M3ExploratoryDataAnalysis-lab1.ipynb

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   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
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   "source": [
    "# **Exploratory Data Analysis Lab**\n"
   ]
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   "source": [
    "Estimated time needed: **30** minutes\n"
   ]
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   "source": [
    "In this module you get to work with the cleaned dataset from the previous module.\n",
    "\n",
    "In this assignment you will perform the task of exploratory data analysis.\n",
    "You will find out the distribution of data, presence of outliers and also determine the correlation between different columns in the dataset.\n"
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   "source": [
    "## Objectives\n"
   ]
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   "source": [
    "In this lab you will perform the following:\n"
   ]
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    "*   Identify the distribution of data in the dataset.\n",
    "\n",
    "*   Identify outliers in the dataset.\n",
    "\n",
    "*   Remove outliers from the dataset.\n",
    "\n",
    "*   Identify correlation between features in the dataset.\n"
   ]
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   "source": [
    "***\n"
   ]
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   "source": [
    "## Hands on Lab\n"
   ]
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   "source": [
    "Import the pandas module.\n"
   ]
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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
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   },
   "source": [
    "Load the dataset into a dataframe.\n"
   ]
  },
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   "source": [
    "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")"
   ]
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   "source": [
    "## Distribution\n"
   ]
  },
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   "source": [
    "### Determine how the data is distributed\n"
   ]
  },
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   "source": [
    "The column `ConvertedComp` contains Salary converted to annual USD salaries using the exchange rate on 2019-02-01.\n",
    "\n",
    "This assumes 12 working months and 50 working weeks.\n"
   ]
  },
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   "source": [
    "Plot the distribution curve for the column `ConvertedComp`.\n"
   ]
  },
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   "outputs": [
    {
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     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ConvertedComp', ylabel='Density'>"
      ]
     },
     "execution_count": 3,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "sns.distplot(df.ConvertedComp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1162ab82",
   "metadata": {
    "papermill": {
     "duration": 0.029843,
     "end_time": "2022-05-25T10:19:12.996853",
     "exception": false,
     "start_time": "2022-05-25T10:19:12.967010",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Plot the histogram for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95765749",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:13.060484Z",
     "iopub.status.busy": "2022-05-25T10:19:13.060184Z",
     "iopub.status.idle": "2022-05-25T10:19:13.248623Z",
     "shell.execute_reply": "2022-05-25T10:19:13.247823Z"
    },
    "papermill": {
     "duration": 0.223147,
     "end_time": "2022-05-25T10:19:13.250495",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.027348",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([9659.,  238.,  115.,  125.,   99.,  131.,   34.,   15.,   15.,\n",
       "         151.]),\n",
       " array([      0.,  200000.,  400000.,  600000.,  800000., 1000000.,\n",
       "        1200000., 1400000., 1600000., 1800000., 2000000.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "plt.hist(df.ConvertedComp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a859a05",
   "metadata": {
    "papermill": {
     "duration": 0.030556,
     "end_time": "2022-05-25T10:19:13.311811",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.281255",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What is the median of the column `ConvertedComp`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fc40a11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:13.375470Z",
     "iopub.status.busy": "2022-05-25T10:19:13.375182Z",
     "iopub.status.idle": "2022-05-25T10:19:13.381382Z",
     "shell.execute_reply": "2022-05-25T10:19:13.380781Z"
    },
    "papermill": {
     "duration": 0.039981,
     "end_time": "2022-05-25T10:19:13.382958",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.342977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57745.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.ConvertedComp.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a45fd4",
   "metadata": {
    "papermill": {
     "duration": 0.031107,
     "end_time": "2022-05-25T10:19:13.445128",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.414021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "How many responders identified themselves only as a **Man**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb70481f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:13.509389Z",
     "iopub.status.busy": "2022-05-25T10:19:13.508624Z",
     "iopub.status.idle": "2022-05-25T10:19:13.516717Z",
     "shell.execute_reply": "2022-05-25T10:19:13.515939Z"
    },
    "papermill": {
     "duration": 0.042257,
     "end_time": "2022-05-25T10:19:13.518476",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.476219",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Man                                                            10480\n",
       "Woman                                                            731\n",
       "Non-binary, genderqueer, or gender non-conforming                 63\n",
       "Man;Non-binary, genderqueer, or gender non-conforming             26\n",
       "Woman;Non-binary, genderqueer, or gender non-conforming           14\n",
       "Woman;Man                                                          9\n",
       "Woman;Man;Non-binary, genderqueer, or gender non-conforming        2\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e81a55",
   "metadata": {
    "papermill": {
     "duration": 0.031162,
     "end_time": "2022-05-25T10:19:13.581025",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.549863",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33aa7544",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:13.645392Z",
     "iopub.status.busy": "2022-05-25T10:19:13.644967Z",
     "iopub.status.idle": "2022-05-25T10:19:13.653252Z",
     "shell.execute_reply": "2022-05-25T10:19:13.652722Z"
    },
    "papermill": {
     "duration": 0.042538,
     "end_time": "2022-05-25T10:19:13.654924",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.612386",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57708.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ConvertedComp[df.Gender == 'Woman'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ef20b0",
   "metadata": {
    "papermill": {
     "duration": 0.032311,
     "end_time": "2022-05-25T10:19:13.719265",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.686954",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Give the five number summary for the column `Age`?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b713f6fa",
   "metadata": {
    "papermill": {
     "duration": 0.031373,
     "end_time": "2022-05-25T10:19:13.782357",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.750984",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Double click here for hint**.\n",
    "\n",
    "<!--\n",
    "min,q1,median,q3,max of a column are its five number summary.\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ede9db9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:13.847907Z",
     "iopub.status.busy": "2022-05-25T10:19:13.847008Z",
     "iopub.status.idle": "2022-05-25T10:19:13.857834Z",
     "shell.execute_reply": "2022-05-25T10:19:13.857121Z"
    },
    "papermill": {
     "duration": 0.045463,
     "end_time": "2022-05-25T10:19:13.859752",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.814289",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11111.000000\n",
       "mean        30.778895\n",
       "std          7.393686\n",
       "min         16.000000\n",
       "25%         25.000000\n",
       "50%         29.000000\n",
       "75%         35.000000\n",
       "max         99.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.Age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40aef1d",
   "metadata": {
    "papermill": {
     "duration": 0.033567,
     "end_time": "2022-05-25T10:19:13.925520",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.891953",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Plot a histogram of the column `Age`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98c36a14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:13.991907Z",
     "iopub.status.busy": "2022-05-25T10:19:13.991656Z",
     "iopub.status.idle": "2022-05-25T10:19:14.154702Z",
     "shell.execute_reply": "2022-05-25T10:19:14.154098Z"
    },
    "papermill": {
     "duration": 0.199046,
     "end_time": "2022-05-25T10:19:14.156613",
     "exception": false,
     "start_time": "2022-05-25T10:19:13.957567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.Age.plot(kind = 'hist',bins = 20, xticks = [10,20,30,40,50,60,70,80,90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9efed5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:14.224766Z",
     "iopub.status.busy": "2022-05-25T10:19:14.224276Z",
     "iopub.status.idle": "2022-05-25T10:19:14.313599Z",
     "shell.execute_reply": "2022-05-25T10:19:14.312995Z"
    },
    "papermill": {
     "duration": 0.125512,
     "end_time": "2022-05-25T10:19:14.315425",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.189913",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sbRHxbxFxJCL+OSKuqXB86YyMu1a6m4EvZOb/AjMR8TbgN4Eu4AbgA8A7ASKiA/hL4NbMfBtwABiqYmjp1fgF2VrptgEfr93+h9r9NcA/ZuYccDwiRmuPbwZ6gEcjAmA1cOz8jis1xrhrxYqI9cC7gDdFRDIf6wT+6UxPASYy853naUSpZS7LaCW7Ffi7zHx9ZnZl5muBbwA/AH6rtva+Afjl2v7/A3RGxOllmoj4hSoGl16NcddKto2fPUr/DPDzwBTwFPD3wJeAFzLzFPP/IeyLiK8CXwH6ztu0UhO8/IBUR0Ssy8yXIuIq4L+ALZl5vOq5pEa55i7VdygirgDWAn9i2LXceOQuSQVyzV2SCmTcJalAxl2SCmTcJalAxl2SCvT/2xPdyhpyeRUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e728a45",
   "metadata": {
    "papermill": {
     "duration": 0.034828,
     "end_time": "2022-05-25T10:19:14.384750",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.349922",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2da2e3",
   "metadata": {
    "papermill": {
     "duration": 0.033682,
     "end_time": "2022-05-25T10:19:14.452711",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.419029",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Finding outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5bde2fb",
   "metadata": {
    "papermill": {
     "duration": 0.033845,
     "end_time": "2022-05-25T10:19:14.520610",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.486765",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Find out if outliers exist in the column `ConvertedComp` using a box plot?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98fd46e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:14.590777Z",
     "iopub.status.busy": "2022-05-25T10:19:14.590250Z",
     "iopub.status.idle": "2022-05-25T10:19:14.742769Z",
     "shell.execute_reply": "2022-05-25T10:19:14.741919Z"
    },
    "papermill": {
     "duration": 0.190184,
     "end_time": "2022-05-25T10:19:14.744856",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.554672",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WaRz0Zin1LwjuhcGt0jjozVJqaGigqqqKhoaGUpdiNiIOerOUtm3bRl9fH9u2bSt1KWYj4qA3S6mvr2/AvVmlcNCbpdTU1MTWrVtpamoqdSlmI+IvTJmlNHiFKbNK4aA3K2DMmDFUV1fT19dHb28vNTU1VFVVcejQoVKXZpZKqqEbSa2SdkkacilA5XxeUqek5yXNydt3k6SXkttNxSrcbLTU1tbS09MzYB59T08PtbW1Ja7MLJ20Y/RfBuYdYf/VwPTktgh4GEDSRHJLD15CbmHwz0iacLTFmpXC/v37R9RuVm5SBX1ErAH2HOGQa4GvRs4zwHhJZwAfAZ6IiD0RsRd4giP/wjAzsyIr1qybs4DtedtdSdtw7e8gaZGkDkkd3d3dRSrLrHj614490hqyZuWobKZXRsSyiGiOiOb6+vpSl2P2Dv0LgXtBcKs0xQr6HUD+98InJ23DtZuZ2SgpVtC3A3+czL75APBGROwEVgJXSZqQfAh7VdJmZmajJNU8ekltwOXAJEld5GbS1ABExBeA7wEfBTqB3wC3JPv2SPossC55qvsi4kgf6pqZWZGlCvqIuLHA/gDuHGZfK9A68tLMzKwYyubDWDMzOz4c9GZmGeegNzPLOAe9mVnGOejNzDLOQW9mlnEOejOzjHPQm5llnIPezCzjHPRmZhnnoDczyzgHvZlZxjnozcwyzkFvZpZxDnozs4xz0JuZZVyqoJc0T9KLkjol3T3E/v8h6dnktkXS63n7DuXtay9i7WZmlkLBFaYkVQMPAR8GuoB1ktoj4oX+YyLiv+Qdvxi4IO8p3oqI84tWsZmZjUiaHv3FQGdEvBIRB4HlwLVHOP5GoK0YxZmZ2bFLE/RnAdvztruStneQNAWYCqzOax4rqUPSM5L+YLgXkbQoOa6ju7s7RVlmZpZGsT+MXQB8KyIO5bVNiYhm4GPA5ySdM9SJEbEsIpojorm+vr7IZZmZnbjSBP0OoCFve3LSNpQFDBq2iYgdyf0rwI8YOH5vZmbHWZqgXwdMlzRV0knkwvwds2ckvReYADyd1zZBUm3yeBIwF3hh8LlmZnb8FJx1ExFvS7oLWAlUA60RsUnSfUBHRPSH/gJgeURE3ukzgS9K6iP3S+WB/Nk6ZmZ2/GlgLpeH5ubm6OjoKHUZZgBIGnZfOf782IlJ0vrk89B38DdjzcwyzkFvZpZxDnozs4xz0JuZZZyD3sws4xz0ZmYZ56A3M8s4B72ZWcY56M3MMs5Bb2aWcQ56M7OMc9CbmWWcg97MLOMc9GZmGeegNzPLuFRBL2mepBcldUq6e4j9N0vqlvRscvtPeftukvRScrupmMWbmVlhBVeYklQNPAR8GOgC1klqH2KlqH+MiLsGnTsR+AzQDASwPjl3b1GqNzOzgtL06C8GOiPilYg4CCwHrk35/B8BnoiIPUm4PwHMO7pSzczsaKQJ+rOA7XnbXUnbYP9O0vOSviWpYYTnImmRpA5JHd3d3SnKMjOzNIr1Yez/ARoj4jxyvfavjPQJImJZRDRHRHN9fX2RyjIzszRBvwNoyNuenLQdFhG7I6In2fwScGHac83M7PhKE/TrgOmSpko6CVgAtOcfIOmMvM35wObk8UrgKkkTJE0ArkrazMxslBScdRMRb0u6i1xAVwOtEbFJ0n1AR0S0A38qaT7wNrAHuDk5d4+kz5L7ZQFwX0TsOQ7vw8zMhqGIKHUN79Dc3BwdHR2lLsMMAEnD7ivHnx87MUlaHxHNQ+3zN2PNzDLOQW9mlnEOejOzjHPQm5llnIPezCzjHPRmZhnnoDczyzgHvZlZxjnozcwyzkFvZpZxDnozs4xz0JuZZZyD3sws4xz0ZmYZ56A3M8s4B72ZWcalCnpJ8yS9KKlT0t1D7P9zSS9Iel7SDyRNydt3SNKzya198LlmZnZ8FVxKUFI18BDwYaALWCepPSJeyDvs50BzRPxG0u3Ag8ANyb63IuL84pZtZmZppenRXwx0RsQrEXEQWA5cm39ARPwwIn6TbD4DTC5umWZmdrTSBP1ZwPa87a6kbTi3Ao/lbY+V1CHpGUl/MNxJkhYlx3V0d3enKMvMzNIoOHQzEpL+I9AMfCiveUpE7JD0u8BqSRsi4uXB50bEMmAZ5BYHL2ZdZmYnsjQ9+h1AQ9725KRtAElXAvcC8yOip789InYk968APwIuOIZ6zcxshNIE/TpguqSpkk4CFgADZs9IugD4IrmQ35XXPkFSbfJ4EjAXyP8Q18zMjrOCQzcR8baku4CVQDXQGhGbJN0HdEREO7AEqAO+KQlgW0TMB2YCX5TUR+6XygODZuuYlUzj3d8dted49YHfO+bXMjtaiii/4fDm5ubo6OgodRlmACSdlyGV48+PnZgkrY+I5qH2+ZuxZmYZ56A3K2C4Xrt781YpHPRmKUQEEcGUT644/NisUjjozcwyzkFvZpZxDnozs4xz0JuZZZyD3sws4xz0ZmYZV9SrV5qV0uy/XsUbb/Ue99cpxqUTjmTcu2p47jNXHdfXsBOLg94y4423ejNxTZnj/YvETjweujEzyzgHvZlZxnnoxjLj1Jl38/6v3F3qMo7ZqTMBKn8IysqHg94y483ND3iM3mwIHroxM8u4VD16SfOA/0luhakvRcQDg/bXAl8FLgR2AzdExKvJvk8BtwKHgD+NiJVFq95skCz0hse9q6bUJVjGFAx6SdXAQ8CHgS5gnaT2QUsC3grsjYhpkhYAfwPcIOl95NaYbQLOBL4vaUZEHCr2GzEbjWGbxru/m4nhITuxpBm6uRjojIhXIuIgsBy4dtAx1wJfSR5/C/i3yq2/di2wPCJ6IuIXQGfyfGZmNkrSDN2cBWzP2+4CLhnumGQx8TeAdyftzww696yhXkTSImARwNlnn52mdrNjcrTDPEdznv8KsFIqm1k3EbEMWAa5xcFLXI6dABy+dqJIM3SzA2jI256ctA15jKQxwDhyH8qmOdfMzI6jNEG/Dpguaaqkk8h9uNo+6Jh24Kbk8fXA6sgtqtkOLJBUK2kqMB34aXFKNzOzNAoO3SRj7ncBK8lNr2yNiE2S7gM6IqIdeAT4mqROYA+5XwYkx30DeAF4G7jTM27MzEaXynE1++bm5ujo6Ch1GWZmFUPS+ohoHmqfvxlrZpZxDnozs4xz0JuZZZyD3sws48ryw1hJ3cDWUtdhNoRJwK9KXYTZEKZERP1QO8oy6M3KlaSO4WY2mJUrD92YmWWcg97MLOMc9GYjs6zUBZiNlMfozcwyzj16M7OMc9CbmWWcg97KjqTfkbRc0suS1kv6nqQZJajjZklnjvCcRkkb87YvlrRG0ouSfi7pS5JOLn61ZsNz0FtZSdYa/jbwo4g4JyIuBD4FnD7KdVQDN5Nb1P5on+N04JvAJyPi3Ii4AHgcOLUoRZql5KC3ctMC9EbEF/obIuI54ClJSyRtlLRB0g0Aki6X9CNJ35L0fyV9XTnzJH2z/zmS41Ykj6+S9LSkn0n6pqS6pP1VSX8j6WfAjUAz8HVJz0p6l6QLJT2Z/JWxUtIZyXkXSnpO0nPAnXnv5U7gKxHxdN57+VZEvCZpoqTvSHpe0jOSzkue668kfUXSjyVtlXSdpAeT9/y4pJq8Wvvbfypp2vH4x7BscNBbuZkFrB+i/TrgfGA2cCWwpD9ogQuAPwPeB/wuMBf4PnCJpFOSY24AlkuaBHwauDIi5gAdwJ/nvc7uiJgTEf+Q7Pt4RJxPbuGcpcD1yV8ZrcD9yTn/G1gcEbNTvheAvwZ+HhHnAfcAX83bdw5wBTAf+AfghxHxfuAtIH+h2zeS9r8DPjfM65g56K1iXAq0RcShiHgNeBK4KNn304joiog+4FmgMSLeJjdM8vvJOsa/B/wL8AFyvxB+IulZcktgTsl7nX8c5vXPJRfcTyTnfRqYLGk8MD4i1iTHfW0E7+drABGxGni3pNOSfY9FRC+wgdyqbo8n7RuAxrznaMu7/2DK17UTUMGlBM1G2SZy6w6PRE/e40P89v/1cuAucstbdkTEm8lnAE9ExI3DPNf+YdoFbIqIAYGaBP1wNgEXkvsFMxI9ABHRJ6k3fvtllz4G/szGMI/NBnCP3srNaqBW0qL+hmT8+nXgBknVkuqByyi80PyTwBzgT8iFPsAzwNz+MW1JpxxhRs+b/PaD0xeBekkfTM6rkdQUEa8Dr0u6NDnu43nn/x1wk6RL8t7LdcmHtD/uP1bS5cCvIuLXBd7PYDfk3T99pAPtxOYevZWViAhJfwh8TtIngQPAq+TG4OuA58j1Xj8REf9P0nuP8FyHkg9gbyY3RENEdEu6GWiTVJsc+mlgyxBP8WXgC5LeIjc0cj3weUnjyP3sfI5cr/0WoFVSAKvyXv81SQuAv5X0HnI98jXkhmL+KjnneeA3/fWN0ITk/B5yHx6bDcmXQDCrQJJeBZojwtfGt4I8dGNmlnHu0ZuZZZx79GZmGeegNzPLOAe9mVnGOejNzDLOQW9mlnH/H/xa4KpDoPAZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.dropna()\n",
    "df.ConvertedComp.plot(kind= 'box')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea5e798",
   "metadata": {
    "papermill": {
     "duration": 0.035801,
     "end_time": "2022-05-25T10:19:14.817041",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.781240",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b14d67e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:14.889661Z",
     "iopub.status.busy": "2022-05-25T10:19:14.889398Z",
     "iopub.status.idle": "2022-05-25T10:19:14.899188Z",
     "shell.execute_reply": "2022-05-25T10:19:14.898364Z"
    },
    "papermill": {
     "duration": 0.04843,
     "end_time": "2022-05-25T10:19:14.900929",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.852499",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.058200e+04\n",
       "mean     1.315967e+05\n",
       "std      2.947865e+05\n",
       "min      0.000000e+00\n",
       "25%      2.686800e+04\n",
       "50%      5.774500e+04\n",
       "75%      1.000000e+05\n",
       "max      2.000000e+06\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.ConvertedComp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "758b8730",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:14.987915Z",
     "iopub.status.busy": "2022-05-25T10:19:14.987340Z",
     "iopub.status.idle": "2022-05-25T10:19:15.001590Z",
     "shell.execute_reply": "2022-05-25T10:19:15.000554Z"
    },
    "papermill": {
     "duration": 0.062912,
     "end_time": "2022-05-25T10:19:15.003448",
     "exception": false,
     "start_time": "2022-05-25T10:19:14.940536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73132.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = df.ConvertedComp.describe()[6] - df.ConvertedComp.describe()[4]\n",
    "IQR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e473e50",
   "metadata": {
    "papermill": {
     "duration": 0.036169,
     "end_time": "2022-05-25T10:19:15.080251",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.044082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Find out the upper and lower bounds.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47104ba3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:15.153318Z",
     "iopub.status.busy": "2022-05-25T10:19:15.153024Z",
     "iopub.status.idle": "2022-05-25T10:19:15.163737Z",
     "shell.execute_reply": "2022-05-25T10:19:15.162926Z"
    },
    "papermill": {
     "duration": 0.049639,
     "end_time": "2022-05-25T10:19:15.165586",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.115947",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper bound: 209698.0,  Lower bound: -82830.0\n"
     ]
    }
   ],
   "source": [
    "# your code goes here\n",
    "whiskers = 1.5*IQR\n",
    "\n",
    "Lower = df.ConvertedComp.describe()[4] - whiskers\n",
    "Upper = df.ConvertedComp.describe()[6] + whiskers\n",
    "\n",
    "print(\"Upper bound: {},  Lower bound: {}\".format(Upper, Lower))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d2046e",
   "metadata": {
    "papermill": {
     "duration": 0.035331,
     "end_time": "2022-05-25T10:19:15.237001",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.201670",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Identify how many outliers are there in the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8281bb70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:15.309952Z",
     "iopub.status.busy": "2022-05-25T10:19:15.309444Z",
     "iopub.status.idle": "2022-05-25T10:19:15.317442Z",
     "shell.execute_reply": "2022-05-25T10:19:15.316877Z"
    },
    "papermill": {
     "duration": 0.046448,
     "end_time": "2022-05-25T10:19:15.318943",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.272495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(879, 85)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df[(df['ConvertedComp'] > Upper) | (df ['ConvertedComp'] < Lower)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "872f5478",
   "metadata": {
    "papermill": {
     "duration": 0.035918,
     "end_time": "2022-05-25T10:19:15.390930",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.355012",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd420893",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:15.464756Z",
     "iopub.status.busy": "2022-05-25T10:19:15.464273Z",
     "iopub.status.idle": "2022-05-25T10:19:15.480731Z",
     "shell.execute_reply": "2022-05-25T10:19:15.480136Z"
    },
    "papermill": {
     "duration": 0.055336,
     "end_time": "2022-05-25T10:19:15.482539",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.427203",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9703.000000\n",
       "mean      59883.208389\n",
       "std       43394.336755\n",
       "min           0.000000\n",
       "25%       24060.000000\n",
       "50%       52704.000000\n",
       "75%       85574.500000\n",
       "max      209356.000000\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df_cleaned = df[df.ConvertedComp <= Upper]\n",
    "df_cleaned.ConvertedComp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfd9052",
   "metadata": {
    "papermill": {
     "duration": 0.035975,
     "end_time": "2022-05-25T10:19:15.554869",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.518894",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cfb720a",
   "metadata": {
    "papermill": {
     "duration": 0.03605,
     "end_time": "2022-05-25T10:19:15.626873",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.590823",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Finding correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca827408",
   "metadata": {
    "papermill": {
     "duration": 0.035839,
     "end_time": "2022-05-25T10:19:15.698952",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.663113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Find the correlation between `Age` and all other numerical columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1fe43e54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T10:19:15.773219Z",
     "iopub.status.busy": "2022-05-25T10:19:15.772674Z",
     "iopub.status.idle": "2022-05-25T10:19:15.791416Z",
     "shell.execute_reply": "2022-05-25T10:19:15.790555Z"
    },
    "papermill": {
     "duration": 0.058099,
     "end_time": "2022-05-25T10:19:15.793382",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.735283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Respondent</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019354</td>\n",
       "      <td>0.010878</td>\n",
       "      <td>-0.016221</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.002180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompTotal</th>\n",
       "      <td>-0.019354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.063561</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.017007</td>\n",
       "      <td>0.006337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedComp</th>\n",
       "      <td>0.010878</td>\n",
       "      <td>-0.063561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034351</td>\n",
       "      <td>-0.088934</td>\n",
       "      <td>0.401821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <td>-0.016221</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.034351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038935</td>\n",
       "      <td>0.032032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.017007</td>\n",
       "      <td>-0.088934</td>\n",
       "      <td>0.038935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.002180</td>\n",
       "      <td>0.006337</td>\n",
       "      <td>0.401821</td>\n",
       "      <td>0.032032</td>\n",
       "      <td>-0.012878</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Respondent  CompTotal  ConvertedComp  WorkWeekHrs  CodeRevHrs  \\\n",
       "Respondent       1.000000  -0.019354       0.010878    -0.016221    0.005293   \n",
       "CompTotal       -0.019354   1.000000      -0.063561     0.004910    0.017007   \n",
       "ConvertedComp    0.010878  -0.063561       1.000000     0.034351   -0.088934   \n",
       "WorkWeekHrs     -0.016221   0.004910       0.034351     1.000000    0.038935   \n",
       "CodeRevHrs       0.005293   0.017007      -0.088934     0.038935    1.000000   \n",
       "Age              0.002180   0.006337       0.401821     0.032032   -0.012878   \n",
       "\n",
       "                    Age  \n",
       "Respondent     0.002180  \n",
       "CompTotal      0.006337  \n",
       "ConvertedComp  0.401821  \n",
       "WorkWeekHrs    0.032032  \n",
       "CodeRevHrs    -0.012878  \n",
       "Age            1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df_cleaned.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1362a304",
   "metadata": {
    "papermill": {
     "duration": 0.036355,
     "end_time": "2022-05-25T10:19:15.868345",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.831990",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c97545",
   "metadata": {
    "papermill": {
     "duration": 0.036684,
     "end_time": "2022-05-25T10:19:15.942141",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.905457",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Ramesh Sannareddy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e705016",
   "metadata": {
    "papermill": {
     "duration": 0.036529,
     "end_time": "2022-05-25T10:19:16.015570",
     "exception": false,
     "start_time": "2022-05-25T10:19:15.979041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7115e509",
   "metadata": {
    "papermill": {
     "duration": 0.036242,
     "end_time": "2022-05-25T10:19:16.088601",
     "exception": false,
     "start_time": "2022-05-25T10:19:16.052359",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Rav Ahuja\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ee059a",
   "metadata": {
    "papermill": {
     "duration": 0.036477,
     "end_time": "2022-05-25T10:19:16.161799",
     "exception": false,
     "start_time": "2022-05-25T10:19:16.125322",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cab4b5a",
   "metadata": {
    "papermill": {
     "duration": 0.036809,
     "end_time": "2022-05-25T10:19:16.235276",
     "exception": false,
     "start_time": "2022-05-25T10:19:16.198467",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n",
    "| ----------------- | ------- | ----------------- | ---------------------------------- |\n",
    "| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9460701",
   "metadata": {
    "papermill": {
     "duration": 0.036382,
     "end_time": "2022-05-25T10:19:16.308292",
     "exception": false,
     "start_time": "2022-05-25T10:19:16.271910",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  }
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