data_visualization_2.ipynb

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              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
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              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
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      ],
      "source": [
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "df=sns.load_dataset(\"titanic\")\n",
        "df.head()\n"
      ]
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        "df.describe().T"
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              "          count       mean        std   min      25%      50%   75%       max\n",
              "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
              "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
              "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
              "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
              "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
              "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292"
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        {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q1 = df['age'].quantile(0.25)\n",
        "q2 = df['age'].quantile(0.50) # equal median\n",
        "q3 = df['age'].quantile(0.75)\n",
        "IQR=q3-q1 #Interquartile Range\n",
        "mean=df[\"age\"].mean()\n",
        "median=df[\"age\"].median()\n",
        "minimum = q1-1.5*IQR\n",
        "maximum = q3+1.5*IQR\n",
        "print(\"mean : \" + str(mean))\n",
        "print(\"q1 : \" + str(q1))\n",
        "print(\"q2 : \" + str(q2))\n",
        "print(\"q3 : \" + str(q3))\n",
        "print(\"IQR : \" + str(IQR))\n",
        "print(\"minimum : \" + str(minimum))\n",
        "print(\"maximum : \" + str(maximum))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSoGOFWPacts",
        "outputId": "01ff0ecc-eb0e-4013-8969-60d85cf582cc"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean : 29.69911764705882\n",
            "q1 : 20.125\n",
            "q2 : 28.0\n",
            "q3 : 38.0\n",
            "IQR : 17.875\n",
            "minimum : -6.6875\n",
            "maximum : 64.8125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df[\"age\"],kde=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "ANUOWRLWHDZG",
        "outputId": "1f3cf061-3590-4e7a-a1d9-48abef3fbb87"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='age',y='fare',data=df);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "aLT9wBDog56C",
        "outputId": "1fc8cb74-566e-414b-e765-64fc22929e59"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_4Ltg0D9nkwW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}