NumPy_Intro.ipynb

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      "name": "NumPy-Intro.ipynb",
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      "authorship_tag": "ABX9TyO88zeaISuBk/4XkGEn95Bo",
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      "name": "python"
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        "<a href=\"https://colab.research.google.com/github/OMER62/NumPy-Intro/blob/main/NumPy_Intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "![Numpy.png](data:image/png;base64,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)"
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      "cell_type": "markdown",
      "source": [
        "###<font color='#ff471a'>What is Numpy?</font>\n",
        "Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors."
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#ff471a'>3 Resaons Why to use NumPy:</font>\n",
        "\n",
        "\n",
        "1.   **Ndarray more fast than using the triditional python array object:**\n",
        "\n",
        "  NumPy aims to provide an array object that is up to 50x faster than traditional Python lists,by executed the ndarray.NumPy Faster Than Lists:they are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. Also it is optimized to work with latest CPU architectures.\n",
        "\n",
        "2.   **Data Science benefits**\n",
        "\n",
        "  Performing arithmetic functions on the elements and Storing data more compactly and effectively.\n",
        "\n",
        "3.   **Matlab:**\n",
        "\n",
        "  Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy.Using the Numpy library allows you to use Matlab methods.\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "###<font color='#009900'>Outlining:</font>\n",
        "  **A.How to Create Ndarray**\n",
        "\n",
        "  **B.Common NdArray DataType**\n",
        "\n",
        "  **C.Indexing & slicing Keywords and Syntax**\n",
        "\n",
        "  **D.NumPy Sorting Arrays**\n",
        "\n",
        "  **E.Common Functions**\n",
        "\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#**<font color='#ff471a'>A. Create NdArray:</font>**"
      ],
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      "source": [
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)"
      ],
      "metadata": {
        "id": "qvtz18nCXLKI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Install Numpuy Libary:\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "nlD62xEvIZ8o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>1.Create By using Python Array object</font>\n"
      ],
      "metadata": {
        "id": "QtNpKkmKLqya"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array((1, 2, 3, 4, 5))\n",
        "#OR\n",
        "arr2 = np.array([1, 2, 3, 4, 5])\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uH22BW2zLre1",
        "outputId": "aa925e4d-8f91-4db7-e1f2-ff1d2f4916cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>2.Creating Zero array</font>"
      ],
      "metadata": {
        "id": "bsD7h6vrRgts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.zeros(7, dtype=int)\n",
        "print (a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQE2KbHNRdoW",
        "outputId": "da659cc8-e919-47bb-c14c-8b010fb87633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>3.Creating an array of Ones(or another Number)</font>"
      ],
      "metadata": {
        "id": "4dsCMMlaTWNH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating an array that each cell has the 1 value\n",
        "np.ones((2,3), dtype=float)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NrhVdjLOTXW0",
        "outputId": "71b2d0b9-315c-4526-8808-962baf287c74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5., 5., 5.],\n",
              "       [5., 5., 5.]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating an array that each cell has the 5 value\n",
        "np.ones((3), dtype=int)*5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3reZYQuET3eB",
        "outputId": "da0e1a88-c9ea-4fa1-c72a-a3acfc3f92a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 5, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>4.Creating arrays containing number sequences</font>"
      ],
      "metadata": {
        "id": "_B26lv1PZCO5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.arange(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-gIDiUzZLNG",
        "outputId": "1a2fcc15-10d9-4dce-8c02-c25936290c70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.arange(2,7)"
      ],
      "metadata": {
        "id": "D2hvaEi6ZZ_d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# start, end, step\n",
        "np.arange(2,7,2) "
      ],
      "metadata": {
        "id": "7Hc52CXsZZ0c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>5.Creating array of random numbers</font>"
      ],
      "metadata": {
        "id": "aEmtciB3Zxos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.rand(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAner2iqZy_P",
        "outputId": "f070a7c6-e14f-4161-a042-9cc731f16ced"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.95013608, 0.79849996, 0.35956111, 0.41741043, 0.25135673])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#**<font color='#ff471a'>B.Common NdArray DataType:</font>**"
      ],
      "metadata": {
        "id": "R2yPOyozxOmm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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      ],
      "metadata": {
        "id": "JNvVJBTaPlzj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a list named \"a\"\n",
        "a = [1, 2, 3, 4, 5]\n",
        "# Creating a numpy array from the list and convert the type from int to float:\n",
        "b=np.array(a, dtype='float64')\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V45T-olyNoJI",
        "outputId": "c1a2c1a3-4c6a-43a0-af8b-3b495857db62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 2. 3. 4. 5.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a numpy array of string object(The type of string is object):\n",
        "c = np.array(['apples', 'foobar', 'cowboy'], dtype=object)\n",
        "print(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ne7zrSOyP-XI",
        "outputId": "b74c63da-4f86-4107-9837-6af96aa3af53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['apples' 'foobar' 'cowboy']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#By using the Keyword _.dtype we can identify the type of the array data.\n",
        "c.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiDh3sIySHbE",
        "outputId": "651a292e-9945-4884-d90e-01d6d10661ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remarks:\n",
        "\n",
        "*By using the keyword “dtype” we can to convert the type of the data in the cells "
      ],
      "metadata": {
        "id": "pXr04G4PN-zQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#**<font color='#ff471a'>C.Indexing & slicing Keywords and Syntax:</font>**"
      ],
      "metadata": {
        "id": "eUnlBBowe3vc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.diag(np.arange(3)) # Diagonal matrix\n",
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDLWQaosfMxz",
        "outputId": "95a48e79-5377-449b-b6ab-ab3633ef3e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0 0]\n",
            " [0 1 0]\n",
            " [0 0 2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>By using the - operator:</font>\n"
      ],
      "metadata": {
        "id": "I869pDXug-wt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For example, a[0:3] means the first 2 rows (rows 0 through 1 (include)) of a.\n",
        "a[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ok9BIbM-fW9i",
        "outputId": "9051d13a-a82f-4238-9291-4bfcdcaf3053"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [0, 1, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#For example, a[0:3] means the first 3 rows (rows 0 through 2) of a.\n",
        "a[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dk_2PbVwf4m9",
        "outputId": "07c2c5f9-76b3-43a0-9d1e-1471c11ca293"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [0, 1, 0],\n",
              "       [0, 0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>By using the negative operator</font>\n"
      ],
      "metadata": {
        "id": "yOWPXTlkhKUI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#For example, a[-1] means the last row of x.\n",
        "print(a[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwg_BzoSgMvO",
        "outputId": "2525604c-b94b-430e-e426-45cab19c959f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>By using the : operator</font>\n"
      ],
      "metadata": {
        "id": "wiXLeKXNhyLa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#For example, a[2:] means the rest of the rows+(include row 2) after row 2.\n",
        "a[2:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r9gaAwoGh5fA",
        "outputId": "d5694401-a668-49a4-f610-7a06a15ed822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Similarly, a[:2] means all the rows up to row 2 (not include row 2).\n",
        "a[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hhplv6pfiKTh",
        "outputId": "51c79067-352e-404c-8a63-40223dc2f461"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [0, 1, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#a[:] means all rows of a.\n",
        "a[:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYqNT5cdiLf4",
        "outputId": "8967f332-69b0-4234-9fa9-da63d8c35767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [0, 1, 0],\n",
              "       [0, 0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>A pattern that is important to remember:(Allowing to Subset an array)</font>\n",
        "\n",
        "###<font color='red'>array[start_row:end_row, start_col:end_col]</font>"
      ],
      "metadata": {
        "id": "fa2BZEhwjfI9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#index all rows of column 2.\n",
        "a[:,1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxpvTBs3j98T",
        "outputId": "d081dff7-0441-4a74-a680-0184566e284b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The following indexes the first row u up to (but not including) the 2th row,and the first column up to (but not including) the 2th column:\n",
        "a[0:2,0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Jf0V0jlkXD3",
        "outputId": "1164fd7f-8b82-4ce2-ba51-6230009e2692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0],\n",
              "       [0, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**<font color='#ff471a'>D.NumPy Sorting Arrays</font>**"
      ],
      "metadata": {
        "id": "NTEAfSTdleoz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>Basic sort:</font>\n"
      ],
      "metadata": {
        "id": "NrFNYJwdmfsk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([3, 2, 0, 1])\n",
        "\n",
        "print(np.sort(arr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--DgLKoGmksn",
        "outputId": "09f6aeb4-5361-4d94-d238-6b622057f943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>The sort Function:</font>\n",
        "\n",
        "###<font color='red'>numpy.sort(a, axis=- 1, kind=None, order=None)</font>\n",
        "\n",
        "  Input:\n",
        "*  A: The array to Sort\n",
        "*  Axis:Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. \n",
        "*   Kind: quicksort/ heapsort/ mergesort/ timesort\n",
        "*   order: When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string\n",
        "\n",
        "Output:\n",
        "\n",
        "\n",
        "*   Return a sorted copy of an array.\n",
        "\n"
      ],
      "metadata": {
        "id": "AQfR2nIBn3Ex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " a = np.array([[4, 3, 5], [1, 2, 1]])\t\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmuxbwLBp51Z",
        "outputId": "706cbfcd-7f91-4fad-9d03-c079ac6326e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4, 3, 5],\n",
              "       [1, 2, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
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      ],
      "metadata": {
        "id": "sFesvPrfrWAi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "  #axis=-1: returns an array,flat the array to one big array and sorted it!\n",
        " print(np.sort(a, axis=-1) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dggnnvT-qj9f",
        "outputId": "26396c9b-3af8-43c7-e5ce-149f2c0b6d2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3 4 5]\n",
            " [1 1 2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # axis=1:returns an array,sorted by rows!\n",
        " print(np.sort(a, axis=1) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqvoapIZqPTx",
        "outputId": "a4341ade-283e-497f-f8ac-d74be6bf9556"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3 4 5]\n",
            " [1 1 2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # axis=0:returns an array,sorted by columns!\n",
        " print(np.sort(a, axis=0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ODoHf5TrI-N",
        "outputId": "b63863b8-d280-483e-8e1a-bc97e4c27b46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 1]\n",
            " [4 3 5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**<font color='#ff471a'>E.Common Functions</font>**"
      ],
      "metadata": {
        "id": "-MhChXrssyc9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " a = np.array([[4, 3, 5], [1, 2, 1]])\t\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "706cbfcd-7f91-4fad-9d03-c079ac6326e1",
        "id": "xP2Z9MUMtX4P"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4, 3, 5],\n",
              "       [1, 2, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>Sum:</font>\n"
      ],
      "metadata": {
        "id": "-soj0lN7tB7D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Sum all Cells:\n",
        "a.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVhKA8v6tUfz",
        "outputId": "dd0b7599-9e76-4cfd-b19b-336293e4406d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sum by rows:\n",
        "a.sum(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "322bf65e-7441-4616-ab15-029fcda33bcb",
        "id": "qdFHsrePtlI1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12,  4])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sum by Columns:\n",
        "a.sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73078e49-badd-4ebd-87c5-24b8bfbeb636",
        "id": "rkRufM9ktvjL"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>Max:</font>\n"
      ],
      "metadata": {
        "id": "lW1BaA9Pt-Wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Sum by Columns:\n",
        "a.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0056e967-084e-4636-9272-c40cbf1d9fa1",
        "id": "JCpOcQlruETT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>Min:</font>\n"
      ],
      "metadata": {
        "id": "hBQPtsvbuVsY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Sum by Columns:\n",
        "a.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b48329c-66d3-4c3d-fa30-defa41634dc4",
        "id": "Ff_86x_quSTk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<font color='#0000e6'>Mean:</font>\n"
      ],
      "metadata": {
        "id": "ctcLvfdPufui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwO5HIzluktu",
        "outputId": "074c47ef-cb32-48c2-f3b1-752c7a62d886"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.6666666666666665"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    }
  ]
}