Academic Python — Scientific Computing

Overview

Execute Python 3.12 with a full scientific computing stack pre-installed. You are running inside the container — use python3 directly, no docker exec needed.

When To Use

• User asks to run Python code, analyze data, or create plots

• User needs scientific computation (linear algebra, statistics, symbolic math)

• User wants charts, visualizations, or data processing

CRITICAL: Output File Location

ALWAYS save outputs to /workspace/output/ — this directory is monitored by the UI.

When you save a file, you MUST report the full path in your response so the UI can display it:

保存完成：/workspace/output/chart.png

The UI will automatically detect paths like /workspace/output/xxx.png and display the file.

Quick Execution

Important: Always use /home/user/.venv/bin/python3 for the full scientific stack.

Visualization Example (CORRECT)

/home/user/.venv/bin/python3 << 'PYTHON' import numpy as np import matplotlib matplotlib.use('Agg') # Required: no display server import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100) y = np.sin(x)

plt.figure(figsize=(10, 6)) plt.plot(x, y, 'b-', linewidth=2) plt.title('Sine Wave') plt.xlabel('x') plt.ylabel('sin(x)') plt.grid(True)

MUST save to /workspace/output/

output_path = '/workspace/output/sine_wave.png' plt.savefig(output_path, dpi=150, bbox_inches='tight') print(f'图表已保存：{output_path}') PYTHON

After running, tell the user: "图表已保存到 /workspace/output/sine_wave.png"

Inline (short scripts)

/home/user/.venv/bin/python3 -c " import numpy as np x = np.array([1, 2, 3, 4, 5]) print(f'Mean: {np.mean(x):.2f}') print(f'Std: {np.std(x):.2f}') "

Data analysis with Pandas

/home/user/.venv/bin/python3 << 'PYTHON' import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'score': [95, 87, 92]} df = pd.DataFrame(data) print(df.describe())

Save results

output_path = '/workspace/output/analysis.csv' df.to_csv(output_path, index=False) print(f'数据已保存：{output_path}') PYTHON

Symbolic math with SymPy

/home/user/.venv/bin/python3 -c " from sympy import symbols, integrate, diff, latex x = symbols('x') f = x3 + 2*x2 - x + 1 print(f'f(x) = {f}') print(f"f'(x) = {diff(f, x)}") print(f'∫f dx = {integrate(f, x)}') "

Machine learning with scikit-learn

/home/user/.venv/bin/python3 << 'PYTHON' from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) clf = RandomForestClassifier(n_estimators=100, random_state=42) clf.fit(X_train, y_train) print(f"Accuracy: {accuracy_score(y_test, clf.predict(X_test)):.2%}") PYTHON

Pre-installed Packages

Category Packages

Numerical numpy, scipy

Data pandas, polars

Visualization matplotlib, seaborn, plotly

Symbolic math sympy

ML/AI scikit-learn, pytorch, transformers

Statistics statsmodels

NLP nltk, spacy

Image Pillow, opencv-python

Other requests, tqdm, pyyaml, h5py

Important Notes

• Always use matplotlib.use('Agg') before importing pyplot (no display server).

• ALWAYS save outputs (plots, CSVs, data) to /workspace/output/ — the UI monitors this directory!

• ALWAYS print the full output path so the UI can detect and display the file.

• For long-running scripts, consider writing progress to stdout.

• R 4.3 with tidyverse is also available: Rscript -e "library(tidyverse); ..." .

• pip is available if you need additional packages: pip install <package> .

File Locations

Purpose Path

Save outputs /workspace/output/ (UI monitored!)

Temp scripts /tmp/

User projects /workspace/projects/

Notebooks /workspace/notebooks/