Master Python Dependency Management: Pip, Pipdeptree, Pip‑autoremove & Conda

Many developers initially resist Python because its environment and dependency management feel chaotic compared to Node's npm, Java's Maven/Gradle, etc. While simple scripts can be assembled quickly, larger projects suffer from hidden dependencies, unclear environment setup, and difficult reproducibility.

Based on Pip

The basic goals of a dependency manager are to:

Quickly configure project dependencies and set up the development environment.

Clearly show which third‑party packages a project uses and their dependency trees.

Allow easy addition, removal, and resolution of dependencies.

These goals are achievable with the Pip toolchain.

Quick environment configuration (pip)

List installed packages with pip list:

$ pip list
Package    Version
---------- -------------------
certifi    2020.6.20
pip        19.3.1
setuptools 44.0.0.post20200106
wheel      0.36.2

Install a package (e.g., Flask) and see the updated list:

$ pip install flask
$ pip list
Package      Version
------------ -------------------
certifi      2020.6.20
click        7.1.2
Flask        1.1.2
itsdangerous 1.1.0
Jinja2       2.11.3
MarkupSafe   1.1.1
pip          19.3.1
setuptools   44.0.0.post20200106
Werkzeug     1.0.1
wheel        0.36.2

Record the exact set of dependencies with pip freeze and save to requirements.txt:

$ pip freeze > requirements.txt
$ cat requirements.txt
certifi==2020.6.20
click==7.1.2
Flask==1.1.2
itsdangerous==1.1.0
Jinja2==2.11.3
MarkupSafe==1.1.1
Werkzeug==1.0.1

Re‑install the same environment elsewhere using pip install -r requirements.txt.

Explicit dependency tree (pipdeptree)

While pip list and pip freeze show what is installed, they do not reveal which packages depend on which. Install pipdeptree to visualize the tree:

$ pip install pipdeptree
$ pipdeptree
certifi==2020.6.20
Flask==1.1.2
  - click [required: >=5.1, installed: 7.1.2]
  - itsdangerous [required: >=0.24, installed: 1.1.0]
  - Jinja2 [required: >=2.10.1, installed: 2.11.3]
    - MarkupSafe [required: >=0.23, installed: 1.1.1]
  - Werkzeug [required: >=0.15, installed: 1.0.1]
pipdeptree==2.0.0
  - pip [required: >=6.0.0, installed: 19.3.1]
setuptools==44.0.0.post20200106
wheel==0.36.2

This makes it clear that Jinja2 is required by Flask, preventing accidental removal.

Dependency cleanup (pip‑autoremove)

Uninstalling a package with pip uninstall flask -y removes Flask but leaves its dependencies behind. Use pip‑autoremove to clean them up:

$ pip install pip-autoremove
$ pip-autoremove flask -y
$ pipdeptree
certifi==2020.6.20
pip-autoremove==0.9.1
pipdeptree==2.0.0
pip==19.3.1
setuptools==44.0.0.post20200106
wheel==0.36.2

The environment is now tidy.

Based on Conda

While Pip works well for single‑project environments, Python’s global interpreter makes it hard to isolate multiple projects. Conda provides a language‑agnostic virtual‑environment system that can manage Python, R, Java, Node, Ruby, and even system tools.

Package, dependency and environment management for any language—Python, R, Ruby, Lua, Scala, Java, JavaScript, C/C++, FORTRAN Conda is an open‑source package and environment manager that runs on Windows, macOS, and Linux. It quickly installs, runs, and updates packages and their dependencies, and easily creates, saves, loads, and switches between environments.

Installation

Choose Miniconda (lightweight) over Anaconda (full scientific stack). Download the installer, run it, and follow the prompts. After installation, the conda command becomes available (you may need to source ~/.bashrc or disable auto‑activation with conda config --set auto_activate_base false).

Environment operations

Create and activate a clean Python 2.7 environment named frida:

$ conda create -n frida python=2.7 -y
$ conda activate frida

List environments and packages:

(frida) $ conda env list
# conda environments:
base                     /home/user/miniconda3
frida               *   /home/user/miniconda3/envs/frida

(frida) $ conda list

Note that conda list shows both Python packages and non‑Python dependencies, unlike pip list.

Deactivate the environment with conda deactivate (may need to run twice if nested).

Dependency management

Export the current environment to a YAML file and recreate it elsewhere:

(frida) $ conda env export > environment.yaml
(frida) $ conda env create -f environment.yaml

IDE integration

Conda environments can be directly linked to IDEs, simplifying configuration and debugging.

Some reflections

Is Conda convenient for virtual environments of other languages? Yes – you can create isolated environments for Java, Node, Ruby, etc., and install language‑specific packages (e.g., conda install openjdk=8.0.152 -y).

How to find packages supported by Conda? Use conda search package_name or browse anaconda.org .

Should Python packages be installed with Conda or Pip? For pure Python libraries, Pip is preferred for simplicity and uniformity. For cross‑language stacks or pre‑built binaries (e.g., TensorFlow), Conda can be advantageous.

References

Anaconda vs Miniconda

Official Conda documentation

pip‑deptree

pip‑autoremove