Correlation & Plotting

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

# Generate data
np.random.seed(42)
df = pd.DataFrame({
    'A': np.random.rand(100),
    'B': np.random.rand(100),
    'C': np.random.rand(100) * 2
})

# Compute and plot correlation
corr_matrix = df.corr()
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

import pandas as pd
df = pd.DataFrame({
    'A': np.random.rand(50),
    'B': np.random.rand(50)
})

# Plot
plt.scatter(df['A'], df['B'], alpha=0.6)
plt.xlabel('A')
plt.ylabel('B')
plt.title('Scatter Plot of A vs. B')
plt.show()

import pandas as pd
df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [10, 9, 8, 7, 6],
    'C': [2, 4, 6, 8, 10]
})

# Pairwise scatter matrix
pd.plotting.scatter_matrix(df, diagonal='hist', figsize=(8, 6))
plt.suptitle("Scatter Matrix")
plt.show()

# Generate correlated data
df = pd.DataFrame({
    'X': range(1, 101),
    'Y': [x * 2 for x in range(1, 101)],
    'Z': [x * 3 for x in range(1, 101)]
})

# Heatmap
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title('Heatmap of Strong Correlations')
plt.show()

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [3, 6, 9]})

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [10, 15, 25, 30]})
pd.plotting.scatter_matrix(df)