updated lists.py file

.vs/ProjectSettings.json

@@ -0,0 +1,3 @@
+{
+  "CurrentProjectSetting": null
+}

.vs/VSWorkspaceState.json

@@ -0,0 +1,6 @@
+{
+  "ExpandedNodes": [
+    ""
+  ],
+  "PreviewInSolutionExplorer": false
+}

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "githubPullRequests.ignoredPullRequestBranches": [
+        "main"
+    ]
+}

151353.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMUKGk7KxLXnEt/s+lWiczh",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DavidMoseNyamamba/python_intro/blob/main/151353.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install scikit-learn\n",
+        "!pip install numpy"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bk_XKdjp0aPe",
+        "outputId": "1f5c70d2-d0f2-47a0-f76f-deaad4849451"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
+            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.4)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.13.1)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "id": "HDfg_e1Nzsux",
+        "outputId": "782d279c-9431-456a-93a1-32c903693e94"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "UFuncTypeError",
+          "evalue": "ufunc 'subtract' did not contain a loop with signature matching types (dtype('<U21'), dtype('int64')) -> None",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mUFuncTypeError\u001b[0m                            Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-4-a6d104ef3c83>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;31m# predict the class of the new data point\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkNearestNeighbor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'The new data point belongs to class: {prediction}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-4-a6d104ef3c83>\u001b[0m in \u001b[0;36mkNearestNeighbor\u001b[0;34m(data, predict, k)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;31m# calculate the Euclidean distance between the new data point and each data point in the data set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mdistance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meuclidean_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0;31m# append the class label and distance to the predictions list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-4-a6d104ef3c83>\u001b[0m in \u001b[0;36meuclidean_distance\u001b[0;34m(x1, x2)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"\"\" x1 and x2 are two numpy arrays\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"\"\" return the Euclidean distance between x1 and x2\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mkNearestNeighbor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'subtract' did not contain a loop with signature matching types (dtype('<U21'), dtype('int64')) -> None"
+          ]
+        }
+      ],
+      "source": [
+        "def euclidean_distance(x1, x2):\n",
+        "    \"\"\" Calculate the Euclidean distance between two points\"\"\"\n",
+        "    \"\"\" x1 and x2 are two numpy arrays\"\"\"\n",
+        "    \"\"\" return the Euclidean distance between x1 and x2\"\"\"\n",
+        "    return np.sqrt(np.sum((x1 - x2) ** 2))\n",
+        "\n",
+        "def kNearestNeighbor(data, predict, k=3):\n",
+        "    \"\"\"predict the class of a new data point based on the k nearest neighbors\n",
+        "\n",
+        "       parameters:\n",
+        "         data(list): a list of lists, where each list is a data point\n",
+        "         predict(list): a list of the new data point\n",
+        "         k(int): the number of neighbors to consider\n",
+        "\n",
+        "         returns:\n",
+        "            int: the predicted class of the new data point\n",
+        "    \"\"\"\n",
+        "    predictions = []\n",
+        "    for row in data:\n",
+        "        # calculate the Euclidean distance between the new data point and each data point in the data set\n",
+        "        distance = euclidean_distance(row[:-1], predict)\n",
+        "        # append the class label and distance to the predictions list\n",
+        "        predictions.append((row[-1], distance))\n",
+        "\n",
+        "    # sort the predictions list by distance\n",
+        "    predictions = sorted(predictions, key=lambda x: x[1])\n",
+        "\n",
+        "    # select the k nearest neighbors\n",
+        "    top_k = predictions[:k]\n",
+        "\n",
+        "    # count the number of each class in the top k\n",
+        "    result = []\n",
+        "    for item in top_k:  # item is a tuple\n",
+        "        result.append(item[0])  # append the class label to the result list\n",
+        "\n",
+        "    # return the most common class label\n",
+        "    return max(set(result), key=result.count)\n",
+        "\n",
+        "data = np.array([\n",
+        "    [1, 4, 'A', 'D', 'Good'],\n",
+        "    [5, 9, 'E', 'I', 'Good'],\n",
+        "    [6, 7, 'F', 'G', 'Good'],\n",
+        "    [1, 1, 'A', 'A', 'Good'],\n",
+        "    [3, 8, 'C', 'H', 'Good'],\n",
+        "    [10, 4, 'J', 'D', 'Bad'],\n",
+        "    [5, 3, 'E', 'C', 'Bad'],\n",
+        "    [2, 3, 'B', 'C', 'Bad'],\n",
+        "    [6, 3, 'F', 'C', 'Bad'],\n",
+        "    [2, 8, 'B', 'H', 'Bad']\n",
+        "])\n",
+        "\n",
+        "# convert categorical data to numerical data\n",
+        "label_encoders = {}\n",
+        "for i in range(2, 4):\n",
+        "    le = LabelEncoder()\n",
+        "    data[:, i] = le.fit_transform(data[:, i])\n",
+        "    label_encoders[i] = le\n",
+        "\n",
+        "# separate features and labels\n",
+        "X = data[:, :-1]\n",
+        "y = data[:, -1]\n",
+        "\n",
+        "# new data point to classify\n",
+        "new_data = np.array([13, 7, 26, 25])  # x1=13, x2=7, x3=Z, x4=Z (Encoded as 26 and 25)\n",
+        "\n",
+        "# predict the class of the new data point\n",
+        "prediction = kNearestNeighbor(data, new_data, k=3)\n",
+        "print(f'The new data point belongs to class: {prediction}')\n",
+        "\n",
+        "# decode the predicted class back to the original label\n",
+        "predicted_label = label_encoders[3].inverse_transform([prediction])[0]\n",
+        "print(f'The new data point belongs to class: {predicted_label}')"
+      ]
+    }
+  ]
+}

151353_q1.py

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+#add new patient to a list
+#add_patient function prompts the user for the patient's name, age, and illness. 
+def add_patient(patients):
+    name = input("Enter patient name: ")
+    age = input("Enter patient age: ")
+    illness = input("Enter patient illness: ")
+ #create a dictionary to represent the patient and adds it to the patients list.
+    patient = {
+        "name": name,
+        "age": age,
+        "illness": illness
+    }
+    patients.append(patient)
+    print(f"Patient {name} added successfully.") 
+
+# display all patients
+#The display_patients function iterates through the patients list
+def display_patients(patients):
+    if not patients:
+        print("No patients found.")
+    else:
+        for patient in patients:
+#print the details of each patient.
+            print(f"Name: {patient['name']},Age: {patient['age']},Illness: {patient['illness']}")    
+
+
+#Define a function to search for a patient by name and display their details if found.
+#The search_patient function to prompt the user for the patient's name
+#searches through the patients list to find and display the details of the patient if found.
+
+def search_patient(patients):
+    name = input("Enter patient name to search: ")
+    found = False
+    for patient in patients:
+        # lower case name of patients
+        if patient['name'].lower() == name.lower():
+            print(f"Name: {patient['name']}, Age: {patient['age']}, Illness: {patient['illness']}")
+            found = True
+            break
+    if not found:
+        print(f"Patient {name} not found.")
+
+#Define a function to remove a patient by name
+#The remove_patient function to prompt the user for the patient's name
+def remove_patient(patients):
+    name = input("Enter patient name to remove: ")
+
+ #search through the patients list to find and remove the patient if found.
+    for patient in patients:
+        if patient['name'].lower() == name.lower():
+            patients.remove(patient)
+            print(f"Patient {name} removed successfully.")
+            return
+    print(f"Patient {name} not found.")
+
+#Use a while loop to keep the program running until the user chooses to exit
+# Demonstrating the functionality
+def main():
+    patients = []
+    while True:
+        print("\nHospital Patient Management System")
+        print("1. Add a new patient")
+        print("2. Display all patients")
+        print("3. Search for a patient by name")
+        print("4. Remove a patient by name")
+        print("5. Exit")
+
+        choice = input("Enter your choice: ")
+
+        if choice == '1':
+            add_patient(patients)
+        elif choice == '2':
+            display_patients(patients)
+        elif choice == '3':
+            search_patient(patients)
+        elif choice == '4':
+            remove_patient(patients)
+        elif choice == '5':
+            print("Exiting the program.")
+            break
+        else:
+            print("Invalid choice. Please try again.")
+
+if __name__ == "__main__":
+    main()

151353_q2.py

@@ -0,0 +1,112 @@
+#Create a Student class with attributes name and grades 
+# (a dictionary with subjects as keys and grades as values).
+class Student:
+# __init__(self, name): Initializes a student with a name and an empty dictionary for grades.
+    def __init__(self, name):
+        self.name = name
+        self.grades = {}
+  # add_grade(self, subject, grade): Adds a grade for a specific subject.
+    def add_grade(self, subject, grade):
+        self.grades[subject] = grade
+
+# get_average_grade(self): Calculates and returns the average grade for the student.
+
+    def get_average_grade(self):
+        if not self.grades:
+            return 0
+        return sum(self.grades.values()) / len(self.grades)
+
+
+#__init__(self): Initializes an empty list of students.
+class Classroom:
+    def __init__(self):
+        self.students = []
+
+#add_student(self, student): Adds a student to the classroom.
+    def add_student(self, student):
+        self.students.append(student)
+        print(f"Student {student.name} added successfully.")
+
+#display_students(self): Displays all students and their grades.
+    def display_students(self):
+        if not self.students:
+            print("No students in the class.")
+        else:
+            for student in self.students:
+                print(f"Name: {student.name}, Grades: {student.grades}")
+
+#get_student_average(self, student_name): Prints the average grade for a specific student by name.
+    def get_student_average(self, student_name):
+        for student in self.students:
+            if student.name.lower() == student_name.lower():
+                average = student.get_average_grade()
+                print(f"Average grade for {student.name}: {average:.2f}")
+                return
+        print(f"Student {student_name} not found.")
+#get_class_average_for_subject(self, subject): Prints the class average for a specific subject.
+    def get_class_average_for_subject(self, subject):
+        total = 0
+        count = 0
+        for student in self.students:
+            if subject in student.grades:
+                total += student.grades[subject]
+                count += 1
+        if count == 0:
+            print(f"No grades recorded for subject: {subject}")
+        else:
+            average = total / count
+            print(f"Class average for {subject}: {average:.2f}")
+
+# Demonstrating the functionality
+#using main() function:
+#Provides a simple command-line interface for interacting with the Classroom and Student classes.
+#Allows adding students,
+#  adding grades, 
+# displaying students,
+#  getting individual student averages, 
+# and getting class averages for subjects.
+def main():
+    classroom = Classroom()
+
+    while True:
+        print("\nClassroom Management System")
+        print("1. Add a new student")
+        print("2. Add grade for a student")
+        print("3. Display all students")
+        print("4. Get average grade of a student")
+        print("5. Get class average for a subject")
+        print("6. Exit")
+
+        choice = input("Enter your choice: ")
+
+        if choice == '1':
+            name = input("Enter student name: ")
+            student = Student(name)
+            classroom.add_student(student)
+        elif choice == '2':
+            name = input("Enter student name: ")
+            subject = input("Enter subject: ")
+            grade = float(input("Enter grade: "))
+            for student in classroom.students:
+                if student.name.lower() == name.lower():
+                    student.add_grade(subject, grade)
+                    print(f"Grade {grade} added for {subject} to student {name}.")
+                    break
+            else:
+                print(f"Student {name} not found.")
+        elif choice == '3':
+            classroom.display_students()
+        elif choice == '4':
+            name = input("Enter student name: ")
+            classroom.get_student_average(name)
+        elif choice == '5':
+            subject = input("Enter subject: ")
+            classroom.get_class_average_for_subject(subject)
+        elif choice == '6':
+            print("Exiting the program.")
+            break
+        else:
+            print("Invalid choice. Please try again.")
+
+if __name__ == "__main__":
+    main()