Day1_DhruvDhayal_AI.ipynb

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          "text": [
            "\n",
            " --------------- Factorial Program! ---------------\n",
            "\n",
            " 1. Enter the Number in order to find it's Factorial: 5\n",
            "\n",
            " --> Factorial of  5  is:  720\n"
          ]
        }
      ],
      "source": [
        "#Day-1 Of_AI_ML_SUMMER_TRAINING.\n",
        "print(\"\\n --------------- Factorial Program! ---------------\");\n",
        "n=int(input(\"\\n 1. Enter the Number in order to find it's Factorial: \"));\n",
        "fact=1;\n",
        "for i in range(1,n+1):\n",
        "  i=i+1;\n",
        "  fact=fact*i;\n",
        "print(\"\\n --> Factorial of \",n,\" is: \",fact);\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sum of the 'n' Numbers.\n",
        "print(\"\\n -------------- Sum of the Two or More Numbers! ------------------\");\n",
        "n=int(input(\"\\n 1. Enter the Number in order to find it's Sum: \"));\n",
        "sum=0;\n",
        "for i in range(1,n+1):\n",
        "  sum=sum+i;\n",
        "  i=i+1;\n",
        "  print(\"\\t\", sum);\n",
        "print(\"\\n --> Sum of \",n,\" is: \",sum);"
      ],
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          "text": [
            "\n",
            " -------------- Sum of the Two or More Numbers! ------------------\n",
            "\n",
            " 1. Enter the Number in order to find it's Sum: 10\n",
            "\t 1\n",
            "\t 3\n",
            "\t 6\n",
            "\t 10\n",
            "\t 15\n",
            "\t 21\n",
            "\t 28\n",
            "\t 36\n",
            "\t 45\n",
            "\t 55\n",
            "\n",
            " --> Sum of  10  is:  55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Adding of the Two Numbers.\n",
        "#1.  Manual Methods.\n",
        "a=10;\n",
        "b=90;\n",
        "c=(a+b);\n",
        "print(\"\\n Total Sum of \",a,\" and \",b,\" is: \",c);\n",
        "\n",
        "#2. User-Input Methods.\n",
        "a=int(input(\"\\n 1. Enter the First Number: \"));\n",
        "b=int(input(\"\\n 2. Enter the Second Number: \"));\n",
        "result=(a+b);\n",
        "print(f\"\\n\\t--> Total Sum of {a} and {b} is: {result}\");"
      ],
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          "text": [
            "\n",
            " Total Sum of  10  and  90  is:  100\n",
            "\n",
            " 1. Enter the First Number: 123\n",
            "\n",
            " 2. Enter the Second Number: 890\n",
            "\n",
            "\t--> Total Sum of 123 and 890 is: 1013\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Using of the While Loop, or never-ending loop.\n",
        "while(True):\n",
        "  print(\"Hello World\");  #We, did not print this because it takes lot of memory and space, which may lead to bann of GoogleVColabLisecing."
      ],
      "metadata": {
        "id": "kHtCytFonor_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#WAP a program to perform Arithmetic Operations (+,-,*,/), on the two no's entered by the user and perform operations on ot!\n",
        "#(Operations has to been perfored)!\n",
        "#Then, Ask the User a Question: Do, You Want to Continue?\n",
        "#If 'n' Exists then its still performing the operations based on the True Valid-Conditions.\n",
        "flag=True;\n",
        "while(flag):\n",
        "  n1=int(input(\"\\n 1. Enter the First Number: \"));\n",
        "  n2=int(input(\"\\n 2. Enter the Second Number: \"));\n",
        "  ch=input(\"\\n --> Enter the Operations You want to Perform: \\n 1. Addition(+) \\n2.Substraction(-) \\n3.Multiplication(*) \\n4.Division(/) \\n\\n\");\n",
        "\n",
        "  #Checking the Operation in order to performs.\n",
        "  if(ch=='+'):\n",
        "    n3=(n1+n2);\n",
        "    print(\"\\n\\n --> Addition of (\",n1,\"+\",n2,\") is = \",n3);\n",
        "  elif(ch=='-'):\n",
        "    n3=(n1-n2);\n",
        "    print(\"\\n --> Substraction of (\",n1,\"-\",n2,\") is = \",n3);\n",
        "  elif(ch==''):\n",
        "     n3=(n1*n2);\n",
        "     print(\"\\n --> Multiplication of (\",n1,\"*\",n2,\") is = \",n3);\n",
        "  elif(ch=='/'):\n",
        "    if(n1>n2):\n",
        "      n3=(n1/n2);\n",
        "      print(\"\\n --> Division of (\",n1,\"/\",n2,\") is = \",n3);\n",
        "    else:\n",
        "      n3=(n2/n1);\n",
        "      print(\"\\n --> Division of (\",n2,\"/\",n1,\") is = \",n3);\n",
        "  else:\n",
        "    print(\"\\n Invalid Input!\");\n",
        "\n",
        "\n",
        "#Main Driver Source Code Logic Of User-Input\n",
        "#print(\"\\n ----- Do, You Want to Still continiue Choose (Y/N): \");\n",
        "  user=input(\"\\n----- Do, You Want to Still continiue Choose (Y/N): \");\n",
        "  flag=0;\n",
        "  if(user==\"Y\" or user==\"y\"):\n",
        "    flag=True;\n",
        "    print(\"\\n Now, It's Again Executed!\");\n",
        "    print(\"\\n---------------------------------------\\n\");\n",
        "  else:\n",
        "    flag=False;\n",
        "    print(\"\\n Program Terminated Thank You!\");\n",
        "    print(\"\\n---------------------------------------\\n\");"
      ],
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      "execution_count": 15,
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          "text": [
            "\n",
            " 1. Enter the First Number: 120\n",
            "\n",
            " 2. Enter the Second Number: 78\n",
            "\n",
            " --> Enter the Operations You want to Perform: \n",
            " 1. Addition(+) \n",
            "2.Substraction(-) \n",
            "3.Multiplication(*) \n",
            "4.Division(/) \n",
            "\n",
            "+\n",
            "\n",
            "\n",
            " --> Addition of ( 120 + 78 ) is =  198\n",
            "\n",
            "----- Do, You Want to Still continiue Choose (Y/N): y\n",
            "\n",
            " Now, It's Again Executed!\n",
            "\n",
            "---------------------------------------\n",
            "\n",
            "\n",
            " 1. Enter the First Number: 1266\n",
            "\n",
            " 2. Enter the Second Number: 24\n",
            "\n",
            " --> Enter the Operations You want to Perform: \n",
            " 1. Addition(+) \n",
            "2.Substraction(-) \n",
            "3.Multiplication(*) \n",
            "4.Division(/) \n",
            "\n",
            "/\n",
            "\n",
            " --> Division of ( 1266 / 24 ) is =  52.75\n",
            "\n",
            "----- Do, You Want to Still continiue Choose (Y/N): n\n",
            "\n",
            " Program Terminated Thank You!\n",
            "\n",
            "---------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "#Performing the Same Logic Program with the Functions.\n",
        "#Starting Phase!\n",
        "\n",
        "def values():\n",
        "  no1=int(input(\"\\n 1. Enter the First Number: \"));\n",
        "  no2=int(input(\"\\n 2. Enter the Second Number: \"));\n",
        "  ch=input(\"\\n --> Enter the Operations You want to Perform: \\n 1. Addition(+) \\n2.Substraction(-) \\n3.Multiplication(*) \\n4.Division(/) \\n\\n\");\n",
        "  return no1,no2,ch;\n",
        "\n",
        "def operations(no1,no2,ch):\n",
        "  #Middle-Phase Performing Operations.\n",
        "  if(ch=='+'):\n",
        "    n3=(no1+no2);\n",
        "    print(\"\\n\\n --> Addition of (\",no1,\"+\",no2,\") is = \",n3);\n",
        "  elif(ch=='-'):\n",
        "    n3=(no1-no2);\n",
        "    print(\"\\n --> Substraction of (\",no1,\"-\",no2,\") is = \",n3);\n",
        "  elif(ch=='*'):\n",
        "    n3=(no1*no2);\n",
        "    print(\"\\n --> Multiplication of (\",no1,\"*\",no2,\") is = \",n3);\n",
        "  elif(ch=='/'):\n",
        "    if(no1>no2):\n",
        "      n3=(no1/no2);\n",
        "      print(\"\\n --> Division of (\",no1,\"/\",no2,\") is = \",n3);\n",
        "    else:\n",
        "      n3=(no2/no1);\n",
        "      print(\"\\n --> Division of (\",no2,\"/\",no1,\") is = \",n3);\n",
        "  else:\n",
        "    print(\"\\n Invalid Input!\");\n",
        "\n",
        "def checking():\n",
        "  #Main-Driver Source Code.\n",
        "  user=input(\"\\n --> Do, You Still Want to Continue? (Y/N): \");\n",
        "  if(user==\"Y\" or user==\"y\"):\n",
        "    return True;\n",
        "    print(\"\\n Now, It's Again Executed!\");\n",
        "    print(\"\\n---------------------------------------\\n\");\n",
        "  else:\n",
        "    return False;\n",
        "    print(\"\\n Program Terminated!\");\n",
        "    print(\"\\n---------------------------------------\\n\");\n",
        "\n",
        "#Main Calling the Values of the Functions!\n",
        "flag=True;\n",
        "while(flag):\n",
        "  no1,no2,ch=values();\n",
        "  operations(no1,no2,ch);\n",
        "  flag=checking();"
      ],
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      "execution_count": 17,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            " 1. Enter the First Number: 120\n",
            "\n",
            " 2. Enter the Second Number: 90\n",
            "\n",
            " --> Enter the Operations You want to Perform: \n",
            " 1. Addition(+) \n",
            "2.Substraction(-) \n",
            "3.Multiplication(*) \n",
            "4.Division(/) \n",
            "\n",
            "/\n",
            "\n",
            " --> Division of ( 120 / 90 ) is =  1.3333333333333333\n",
            "\n",
            " --> Do, You Still Want to Continue? (Y/N): y\n",
            "\n",
            " 1. Enter the First Number: 89\n",
            "\n",
            " 2. Enter the Second Number: 6\n",
            "\n",
            " --> Enter the Operations You want to Perform: \n",
            " 1. Addition(+) \n",
            "2.Substraction(-) \n",
            "3.Multiplication(*) \n",
            "4.Division(/) \n",
            "\n",
            "+\n",
            "\n",
            "\n",
            " --> Addition of ( 89 + 6 ) is =  95\n",
            "\n",
            " --> Do, You Still Want to Continue? (Y/N): n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import third party Library in Python Instance.\n",
        "#Command to install third_party_Library.\n",
        "#Pip Install Name of the Three Modules.\n",
        "\n",
        "!pip install numpy;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WmHciBBaxJem",
        "outputId": "72e65a45-1fbb-4ccd-e79b-4c64b493baf8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<!  Starting of the Numpy Library and its's Functions & Operations.>"
      ],
      "metadata": {
        "id": "msP6PWlczYdi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Now, Importing the Numpy & then perform all functions and it's operatiosns.\n",
        "\n",
        "import numpy as np\n",
        "V=np.array([1,2,3,4,5]);\n",
        "D=[1,2,3,4,5];\n",
        "print(\"\\n Values of \",V,\" and type of item it Contains: \",type(V));\n",
        "print(\"\\n Values of \",D,\" and type of item it Contains: \",type(D));"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 0
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      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values of  [1 2 3 4 5]  and type of item it Contains:  <class 'numpy.ndarray'>\n",
            "\n",
            " Values of  [1, 2, 3, 4, 5]  and type of item it Contains:  <class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the Dimensions of the Array.\n",
        "#NOTE: In, Python Numpy we used to call Dimensions as an axes!\n",
        "import numpy as np;\n",
        "check=np.array([[1,2] , [3,4] , [5,6],  [7,8]]);\n",
        "print(\"\\n Dimensions of the Array: \",check.ndim);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Hkc4zvx9z-wl",
        "outputId": "b81a3b7e-cbb4-4f59-af2f-044bc92cdbe4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Dimensions of the Array:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating the Total Size of an Array!\n",
        "#NOTE: Both, shape() & size() is similar to find the total length of item in any dimension of numpy array!\n",
        "import numpy as np;\n",
        "checking=np.array([[[1,2,3,4], [4,5,6,7], [6,7,8,9], [3,44,56,100]]]);\n",
        "\n",
        "#Checking the Dimensions of the Matrix.\n",
        "print(\"\\n 1. Dimension of the Matrix is: \",checking.ndim);\n",
        "print(\"\\n 3. Size of the Matrix are: \",checking.size);\n",
        "print(\"\\n 2. Total Axes of the Matrix are: \",checking.shape);"
      ],
      "metadata": {
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          "height": 0
        },
        "id": "DcvXK1jv0eHA",
        "outputId": "758e329f-da58-409a-9f1f-713122fb426c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 1. Dimension of the Matrix is:  3\n",
            "\n",
            " 3. Size of the Matrix are:  16\n",
            "\n",
            " 2. Total Axes of the Matrix are:  (1, 4, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#chekcing the functions like data, itemsize, dtype.\n",
        "import numpy as np;\n",
        "checking=np.array([[1,2,3,4], [4,5,6,7], [6,7,8,9], [3,44,56,100]]);\n",
        "\n",
        "#Performing the Operations in the Matrix.\n",
        "print(\"\\n 1. Size of the Matrix: \",checking.shape);\n",
        "print(\"\\n 2. Item-Size in the matrix: \",checking.itemsize);\n",
        "print(\"\\n 3. Checking the Data-type of item presented in Matrix are: \",checking.dtype);\n",
        "print(\"\\n 4. Checking the Data-type of item presented in Matrix are: \",checking.dtype.itemsize);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pbSktuXi1sRe",
        "outputId": "8f18ff3e-c8a6-4b74-a048-6433ad7bcd67"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 1. Size of the Matrix:  (4, 4)\n",
            "\n",
            " 2. Item-Size in the matrix:  8\n",
            "\n",
            " 3. Checking the Data-type of item presented in Matrix are:  int64\n",
            "\n",
            " 4. Checking the Data-type of item presented in Matrix are:  8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Arange() functions simply gives you the initial-ending point values within the Ranges.\n",
        "import numpy as np;\n",
        "a=np.arange(10);\n",
        "print(\"\\n Values: \",a);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Kcmb7ogF2lxf",
        "outputId": "372880c8-6cf9-4a12-ba1e-7cf2ab10544b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values:  [0 1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Some Functions based on Arange() Functions.\n",
        "import numpy as np;\n",
        "aa=np.arange(1,10,2);   #(START | END | STEP-SIZE)!\n",
        "print(\"\\n The Values of aa: \",aa);\n",
        "\n",
        "#We, will also using floating points values within the Arange Functions.\n",
        "b=np.arange(1,10,0.5);   #(START | END | STEP-SIZE)!\n",
        "print(\"\\n Hence, Values of b: \",b);\n",
        "\n",
        "#Total Size of the Array will be Calculated!\n",
        "c=np.arange(1,10,0.2);   #(START | END | STEP-SIZE)!\n",
        "print(\"\\n Values of c: \",c,\" and size is: \",c.size,\" Similar in terms with the shape(): \",c.shape);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IIH2QV1a27W7",
        "outputId": "7ffa1015-4246-4c98-9c6c-5b5d7b623d38"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " The Values of aa:  [1 3 5 7 9]\n",
            "\n",
            " Hence, Values of b:  [1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5 6.  6.5 7.  7.5 8.  8.5 9.  9.5]\n",
            "\n",
            " Values of c:  [1.  1.2 1.4 1.6 1.8 2.  2.2 2.4 2.6 2.8 3.  3.2 3.4 3.6 3.8 4.  4.2 4.4\n",
            " 4.6 4.8 5.  5.2 5.4 5.6 5.8 6.  6.2 6.4 6.6 6.8 7.  7.2 7.4 7.6 7.8 8.\n",
            " 8.2 8.4 8.6 8.8 9.  9.2 9.4 9.6 9.8]  and size is:  45  Similar in terms with the shape():  (45,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The type of the array can also be explicitly specified at creation time:\n",
        "import numpy as np;\n",
        "d=np.array([11,24.4,89.7,6.35], dtype=\"complex\");\n",
        "print(\"\\n Values of the d: \",d);\n",
        "c=np.array([45,56.9,8.1,23,34,5], dtype=\"float\");\n",
        "print(\"\\n Vaues of c: \",c);\n",
        "e=np.array([45.1,56.9,8.1,2.93,3.4,5.567], dtype=\"int\");\n",
        "print(\"\\n Vaues of e: \",e);"
      ],
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        },
        "id": "THWf159g4OSC",
        "outputId": "4a54b161-eec0-4368-df36-4da6b924ecd3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values of the d:  [11.  +0.j 24.4 +0.j 89.7 +0.j  6.35+0.j]\n",
            "\n",
            " Vaues of c:  [45.  56.9  8.1 23.  34.   5. ]\n",
            "\n",
            " Vaues of e:  [45 56  8  2  3  5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Putting all Zeros and ones in numpy array.\n",
        "import numpy as np;\n",
        "a=np.zeros(10);\n",
        "print(\"\\n Values are: \",a);\n",
        "c=np.zeros((3,4));\n",
        "print(\"\\n Values of Matrix are: \",c);\n",
        "\n",
        "#Putting Values in form of Ones.\n",
        "b=np.ones((2,3,4));\n",
        "print(\"\\n Matrix: \",b);\n",
        "e=np.ones((2,3), dtype=\"complex\");\n",
        "print(\"\\n Complex-Matrix: \",e);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "T7busgFM5Omd",
        "outputId": "e9459ff3-c5e7-48d2-cf92-7935073dadb8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values are:  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "\n",
            " Values of Matrix are:  [[0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]]\n",
            "\n",
            " Matrix:  [[[1. 1. 1. 1.]\n",
            "  [1. 1. 1. 1.]\n",
            "  [1. 1. 1. 1.]]\n",
            "\n",
            " [[1. 1. 1. 1.]\n",
            "  [1. 1. 1. 1.]\n",
            "  [1. 1. 1. 1.]]]\n",
            "\n",
            " Complex-Matrix:  [[1.+0.j 1.+0.j 1.+0.j]\n",
            " [1.+0.j 1.+0.j 1.+0.j]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Conversion of lst in the form of an Array!\n",
        "import numpy as np;\n",
        "p=np.array([1,23,4,5]);\n",
        "q=[67,89,90,12,2,34];\n",
        "#Convert(list-form into an array!);\n",
        "r=np.array(q);\n",
        "print(\"\\n Values of r: \",r);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Vt5Cv3GF66QM",
        "outputId": "2a15353c-c7bd-48b9-cc13-edcdc4bd8ac6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values of r:  [67 89 90 12  2 34]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Arange Functions is used to give data values within the range but not-precise & Accurate takes mre time in calculation for complex data-sets.\n",
        "#The Linespace() function is used in place of arange() values.\n",
        "# linespace() gives correct & accurate results data-sets.\n",
        "import numpy as np;\n",
        "a=np.linspace(1,10,9);\n",
        "print(\"\\n Values of a: \",a);\n",
        "print(\"\\n Total Size of data from linespace are: \",a.size);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "K8jxPJxR7sgF",
        "outputId": "147ff63a-c975-4414-d6f0-ce7ea235a64a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values of a:  [ 1.     2.125  3.25   4.375  5.5    6.625  7.75   8.875 10.   ]\n",
            "\n",
            " Total Size of data from linespace are:  9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Printing of an Arrays.\n",
        "a=np.arange(15);\n",
        "print(\"\\n 1. Arrange Values: \",a);\n",
        "\n",
        "b=np.arange(1,10,0.5); #(START | END | STEP-SIZE);\n",
        "print(\"\\n 2. Values of b: \",b);\n",
        "\n",
        "c=np.array([[12,23,34,54],[34,44,56,67]], dtype=\"complex\");\n",
        "print(\"\\n 3. Array of c: \",c);\n",
        "\n",
        "d=np.arange(12).reshape(3,4);\n",
        "print(\"\\n 4. Reshaping of d: \",d);\n",
        "\n",
        "e=np.arange(24).reshape(2,3,4);\n",
        "print(\"\\n 5. Reshaping values of e: \",e);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hcrg-f_2_FhB",
        "outputId": "e3a338c2-dfb6-4730-f075-7f5a08e5d9b1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 1. Arrange Values:  [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
            "\n",
            " 2. Values of b:  [1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5 6.  6.5 7.  7.5 8.  8.5 9.  9.5]\n",
            "\n",
            " 3. Array of c:  [[12.+0.j 23.+0.j 34.+0.j 54.+0.j]\n",
            " [34.+0.j 44.+0.j 56.+0.j 67.+0.j]]\n",
            "\n",
            " 4. Reshaping of d:  [[ 0  1  2  3]\n",
            " [ 4  5  6  7]\n",
            " [ 8  9 10 11]]\n",
            "\n",
            " 5. Reshaping values of e:  [[[ 0  1  2  3]\n",
            "  [ 4  5  6  7]\n",
            "  [ 8  9 10 11]]\n",
            "\n",
            " [[12 13 14 15]\n",
            "  [16 17 18 19]\n",
            "  [20 21 22 23]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#NOTE: If, the Values too much greater then numpy automatically skips the central data and show only corners-Values.\n",
        "a=np.arange(10000);\n",
        "print(\"\\n Values of a: \",a);   #Larger Values it skip central data without showing corner-data has been visible.\n",
        "\n",
        "b=np.arange(100).reshape(10,10);\n",
        "print(\"\\n Values of b: \",b);   #All Value visible without skip because data-set avilable in smaller-size.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "URoYq4sZCeoF",
        "outputId": "e3481a21-5d2d-4066-e7d3-cf4d8950a2a8"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values of a:  [   0    1    2 ... 9997 9998 9999]\n",
            "\n",
            " Values of b:  [[ 0  1  2  3  4  5  6  7  8  9]\n",
            " [10 11 12 13 14 15 16 17 18 19]\n",
            " [20 21 22 23 24 25 26 27 28 29]\n",
            " [30 31 32 33 34 35 36 37 38 39]\n",
            " [40 41 42 43 44 45 46 47 48 49]\n",
            " [50 51 52 53 54 55 56 57 58 59]\n",
            " [60 61 62 63 64 65 66 67 68 69]\n",
            " [70 71 72 73 74 75 76 77 78 79]\n",
            " [80 81 82 83 84 85 86 87 88 89]\n",
            " [90 91 92 93 94 95 96 97 98 99]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Basic Operations In Numpy.\n",
        "#Random Values Generations.\n",
        "import numpy as np\n",
        "a=np.random.random((2,3));\n",
        "print(\"\\n Values of a: \",a);\n",
        "\n",
        "#After, every execution it can generates random values.\n",
        "\n",
        "#We, will talk about 'seed()' part of the random functions.\n",
        "b=np.random.seed((1,50,2));\n",
        "print(\"\\n Values of b: \",b);\n",
        "\n",
        "#generating random values and then finding the values like: max() & min().\n",
        "import numpy as np;\n",
        "e=np.random.random((2,3));\n",
        "print(\"\\n\",e);\n",
        "print(\"\\n Maximum Values: \",e.max());\n",
        "print(\"\\n Minimum Values: \",e.min());\n",
        "print(\"\\n Total Sum is: \",e.sum());"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JIWuHCzEDMtC",
        "outputId": "8dafed38-bd0a-4d07-de62-8e786a5ebd64"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values of a:  [[0.59221332 0.50408614 0.22183998]\n",
            " [0.38476062 0.13912162 0.9979631 ]]\n",
            "\n",
            " Values of b:  None\n",
            "\n",
            " [[0.45666348 0.35765781 0.80253672]\n",
            " [0.38600085 0.97943691 0.16054335]]\n",
            "\n",
            " Maximum Values:  0.9794369113257708\n",
            "\n",
            " Minimum Values:  0.16054335039498624\n",
            "\n",
            " Total Sum is:  3.14283912438308\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Integer Number Generations.\n",
        "import numpy as np;\n",
        "no1=np.random.randint(10,50,size=(50));\n",
        "print(\"\\n Values of no1: \",no1);\n",
        "print(\"\\n Total Size of an Arrany: \",no1.size);\n",
        "\n",
        "#Generating Random Integer Numbers."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VaGOLKDVIExD",
        "outputId": "263bbf3d-456a-47a7-e63b-113bf13fb519"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Values of no1:  [30 11 36 44 37 25 14 16 28 37 48 15 14 30 39 24 44 11 33 34 34 12 18 26\n",
            " 44 28 39 23 48 45 24 48 46 41 25 23 26 15 22 14 28 18 41 16 14 13 14 26\n",
            " 49 49]\n",
            "\n",
            " Total Size of an Arrany:  50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# few more numpy functions\n",
        "\n",
        "d = np.zeros((10))\n",
        "print(d)\n",
        "r = np.ones((10))*2\n",
        "print(r)\n",
        "\n",
        "g = np.full((10),100)\n",
        "print(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RlX1-lRoIp3_",
        "outputId": "9a9f7a93-406e-4bf7-a185-b70e779db812"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "[2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]\n",
            "[100 100 100 100 100 100 100 100 100 100]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# statistical function\n",
        "np.random.seed(10)\n",
        "var2 = np.random.randint(10,100,(50))\n",
        "\n",
        "s = var2.sum()\n",
        "t = var2.min()\n",
        "m = var2.max()\n",
        "max_ind = var2.argmax()\n",
        "min_ind = var2.argmin()\n",
        "avg = var2.mean()\n",
        "print(var2)\n",
        "print()\n",
        "print(\"min: \",t,'Pos:',min_ind)\n",
        "print(\"max: \",m,'Pos:',max_ind)\n",
        "print(\"average: \",avg)\n",
        "print(\"sum of all the elements: \",s )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gK1mNAHeJgyM",
        "outputId": "53bf4629-1eba-43ad-dc5a-5adbad39d56f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[19 25 74 38 99 39 18 83 10 50 46 26 21 64 98 72 43 82 88 59 61 64 87 79\n",
            " 23 35 23 96 40 40 99 22 75 41 67 46 37 28 87 32 33 21 38 84 98 19 25 28\n",
            " 90 81]\n",
            "\n",
            "min:  10 Pos: 8\n",
            "max:  99 Pos: 4\n",
            "average:  53.06\n",
            "sum of all the elements:  2653\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Userlibraries and custom libraries as well!\n",
        "import matplotlib.pyplot as plt;\n",
        "import numpy as np;\n",
        "\n",
        "X=np.linspace(1,20,100);\n",
        "Y=np.sin(X);\n",
        "Z=np.cos(X);\n",
        "W=2*np.sin(X)+np.cos(X);\n",
        "P=np.sin(2*X)-2*np.cos(X);\n",
        "plt.figure(1,figsize=(5,2));\n",
        "plt.plot(X,Y,'r',linewidth='4');\n",
        "plt.title(\"Clock-Pendulum Motion!\");\n",
        "plt.xlabel(\"Sin-Functions\");\n",
        "plt.ylabel(\"Cos-Functions\");\n",
        "plt.grid(\"on\");\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "toT4_BFrP5f-",
        "outputId": "1f2aa27f-286d-4601-9786-fb0654811049"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt;\n",
        "import numpy as np;\n",
        "\n",
        "#We, takes the Values of the Data-sets Conditions.\n",
        "X=np.linspace(1,20,100);\n",
        "Y=np.sin(X);\n",
        "Z=np.cos(X);\n",
        "W=2*np.sin(X)+np.cos(X);\n",
        "P=np.sin(2*X)-2*np.cos(X);\n",
        "plt.figure(1,figsize=(5,2));\n",
        "plt.plot(X,Y,'m',linewidth='4');\n",
        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
        "plt.xlabel(\"Time\");\n",
        "plt.ylabel(\"Speed\");\n",
        "plt.grid(\"on\");\n",
        "\n",
        "#Another Graphical Manner!\n",
        "plt.figure(1,figsize=(5,2));\n",
        "plt.plot(X,Z,'c',linewidth='4');\n",
        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
        "plt.xlabel(\"Time\");\n",
        "plt.ylabel(\"Speed\");\n",
        "plt.grid(\"on\");\n",
        "\n",
        "#3rd Graphical Forms!\n",
        "plt.figure(1,figsize=(5,2));\n",
        "plt.plot(X,P,'r',linewidth='4');\n",
        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
        "plt.xlabel(\"Time\");\n",
        "plt.ylabel(\"Speed\");\n",
        "plt.grid(\"on\");\n",
        "\n",
        "#4th Graphical Forms!\n",
        "plt.figure(1,figsize=(5,2));\n",
        "plt.plot(X,W,'b',linewidth='4');\n",
        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
        "plt.xlabel(\"Time\");\n",
        "plt.ylabel(\"Speed\");\n",
        "plt.grid(\"on\");\n",
        "\n",
        "#5th Graphical Forms!\n",
        "plt.figure(1,figsize=(5,2));\n",
        "plt.plot(X,Y,'d',linewidth='4');\n",
        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
        "plt.xlabel(\"Time\");\n",
        "plt.ylabel(\"Speed\");\n",
        "plt.legend();\n",
        "plt.grid(\"on\");\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "tpLxMeUFRmFp",
        "outputId": "1d587b05-5e13-4e65-90e0-cf2084130f2c"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate a Graph with (Acceleration - Velocity Time Graph!).\n",
        "import matplotlib.pyplot as plt;\n",
        "import numpy as np;\n",
        "\n",
        "#Putting & Generating the Values of the Data.\n",
        "X=np.linspace(1,50,100);\n",
        "Y=np.sin(X);\n",
        "Z=np.cos(X);\n",
        "W=2*np.sin(X)+np.cos(X);\n",
        "P=np.sin(2*X)-2*np.cos(X);\n",
        "\n",
        "#Designing the Figure suitable for the Human-Interface!\n",
        "plt.figure(1,figsize=(10,4),label=\"Sin(X)\");\n",
        "plt.plot(X,Y,\"r\",linewidth='4',label=\"Sin(X)\");\n",
        "plt.plot(X,Z,\"b\",linewidth='4',label=\"Cos(X)\");\n",
        "plt.plot(X,W,\"g\",linewidth='4',label=\"Func1(X)\");\n",
        "plt.plot(X,P,\"m\",linewidth='4',label=\"Func2(X)\");\n",
        "\n",
        "#Finalizing & Designing , Mentioning the Details related to the Graph!\n",
        "plt.title(\"Velocity/Acceleraation (Time-Graph)\");\n",
        "plt.xlabel(\"Velocity\");\n",
        "plt.ylabel(\"Acceleration\");\n",
        "plt.legend();\n",
        "plt.grid(\"on\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "n9DY80THT8pa",
        "outputId": "b1c2c3f0-7f50-4618-8ad2-d0e99054698f"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}