Loading_CSV_To_DataFrame.ipynb

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  "metadata": {
    "colab": {
      "name": "Loading_CSV_To_DataFrame.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOIvfy8e7B2V2rpGYXjAMI2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BabritB/step-by-step-ml-beginner/blob/master/Loading_CSV_To_DataFrame.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ww1HR1Tg8qaz",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvYMAFjc8n1g",
        "colab_type": "text"
      },
      "source": [
        "## Load CSV from URL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqoerNAw81Az",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opzoxTge84Om",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url=\"https://goo.gl/bDdBiA\"\n",
        "names=['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] ## These are column names"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cwn8H7GF9Dec",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_frame_url=pd.read_csv(url,names=names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TdyhNqL9N82",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "3304ac3e-37a6-4eaa-afe9-080541832732"
      },
      "source": [
        "print(data_frame_url.head())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
            "0     6   148    72    35     0  33.6  0.627   50      1\n",
            "1     1    85    66    29     0  26.6  0.351   31      0\n",
            "2     8   183    64     0     0  23.3  0.672   32      1\n",
            "3     1    89    66    23    94  28.1  0.167   21      0\n",
            "4     0   137    40    35   168  43.1  2.288   33      1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdOzGl3F9Yuj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "331e7e9a-ad43-4603-dd75-78b92f7aec08"
      },
      "source": [
        "print(data_frame_url.describe())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             preg        plas        pres  ...        pedi         age       class\n",
            "count  768.000000  768.000000  768.000000  ...  768.000000  768.000000  768.000000\n",
            "mean     3.845052  120.894531   69.105469  ...    0.471876   33.240885    0.348958\n",
            "std      3.369578   31.972618   19.355807  ...    0.331329   11.760232    0.476951\n",
            "min      0.000000    0.000000    0.000000  ...    0.078000   21.000000    0.000000\n",
            "25%      1.000000   99.000000   62.000000  ...    0.243750   24.000000    0.000000\n",
            "50%      3.000000  117.000000   72.000000  ...    0.372500   29.000000    0.000000\n",
            "75%      6.000000  140.250000   80.000000  ...    0.626250   41.000000    1.000000\n",
            "max     17.000000  199.000000  122.000000  ...    2.420000   81.000000    1.000000\n",
            "\n",
            "[8 rows x 9 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8IRdAKU9d_0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "71bcbe73-32c3-4285-d6f2-cfe6ea22f3bc"
      },
      "source": [
        "print(data_frame_url.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(768, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gy5wzfXw9jkm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "36386f85-c1db-4f52-b4c7-d93b100b613e"
      },
      "source": [
        "print(data_frame_url.info())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   preg    768 non-null    int64  \n",
            " 1   plas    768 non-null    int64  \n",
            " 2   pres    768 non-null    int64  \n",
            " 3   skin    768 non-null    int64  \n",
            " 4   test    768 non-null    int64  \n",
            " 5   mass    768 non-null    float64\n",
            " 6   pedi    768 non-null    float64\n",
            " 7   age     768 non-null    int64  \n",
            " 8   class   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4InhUMMw9qFt",
        "colab_type": "text"
      },
      "source": [
        "## Load CSV from local system"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1rL1yDj9tch",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_frame_local=pd.read_csv(\"Iris.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qk2TBMpj-2Md",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "2eb5fa2b-1c94-4d59-f039-9b41e2c31147"
      },
      "source": [
        "print(data_frame_local.head())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
            "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
            "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
            "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
            "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
            "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSNOu85g_D_A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "090a01fb-cb63-4764-9bb8-1733fbc8e6a8"
      },
      "source": [
        "print(data_frame_local.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(150, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SffRuscS_GNS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "b36482ec-a5f8-43b9-fbdb-f81e501bfdb3"
      },
      "source": [
        "print(data_frame_local.describe())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
            "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
            "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
            "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
            "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
            "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
            "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
            "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
            "max    150.000000       7.900000      4.400000       6.900000      2.500000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZeW0TarP_IzL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "070eecf4-0be1-4741-9d6c-ed290cc31d01"
      },
      "source": [
        "print(data_frame_local.info())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05gYZcmA_K_3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}