๐ฅ Seaborn Heatmaps
Seaborn makes it easy to create attractive heatmaps to visualize correlation matrices or 2D data.
๐ก Quick Tip:
Mastering this concept will significantly boost your Python data science skills!
๐ป Code Example:
import seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np # 1. Real-world correlation heatmap np.random.seed(42) df = pd.DataFrame({ "Python Score": np.random.randint(60, 100, 50), "AI Score" : np.random.randint(55, 100, 50), "DevOps Score": np.random.randint(50, 100, 50), "Study Hours" : np.random.randint(2, 12, 50), "Projects Done": np.random.randint(1, 10, 50), }) corr = df.corr() plt.figure(figsize=(8, 5)) sns.heatmap( corr, annot=True, fmt=".2f", cmap="coolwarm", linewidths=0.5, square=True, cbar_kws={"shrink": 0.8}, ) plt.title("Pynfinity Learner Correlation Matrix", fontsize=13, pad=12) plt.tight_layout() # 2. Distribution visualisation fig, axes = plt.subplots(1, 2, figsize=(12, 4)) sns.boxplot(data=df, ax=axes[0]) axes[0].set_title("Score Distributions (Box Plot)") sns.pairplot(df[["Python Score", "AI Score", "Study Hours"]]) plt.suptitle("Pair Plot โ Pynfinity Learners", y=1.02) plt.show()
Keep exploring and happy coding! ๐ป
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