How to Use Pre-Installed Data Science Packages (NumPy, Pandas, scikit-learn) in LabNow AI

LabNow AI Docker images ship with NumPy, Pandas, and scikit-learn ready to import when built using the datascience Python profile, eliminating the need for manual pip installation inside running containers.

LabNow AI eliminates setup friction for data science workflows by baking essential libraries directly into its container images. According to the lab-foundation source code, these pre-installed data science packages are provisioned during the Docker build process via conditional profile arguments. This guide explains how the packages are installed and how to use them immediately upon container startup.

How LabNow AI Installs Data Science Packages Automatically

During image construction, the docker_core/Dockerfile checks the build argument ARG_PROFILE_PYTHON for the token datascience. When detected, the build appends docker_core/work/install_list_PY_datascience.pip to the installation queue.

The relevant logic appears in the Dockerfile:

&& echo "If installing Python packages" \
&& ( $(grep -q "datascience" <<< "${ARG_PROFILE_PYTHON}") && ( \
    ( which R && echo "rpy2  % Install rpy2 if R exists" >> /opt/utils/install_list_PY_datascience.pip || echo "Skip rpy2 install" ) \
 && ( which java && echo "py4j  % Install py4j if Java exists" >> /opt/utils/install_list_PY_datascience.pip || echo "Skip py4j install" ) \
) || echo "Skip Python datascience packages install" ) \
...
&& for profile in $(echo $ARG_PROFILE_PYTHON | tr "," "\n") ; do ( \
   [ -f "/opt/utils/install_list_PY_${profile}.pip" ] && install_pip "/opt/utils/install_list_PY_${profile}.pip" \
); done \

The install_pip helper function, defined in docker_core/work/script-setup.sh, executes pip install -r against the specified requirement file. This mechanism ensures NumPy, Pandas, and scikit-learn are baked into the image rather than installed at runtime.

Runtime Environment and Import Paths

The packages reside in the base Conda environment (${CONDA_PREFIX}). The Dockerfile links the Conda-installed python3.* binary to /usr/bin/python, making the pre-installed libraries available system-wide.

Because the installation occurs during the build phase, you can import these modules immediately upon entering the container:

import numpy as np
import pandas as pd
import sklearn

No virtual environment activation or additional pip install commands are required.

Practical Code Examples

The following snippets demonstrate typical usage patterns. Execute these inside any LabNow AI container without prerequisite setup steps.

NumPy Array Operations

import numpy as np

# Create a 3×3 matrix of random numbers

A = np.random.rand(3, 3)
print("Matrix A:")
print(A)

# Compute its transpose and determinant

A_T = A.T
det = np.linalg.det(A)
print("\nTranspose of A:")
print(A_T)
print(f"\nDeterminant of A = {det:.4f}")

Pandas Data Manipulation

import pandas as pd

# Load a CSV from a URL

url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv"
df = pd.read_csv(url)

# Display first rows

print(df.head())

# Compute mean sepal length per species

means = df.groupby("species")["sepal_length"].mean()
print("\nMean sepal length per species:")
print(means)

scikit-learn Model Training

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

# Load the Iris dataset

X, y = load_iris(return_X_y=True)

# Split data

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train Random Forest

clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# Evaluate

y_pred = clf.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

Key Source Files in lab-foundation

Understanding these files helps troubleshoot or extend the default package set:

• docker_core/Dockerfile: Orchestrates the build and triggers profile-based installation (lines 58-63, 90-93).
• docker_core/work/install_list_PY_datascience.pip: Contains the explicit pip requirements including pandas and scikit-learn.
• docker_core/work/script-setup.sh: Defines the install_pip function that executes the actual pip install -r command.

Summary

• LabNow AI pre-installs NumPy, Pandas, and scikit-learn when the datascience token is present in ARG_PROFILE_PYTHON.
• The installation occurs during Docker build via docker_core/work/install_list_PY_datascience.pip and the install_pip helper.
• Packages are available in the base Conda environment and accessible via /usr/bin/python immediately upon container startup.
• No manual pip install is required inside running containers.

Frequently Asked Questions

Do I need to run pip install for NumPy or Pandas in LabNow AI?

No. If your LabNow AI image was built with the datascience profile, these packages are already present in the global Python environment. Simply import them directly in your scripts.

How can I verify which data science packages are pre-installed?

Run pip list inside the container to view all installed packages. You can also inspect the file docker_core/work/install_list_PY_datascience.pip in the lab-foundation repository to see the intended package set before building.

Can I add additional packages to the pre-installed list?

Yes. Modify docker_core/work/install_list_PY_datascience.pip to include additional pip packages, or create a new profile file (e.g., install_list_PY_custom.pip) and add your custom profile name to the ARG_PROFILE_PYTHON build argument.

Which Python version does LabNow AI use for these packages?

LabNow AI uses the Python version installed in the base Conda environment (${CONDA_PREFIX}), which is linked to /usr/bin/python. The specific version depends on the base image tag used during the Docker build process.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}