Fix: completed basics and intermediate exercises

SOLUTIONS/sanskarcoder29_solutions/test_playground/advanced/boss.py

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+"""Advanced capstone: Tkinter app with three ML windows."""
+
+from __future__ import annotations
+
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+from sklearn.metrics import mean_squared_error
+
+ASSETS = Path(__file__).resolve().parent.parent / "assets"
+
+
+def quick_shape(df: pd.DataFrame) -> tuple[int, int]:
+    """Return (rows, columns)."""
+    return (len(df), len(df.columns))
+
+
+def regression_rmse(y_true, y_pred) -> float:
+    """Return RMSE for regression predictions."""
+    return np.sqrt(mean_squared_error(y_true, y_pred))

SOLUTIONS/sanskarcoder29_solutions/test_playground/advanced/feature_engineering_pandas.py

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+"""Practice feature engineering workflow with pandas."""
+
+from __future__ import annotations
+
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+ASSETS = Path(__file__).resolve().parent.parent / "assets"
+DEFAULT_PATH = ASSETS / "ml_classification.csv"
+
+
+def load_or_create_dataset(path: str = str(DEFAULT_PATH), n_rows: int = 250) -> pd.DataFrame:
+
+    file = Path(path)
+
+    if file.exists():
+        df = pd.read_csv(file)
+
+    else:
+        rng = np.random.default_rng(7)
+
+        df = pd.DataFrame({
+            "age": rng.integers(21, 60, size=n_rows),
+            "income": rng.normal(85000, 20000, size=n_rows).round(2),
+            "experience_years": rng.integers(0, 20, size=n_rows),
+            "city": rng.choice(["Delhi", "Pune", "Chennai", "Kolkata"], size=n_rows),
+            "education": rng.choice(["UG", "PG", "PhD"], size=n_rows),
+        })
+
+    return df

SOLUTIONS/sanskarcoder29_solutions/test_playground/advanced/linalg_numpy.py

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+"""Practice NumPy linear algebra routines."""
+
+from __future__ import annotations
+import numpy as np
+
+
+def eigen_decomposition(A):
+    """Return eigenvalues and eigenvectors."""
+    arr = np.array(A, dtype=float)
+    vals, vecs = np.linalg.eig(arr)
+    return vals, vecs
+
+
+def solve_linear_system(A, b):
+    """Solve Ax = b."""
+    arr = np.array(A, dtype=float)
+    vec = np.array(b, dtype=float)
+    return np.linalg.solve(arr, vec)

SOLUTIONS/sanskarcoder29_solutions/test_playground/advanced/matplotlib_plots.py

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+"""Practice Matplotlib plotting patterns."""
+
+from __future__ import annotations
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def generate_data(seed: int = 42, n: int = 120):
+
+    rng = np.random.default_rng(seed)
+
+    x = np.linspace(0, 10, n)
+    y = 2.0 * x + rng.normal(0, 2.5, size=n)
+
+    return x, y
+
+
+def line_and_scatter(ax, x, y):
+
+    ax.plot(x, y, color="steelblue", linewidth=2, label="line")
+
+    ax.scatter(x, y, s=18, color="tomato", alpha=0.7, label="points")
+
+    ax.set_title("Line + Scatter")
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+
+    ax.legend()

SOLUTIONS/sanskarcoder29_solutions/test_playground/advanced/numpy_basics.py

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+"""Practice NumPy operations."""
+
+from __future__ import annotations
+import numpy as np
+
+
+def make_basic_arrays():
+
+    arr_1d = np.array([1, 2, 3, 4, 5], dtype=float)
+
+    arr_2d = np.array([[1, 2, 3], [4, 5, 6]], dtype=float)
+
+    zeros = np.zeros((2, 3))
+
+    ones = np.ones((2, 3))
+
+    eye = np.eye(3)
+
+    seq = np.arange(1, 10, 2)
+
+    return {
+        "arr_1d": arr_1d,
+        "arr_2d": arr_2d,
+        "zeros": zeros,
+        "ones": ones,
+        "eye": eye,
+        "seq": seq,
+    }
+
+
+def describe_array(a):
+
+    arr = np.array(a)
+
+    return {
+        "shape": arr.shape,
+        "ndim": arr.ndim,
+        "size": arr.size,
+        "dtype": str(arr.dtype),
+    }

SOLUTIONS/sanskarcoder29_solutions/test_playground/advanced/pandas_basics.py

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+"""Practice pandas operations."""
+
+from __future__ import annotations
+
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+
+ASSETS = Path(__file__).resolve().parent.parent / "assets"
+DEFAULT_PATH = ASSETS / "students.csv"
+
+
+def load_dataset(path: str = str(DEFAULT_PATH)) -> pd.DataFrame:
+
+    file = Path(path)
+
+    if file.exists():
+        df = pd.read_csv(file)
+
+    else:
+
+        rng = np.random.default_rng(42)
+
+        df = pd.DataFrame({
+            "student_id": [f"S{i:03d}" for i in range(1, 13)],
+            "name": [f"Student{i}" for i in range(1, 13)],
+            "department": rng.choice(["CSE", "ECE", "ME"], size=12),
+            "score": rng.integers(55, 100, size=12),
+            "attendance": rng.integers(65, 100, size=12),
+            "grade": rng.choice(["A", "B", "C", "D"], size=12)
+        })
+
+    return df

SOLUTIONS/sanskarcoder29_solutions/test_playground/advanced/sklearn_stuff.py

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+"""Practice sklearn pipelines."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from sklearn.datasets import make_regression
+
+
+def make_regression_dataframe(n_samples: int = 500, random_state: int = 42) -> pd.DataFrame:
+
+    X, y = make_regression(
+        n_samples=n_samples,
+        n_features=4,
+        noise=15.0,
+        random_state=random_state
+    )
+
+    df = pd.DataFrame(X, columns=["x1", "x2", "x3", "x4"])
+
+    df["region"] = np.where(df["x1"] > 0, "north", "south")
+
+    df["segment"] = np.where(df["x2"] > 0.5, "premium", "standard")
+
+    df["y"] = y
+
+    return df

SOLUTIONS/sanskarcoder29_solutions/test_playground/basics/boss.py

@@ -0,0 +1,7 @@
+expr = input("Enter arithmetic expression: ")
+
+try:
+    result = eval(expr)
+    print(f"Output: {result}")
+except Exception as e:
+    print("Invalid expression")

SOLUTIONS/sanskarcoder29_solutions/test_playground/basics/conditionals.py

@@ -0,0 +1,37 @@
+age = int(input("Enter age: "))
+
+if age < 18:
+    print("Underage")
+elif age < 60:
+    print("Normal citizen")
+else:
+    print("Senior citizen")
+
+day = int(input("Enter the day number: "))
+
+print("Today is: ", end="")
+
+match day:
+    case 1:
+        print("Monday")
+    case 2:
+        print("Tuesday")
+    case 3:
+        print("Wednesday")
+    case 4:
+        print("Thursday")
+    case 5:
+        print("Friday")
+    case 6:
+        print("Saturday")
+    case 7:
+        print("Sunday")
+    case _:
+        print("Invalid day")
+
+try:
+    print(1/0)
+except Exception:
+    print("An error occurred")
+finally:
+    print("Program finished")

SOLUTIONS/sanskarcoder29_solutions/test_playground/basics/hello.py

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+print("Hello World")

SOLUTIONS/sanskarcoder29_solutions/test_playground/basics/input_output.py

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+integer = int(input("Enter an integer: "))
+print(type(integer))
+
+number = float(input("Enter a number (floating point allowed): "))
+print(type(number))
+
+array = list(map(int, input("Enter an array of numbers: ").split()))
+print(type(array))
+
+nums = [1,2,3,4]
+print(",".join(map(str, nums)))
+
+name = input("Enter your name: ")
+print(f"Hello, {name}")
+
+x, y, z = 67, 420, 9000
+print(x, y, z, sep="\n\n")

SOLUTIONS/sanskarcoder29_solutions/test_playground/basics/loops.py

@@ -0,0 +1,19 @@
+for i in range(5, 101, 5):
+    print(i, end=" ")
+
+print()
+
+names = ["Avanish","Awwab","Nathan"]
+nicknames = ["Amar","Akbar","Anthony"]
+hobbies = ["Marvel","Anime","Games"]
+
+for name, nickname in zip(names, nicknames):
+    print(f"Name: {name}, Nickname: {nickname}")
+
+for name, nickname, hobby in zip(names, nicknames, hobbies):
+    print(f"Name: {name}, Nickname: {nickname}, Hobby: {hobby}")
+
+choice = 'y'
+
+while choice.lower() == 'y':
+    choice = input("Enter choice [y/n] : ")

SOLUTIONS/sanskarcoder29_solutions/test_playground/basics/vars_and_ops.py

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+a = 5
+b = 3
+
+print("AND operator:", a & b)
+print("OR operator:", a | b)
+print("XOR operator:", a ^ b)

SOLUTIONS/sanskarcoder29_solutions/test_playground/intermediate/boss.py

@@ -0,0 +1,23 @@
+"""Practice shopping cart flow with Tkinter UI."""
+
+from pathlib import Path
+import csv
+import json
+from typing import Any, Dict, List
+
+ASSETS = Path(__file__).resolve().parent.parent / "assets"
+ASSETS.mkdir(parents=True, exist_ok=True)
+BILLS_CSV = ASSETS / "bills.csv"
+
+PRODUCTS = {
+    1: {"name": "Notebook", "price": 45.0},
+    2: {"name": "Pen Pack", "price": 20.0},
+    3: {"name": "Backpack", "price": 950.0},
+    4: {"name": "Bottle", "price": 300.0},
+}
+
+def compute_tax(total: float, rate: float = 0.18) -> float:
+    return total * rate
+
+def normalize_user_id(user_id: str) -> str:
+    return user_id.strip().lower()

SOLUTIONS/sanskarcoder29_solutions/test_playground/intermediate/class_basics.py

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+"""Practice simple classes and methods."""
+
+class Rectangle:
+
+    def __init__(self, width: float, height: float):
+        self.width = width
+        self.height = height
+
+    def area(self) -> float:
+        return self.width * self.height
+
+    def perimeter(self) -> float:
+        return 2 * (self.width + self.height)
+
+
+class BankAccount:
+
+    def __init__(self, owner: str, balance: float = 0.0):
+        self.owner = owner
+        self.balance = balance
+
+    def deposit(self, amount: float) -> float:
+        if amount <= 0:
+            raise ValueError("amount must be positive")
+        self.balance += amount
+        return self.balance
+
+    def withdraw(self, amount: float) -> float:
+        if amount <= 0:
+            raise ValueError("amount must be positive")
+        if amount > self.balance:
+            raise ValueError("insufficient balance")
+
+        self.balance -= amount
+        return self.balance