Aujasvit Datta (220254) Assignments 1,2,3

220254_Aujasvit/Assignment_1/q6.py

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+def input_info():
+    name = input("Enter name: ").strip()
+    roll_no = int(input("Enter Roll Number: ").strip())
+    return (name, roll_no)
+
+def print_info(name, roll_no):
+    print("Name:", name)
+    print("Roll Number:", roll_no)
+
+
+inp_name, inp_roll = input_info()
+print('\n') #makes output more readable
+print_info(inp_name, inp_roll)

220254_Aujasvit/Assignment_1/q7.py

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+def hello_world():
+    print("Hello, world!")
+
+
+hello_world() #function call which prints hello world

220254_Aujasvit/Assignment_1/q8.py

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+sum = 0
+for i in range(1, 11):
+    sum += i
+
+print("Sum of first ten natural numbers is", sum)

220254_Aujasvit/Assignment_2/q1.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+plt.bar(['A', 'B', 'C'],[2,3,4], width = 0.5, color = ['red', 'blue', 'green'])
+plt.show()

220254_Aujasvit/Assignment_2/q2.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+#Marks in various subjects
+math = pd.Series([94, 98, 64], index = ["Jon", "Jane", "Charlie"])
+science = pd.Series([88, 82, 60], index = ["Jon", "Jane", "Charlie"])
+sst = pd.Series([79, 66, 80], index = ["Jon", "Jane", "Charlie"])
+
+x = np.arange(3)
+df = pd.DataFrame({'Math': math, 'Science': science, "SST": sst})
+
+plt.bar(x - 0.1, df['Math'].values, width = 0.1)
+plt.bar(x, df['Science'].values, width = 0.1)
+plt.bar(x + 0.1, df['SST'].values, width = 0.1)
+plt.xticks(x, ['Jon', 'Jane', 'Aujasvit'])
+plt.xlabel('Student')
+plt.ylabel('Marks')
+plt.legend(['Math', 'Science', 'SST'])
+plt.show()

220254_Aujasvit/Assignment_2/q3.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+x = [1,2,3,4,6, 7]
+y = [2,1,5,3,10,7]
+plt.scatter(x, y, color = 'red')
+plt.plot(x, y)
+plt.show()

220254_Aujasvit/Assignment_2/q4.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+a = np.array([1,2, 5, 3])
+print('a:', a)
+print("Sum of all elements in a:", np.sum(a))
+print("Product of all elements in a:", np.prod(a))
+print("a squared:", a**2)
+print("2 to the power a:", 2**a)
+print("a + 1:", a + 1)
+print("a * 10:", a * 10)
+print("Mean of elements of a:", np.mean(a))
+print("Median of elements of a:", np.median(a))
+print("Standard Deviation of elements of a:", np.std(a))

220254_Aujasvit/Assignment_3/problem1/activation.py

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+import numpy as np
+
+
+def sigmoid(a):
+  return 1/(1 + np.exp(-1*a))
+
+def tanh(a):
+  return (np.exp(a) - np.exp(-a))/(np.exp(a) + np.exp(-a))

220254_Aujasvit/Assignment_3/problem1/graph.py

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+import numpy as np
+import matplotlib.pyplot as plt
+plt.style.use('seaborn-whitegrid')
+
+
+def sigmoid(a):
+  return 1/(1 + np.exp(-1*a))
+
+def tanh(a):
+  return (np.exp(a) - np.exp(-a))/(np.exp(a) + np.exp(-a))
+
+x = np.linspace(-10, 10, 1000000)
+
+plt.plot(x, sigmoid(x))
+plt.xlabel('x')
+plt.ylabel('sigmoid(x)')
+plt.show()
+
+plt.plot(x, tanh(x))
+plt.xlabel('x')
+plt.ylabel('tanh(x)')
+plt.show()

220254_Aujasvit/Assignment_3/problem2.py

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+import numpy as np
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+plt.style.use('default')
+
+x = pd.read_csv('/content/World Press Index 2021.csv')
+temp = np.arange(2)
+
+best_2019 = x['Global Score 2019'].min()
+best_2019_country = str(x[x['Global Score 2019'] == best_2019]['Country Name']).split()[1]
+
+worst_2021 = x['Global Score 2021'].max()
+worst_2021_country = str(x[x['Global Score 2021'] == x['Global Score 2021'].max()]['Country Name']).split()[1]
+
+plt.bar([worst_2021_country + ': Worst Global Score 2019', best_2019_country + ': Best Global Score 2021'], [worst_2021,best_2019,], width = 0.1)
+plt.show()