Session_1/(5) Numpy Notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Summer_School_Session2_Numpy.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Datasets- Datasets contain rows and columns tabulating data from experimental or observational facts. Tabular data can then be processed for purposes like machine learning. "
      ],
      "metadata": {
        "id": "7H95cYJsyufB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is what datasets look like. "
      ],
      "metadata": {
        "id": "poVBrLksxIXX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "jT2fLYY4jJwV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's extract the data in the csv file using the csv module in python"
      ],
      "metadata": {
        "id": "8tvuB_OUsMhU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2mcX2KeXO6ea"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "\n",
        "file = open('cereal.csv')\n",
        "type(file)\n",
        "csv_file_reader_object = csv.reader(file)\n",
        "\n",
        "# first we extract the column headings\n",
        "headings = []\n",
        "headings = next(csv_file_reader_object)\n",
        "print(headings)\n",
        "\n",
        "# now for the data \n",
        "rows = []\n",
        "for row in csv_file_reader_object:\n",
        "  rows.append(row)\n",
        "\n",
        "print(rows)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data extraction successful !"
      ],
      "metadata": {
        "id": "DxeXvIPTwMiH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Numpy is a very important, useful and famous library as it offers multi-dimensional array capabilities. Lists store values, numpy arrays have built in functions that we can use to manipulate the data en masse. "
      ],
      "metadata": {
        "id": "PWAlrTh-yEN3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let us turn our attention to using Numpy to manipulate the data. Remember that 'rows' contains our data and 'columns' contains the headings."
      ],
      "metadata": {
        "id": "ohj4_lUpyYKy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "\n",
        "# The data is in python lists. To use numpy we need to convert them into numpy arrays\n",
        "# To keep it simple for analysis lets use just some of this data\n",
        "\n",
        "rows_array = np.array(rows)\n",
        "\n",
        "print(\"The array - \\n\", rows_array)\n",
        "print(\"Dimension- \\n\", rows_array.ndim) #prints how many rows present\n",
        "print(\"Shape - \\n\" ,rows_array.shape) #prints the shape of the array in (rows, columns)\n"
      ],
      "metadata": {
        "id": "rssbLYnAtO9o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now lets see some of the arithmetic operations offered by numpy\n",
        "# first we will need the numerical columns in the dataset. Lets use calories and protein\n",
        "\n",
        "calories = rows_array[:,3] # column slicing\n",
        "protein = rows_array[:,4] # negative slicing\n",
        "\n",
        "calories = calories.astype(np.int64)\n",
        "protein = protein.astype(np.int64)\n",
        "\n",
        "print(\"Calories array - \\n\", calories)\n",
        "print(\"Protein array - \\n\", protein)"
      ],
      "metadata": {
        "id": "_-593c4N0Df4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lets find out more about the calories array\n",
        "print(\"Datatype of the calories array-\", calories.dtype) #prints the datatype of the array\n",
        "\n",
        "print(\"Item size -\", calories.itemsize) #prints the size of each item, we took int8, its 1 byte per item\n",
        "\n",
        "print(\"Net array size -\", calories.size) #prints net size of the array."
      ],
      "metadata": {
        "id": "rQZjRHBFl7f5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# how about if we want a particular element? \n",
        "\n",
        "print(rows_array[1,0])"
      ],
      "metadata": {
        "id": "E1Bc8ukv6F7x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now for operations. First lets square all elements in the second column \n",
        "# and find the sine value of elements in the third column\n",
        "\n",
        "calorie_squares = np.square(calories)\n",
        "protein_sines = np.sin(protein)\n",
        "calorie_squares_alternate = np.power(calories,2)\n",
        "\n",
        "print(\" Calories ^2 - \\n\", calorie_squares)\n",
        "print(\" sin(protein) - \\n\", protein_sines)\n",
        "print(\"Calories^2 alternate-\\n\", calorie_squares_alternate)"
      ],
      "metadata": {
        "id": "H9j716_B7BRn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lets see how the add, subtract and multiply commands work\n",
        "\n",
        "calories_plus_protein = np.add(calories,protein)\n",
        "calories_minus_protein = np.subtract(calories, protein)\n",
        "calories_times_calories = np.multiply(calories, calories)\n",
        "\n",
        "print(\"addition -\\n \", calories_plus_protein)\n",
        "print(\"subtraction -\\n \", calories_minus_protein)\n",
        "print(\"multiplication -\\n \", calories_times_calories) # compare this with calories^2 in the earlier cell"
      ],
      "metadata": {
        "id": "0k-ai-smp0M1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets try stacking "
      ],
      "metadata": {
        "id": "LNHA6qbb1f8_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For more clarity on this lets use an even smaller set of values from the \n",
        "# calories and protein values\n",
        "\n",
        "a = calories[0:3]\n",
        "b = protein[0:3]\n",
        "\n",
        "print(a)\n",
        "print(b)"
      ],
      "metadata": {
        "id": "DkUblEanqjja"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "horizontal = np.hstack((a,b))\n",
        "vertical = np.vstack((a,b))\n",
        "column = np.column_stack((a,b))\n",
        "\n",
        "print(\"horizontal stacking - \\n\", horizontal)\n",
        "print(\"vertical stacking - \\n\",vertical)\n",
        "print(\"stacking columns - \\n\",column)"
      ],
      "metadata": {
        "id": "SxQiyEz-rGj4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shape manipulation is an important part of numpy usage. Lets see how we can do that:"
      ],
      "metadata": {
        "id": "87BEwXDesTpv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lets reshape the vertical stack\n",
        "print(\"unravelled -\\n\", vertical.ravel())\n",
        "\n",
        "# reshape command-\n",
        "reshaped = vertical.reshape(3,2)\n",
        "print(\"after using reshape-\\n\", reshaped)\n",
        "print(\"original array-\\n\", vertical)\n",
        "\n",
        "#resize command:\n",
        "vertical.resize(3,2)\n",
        "print(\"original array has changed-\\n\", vertical)"
      ],
      "metadata": {
        "id": "i8ajNL0tsTD_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "An important difference between reshape and resize is highlighted here. Reshape return a new array leaving the old one unchanged while resize changes the original array. "
      ],
      "metadata": {
        "id": "hSrtvlBpyrQf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets now try out the ones and zeroes functions !"
      ],
      "metadata": {
        "id": "bWfm9U3byrM8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = np.zeros((2,3))\n",
        "j = np.full((2,3), 99)\n",
        "k = np.identity(3)\n",
        "print(\"All 0s-\\n\", i)\n",
        "print(\"All 99s-\\n\", j)\n",
        "print(\"A 3x3 identity matrix-\\n\", k)"
      ],
      "metadata": {
        "id": "1j0LTXkVK2Ch"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}