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from typing import List import math Vector=List[float]
height_weight_age=[70,170,40] grades=[95,80,75,62]
def add(v:Vector,w:Vector): assert len(v) == len(w) , "vectors must be of same length" return [v_i + w_i for v_i,w_i in zip(v,w)]
def subtract(v:Vector,w:Vector): assert len(v) == len(w) , "vectors must be of same length" return [v_i - w_i for v_i,w_i in zip(v,w)]
def vector_sum(vectors:List[Vector])->Vector: """Sums all corresponding elements""" assert vectors,"no vectors provided!" num_elements=len(vectors[0]) assert all(len(v) == num_elements for v in vectors),"different sizes!" return [sum(vector[i] for vector in vectors) for i in range(num_elements)]
def scalar_multiply(c:float,v:Vector)->Vector: """Multiplies every element by c""" return [c*v_i for v_i in v]
def vector_mean(vectors:List[Vector])->Vector: """Computes element wise average""" n=len(vectors) return scalar_multiply(1/n,vector_sum(vectors))
def dot(v:Vector,w:Vector)->float: assert len(v) == len(w) , "vectors must be the same length" return sum(v_i*w_i for v_i,w_i in zip(v,w))
def sum_of_squares(v:Vector)->float: return dot(v,v)
def magnitude(v:Vector)->float: return math.sqrt(sum_of_squares(v))
def squarred_distance(v:Vector,w:Vector)->float: return sum_of_squares(subtract(v,w))
def distance(v:Vector,w:Vector)->float: return math.sqrt(squarred_distance(v,w)) if name == "main":
print("1.Add Vectors:",add([1,2,3],[4,5,6]))
print("2.Subtract Vectors",subtract([5,7,9],[4,5,6]))
print("3. Vector Sum",vector_sum([[1,2],[3,4],[5,6],[7,8]]))
print("4. Scalar Multiply",scalar_multiply(2,[1,2,3]))
print("5. Vector Mean:",vector_mean([[1,2],[3,4],[5,6]]))
print("6. Dot Product;",dot([1,2,3],[4,5,6]))
print("7 Sum of Squares:",sum_of_squares([1,2,3]))
print("8. Magnitude:",magnitude([3,4]))
print("9.Distance:",distance([1,2,3],[4,5,6]))
Program 2
from typing import List from collections import Counter
import matplotlib.pyplot as plt
import math
num_friends = [100, 49, 41, 40, 25]
daily_minutes = [] # Your data here
daily_hours = [] # Your data here
friend_counts = Counter(num_friends)
xs = range(101)
ys = [friend_counts[x] for x in xs]
plt.bar(xs, ys)
plt.axis([0, 101, 0, 25])
plt.title("Histogram of Friend Counts")
plt.xlabel("# of friends")
plt.ylabel("# of people")
plt.show()
num_points = len(num_friends)
largest_value = max(num_friends)
smallest_value = min(num_friends)
sorted_values = sorted(num_friends)
smallest_value = sorted_values[0]
second_smallest_value = sorted_values[1] # 1 second_largest_value = sorted_values[-2] # 49
def mean(xs: List[float]) -> float:
return sum(xs) / len(xs)
print("Mean:", mean(num_friends))
def _median_odd(xs: List[float]) -> float: """If len(xs) is odd, the median is the middle element""" return sorted(xs)[len(xs) // 2]
def _median_even(xs: List[float]) -> float: """If len(xs) is even, it's the average of the middle two elements""" sorted_xs = sorted(xs) hi_midpoint = len(xs) // 2 # e.g. length 4 => hi_midpoint 2 return (sorted_xs[hi_midpoint - 1] + sorted_xs[hi_midpoint]) / 2
def median(v: List[float]) -> float: """Finds the 'middle-most' value of v""" return _median_even(v) if len(v) % 2 == 0 else _median_odd(v)
print("Median:", median(num_friends)) # 6
def quantile(xs: List[float], p: float) -> float: """Returns the pth-percentile value in x""" p_index = int(p * len(xs)) return sorted(xs)[p_index]
print("Quantile (10th percentile):", quantile(num_friends, 0.10)) print("Quantile (25th percentile):", quantile(num_friends, 0.25)) print("Quantile (75th percentile):", quantile(num_friends, 0.75)) print("Quantile (90th percentile):", quantile(num_friends, 0.90))
def mode(x: List[float]) -> List[float]: """Returns a list, since there might be more than one mode""" counts = Counter(x) max_count = max(counts.values()) return [x_i for x_i, count in counts.items() if count == max_count] print("Mode:", mode(num_friends))
def data_range(xs: List[float]) -> float: return max(xs) - min(xs) print("Data Range:", data_range(num_friends))
def de_mean(xs: List[float]) -> List[float]: """Translate xs by subtracting its mean (so the result has mean 0)""" x_bar = mean(xs) return [x - x_bar for x in xs]
def sum_of_squares(xs: List[float]) -> float: """Returns the sum of squares of elements in xs""" return sum(x * x for x in xs)
def variance(xs: List[float]) -> float: """Returns the variance of xs""" assert len(xs) >= 2, "variance requires at least two elements" n = len(xs) deviations = de_mean(xs) return sum_of_squares(deviations) / (n - 1) print("Variance:", variance(num_friends))
def standard_deviation(xs: List[float]) -> float: """Returns the standard deviation of xs""" return math.sqrt(variance(xs)) print("Standard Deviation:", standard_deviation(num_friends))
def interquartile_range(xs: List[float]) -> float: """Returns the interquartile range of xs""" return quantile(xs, 0.75) - quantile(xs, 0.25) print("Interquartile Range:", interquartile_range(num_friends))
Program 3 import matplotlib.pyplot as plt hours_studied=[10,9,2,15,10,16,11,16] exam_scores=[95,80,10,50,45,98,38,93] plt.plot(hours_studied,exam_scores,marker = '*' , color = 'red') plt.xlabel('Number of hours studied') plt.ylabel('Score in final exam') plt.title('Effect of hours studied on Exam Performance') plt.grid(True) plt.show()
Program 4 import pandas as pd import matplotlib.pyplot as plt df=pd.read_csv('mtcars.csv') plt.hist(df['mpg'] , bins=10 , color= 'skyblue', edgecolor='black') plt.xlabel('Miles per gallon(mpg)') plt.ylabel('Frequency') plt.title('Histogram of miles per Gallon/mpg') plt.show()
Program 5 import numpy as np import pandas as pd import re df=pd.read_csv('BL-Flickr-Images-Book.csv') irrelevant_columns=['Edition Statement','Corporate Author','Corporate Contributors','Former owner','Engraver','Contributors','Issuance type','Shelfmarks'] df.drop(columns=irrelevant_columns,inplace=True) df.set_index('Identifier',inplace=True) df['Date of Publication']=df['Date of Publication'].str.extract(r'^(\d{4})',expand=False) df['Date of Publication']=pd.to_numeric(df['Date of Publication'],errors='coerce') print(df.head())