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 |
All the code below can be cut-and-pasted in single cells in https://colab.research.google.com/
Barplot#
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# 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 matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# 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()
- np.random.rand(5, 5): Generates a 5x5 matrix of random values between 0 and 1.
- 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.)
