Seaborn

Function	Purpose
sns.barplot()	Bar charts with statistical aggregation
sns.histplot()	Histograms for distribution visualization
sns.boxplot()	Box-and-whisker plots for outlier detection
sns.violinplot()	Combines boxplot and KDE for distribution
sns.scatterplot()	Scatter plots for relationships
sns.lineplot()	Line graphs for trends over time
sns.heatmap()	Correlation heatmaps & matrix visualizations
sns.pairplot()	Scatterplot matrix for pairwise relationships
sns.lmplot()	Linear regression plots

Barplot

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

# Sample Data
data = pd.DataFrame({
    "category": ["A", "B", "C", "D"],     # First dataframe's column
    "values": [10, 15, 7, 12]             # Second dataframe's column
})

# Create Bar Plot
sns.barplot(x="category", y="values", data=data)

# Show the Plot
plt.show()

Heatmap

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

# Create a DataFrame
data = pd.DataFrame(np.random.rand(5, 5), columns=["A", "B", "C", "D", "E"])  # (1)!

# Heatmap using DataFrame
sns.heatmap(data, annot=True, cmap="YlGnBu")  # (2)!

plt.show()

1. np.random.rand(5, 5): Generates a 5x5 matrix of random values between 0 and 1.
2. sns.heatmap(): Creates the heatmap. annot=True: Displays numerical values inside the cells. cmap="coolwarm": Sets the color map (you can try others like "viridis", "Blues", "magma", etc.)