Unit-5

24) #complex object or not
import json
def is_complex_json(json_string):
    def check_complex(obj):
        if isinstance(obj, (dict, list)):
            return any(isinstance(item, (dict, list)) or check_complex(item) for item in (obj.values() if isinstance(obj, dict) else obj))
        return False
    try:
        data = json.loads(json_string)
        return check_complex(data)
    except json.JSONDecodeError:
        print("Invalid JSON string.")
        return False
json_string = '{"name": "John", "address": {"city": "New York", "zipcode": "10001"}, "hobbies": ["reading", "sports"]}'
print("Contains complex object:", is_complex_json(json_string))


25) #NumPy arrays creation using array
import numpy as np
a_1d = np.array([1, 2, 3, 4, 5])
a_2d = np.array([[1, 2, 3], [4, 5, 6]])
a_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print("1D Array:\n", a_1d)
print("\n2D Array:\n", a_2d)
print("\n3D Array:\n", a_3d)


26)
import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
print(a)
print("Number of dimensions (ndim):", a.ndim)
print("Shape of the array (shape):", a.shape)
print("Total number of elements (size):", a.size)
print("Data type of elements (dtype):", a.dtype)
print("Size of each element (itemsize):", a.itemsize)


27)
import numpy as np

# Creating a 2D array
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(arr)

# Row selection
print("\nFirst row:", arr[0])
print("Second row:", arr[1])

# Column selection
print("\nFirst column:", arr[:, 0])
print("Second column:", arr[:, 1])

# Slicing the array to extract a sub-array
print("\nBasic Slicing (sub-array):", arr[1:3, 1:3])

# Integer indexing to access specific elements
print("\nInteger Indexing (specific element):", arr[0, 2])

# Accessing multiple elements using integer arrays
print("Integer Indexing (multiple elements):", arr[[0, 1, 2], [0, 1, 2]])

# Boolean indexing to extract elements greater than 5 
bool_array = arr > 5 
print("\nBoolean Indexing (condition mask):") 
print(bool_array) 
filtered_elements = arr[bool_array] 
print("\nFiltered elements (greater than 5):", filtered_elements)


28)
import numpy as np
# Creating an array
arr = np.array([1, 2, 3, 4, 5])
print("Minimum value:", arr.min())
print("Maximum value:", arr.max())
print("Sum of the array:", arr.sum())
print("Cumulative sum of the array:", arr.cumsum())
# Finding the cumulative sum of the array elements
arr2 = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print("\n2D Array:")
print(arr2)
print("Column Sum:", np.sum(arr2, axis=1))
print("Row Sum:", np.sum(arr2, axis=0))
print("Cumulative Sum:", np.cumsum(arr2))


29)
import pandas as pd
data = {
    'Name': ['Praveen', 'Ram', 'Roja', 'David', 'Uday', 'Prabhakar', 'Tulasi', 'Swathi', 'Satya', 'Vasu'],
    'Age': [25, 30, 22, 35, 28, 40, 32, 45, 29, 26],
    'Height': [5.5, 6.0, 5.8, 6.2, 5.6, 5.9, 6.1, 5.7, 6.3, 5.4],
    'Weight': [65, 70, 68, 75, 72, 80, 78, 85, 74, 69],
    'Score': [85, 90, 88, 95, 80, 92, 87, 93, 89, 86]
}
df = pd.DataFrame(data)
print(df.head())
print("\nSelecting the 'Name' column:")
print(df['Name'])
print("\nSelecting the 'Name' and 'Age' columns:")
print(df[['Name', 'Age']])
print("\nSelecting the first three rows (0, 1, 2):")
print(df.iloc[:3])
print("\nSelecting rows where 'Age' is greater than 30:")
print(df[df['Age'] > 30])
print("\nSelecting rows where 'Age' > 30 and 'Score' > 90:")
print(df[(df['Age'] > 30) & (df['Score'] > 90)])
print("\nSelecting rows 1 and 3 for columns 'Name' and 'Height':")
print(df.loc[[1, 3], ['Name', 'Height']])


30)
import pandas as pd
import matplotlib.pyplot as plt
# Creating a DataFrame from the dictionary
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank', 'Grace', 'Heidi', 'Ivan', 'Judy'],
    'Age': [23, 30, 35, 40, 22, 28, 32, 36, 29, 25],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'Austin'],
    'Salary': [70000, 80000, 120000, 110000, 95000, 88000, 91000, 76000, 99000, 85000],
    'Department': ['HR', 'IT', 'Finance', 'IT', 'HR', 'Finance', 'IT', 'HR', 'Finance', 'IT']
})
# Creating a scatter plot to observe the relationship between Age and Salary
plt.figure(figsize=(10, 6))
plt.scatter(df['Age'], df['Salary'], color='blue', marker='o')
plt.title('Scatter Plot of Age vs. Salary')
plt.xlabel('Age')
plt.ylabel('Salary')
plt.grid(True)
plt.show()