01_NumPy_Arrays.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Grundmane/Udemy_Python_Bootcamp/blob/main/01_NumPy_Arrays.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "___\n",
        "\n",
        "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
        "___"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_IHAKqbkPlJ-"
      },
      "source": [
        "# NumPy\n",
        "\n",
        "NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks.\n",
        "\n",
        "Numpy is also incredibly fast, as it has bindings to C libraries. For more info on why you would want to use Arrays instead of lists, check out this great [StackOverflow post](http://stackoverflow.com/questions/993984/why-numpy-instead-of-python-lists).\n",
        "\n",
        "We will only learn the basics of NumPy, to get started we need to install it!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bji2A272PlJ-"
      },
      "source": [
        "## Installation Instructions\n",
        "\n",
        "**It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries) all sync up with the use of a conda install. If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:**\n",
        "    \n",
        "    conda install numpy\n",
        "    \n",
        "**If you do not have Anaconda and can not install it, please refer to [Numpy's official documentation on various installation instructions.](http://docs.scipy.org/doc/numpy-1.10.1/user/install.html)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "voFVWY0LPlJ-"
      },
      "source": [
        "## Using NumPy\n",
        "\n",
        "Once you've installed NumPy you can import it as a library:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "id": "DsY7RKZOPlJ_"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HF5SzoYxPlJ_"
      },
      "source": [
        "Numpy has many built-in functions and capabilities. We won't cover them all but instead we will focus on some of the most important aspects of Numpy: vectors,arrays,matrices, and number generation. Let's start by discussing arrays.\n",
        "\n",
        "# Numpy Arrays\n",
        "\n",
        "NumPy arrays are the main way we will use Numpy throughout the course. Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column).\n",
        "\n",
        "Let's begin our introduction by exploring how to create NumPy arrays.\n",
        "\n",
        "## Creating NumPy Arrays\n",
        "\n",
        "### From a Python List\n",
        "\n",
        "We can create an array by directly converting a list or list of lists:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
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        "id": "HzTxPg6fPlJ_",
        "outputId": "4b5dc294-f22c-4f03-a5c6-a71461a05284"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "my_list = [1,2,3]\n",
        "my_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGN0gdYYPlKA",
        "outputId": "5df1917b-9ab4-4642-add7-5ae8e363c017"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "np.array(my_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAqlkZZpPlKA",
        "outputId": "452cdec9-d9bc-407a-a38a-8f8f147d3ff7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "my_matrix = [[1,2,3],[4,5,6],[7,8,9]]\n",
        "my_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unY0Fjj-PlKA",
        "outputId": "4343c840-138b-4830-f9a6-39b70c5e4926"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "np.array(my_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3ahjR7dPlKB"
      },
      "source": [
        "## Built-in Methods\n",
        "\n",
        "There are lots of built-in ways to generate Arrays"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLWEUMf4PlKB"
      },
      "source": [
        "### arange\n",
        "\n",
        "Return evenly spaced values within a given interval."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmGAg73SPlKB",
        "outputId": "daaffe59-5fa3-4ded-a574-f414b847f7fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "np.arange(0,10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "okQF5To6PlKB",
        "outputId": "d2cf3f59-3f09-4949-cfad-a847c44b3a9e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 3, 6, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "np.arange(0,11,3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcZU3OhTPlKB"
      },
      "source": [
        "### zeros and ones\n",
        "\n",
        "Generate arrays of zeros or ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyU79DgLPlKB",
        "outputId": "c6e09c56-ecb4-47cf-c6cb-053456b7a1d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "np.zeros(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YctIwYPwPlKC",
        "outputId": "14152e92-0726-4174-eaa5-226d653704ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "np.zeros((5,5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVUYfNKlPlKC",
        "outputId": "0e86744a-9dbe-44d8-8780-44a0a2fe7476"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "np.ones(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avU2ZWtjPlKC",
        "outputId": "65991442-fd44-462d-e932-355b4c8feb39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1.],\n",
              "       [1., 1., 1.],\n",
              "       [1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "np.ones((3,3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LFSZLZaPlKC"
      },
      "source": [
        "### linspace\n",
        "Return evenly spaced numbers over a specified interval."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BT-XQlEVPlKC",
        "outputId": "f225d248-1bd9-4162-99c4-b4a8e3596aad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  5., 10.])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "np.linspace(0,10,3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnapv5qdPlKC",
        "outputId": "aae420d7-180c-44d4-c4c5-583231a6a92a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  0.20408163,  0.40816327,  0.6122449 ,  0.81632653,\n",
              "        1.02040816,  1.2244898 ,  1.42857143,  1.63265306,  1.83673469,\n",
              "        2.04081633,  2.24489796,  2.44897959,  2.65306122,  2.85714286,\n",
              "        3.06122449,  3.26530612,  3.46938776,  3.67346939,  3.87755102,\n",
              "        4.08163265,  4.28571429,  4.48979592,  4.69387755,  4.89795918,\n",
              "        5.10204082,  5.30612245,  5.51020408,  5.71428571,  5.91836735,\n",
              "        6.12244898,  6.32653061,  6.53061224,  6.73469388,  6.93877551,\n",
              "        7.14285714,  7.34693878,  7.55102041,  7.75510204,  7.95918367,\n",
              "        8.16326531,  8.36734694,  8.57142857,  8.7755102 ,  8.97959184,\n",
              "        9.18367347,  9.3877551 ,  9.59183673,  9.79591837, 10.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "np.linspace(0,10,50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qeHejVNPlKD"
      },
      "source": [
        "## eye\n",
        "\n",
        "Creates an identity matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8d1uzIgoPlKD",
        "outputId": "67a87846-f16b-4a19-c150-ec4fd6c5c7ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "np.eye(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7eWwmkPPlKD"
      },
      "source": [
        "## Random\n",
        "\n",
        "Numpy also has lots of ways to create random number arrays:\n",
        "\n",
        "### rand\n",
        "Create an array of the given shape and populate it with\n",
        "random samples from a uniform distribution\n",
        "over ``[0, 1)``."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vg2GRd_UPlKD",
        "outputId": "5d520392-166b-4975-e204-e973232fd41b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.88442482, 0.00388058])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "np.random.rand(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bymqvaTPlKD",
        "outputId": "9f0d6e17-416e-4fb8-f66b-e85d9bab17cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.91523755, 0.44364956, 0.28020073, 0.16646082, 0.01284057],\n",
              "       [0.44958328, 0.28139091, 0.35208932, 0.0978535 , 0.01063343],\n",
              "       [0.79217933, 0.04631681, 0.66087021, 0.90612833, 0.45506069],\n",
              "       [0.78683693, 0.66203141, 0.75091078, 0.71345744, 0.84966294],\n",
              "       [0.50537031, 0.27400162, 0.15051435, 0.99977501, 0.60403603]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "np.random.rand(5,5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SWtNGLoDPlKD"
      },
      "source": [
        "### randn\n",
        "\n",
        "Return a sample (or samples) from the \"standard normal\" distribution. Unlike rand which is uniform:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yl21VePdPlKD",
        "outputId": "f7d936b3-2f65-40c4-baba-ac398005d2e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.9863399 , -2.48148602])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "np.random.randn(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vpckIYAbPlKD",
        "outputId": "dc1d67dd-4d3b-4ec3-b063-52c760aa5730"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.48389321,  0.70048411, -0.30659843,  0.99973917, -0.41401632],\n",
              "       [ 0.92354407, -1.70306294,  2.04714325, -1.03360691, -0.30965494],\n",
              "       [-0.56505016,  0.51550068,  0.49698572, -0.79971553, -0.6203657 ],\n",
              "       [ 0.81694817,  2.10217402,  1.64785192,  0.54638569, -1.00901862],\n",
              "       [ 0.12190281, -0.26481642, -0.45503854,  0.87362498,  1.61330194]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "np.random.randn(5,5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPevFvjcPlKE"
      },
      "source": [
        "### randint\n",
        "Return random integers from `low` (inclusive) to `high` (exclusive)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wbCuPRyPlKE",
        "outputId": "cab0ad5a-1391-4c8e-c78d-3b8fd22d5055"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "np.random.randint(1,100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHysKJ5UPlKE",
        "outputId": "9bb89baa-96a5-4a61-e4b4-d5b08d42dbe7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([97, 71, 32,  2, 53, 81, 32, 89, 52, 80])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "np.random.randint(1,100,10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KaKyRStWPlKE"
      },
      "source": [
        "## Array Attributes and Methods\n",
        "\n",
        "Let's discuss some useful attributes and methods or an array:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "collapsed": true,
        "id": "kRUt6Uj_PlKE"
      },
      "outputs": [],
      "source": [
        "arr = np.arange(25)\n",
        "ranarr = np.random.randint(0,50,10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shViuhHNPlKE",
        "outputId": "71295d3a-4e4f-4515-9a97-a6bfd9f5eccd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-K47OkHPlKE",
        "outputId": "f1421170-44b1-4000-d720-bbbe8c1ea08e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([25,  8, 12, 47, 20,  2, 18, 33, 15, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "ranarr"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ov5jKmPSPlKF"
      },
      "source": [
        "## Reshape\n",
        "Returns an array containing the same data with a new shape."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-8cySsKPlKF",
        "outputId": "312e7af1-29cb-490e-f04d-11b6bdab9838"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3,  4],\n",
              "       [ 5,  6,  7,  8,  9],\n",
              "       [10, 11, 12, 13, 14],\n",
              "       [15, 16, 17, 18, 19],\n",
              "       [20, 21, 22, 23, 24]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "arr.reshape(5,5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ce8gDTDrPlKF"
      },
      "source": [
        "### max,min,argmax,argmin\n",
        "\n",
        "These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSAo6Q-IPlKF",
        "outputId": "0562d1a5-15a3-4f20-d847-abe961a018d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([25,  8, 12, 47, 20,  2, 18, 33, 15, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "ranarr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSQv0YCSPlKF",
        "outputId": "e9a3ab68-969b-4699-d66f-246e2c01d812"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "ranarr.max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Se3pqGyMPlKF",
        "outputId": "9ef096c1-e4c7-48e1-96e7-03960fafbe45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "ranarr.argmax()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nslOLq7_PlKG",
        "outputId": "7600cb03-a0b4-4c25-8a7c-1e687159353b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "ranarr.min()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNd2apwbPlKG",
        "outputId": "ce019466-5054-41e1-fe09-533cc005d13a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "ranarr.argmin()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_3CdPjmPlKG"
      },
      "source": [
        "## Shape\n",
        "\n",
        "Shape is an attribute that arrays have (not a method):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1PgQZkYbPlKG",
        "outputId": "34062052-9b7b-4dc4-fb11-23949ac10455"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25,)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# Vector\n",
        "arr.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LC_Nkag4PlKG",
        "outputId": "8cfe0a1e-2072-4e07-802d-12ad1dc3039b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
              "        16, 17, 18, 19, 20, 21, 22, 23, 24]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Notice the two sets of brackets\n",
        "arr.reshape(1,25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUZqs9lyPlKG",
        "outputId": "02d63b4b-65c3-44dd-b590-0aa04df45749"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 25)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "arr.reshape(1,25).shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsUrVUP2PlKG",
        "outputId": "9834bd60-81a5-4bb5-95a3-020bbd3033d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0],\n",
              "       [ 1],\n",
              "       [ 2],\n",
              "       [ 3],\n",
              "       [ 4],\n",
              "       [ 5],\n",
              "       [ 6],\n",
              "       [ 7],\n",
              "       [ 8],\n",
              "       [ 9],\n",
              "       [10],\n",
              "       [11],\n",
              "       [12],\n",
              "       [13],\n",
              "       [14],\n",
              "       [15],\n",
              "       [16],\n",
              "       [17],\n",
              "       [18],\n",
              "       [19],\n",
              "       [20],\n",
              "       [21],\n",
              "       [22],\n",
              "       [23],\n",
              "       [24]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "arr.reshape(25,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IE2AYSLRPlKG",
        "outputId": "29d8d8ec-4fc2-4c87-fd87-d62c14e025e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "arr.reshape(25,1).shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQg9gpAlPlKH"
      },
      "source": [
        "### dtype\n",
        "\n",
        "You can also grab the data type of the object in the array:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYngMlVlPlKH",
        "outputId": "3e30c559-bed7-44cf-9d47-5d64c61bf361"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "arr.dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7DE2jlHPlKH"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.2"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}