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In R generate 1000 data points normally distributed, with mean=x, and variance=y. Perform summary stat on the data points, explain stat metrics. Create density plot. Do the same in python script, in codility.com editor

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import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Parameters
x = 5       # mean
y = 4       # variance
sd = np.sqrt(y)

# Generate 1000 normally distributed data points
data = np.random.normal(loc=x, scale=sd, size=1000)

# Summary statistics
mean = np.mean(data)
median = np.median(data)
minimum = np.min(data)
maximum = np.max(data)
q1 = np.percentile(data, 25)
q3 = np.percentile(data, 75)
variance = np.var(data, ddof=1)  # sample variance
std_dev = np.std(data, ddof=1)

# Print summary
print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Min: {minimum}")
print(f"1st Quartile (Q1): {q1}")
print(f"3rd Quartile (Q3): {q3}")
print(f"Max: {maximum}")
print(f"Variance: {variance}")
print(f"Standard Deviation: {std_dev}")

# Explanation:
# Mean: average value
# Median: middle value
# Min/Max: smallest/largest
# Q1/Q3: 25th and 75th percentiles

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Interview question asked to Data Scientists interviewing at OpenDoor, Cure.Fit, Criteo and others: In R generate 1000 data points normally distributed, with mean=x, and variance=y. Perform summary stat on the data points, explain stat metrics. Create density plot. Do the same in python script, in codility.com editor.