Anaconda

TL;DR

• A free, open-source Python and R distribution focused on data science and scientific computing.
• Includes conda (package and environment manager) and more than 1,500 Python/R data science packages, including NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, and Keras.
• Provides Anaconda Navigator to launch and manage tools like Jupyter Notebook, Spyder, and RStudio.

Definition

Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.) that aims to simplify package management and deployment. It is the most popular Python data science platform and is used by over 7 million users worldwide.

Explanation

• The Anaconda distribution bundles more than 1,500 Python/R data science packages, including widely used libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, and Keras.
• It includes conda, a package and environment manager that lets users install, update, and manage packages and isolated environments, handling dependencies automatically.
• Anaconda also provides an integrated interface, Anaconda Navigator, which simplifies access to and launching of included tools and applications (for example, Jupyter Notebook, Spyder, and RStudio).

Examples

Installing and managing packages and environments

To create a new environment named “myenv” with NumPy, Pandas, and Matplotlib:

conda create -n myenv numpy pandas matplotlib

To activate and switch to that environment:

conda activate myenv

This creates an isolated environment containing the specified packages and installs any required dependencies.

Accessing and using Jupyter Notebook

To launch Jupyter Notebook included with Anaconda:

jupyter notebook

This opens a browser window where users can create and run notebooks composed of cells containing live code, equations, visualizations, and narrative text.

Importing and using packages

Example imports commonly used in a Jupyter Notebook:

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

To load a CSV file into a Pandas DataFrame:

df = pd.read_csv("myfile.csv")

Use cases

• Installing and managing packages and isolated environments for reproducible workflows.
• Interactive development, exploration, and visualization via Jupyter Notebook.
• Importing and using bundled data science libraries for analysis and modeling.

Related terms

• conda
• Anaconda Navigator
• Jupyter Notebook
• Spyder
• RStudio
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• TensorFlow
• Keras