Installing Data Science Libraries (pip & conda)

Data science libraries you’ll use often

A common starter stack for data analytics includes:

• NumPy: numerical computing
• Pandas: data manipulation
• Matplotlib: plotting foundation
• Seaborn: statistical visualization
• Plotly: interactive charts
• Jupyter: notebooks
• SciPy (optional early): scientific utilities
• scikit-learn (later): ML utilities

pip vs conda (how to choose)

Use conda when

• You’re using Anaconda/Miniconda
• You want fewer build/compile issues
• You need compiled dependencies (common in data science)

Use pip when

• You installed CPython from python.org
• You’re inside a venv
• A package isn’t available via conda

Installing with conda

Step 1: Create and activate an environment

conda create -n analytics python=3.12

conda activate analytics

Step 2: Install the core stack

conda install numpy pandas matplotlib seaborn jupyter

Step 3: Install Plotly

Plotly is often available via conda, but some users prefer pip. Try conda first:

conda install plotly

If not available in your channels, use pip:

pip install plotly

Installing with pip (venv)

Step 1: Create and activate

python -m venv .venv

source .venv/bin/activate

Step 2: Install packages

pip install numpy pandas matplotlib seaborn plotly jupyter

Verifying installs in Python

After installing packages, verify them in a Python session or notebook:

import numpy as np
import pandas as pd
import matplotlib
import seaborn as sns
import plotly
 
print("NumPy:", np.__version__)
print("Pandas:", pd.__version__)
print("Matplotlib:", matplotlib.__version__)
print("Seaborn:", sns.__version__)
print("Plotly:", plotly.__version__)

Installing Jupyter kernel for your environment

Sometimes Jupyter is installed globally but you want the kernel to point at your environment.

Install ipykernel:

pip install ipykernel

Register the kernel:

python -m ipykernel install --user --name analytics --display-name "Python (analytics)"

Now your environment appears in Jupyter kernel selection.

Reproducibility: pinning versions

For long projects, pin versions so your notebook still runs months later.

pip: requirements.txt

numpy==2.1.0
pandas==2.2.3
matplotlib==3.9.2
seaborn==0.13.2
plotly==5.24.1
jupyter==1.1.1

conda: environment.yml

name: analytics
channels:
  - conda-forge
dependencies:
  - python=3.12
  - numpy
  - pandas
  - matplotlib
  - seaborn
  - plotly
  - jupyter

Visualize it

Use this decision path whenever you're not sure which installer to reach for.

  flowchart TD
  A["Need to install a package"] --> B{"Environment created with conda?"}
  B -->|"Yes"| C["Try: conda install package"]
  C --> D{"Available on conda-forge?"}
  D -->|"Yes"| E["Installed via conda"]
  D -->|"No"| F["Fallback: pip install package"]
  B -->|"No (venv/plain Python)"| G["pip install package"]

Common errors and fixes

Error: ModuleNotFoundError: No module named 'pandas'

• You installed in one environment but are running Python from another.
• Solution: activate the correct environment and reinstall.

Error: Jupyter doesn’t show the right kernel

• Install and register ipykernel as shown above.

Error: pip installs but import fails

• Check you’re using the intended pip:
  • In a terminal inside the environment, run which pip (macOS/Linux)
  • Or where pip (Windows)

Check your understanding

Next

Phase 1 is complete. Next we’ll start Phase 2: Numerical Computing (NumPy) with an Introduction to NumPy.

🧪 Try It Yourself

Exercise 1 – Choose pip or conda

Press Run to see what your code prints.

Exercise 2 – Build a requirements.txt From a Dict

Press Run to see what your code prints.

Exercise 3 – Verify Imports Like a Setup Script

Press Run to see what your code prints.