Interactive PCA Biplots Made Simple

Sample code / prompt

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

# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
    [1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
    [0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],

Sample code / prompt

import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd

# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50

Sample code / prompt

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
import pandas as pd

# EV Model comparison data (0-100 scale)
categories = ['Range', 'Acceleration', 'Charging Speed',
              'Interior Quality', 'Technology', 'Value']
tesla_scores = [85, 90, 88, 70, 95, 80]
bmw_scores = [70, 80, 75, 90, 85, 65]

Sample code / prompt

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30