Project/Project Solution/Project_Solution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project Solution.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sh4ScKu3QSSP"
      },
      "source": [
        "Name = Kishan Kumar Rai\n",
        "\n",
        "Email = kishan.rai99693@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6NqruvjQsR5"
      },
      "source": [
        "# Tasks to be performed:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GwhXgAy1Sl9B"
      },
      "source": [
        "# 1. Read the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EuhGvVfR5I6"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2y71_c4cPvsK",
        "outputId": "5583d9d3-85f5-4e6e-8e24-a470e7b8c51d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data = pd.read_csv(\"/content/pubg - Dr. Darshan Ingle.csv\")\n",
        "print(data)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  Id         groupId  ... winPoints  winPlacePerc\n",
            "0     2f262dd9795e60  78437bcd91d40e  ...      1470        0.0000\n",
            "1     a32847cf5bf34b  85b7ce5a12e10b  ...      1531        0.2222\n",
            "2     1b1900a9990396  edf80d6523380a  ...         0        0.8571\n",
            "3     f589dd03b60bf2  804ab5e5585558  ...         0        0.3462\n",
            "4     c23c4cc5b78b35  b3e2cd169ed920  ...      1557        0.0690\n",
            "...              ...             ...  ...       ...           ...\n",
            "9995  ef4f474acd8e85  2eca2a8391f75d  ...      1471        0.8333\n",
            "9996  cf0bf82fb4d80e  2eaf2765f93adb  ...      1500        0.7174\n",
            "9997  a0a31a0b1dcbe1  8d50c64ccc5071  ...      1434        0.2083\n",
            "9998  f6874657399d69  d31843d7e62ccb  ...      1534        0.2449\n",
            "9999  90359b0b8f8b0d  61d5b1bb8da43f  ...         0        0.1875\n",
            "\n",
            "[10000 rows x 29 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYBTNDwZSb18"
      },
      "source": [
        "# 2. Check the datatype of all the columns.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvl37UOISM_p",
        "outputId": "7fbb00cb-cb55-4440-ba31-437ffd925c0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "DataType = data.dtypes\n",
        "print(DataType)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Id                  object\n",
            "groupId             object\n",
            "matchId             object\n",
            "assists              int64\n",
            "boosts               int64\n",
            "damageDealt        float64\n",
            "DBNOs                int64\n",
            "headshotKills        int64\n",
            "heals                int64\n",
            "killPlace            int64\n",
            "killPoints           int64\n",
            "kills                int64\n",
            "killStreaks          int64\n",
            "longestKill        float64\n",
            "matchDuration        int64\n",
            "matchType           object\n",
            "maxPlace             int64\n",
            "numGroups            int64\n",
            "rankPoints           int64\n",
            "revives              int64\n",
            "rideDistance       float64\n",
            "roadKills            int64\n",
            "swimDistance       float64\n",
            "teamKills            int64\n",
            "vehicleDestroys      int64\n",
            "walkDistance       float64\n",
            "weaponsAcquired      int64\n",
            "winPoints            int64\n",
            "winPlacePerc       float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0urEj0AhTP-g"
      },
      "source": [
        "# 3. Find the summary of all the numerical columns and write your findings about it.\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMGiFJ6JTFli",
        "outputId": "7ce7e0d8-11e0-47b8-fbdc-afb9e5a65030",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "Description = data.describe()\n",
        "print(Description)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            assists        boosts  ...   winPoints  winPlacePerc\n",
            "count  10000.000000  10000.000000  ...  10000.0000  10000.000000\n",
            "mean       0.234600      1.088500  ...    609.3440      0.469926\n",
            "std        0.575149      1.703279  ...    739.7924      0.304508\n",
            "min        0.000000      0.000000  ...      0.0000      0.000000\n",
            "25%        0.000000      0.000000  ...      0.0000      0.200000\n",
            "50%        0.000000      0.000000  ...      0.0000      0.458300\n",
            "75%        0.000000      2.000000  ...   1495.0000      0.735100\n",
            "max        7.000000     18.000000  ...   1863.0000      1.000000\n",
            "\n",
            "[8 rows x 25 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLOj7N96T4jW"
      },
      "source": [
        "# 4. The average person kills how many players?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_VJg6cFURQJ",
        "outputId": "c4214ede-3245-497f-946a-552224869c85",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "average = data[\"kills\"].mean()\n",
        "print(average)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9134\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYck7U7IUfQw"
      },
      "source": [
        "# 5. 99% of people have how many kills?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeKNL48iTyjY",
        "outputId": "28e35f88-4b97-465a-bebc-e4b38d637ea7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "killed = data[\"kills\"].quantile(0.99)\n",
        "print(killed, \"kills\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7.0 kills\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7P87-q6tVCHf"
      },
      "source": [
        "# 6. The most kills ever recorded are how much?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-O5oOEq5U_zd",
        "outputId": "a23ca680-f6f1-43b6-c4d8-67791e5a5ded",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "max_kill = data[\"kills\"].max()\n",
        "print(max_kill, \"kills\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "35 kills\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-pMivFFtVkuo"
      },
      "source": [
        "# 7. Print all the columns of the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RwwmtlKVY53",
        "outputId": "c6d385c5-cd14-495b-f7ca-014509d1f0b3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "column = data.columns\n",
        "print(column)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Id', 'groupId', 'matchId', 'assists', 'boosts', 'damageDealt', 'DBNOs',\n",
            "       'headshotKills', 'heals', 'killPlace', 'killPoints', 'kills',\n",
            "       'killStreaks', 'longestKill', 'matchDuration', 'matchType', 'maxPlace',\n",
            "       'numGroups', 'rankPoints', 'revives', 'rideDistance', 'roadKills',\n",
            "       'swimDistance', 'teamKills', 'vehicleDestroys', 'walkDistance',\n",
            "       'weaponsAcquired', 'winPoints', 'winPlacePerc'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfCi5y02V7Cq"
      },
      "source": [
        "# 8. Comment on distribution of the match's duration. Use seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzH66sKYV5u0",
        "outputId": "38217074-c2f4-406c-c26a-b31257e03a69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.displot( data[\"matchDuration\"] );"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOoGkd1iW49P"
      },
      "source": [
        "# 9. Comment on distribution of the walk distance. Use seaborn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3nQ5rz1WGgM",
        "outputId": "e4b0898b-e143-4983-dcd9-746a93fecfae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "sns.displot(data[\"walkDistance\"]);\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUEEZkGBXYt2"
      },
      "source": [
        "# 10. Plot distribution of the match's duration vs walk distance one below the other"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ffqpjxLXNCX",
        "outputId": "f4c9afe3-57f3-4b0f-e14e-29e75c0fae41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.style.use('classic')\n",
        "%matplotlib inline \n",
        "plt.figure()\n",
        "\n",
        "plt.subplot(2,1,1)\n",
        "plt.plot(data[\"matchDuration\"],'-')\n",
        "plt.xlabel(\"Match Duration\")\n",
        "plt.subplot(2,1,2)\n",
        "plt.plot(data[\"walkDistance\"],\"--\");\n",
        "plt.xlabel(\"Walk Distance\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Walk Distance')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrgNLELDZkuQ"
      },
      "source": [
        "# 11. Plot distribution of the match's duration vs walk distance side by side"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrv4zA9tZXhO",
        "outputId": "faa1b22e-20b8-4a24-9402-d7849a99661d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(data[\"matchDuration\"],'-');\n",
        "plt.xlabel(\"Match Duration\")\n",
        "\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(data[\"walkDistance\"],\"--\");\n",
        "plt.xlabel(\"Walk Duration\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Walk Duration')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUUYoFmpa-MG"
      },
      "source": [
        "# 12. Pairplot the dataframe. Comment on kills vs damage dealt, Comment on maxPlace vs numGroups."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9iFkhfSdl-Q",
        "outputId": "6d1d110d-14c3-4db3-833d-5b526891a5d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sns.pairplot(data.head(20));"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 4500x4500 with 650 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8Re-i1_fFlf"
      },
      "source": [
        "# 13. How many unique values are there in 'matchType' and what are their counts?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCrUrOLLeNhw",
        "outputId": "b78d80e1-0e3b-4e86-e423-31d832144e8e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "unique = data[\"matchType\"].unique()\n",
        "print(unique)\n",
        "print(\"Count is\", len(unique))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['squad-fpp' 'squad' 'duo-fpp' 'solo-fpp' 'duo' 'solo' 'crashfpp'\n",
            " 'flaretpp' 'normal-squad-fpp' 'normal-duo-fpp' 'flarefpp' 'normal-squad'\n",
            " 'normal-solo-fpp' 'crashtpp']\n",
            "Count is 14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sj1Kxlc_gmn-"
      },
      "source": [
        "# 14. Plot a barplot of ‘matchType’ vs 'killPoints'. Write your inferences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DdqDpmJf6RH",
        "outputId": "3d48272b-8503-4ae5-f319-aea36bc1b8a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "source": [
        "sns.barplot(data[\"matchType\"], data[\"killPoints\"]);"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "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": "tQF6i5oSiEym"
      },
      "source": [
        "# 15. Plot a barplot of ‘matchType’ vs ‘weaponsAcquired’. Write your inferences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VGkPWhzhFFg",
        "outputId": "0bf4e7eb-d76d-4c75-858f-acbf42649605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "source": [
        "sns.barplot(data[\"matchType\"], data[\"weaponsAcquired\"]);"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "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": "tGVgtBZTivY-"
      },
      "source": [
        "# 16. Find the Categorical columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qM6AFIqioqi",
        "outputId": "d1d8ac65-1b30-492b-bee4-8f2fbc18ccd0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "categorical = data.select_dtypes(\"category\").columns\n",
        "print(categorical)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index([], dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BK3Ez6fxkA3l"
      },
      "source": [
        "# 17. Plot a boxplot of ‘matchType’ vs ‘winPlacePerc’. Write your inferences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCFD0RSjjf6J",
        "outputId": "63666e2f-b7a1-48fd-8a5f-a4f29e89035d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "source": [
        "sns.barplot(data[\"matchType\"], data[\"winPlacePerc\"]);"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "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": "nkujnxhQkZMv"
      },
      "source": [
        "# 18. Plot a boxplot of ‘matchType’ vs ‘matchDuration’. Write your inferences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74t313XHkXfN",
        "outputId": "091d47df-6ff6-40dd-a824-5d4f9d22ebf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "source": [
        "sns.barplot(data[\"matchType\"], data[\"matchDuration\"]);"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eW9RkumkTwlNm9gvgqvj/6az9KF8REekELmkcVZPppj3M9Q1gJfAH4PfACuDreQUlIiL1Je2eyZHufm5ygJkdB9xZ/ZBERKTepN0zOS/lMBER2QiV3TMxsyMIj9IdYGZXJD7aCliVZ2AiIlI/Kh3magWeAj7N2ifclwK6gF5ERIDKv4B/FnjWzG5393c7KCYRkS6rT48+tQ4hF2lPwA8xs0uAPYCehYHuvnMuUYmIdFEXDxtd6xBykfYE/I3AbwjnSQ4GbgFuzSsoERGpL2mTSS93fwQwd5/n7qOBT+UXloiI1JO0h7neMbNNgJlmdgawAKjNb/ZFRKTTSbtnchbQGzgT+AjwBeCkvIISEZH6knbPxIHfAYOBTeOwa4E98whKRKRWtujZNa+2ylvaZHIb8B1gKrAmv3BERGrrjKMurXUIdSltMnnV3cflGomIiNSttMnkfDO7DngEeKcw0N3vySUqERGpK2mTySnAboTzJYXDXA4omYiISOpksp+775prJCIiUrfSXhr8mJntkWskIiJSt9LumRwITDazOYRzJga4u+vSYBERSZ1MDs81ChERqWupkom7z8s7EBERqV9pz5mIiIiUpGQiIiKZKZmIiEhmSiYiIpKZkomIiGSmZCIiIpkpmYiISGZKJiIikpmSiYiIZKZkIiIimSmZiIhIZkomIiKSWc2SiZnNNbOpZjbZzJ6Kw7Yxs/FmNjP+7RuHm5ldYWazzGyKme1Tq7hFRGRdtd4zOdjd93L3feP/5wKPuPtQwvPmz43DjwCGxtdpwG86PFIRESmp1smkvaOBm+P7m4FjEsNv8eAJYGsz26EWAYqIyLpqmUwc+KuZPW1mp8Vh/d19YXz/MtA/vh8ANCfGbYnDRESkE0j7pMU8fNTdF5jZ9sB4M3s++aG7u5n5+lY6evTo995PeHEGw3bZPXOgIiJdyYSZk3hs1mRg7T4zi5rtmbj7gvh3EXAvsD/wSuHwVfy7KBZfADQkRh8Yh61j9OjR7zWOEomIyLqGDd2b7xxxClDnycTMNjezLQvvgcOAacA44ORY7GTgvvh+HHBSvKrrQOCNxOEwERGpsVod5uoP3GtmhRhud/eHzGwiMNbMTgXmAcfH8g8CRwKzgOXAKR0fsoiIlFKTZOLus4EPFxn+GnBIkeEOnN4BoYmIyAbobJcGi4hIHVIyERGRzJRMREQkMyUTERHJTMlEREQyUzIREZHMlExERCQzJRMREclMyURERDJTMhERkcyUTEREJDMlExERyUzJREREMlMyERGRzJRMREQkMyUTERHJTMlEREQyUzIREZHMlExERCQzJRMREclMyURERDJTMhERkcyUTEREJDMlExERyUzJREREMlMyERGRzJRMREQkMyUTERHJTMlEREQyUzIREZHMlExERCQzJRMREclMyURERDJTMhERkcyUTEREJDMlExERyUzJREREMlMyERGRzJRMREQkMyUTERHJTMlEREQyUzIREZHMlExERCQzJRMREclMyURERDJTMhERkczqKpmY2eFm9oKZzTKzc2sdj4iIBHWTTMysG3AVcASwBzDCzPbIUueEF2dUI7SSHntxQa71T3rhtVzrf+75xbnW/9Jz+db/2tSX861/2qx865/+XK71/2f6s7nWv2jak7nWP2PahNzqfub5/OoGeOylx3Otf8LMSbnWX0zdJBNgf2CWu89295XA74Gjs1SYdzJ5fKaSSTmzc04mi6flnEym551M8l0+/zN9Sq71vzo932Ty/PQ8k8ljudUN8NhLT+Rb/6zJudZfTD0lkwFAc+L/ljhMRERqrJ6SiYiIdFLm7rWOIRUzOwgY7e6fjP+fB+DulyTK1MfMiIh0Mu5uWcavp2TSHXgROARYAEwETnT36TUNTERE6F7rANJy91VmdgbwF6AbcIMSiYhI51A3eyYiItJ56QR8ZGYjzezKEp8dZ2YzzOzRHKY72szOSRNHhXq2M7N/m9kkM/vvlOM0mdm+6zmdn5rZdDP76XqOt9Z8dqQNmc8UdQ4xs2klPvuxma0ws5VmdkuVptdoZv9VocxcM+tXjemtj/bTLdXesdzk2Davb8hynqjrvXXSzDYzs4dj3SdsaJ3t4qxKO2apq9CO5Za1EuMNiW2zTn8Q+5cdNySeSurmMFeNnQp8xd3/VetAyjgEmOruX855OqcB27j76pyn06HMrLu7r6pSdacBVwDPAamSWDwnSJkYGoG3gKr+AKLK811JN8IPjncGDiVl20DRON9bJ83sQAB336vQjtXQwW1TTrcNGGcLYEKR/mAkMA1ozRrUOty9076AzYEHgGdjA5wAHA48DzxDWGHvj2VHA+ckxp0GDInv/wg8DUwHTkuUOYVwUv9J4FrgyiIx/IiwEr8FLCL8vmUiMAVYGb+UK4BHYlxTgFeAu4DeJeJ4Jb7+BdwB/AFYHsteC1wHzI3j9ARuBKYCk4CDi8S4FzAfeDVO/8/AauA/hN/mPBPHnwosBK4EJgPLgJNjHSPi59OAn5T4PsbFeifH7+Im4GrgqdiOR8VyI4H7gNmxjebF+TwHaAL2jeX6rc98plg2DonjTgVuADaLZZsIHddJMZa3gddjuauBfwO/IPww9vFYx2PArnH8D8RlZHJs36HAEGBG/L6mA38FdiUsJx5fy4BbgTnAm8Ca2B7/APoDKwjLz6r4/j8xniUxxukx9jmx3gUxhn/GsouBubGdm4Gl8ftdFKcxLw5/KTG/k+L4v4rlFsXpzYnlZsSY7gdmAtcDTYl2fCvG9lKifeYB98Rx743tuS+wbWyX6YRlenVslxXAa3F+Lo/v3wVeAJ4grBurY7usJqwbY4GDE2U9zttVsU1Xx9fi+PnEGOea+D2sjOO8SVgH58X2Wh6HnZ2YxzdjO387/v9LwjI+L8b4epyHywAjrE9L43hLgatjXXMJ62W/2B6Fdtw/tsnyGNtzhA2QqbHe1THe1wnL9nWxvR+PcS2KcUwi9GHtl80jYxu8HV8Px+/t/lj3sjitXnF6T8Vyy4HPxBhvosi6XXadrHXCqNBhDAeuTfzfJy5AQ+OXOJZ0yWSb+LdXHL4tsEP8orcDegATKJJMEnXdm+goXyasCB+Mn40nJBMnrHznEDqzc9rHAXwkDpsODCasxEvi9A+Mf5PJ5NuEiw0Adosx9ywS40jCQj2c0ME58PnYZm8At8ZyLwOPx/eTgFnAjom26A78DTimRFu8lXh/E/AQ4XDpUEKi7RljeZWwkmwT/zZTPpmkms8Uy8Yu8f9bWLuDOJ6wQrbE+dyO0PFOBLrFclsB3eP7Q4G74/v/D3w+vu9BWI6GEDq7veLwscDZcdhSwkbIk4SO24HvEJbZSYSV8+eEFbmF0LmdSugonwM+CpxF6FRnApsROqofJZajmwgd9TTg03EarcAZhI7hq4QOYzYhsdxISCBbAH8nbHCMJ2z1PhZj+Wyc7ipCAt0sxvCvON3BhOWjF2G5/VMcvhi4Lb7fM46/L2EjqxDzp2KMK4G943wvICyrP4zxTYvf4Qux7DOE5Wh5nK8lwBmxvmbgjfh+fpzmtjG2NXHcIbQl9kMJy/qa2DbPx7p7AhcTkvO2tCWTfoll5yeJfuBVQt+xTZyHk2I79iOsR6/HeLaldDLZitCxD4txPUJYJu6IbdKPsF454fZRQwjLRXfCevIQcDdhPVkKjCyybK4Gfp9YNgv9QRMhSVwZP1sBTIzvLwLeLLdul+uvO/s5k6nAJ8zsJ/G4307AHHef6WGOb01Zz5lm9ixhq6eB0DgHEL7cVz3cnuUPZcZfBhxgZj8BdiF0QC+5+zTCFtncWK6Ztl/p30roFNaKg7B13w8YSFgopxA6uXcJK0T7OD5amE93f56wdbRLmVinAp8gLDwLCAvWC4TEByGZFHbd3yB0Lo20tcUq4DbgY2WmkTTW3de4+0xCx7VbHD4XuMvdFxP20irde2SD5jOxbAwhLBsvxs9vbjcP+xI67b/F+Xw1xvuatx2y6wPcGY9P/5LQoULYIvyemX0XGOzub8fhc9y9cN+Kpwnf6RzCilxogy3j/w8RrkTcFnh/ou4nCMvELYROZgfCRsE3CHu2vYFNCR3p1nGcMwkJZDfC8tyN0CkUPEfY8+lDaNM3CB348FiHE7ZU74jzvjLGUWjXd4FX3P0dwjLUM352FiHhLQYGAR+Kw3sCdwK4+xTCMg2h/Qvf6QMxjvbuiHF+jPB9v4+QxBYTEvG7hD2eYbF9vmxmkwmdeW8z2yLWs9jdX4vfzRrCHiuEJIG7P0xYBt8FPhzbdXksdyzQl9AvQFjfk8bFv48TOuzj4vizgU/GeTiDkKAhfMdDKa0PYXkdT/je9yS0336EpPwfwnq6DNg9jrNJLHN+bKcPxPVkAfCDIsvmq4V5JyybTlvf8mfW7pt+Ff+OBjY3s8JyVmrdLqpTJ5PYMexDWMB/TFiBSlnF2vPTE8KJS0L2P8jdP0zYMuy5ztiRmXWLJ/Imm9kFcfDbwBdiHJ8hdBpFQ24XhxeJ41fAb4vEkRyv4jFSM7uoEOdaAbS1GcAY4JhELBR5X+z/wjQOSLRFqbYvVVepywST81nye6ikyLJxTPkxSno38f5C4FF3/yDwfwvxufvthGXvbeBBM/t4LP9OYtzVhC3H5LA1hM4Hwt7NlcB5sZ7CvK9k7c7LCHuoXwb+7u4D3L1w6GyTxHL0AKFjmUTofNfQ1ub9gNMJHfMWhMMc5xHafQKhI0zOdyHWwt81tH1HBlic7ueBC9y9F2GDaq3vz8xOj8vjbjGGPsD9cflJnh9ZFeslxjycsBf3CiFpdqctIRfiOiLG9IC770X43l+KbZMsV5Ccv/bL/laENtuasGdxFCGx9EyWN7PTCRshN8ST1rcRDif1IrRjzzgfuxL7GMJe/dz4WfJ8ywhg39g+PyP0AQcQksk2hATUXjLu/sCjhL2Y7ydifZWwcfFxYJqZFTbaktMudn4zWXf7tiu1Dpe99LdTJ5P4BS5391uBnwL/BQwxs/fHIiMSxecSO1Ez24ewFwNhgV7i7svNbDfCigrhuO7/mNm2ZrYpYWsDd1/t7nvF149i2R7AihjHnwlbCzvHuxYfQ9hKI/7tHuM4kbCVulYchIXhhBhHL8LW3fsJh1z2i3H8n8R8/ZOwEmNmu8RpvODu3y/EWazNCAv5E4QFfBfCygehgykkqz6ELdSm2Bb94t2ZRxA6sn8n2mIcxR1nZpvE72Rnwl4Qcb6Hm1lfQgIufGdzCYf6IBxWKTufJaZZbNk4iLBsFNrui4TDOQVPxekeHOezX4wxeWvePoQtPQiH6grT2hmY7e5XEM4F7VkqriJWEtp751j3twltXlBolxMIW+5PEPZKCtMufL+rCEmgsBytjjEeCGxPWEYLHcjWhMNGzxOS4FxCxzWWkAR6E5LQCfH77sHaJ8OX0/YdFa5E6kPodObG9ShZfgUw3N2vImx09SBsFf8JuD0uo9vFOiB0gFsRltERMf7ehENE3eP4WxL2pubHtrs+ft431vE+wtZyQV8z28bMehH6tcK0+tHWWe4cp1k4h9KTsGfyTcK6uJY4P08RDnG2Eg5jLXX3n7Rrx08QvpM+hPN2hb2SuYSkRYzpqdgWPYBV7l44R/IOYePlSeDEuGxuT9gQKPyWrhth+fkn4ZBpYT3ZGXjEw51BChssxRhhfVtKWCaSFxOdFf/+EFjm7oU9yFLrdlGd/WquDwE/NbM1hC2NrxEWjgfMbDmhYbeMZe8GTjKz6YREUTjc8RDwVTObQdsJPtx9oZmNJuy6vk7Y4ihlc+BmM1tGWAmeICzUkwkrzaOEw0gvELZSjiIswPe2j4OwdbM6fn5ZrKsbYbdzOGFFnpOY9q+B35jZVEJnMTIefijbZrH+kwkd1xxg71iHA8+Y2SRCkvl6bItz43wYYevvvjLTSJpPWAm2Ar7q7ivMDMKW256Erc2FtC28PwPGmtlphK3rTPPZbtkoHKbqTljZr06Unw1cQNiLaabtfMXERJnLCN/zD9rFdjzwRTN7l3D44eI4v2nNJWx5Tozzdh3hqiYInc23CB3FS4Tv7jTCsrJ9/PtVwuGlgwlJ4W3gvwkd2LvAuYTzJYUtx+YY8ybxdT2hA2eLnx8AAAQJSURBVGsmHIJ6jfBdDyYk0+1o29iAcJ7mcjO7MDHsoTgft8fpL6Cto1xKODwyg3BI6uk4fAxwR1wnH4vjbB/b4D7gc4STxasJ68yLhENKIwh9036Ejv/hOI1jgbvM7BTCob/kbY+XEvqAgbG+FYQNv5WEPboZsQ3eifUsj233emzHNLfx/SzwMTObQujkC+24E2EPZyZhfWtJzP/fYvwPJ+q5jLCH+3PaLsz4EWE56EfbBQgvx2lASMCX0LahuCPhsNUDhPW50rL5LuHE/4cJy+LsmHgBupnZ24Tl5wuJcdZZt8u2TrkTKp39RTjWf38HT3Mk7U7UxzgeAabVuk0SMb1VYngT8QR4FaZxE/DZNG20sb4Ix8aLLheEzrlfZ2j7UvXl3DZFl0XaXUyTop615rnQru3bviPmsRbtmDKuUv3BCuCQasxHpz7MJSIi9UG3UxERkcy0ZyIiIpkpmYiISGZKJiIikpmSiYiIZKZkIpKBme1lZkemKPdWkWHfT9xhYHXi/Zn5RCuSH13NJZKBmY0k/FbijArl3nL3LTb0c5HOTnsmstGLDxN63sxuMrMXzew2MzvUzCaY2Uwz2z++HrfwsKHHzGxXM+tB+FX9CXGP4gQz28LMbjSzqWY2xcyGJ6ZzkZk9a2ZPmFn/ErFcYGZntxvnLAsPx/qHmT1gZi+Y2dVmtkksc1iM7Rkzu9Pabn4o0mG0ZyIbPTMbQrij7N6E22RMJNyz6VTCfYxOIdwuY7m7rzKzQ4Gvufvw9nsmFu4svZm7nx3/7+vuS8zMgU+7+5/M7DLCrb5/nIjhLXffIsZyj7vvE5PFTMJtMD5EuKXJHoQ7Kj8EXEP4Ffk9wBHuvszC3WM3c/fCTUpFOkRnvzeXSEeZ4+HGe8R7ST3i7h7vFTaEcN+vm81sKOEeRpuWqOdQwj2nAHD3JfHtSsIzLCDcu+oTxUZ297lm9pqZ7U24U+wkd38t3u/sSXefHWO8g3A/txWEBDMhlulBuN+cSIdSMhEJ2t86/p3E++603Z7+2Lj30LSe9b/rbYcBCrerL+U6wv2m3kd4yFpBsVuCGzDe3UcgUkM6ZyKSTtHb0xPuVrtl4v/xhGeJAOEw1wZM617C46n3IzxQq2B/M9spHv46gXAn5ieAYYVb75vZ5vHW5CIdSslEJJ3LgEvirfuTexWPAnsUTsATbnHf18ymWXi658HrOyEPT/58lPCku+SDjSYSnlcxg/BYgXs9PDFyJOFW71MIh7jKPhFPJA86AS/SycQ9j2eA4zw8MrXwpM5z3P2oWsYmUor2TEQ6EQtP75xFuABgZq3jEUlLeyYiIpKZ9kxERCQzJRMREclMyURERDJTMhERkcyUTEREJDMlExERyex/AdTSOsnDto8IAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDW8y_KuklkN"
      },
      "source": [
        "# 19. Change the orientation of the above plot to horizontal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwKU5CvWkkGh",
        "outputId": "05fa8509-5425-4482-82a6-53b433242f63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "sns.boxplot(x='matchType', y='matchDuration', data=data);"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "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": "Gfjv4gk7llni"
      },
      "source": [
        "# 20. Add a new column called ‘KILL’ which contains the sum of following columns viz. headshotKills, teamKills, roadKills"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nja2ahmblEg1",
        "outputId": "6a6913e1-2806-405e-f379-2283ac6524e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data[\"KILL\"] = data[\"headshotKills\"] + data[\"teamKills\"] + data[\"roadKills\"]\n",
        "print(data[\"KILL\"])"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0       0\n",
            "1       1\n",
            "2       1\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "9995    0\n",
            "9996    0\n",
            "9997    0\n",
            "9998    0\n",
            "9999    0\n",
            "Name: KILL, Length: 10000, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyg5OwrQnCG6"
      },
      "source": [
        "# 21. Round off column ‘winPlacePerc’ to 2 decimals."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-_gW5DboXIA",
        "outputId": "21de0647-3e7e-40c0-c598-77e8e8776a61",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data[\"winPlacePerc\"] = round(data[\"winPlacePerc\"], 2)\n",
        "print(data[\"winPlacePerc\"])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0       0.00\n",
            "1       0.22\n",
            "2       0.86\n",
            "3       0.35\n",
            "4       0.07\n",
            "        ... \n",
            "9995    0.83\n",
            "9996    0.72\n",
            "9997    0.21\n",
            "9998    0.24\n",
            "9999    0.19\n",
            "Name: winPlacePerc, Length: 10000, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJMaMgS6oQnV"
      },
      "source": [
        "# 22. Take a sample of size 50 from the column damageDealt for 100 times and calculate its mean. Plot it on a histogram and comment on its distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DM_dgQ8emjQh",
        "outputId": "0e3de80f-5f73-4658-9567-4fe72a653d97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "mean = []\n",
        "for i in range(100):\n",
        "  for x in range(0,1001,50):\n",
        "    mean1 = data[\"damageDealt\"].head(x).mean()\n",
        "    mean.append(mean1)\n",
        "\n",
        "sns.histplot(data=data[\"damageDealt\"], x=mean);\n",
        "\n",
        "  \n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "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"
          }
        }
      ]
    }
  ]
}