAoALmPmDAC4C8AQzL4RR6KXDZb8GAO3m8f/HdqzbAHyTmU8E8B0AP7Ht0wLgNGb+luY318FIoDYPwDYAliJaBOBPMFIM9DPzSQBOAvBVM4/Wx8zrcTKMtBMnEtEZ1o+a+YjWAPgSM2/MdkGEYCLTVKES2c7MVkf9AoBjAExi5v8x234BI+dMNn7PzFbxj40A7jSzuD5k+30VKQD3m3/fA+DXZgrw0wA8aORDA2DkwbF4kJmTLr/5LIxiM2/CqDB1qVmRq4+Z95sd/nwispKrTYShAD5m/vuL2d5otr8FYDKMxGSfZuatLscWAo7MCIRKJG77Owlgksu2Izj0nNc5vttv/cFGRagzYKTvvcsyzXiEzWPsZeaFtn9/ozqWhmdgzAI+AOCPMArPXABDQQBGYrFv2n57FjP/zmz/oa29lZl/bu7TD0MhnJ7DuQgBRBSBUA30A+iz2fW/CMNkAwBvADjR/PsCaCCiGQB2M/PtMMpeuhUAD9l+6yIAz7FRCGg7EX3G/D0iogVeT4CZdwBoBjCbmbcBeA6GeekZc5MnAPyDOWMBER1rViV7AsDfmzMSENE0Ippi7jMM4FMwUhRf5FUWIXiIaUioFv4OwK1ENA6Gjf3LZvtNAB4goksB/NZl/w8BuIKIEgAGAbjNCPYDOJmI/jeM4iCfNds/D+CnZnsERrGcTTmcw/MwCrIDxkzghzAUAmAop5kAXjRz8fcA+CQz/46I/gbAetMkNQjgCzBmSjDNSucA+D0RDTJzVdbkEApD0lALQo6YHWpjueUQhGIhpiFBEISAI6YhQdBg+v1HHc1fLGQ2QERfBnC5o3kdM38j398UhEIR05AgCELAEdOQIAhCwBFFIAiCEHBEEQiCIAQcUQSCIAgB5/8DabrXTkPCNX4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9HKc2CYIkYX",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   employee who worked 40 hour per week get salary get good salary as compared to others\n",
        "*   very few employee who worked more then 60 hour per week\n",
        "\n",
        "*   most of employee working per week 10 to 60 hours\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roHMIKP-S7G7",
        "colab_type": "text"
      },
      "source": [
        "***PROBLEM #4 -\tFor each Education type , showcase the distribution of Salary .Use Seaborn Facetgrid & Line plot.Identify the key observations and mention them***\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNssrUcobDPW",
        "colab_type": "code",
        "outputId": "628c4b75-f5ce-486a-acaa-ac4c21ec89a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        }
      },
      "source": [
        "a=sn.FacetGrid(df,col='education',col_wrap=4,height=3)\n",
        "a=a.map(plt.hist,'salary')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwsqJivIL8Jd",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   employee whose education from HS-grade gets good salary as compared to other\n",
        "*   there is only few(<1%) employee whose education is preschool gets very poor salary\n",
        "*   most of employees receive salary approx <0.5 million .\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-KYQCMITBUP",
        "colab_type": "text"
      },
      "source": [
        "***PROBLEM #5 - Categorizing each Gender & Relationship status (combination of all values) , plot the hours_per_week worked.Choose appropriate plots.Mention the inferences drawn***\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0tvnF-1bQvq",
        "colab_type": "code",
        "outputId": "8ca554e8-f92c-4cd1-bf6d-ccb5fb81bc1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "g=sn.FacetGrid(df,col='Gender',row='relationship',palette=\"G nBu_d\",gridspec_kws={\"width_ratios\": [5, 5]}, height=3,aspect=2)\n",
        "g=g.map(plt.hist,\"hours_per_week\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x1296 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xu0EdhxiK3wj",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "*   Most of male employee is there and work max of hour per week\n",
        "\n",
        "*   female(wife) and male(own child) do not do any work ( approx 0 hour per week)\n",
        "\n",
        "*   married female also do good work as comapre to others\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJsZNyEBTGM7",
        "colab_type": "text"
      },
      "source": [
        "***PROBLEM #6 - How many Males that have Marital status Married-civ-spousea are earning above 150,000 ?***\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3CUrXcQEbV0u",
        "colab_type": "code",
        "outputId": "6e08c3b6-7b5a-4da0-c158-a06a76e9915c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        }
      },
      "source": [
        "df[(df['Gender'] == 'Male') & (df['marital-status'] == 'Married-civ-spouse') & (df['salary']>150000)].count()\n",
        "\n",
        "#k['Gender'].count() #count the gender value whose Marital status Married-civ-spousea are earning above 150,000."
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                226\n",
              "employment_type    226\n",
              "salary             226\n",
              "education          226\n",
              "marital-status     226\n",
              "relationship       226\n",
              "Gender             226\n",
              "hours_per_week     226\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-I1cwrTargF",
        "colab_type": "code",
        "outputId": "1fec06f7-3734-4bc2-c8fb-3c379d555f54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "df[(df['Gender'] == 'Male') & (df['marital-status'] == 'Married-civ-spouse') & (df[\"salary\"]>150000)].count()[['Gender']]"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender    226\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QBnvRx9bmCP",
        "colab_type": "code",
        "outputId": "a23d19fa-b5e9-4a2d-a1c7-f0722e9c2287",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['Gender'].unique()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Male', 'Female'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqPiMfk5QULc",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   *there is **226** males gender whose Marital status Married-civ-spousea are earning above 150,000*\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAoNeTmFTIQl",
        "colab_type": "text"
      },
      "source": [
        "***PROBLEM #7 - Are Divorced Individuals of higher age compared to Individuals that are Not-in-family ? Using mean as a measure , answer the above question & mention the respective age of the two groups.***\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YRwq6SaRoUK",
        "colab_type": "code",
        "outputId": "61924cbb-a8a0-4979-e607-03175805f7a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "d=df[(df['marital-status']=='Divorced') & (df['relationship'].isin(['Unmarried','Own-child','Other-relative']))]\n",
        "d.age.mean()\n"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.67272727272727"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lN3REcpJYR1Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "081176b6-ba2a-4abc-ff59-d10739ce7b8b"
      },
      "source": [
        "d1=df[(df['relationship']=='Not-in-family') & (df['marital-status'].isin(['Never-married','Separated',' Widowed']))]\n",
        "d1.age.mean()"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33.06936416184971"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4x-CeaJUSJ1K",
        "colab_type": "text"
      },
      "source": [
        "***Inference:***\n",
        "\n",
        "\n",
        "Yes, divorced individuals are of higher age as compared to the individuals that are not in family\n",
        "\n",
        "---\n",
        "\n"
      ]
    }
  ]
}