3_Indexing.ipynb

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    "colab": {
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/theheking/intro-to-python/blob/gh-pages/3_Indexing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Indexing, Slicing and Subsetting\n",
        "Objectives: \n",
        "-    Understand how a dataframe is indexed\n",
        "-    Be able to take *slices* from a dataframe \n",
        "-    Subset a dataframe on certain conditions\n",
        "\n"
      ],
      "metadata": {
        "id": "bpM-okBxsO8K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make sure the `pandas` package is installed\n",
        "!pip install pandas matplotlib"
      ],
      "metadata": {
        "id": "QpgCVAqIscEx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading our data\n",
        "\n",
        "We will continue to use the patient dataset that we worked with in the last section."
      ],
      "metadata": {
        "id": "RpvxmaEIsg_P"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mmsgtI4IzBn"
      },
      "source": [
        "# Download a file using python\n",
        "import urllib.request # this is the library we need \n",
        "\n",
        "url = 'https://raw.githubusercontent.com/theheking/intro-to-python/gh-pages/docs/patient_data.csv'\n",
        "#retrieve the file\n",
        "\n",
        "urllib.request.urlretrieve(url, 'patient_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbr_HhYGMEVc"
      },
      "source": [
        "#import pandas as a package\n",
        "import pandas as pd \n",
        "\n",
        "#read in the patient data \n",
        "patient_df=pd.read_csv('patient_data.csv', sep=',')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Indexing and Slicing in Python\n",
        "\n",
        "We often want to work with subsets of a DataFrame object. There are different ways to accomplish this including: using labels (column headings), numeric ranges, or specific x,y index locations."
      ],
      "metadata": {
        "id": "Ou3qxi4-s1jM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# method 1- select a 'subset' of the data using the column name using []"
      ],
      "metadata": {
        "id": "vGC65SMHRO0z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# method 2- select a 'subset' of the data using the column name using . \n"
      ],
      "metadata": {
        "id": "j2ZYl9Y6ROpB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create a new object that contains only the data within the illness column"
      ],
      "metadata": {
        "id": "oAFMNKbQRvHH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create an object, surveys_species, that only contains illness and site id \n",
        "\n",
        "#what happens if you subset for a column that doesn't exist\n",
        "\n",
        "#what happens when you flip the order \n"
      ],
      "metadata": {
        "id": "Ul5JKjittSMf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Slicing Subsets of Rows in Python\n",
        "\n",
        "Slicing using the [  ] operator selects a set of rows and columns from a data frame. \n",
        "\n",
        "To slice out a bunch of rows, you use the following syntax: data[start:stop].\n",
        "\n",
        "When slicing in pandas, the start bound is included in the output. The stop bound is one step BEYOND the row you want to select. \n",
        " \n",
        "\n",
        "So if you want to select rows 0, 1 and 2 your code would look like this with our data:\n",
        "\n"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        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)\n",
        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)"
      ],
      "metadata": {
        "id": "tChjCO5KubPL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#form a list  a=[5,6,82,3,5,4]\n",
        "\n",
        "#subset from the first to third "
      ],
      "metadata": {
        "id": "a49ouuf6TPvr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select rows 0, 1, 2 (row 3 is not selected)"
      ],
      "metadata": {
        "id": "vcu5IvnYTzHW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#subset from the beginning row without using 0"
      ],
      "metadata": {
        "id": "8AdBNTLIv-WZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#subset the 500th row to last row"
      ],
      "metadata": {
        "id": "F_0sDFCxUHJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#subset the last record in the row  \n"
      ],
      "metadata": {
        "id": "0TjjwGcFwBlp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Slicing Subsets of Rows and Columns in Python\n",
        "\n",
        "We can select specific ranges of our data in both the row and column directions using either label or integer-based indexing.\n",
        "\n",
        "-    loc is primarily label based indexing. Integers may be used but they are interpreted as a label.\n",
        "-    iloc is primarily integer based indexing\n",
        "\n",
        "To select a subset of rows and columns from our DataFrame, we can use the iloc method. For example, we can select site_id, sex and time (columns 2, 3 and 4 if we start counting at 1), like this:\n",
        "\n",
        "<b> iloc[row slicing, column slicing] </b>"
      ],
      "metadata": {
        "id": "QzP5s-vKwJps"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#subset the first three rows (0:3) and second to fourth column (1:4) using iloc"
      ],
      "metadata": {
        "id": "gwv5oZ6HwSTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select all columns using : and only select the rows 0 and 10 using iloc"
      ],
      "metadata": {
        "id": "kBuPEe1mwa7e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#subset the 300th row with columns illness, site id and weight"
      ],
      "metadata": {
        "id": "a_labvpCVLsp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#is there a difference between running iloc and loc \n",
        "\n",
        "#NOTE: Labels must be found in the DataFrame or you will get a KeyError.\n"
      ],
      "metadata": {
        "id": "mATK8eoaVBDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#change the range patient_df.iloc[0:4, 1:4] - what is the difference"
      ],
      "metadata": {
        "id": "2U7vgdvTVvsl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Subsetting Data using Criteria\n",
        "\n",
        "We can also select a subset of our data using criteria. For example, we can select all rows that have a year value of 2002:"
      ],
      "metadata": {
        "id": "DUONnZUgxNyZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#subset all rows with months except 11 "
      ],
      "metadata": {
        "id": "hNizqFYaWx4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#extract all the entries between and equal to febuary and august patient_df[(patient_df.month >= 2) & (patient_df.month <= 8 )]"
      ],
      "metadata": {
        "id": "IShA0sFlxlLW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Python Syntax Cheat Sheet\n",
        "\n",
        "Use can use the syntax below when querying data by criteria from a DataFrame. Experiment with selecting various subsets of the \"patients\" data.\n",
        "\n",
        "-    Equals: ==\n",
        "-    Not equals: !=\n",
        "-    Greater than, less than: > or <\n",
        "-    Greater than or equal to >=\n",
        "-    Less than or equal to <=\n"
      ],
      "metadata": {
        "id": "OSUl6WzoxsAJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#find patients from 2022, before august and weighs less than 50 patient_df[(patient_df[\"year\"] == 2022) & (patient_df[\"month\"] <= 8) & (patient_df[\"weight\"] < 50)]"
      ],
      "metadata": {
        "id": "lj89DT7AXrUz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create a list that form listGoesHere\n",
        "\n",
        "#subset the illness in list using isin"
      ],
      "metadata": {
        "id": "Xvpm465vyB7r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHq8oDgnMa8b"
      },
      "source": [
        "#find the number of site ids using nunique\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQs3kfBlQ9M0"
      },
      "source": [
        "#print the subset patient_df for site number 2\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}