Q1. Scenario: Generate 1000 random numbers from a normal distribution and plot a histogram with 30 bins. Also plot the kernel density estimate.
data = np.random.randn(1000); plt.hist(data, bins=30, density=True, alpha=0.6, label=''Histogram''); plt.hist(data, bins=30, density=True, cumulative=True) for CDF. For KDE: from scipy.stats import gaussian_kde; kde = gaussian_kde(data); x = np.linspace(-4,4,200); plt.plot(x, kde(x), label=''KDE'').
Q2. Scenario: Create a scatter plot of x and y data: x = [1,2,3,4,5], y = [2,4,5,4,3]. Add regression line using numpy polyfit.
plt.scatter(x, y); coeffs = np.polyfit(x, y, 1); line = np.poly1d(coeffs); plt.plot(x, line(x), color=''red''); plt.show(). Scatter plots show relationship between two variables.
Q3. Scenario: Color scatter points by a third variable (size or color) using c parameter. Use a colormap.
plt.scatter(x, y, c=z, cmap=''viridis''); plt.colorbar(label=''Z value''). This adds a colorbar. Useful for visualizing clusters or gradients.
Q4. Scenario: Plot two histograms on the same axes with transparency to compare distributions of two datasets.
plt.hist(data1, bins=20, alpha=0.5, label=''Data1''); plt.hist(data2, bins=20, alpha=0.5, label=''Data2''); plt.legend(); plt.show(). Alpha controls transparency.
Q5. Scenario: Use plt.subplots to create a 1x2 grid of plots: left histogram, right scatter.
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,4)); ax1.hist(data); ax1.set_title(''Histogram''); ax2.scatter(x,y); ax2.set_title(''Scatter''); plt.tight_layout(); plt.show().