Assigment_2_kush_Kumar.ipynb

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  "metadata": {
    "colab": {
      "name": "Assigment_2_kush_Kumar.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Bn_eYOWtlL6",
        "colab_type": "text"
      },
      "source": [
        "**Assingment-2 \n",
        "Subbmited by Kush Kumar**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEf1PBIOhKc9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "11486737-c3ae-45fb-e47f-0af8888adaab"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uh-oq-wWh5qn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "d0c231d6-e283-4dfc-eea6-4bcdeecd1830"
      },
      "source": [
        "data_csv=pd.read_csv(\"/content/employee_data.csv\",sep=\"\\t\")\n",
        "data_csv.head()"
      ],
      "execution_count": 2,
      "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",
              "  </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",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JhWI_eQir7a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2be53a2e-8910-4f17-ae34-9bd47dab3886"
      },
      "source": [
        "data_csv.shape\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(978, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2jOE0vki4dh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "0ae0db0e-4ffd-4657-bfef-be02ffb6c100"
      },
      "source": [
        "data_csv.columns"
      ],
      "execution_count": 4,
      "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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hol6lcYqi73E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNe0UwGJi90C",
        "colab_type": "text"
      },
      "source": [
        "# Problem Statement"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i28Gf7JvjHK_",
        "colab_type": "text"
      },
      "source": [
        "**Identify Categorical variables & Continuous Variables**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxF3KzMlj-Ko",
        "colab_type": "text"
      },
      "source": [
        " **Continous Variables in dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5iRdGP5jAvj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e3185ac3-096b-48f6-fb57-d32d442fe7f3"
      },
      "source": [
        "# age\n",
        "len(data_csv['age'].unique())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slE2si92kzTo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "388f3d8f-8461-42fb-e95e-2796b35ffb2d"
      },
      "source": [
        "# salary\n",
        "len(data_csv['salary'].unique())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "966"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xds6UhyFlaWo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "29361320-ae8e-44cf-e430-04ef2a6b5bf8"
      },
      "source": [
        "# hours_per_week\n",
        "len(data_csv['hours_per_week'].unique())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuPAue_6GxMR",
        "colab_type": "text"
      },
      "source": [
        "Continous Variables in dataset are\n",
        "1. Age\n",
        "2. Salary\n",
        "3. hours_per_week"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRkVLqHTmPWO",
        "colab_type": "text"
      },
      "source": [
        "**Categorical Variables in the Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORx-3xw2mGWR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "e6d90314-275c-4c1c-bb24-47ab4bf5bc91"
      },
      "source": [
        "data_csv['employment_type'].unique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov',\n",
              "       ' Local-gov', ' ?', ' Self-emp-inc'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ReYlPxZs1sc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        },
        "outputId": "5305f9df-fb17-4f31-938d-ce027494cc4e"
      },
      "source": [
        "data_csv['education'].unique()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Bachelors', ' HS-grad', ' 11th', ' Masters', ' 9th',\n",
              "       ' Some-college', ' Assoc-acdm', ' Assoc-voc', ' 7th-8th',\n",
              "       ' Doctorate', ' Prof-school', ' 5th-6th', ' 10th', ' 1st-4th',\n",
              "       ' Preschool', ' 12th'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBBh7YYbs_gL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "8ed2f01d-e4ac-483e-926e-e72901de8045"
      },
      "source": [
        "data_csv['marital-status'].unique()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Never-married', ' Married-civ-spouse', ' Divorced',\n",
              "       ' Married-spouse-absent', ' Separated', ' Married-AF-spouse',\n",
              "       ' Widowed'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2-LKHlCtBuj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "41645fd0-3f3f-4f4e-c60e-17355715b871"
      },
      "source": [
        "data_csv['relationship'].unique()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Not-in-family', ' Husband', ' Wife', ' Own-child', ' Unmarried',\n",
              "       ' Other-relative'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsMeULlJtCY6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8f82c047-2bef-44bc-9520-949c00a35854"
      },
      "source": [
        "data_csv['Gender'].unique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Male', ' Female'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDCqtbTpHAPZ",
        "colab_type": "text"
      },
      "source": [
        "Categorical Variables in the Dataset are\n",
        "\n",
        "1.employee_type\n",
        "\n",
        "2.education\n",
        "\n",
        "3.marital-status\n",
        "\n",
        "4.relationship\n",
        "\n",
        "5.Gender"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PHKMD_0tehR",
        "colab_type": "text"
      },
      "source": [
        "**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**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSY9HCRdt8NS",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**Categorical Variables in the Dataset ploting**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjqIiASmUsLO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "8b420dd8-b7fe-45e9-adf1-89a04797d5de"
      },
      "source": [
        "data_csv['employment_type'].value_counts().plot.bar()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f575a0f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "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": "BLwxWfnRVXUd",
        "colab_type": "text"
      },
      "source": [
        "**Inference**\n",
        "* Number of employes are maximum in private catagory\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1JHO1b1V8cN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "2c37fb2f-92f6-412d-a17f-ea02bc08bf2a"
      },
      "source": [
        "data_csv['education'].value_counts().plot.bar()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f5748bd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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": "fbGMq101V8Bs",
        "colab_type": "text"
      },
      "source": [
        "**Inference**\n",
        "* Number of employes are maximum in HS_grade catagory\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6G_CMgVdU4iC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "375fb676-fcb4-424b-ab1c-376e1cebbb2b"
      },
      "source": [
        "data_csv['marital-status'].value_counts().plot.bar()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f56ff65f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "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": "wbcJVdN0Vz0E",
        "colab_type": "text"
      },
      "source": [
        "**Inference**\n",
        "* Number of employes are maximum in married-civ-spouse catagory\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILWxmJrqU_oV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "ce95c0e9-fea1-4780-c517-101c9faf5c35"
      },
      "source": [
        "data_csv['relationship'].value_counts().plot.bar()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f56f65908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "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": "code",
      "metadata": {
        "id": "m2onSGpcVE1t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "4cde11fe-40f1-4227-c540-d45b07119d02"
      },
      "source": [
        "data_csv['Gender'].value_counts().plot.bar()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f56ee3be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "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": "fMn4pKyGWkrZ",
        "colab_type": "text"
      },
      "source": [
        "**Inference**\n",
        "* The weightage of male employes are maximum rather than female employes \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hXXFmOxW26e",
        "colab_type": "text"
      },
      "source": [
        "**Histogram plot for Continuous Variables**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pQN1kOct76h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "outputId": "94a0d7ce-0d0c-488b-eefc-29810de9270c"
      },
      "source": [
        "data_csv.hist(figsize=[20,8])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f56f65c18>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f56e70080>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f56e222e8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f56dd5550>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-4FBi09HYIA",
        "colab_type": "text"
      },
      "source": [
        "**Inference drawn from above plot**\n",
        "\n",
        "1.**Age**\n",
        "* Maximun age of the employees are in span of 20-45\n",
        "2.**Salary**\n",
        "* Their are so little employes have the salary above 450000 \n",
        "3.**hours_per_week**\n",
        "* Maximum employes worked for 30 to 40 hours/week \n",
        "* Among all 100 employes are working in the 10 to 70 hours/week\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvbCpn3AvQDR",
        "colab_type": "text"
      },
      "source": [
        "**Show a bivariate analysis between Salary and hour_per_week.Choose a plot of your choice. Mention the inferences drawn**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdFlHYDdvhPe",
        "colab_type": "text"
      },
      "source": [
        "**Bivariate Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vaHDdaMumWs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "87ec726e-d085-4a41-e0bf-a2c8c09d263f"
      },
      "source": [
        "data_csv.plot.scatter(x='hours_per_week', y='salary')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f56f659b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "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": "hFxGegWeLYOs",
        "colab_type": "text"
      },
      "source": [
        "**Inference drawn from above plot**\n",
        "\n",
        "1.Those Employes having salary below 4,00,000 work atleast 25 to 50 hours per week\n",
        "\n",
        "2.Employee working around 40 hours per week earns comparatively higher salary.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYdrP468wVRv",
        "colab_type": "text"
      },
      "source": [
        "**For each Education type , showcase the distribution of Salary .Use Seaborn Facetgrid & Line plot.Identify the key observations and mention them**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_TnkupFtStj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "outputId": "70e569d4-c2bc-49f8-f04c-a44f176821e2"
      },
      "source": [
        "facetgrid = sb.FacetGrid(data_csv,col='education',col_wrap=4,height=3)\n",
        "facetgrid = facetgrid.map(plt.plot,\"salary\")\n",
        "facetgrid"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f2f56c73048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "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": "SfpuUGdFRDFV",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Inference from the above grid\n",
        "* Hs_grad employed have lesser amount of salaries among others\n",
        "* Most of the employes gets salary below 50000\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuQbOI8y0c0c",
        "colab_type": "text"
      },
      "source": [
        "**Categorizing each Gender & Relationship status (combination of all values) , plot the hours_per_week worked.Choose appropriate plots.Mention the inferences drawn**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftcH0orpxIXl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cb026597-65ee-48ed-b10e-6d11deaf5fe2"
      },
      "source": [
        "nvb = sb.FacetGrid(data_csv, col='Gender', row='relationship',aspect=3)\n",
        "nvb.map(plt.hist, 'hours_per_week')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f2f53f37400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1296 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ntiwov551lPW",
        "colab_type": "text"
      },
      "source": [
        "**How many Males that have Marital status ‘Married-civ-spouse’ are earning above 150,000 ?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1R2fIyc2rxk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1531b35d-c443-4934-ac2d-1e3f75886726"
      },
      "source": [
        "males=data_csv[(data_csv['Gender'] == ' Male') & (data_csv['marital-status'] == ' Married-civ-spouse') & (data_csv['salary']>150000)]['Gender'].count()\n",
        "print(f\"Males that have Marital status ‘Married-civ-spouse’ are earning above 150,000 is {males}\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Males that have Marital status ‘Married-civ-spouse’ are earning above 150,000 is 226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMZxiCAhDOeC",
        "colab_type": "text"
      },
      "source": [
        "**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**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckx6BWLuBfFN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "fbe8f088-4c56-4b8a-9b68-f3d4f43f5a63"
      },
      "source": [
        "dev=data_csv[(data_csv['marital-status'] == ' Divorced') & (data_csv['relationship'] != ' Not-in-family')]['age'].mean(0)\n",
        "dev1=data_csv[(data_csv['relationship'] == ' Not-in-family') & (data_csv['marital-status'] != ' Divorced')]['age'].mean(0)\n",
        "print(dev)\n",
        "print(dev1)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "40.67272727272727\n",
            "35.353846153846156\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3G-oalZ7UR0z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}