Automatic structured summaries of Python objects — DataFrames, arrays, models, plots, and more.
pip install pretty-little-summaryOptional adapters are enabled automatically when their libraries are installed.
- Single function API:
pls.describe(obj) - 40+ adapters across data, viz, and ML libraries
- Works with built-ins out of the box (no required deps)
- Jupyter/IPython history capture for better context
import pretty_little_summary as pls
import pandas as pd
df = pd.DataFrame({
"product": ["Widget", "Gadget", "Doohickey"],
"price": [19.99, 29.99, 39.99],
"quantity": [100, 50, 75]
})
result = pls.describe(df)
print(result.content)
print(result.meta)import pretty_little_summary as pls
print(pls.describe([1, 2, 3]).content)
print(pls.describe({"name": "Alice", "age": 30}).content)import numpy as np
import pretty_little_summary as pls
arr = np.random.rand(100, 50)
result = pls.describe(arr)
print(result.content)import pandas as pd
import pretty_little_summary as pls
df = pd.read_csv("data.csv")
result = pls.describe(df)
print(result.content)import matplotlib.pyplot as plt
import pretty_little_summary as pls
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
result = pls.describe(fig)
print(result.content)When running inside Jupyter, pretty_little_summary can capture recent code history that created your object:
import pandas as pd
import pretty_little_summary as pls
df = pd.read_csv("data.csv")
df_clean = df.dropna()
result = pls.describe(df_clean)
print(result.history)- Ensure you installed the package in the current environment.
- Restart your kernel or interpreter.
If an adapter isn’t available, install its library:
pip install pandas numpy matplotlibOr install all optional dependencies:
pip install pretty-little-summary[all]