Seaborn — Statistical Visualisation

import seaborn as sns

fig, ax = plt.subplots(figsize=(10, 6))
sns.boxplot(
    data=df, x="department", y="salary",
    palette="Set2", ax=ax
)
ax.set_title("Salary Distribution by Department")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Box plots show median, quartiles, and outliers simultaneously — much more informative than a bar chart showing just the mean. palette="Set2" is a good colour-blind-friendly palette.

corr = df[["salary","age","experience","score"]].corr()

fig, ax = plt.subplots(figsize=(8, 6))
sns.heatmap(
    corr, annot=True, fmt=".2f",
    cmap="RdYlGn", center=0,
    square=True, ax=ax
)
ax.set_title("Correlation Matrix")
plt.show()

annot=True shows the numbers. fmt=".2f" formats to 2 decimal places. center=0 ensures 0 correlation is white, positive is green, negative is red. Essential for feature selection in any ML or analytics project.

# Count plot — counts category frequency:
sns.countplot(data=df, x="city", palette="Blues_d", order=df["city"].value_counts().index)

# Bar plot — aggregated value:
sns.barplot(data=df, x="department", y="salary", estimator="mean", palette="Set1")

countplot() is for frequency distributions — equivalent to value_counts() visualised. order= sorts bars by frequency. barplot() aggregates by mean by default and shows confidence intervals automatically.

sns.pairplot(
    df[["salary","age","experience","score"]],
    diag_kind="hist",
    plot_kws={"alpha": 0.5}
)
plt.show()

pairplot() creates a grid of scatter plots for every pair of numeric columns, with histograms on the diagonal. It is the fastest way to spot correlations and outliers across all numeric columns at once — essential for EDA.

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Histogram with KDE overlay:
sns.histplot(df["salary"], kde=True, ax=axes[0], color="#FF6B00")
axes[0].set_title("Salary Distribution")

# KDE only (smooth density):
sns.kdeplot(df["salary"], ax=axes[1], fill=True, color="#1d4ed8")
axes[1].set_title("Salary Density")

plt.tight_layout()
plt.show()

kde=True overlays a smooth density curve on the histogram. kdeplot() shows only the density curve — useful for comparing distributions of two groups on the same axes.