7 Essential Python Tools Every Data Scientist Should Master

If you aim to become a data specialist, maintain curiosity, keep exploring, learning, and asking questions. While online tutorials can help you start, the best preparation comes from mastering tools already used in production environments. Below are seven Python tools that every data expert should know.

IPython

IPython is an interactive command‑line shell that supports multiple programming languages, originally built for Python. It offers enhanced introspection, rich media, extended shell syntax, tab completion, and a comprehensive history.

Powerful interactive shell (Qt‑based terminal)

Browser‑based notebook supporting code, plain text, math formulas, built‑in charts, and other rich media

Support for interactive data visualization and GUI tools

Embeddable interpreter that can be loaded into any project

Simple to use, high‑performance tool for parallel computing

Provided by data‑analysis director and Galvanize expert Nir Kaldero

GraphLab Create

GraphLab Create is a Python library backed by a C++ engine, enabling rapid construction of large‑scale, high‑performance data products.

Analyzes massive datasets at interactive speed on a single machine

Handles tabular data, curves, text, and images on a unified platform

Includes state‑of‑the‑art machine‑learning algorithms such as deep learning, evolutionary trees, and factorization machines

Runs on Hadoop YARN or EC2 clusters, whether on a laptop or distributed system

Flexible API for focusing on tasks or machine‑learning models

Provides cloud‑based prediction services for easy data‑product deployment

Creates visualizations for exploration and product monitoring

Presented by Galvanize data scientist Benjamin Skrainka

pandas

pandas is an open‑source library (BSD license) that offers high‑performance, easy‑to‑use data structures and analysis tools for Python. It fills the gap in Python’s data‑analysis and modeling capabilities, allowing convenient data manipulation without switching to languages like R.

Integrated with IPython and other libraries, pandas delivers excellent performance, speed, and compatibility for data‑analysis development. While it does not cover advanced modeling beyond linear and panel regression, tools such as statsmodels and scikit‑learn complement it.

Provided by Galvanize expert and data scientist Nir Kaldero.

PuLP

PuLP is a Python library for linear programming. It generates linear files and can invoke highly optimized solvers such as GLPK, COIN‑CLP/CBC, CPLEX, and Gurobi to solve linear optimization problems.

Presented by Galvanize data scientist Isaac Laughlin.

matplotlib

matplotlib is a 2D plotting library for Python that produces publication‑quality figures for print and interactive environments across platforms.

It works in Python scripts, IPython shells, web servers, and various GUI toolkits. With a few lines of code you can create histograms, power spectra, bar charts, error charts, scatter plots, and more.

The pyplot interface offers a MATLAB‑like experience, especially when combined with IPython, while advanced users can fully customize styles, fonts, and axes via an object‑oriented API.

Scikit‑Learn

Scikit‑Learn is a simple yet powerful library for data mining and analysis, built on NumPy, SciPy, and matplotlib. It is open‑source (BSD license) and usable in commercial settings.

Classification – identifying the category of an object

Regression – predicting continuous values

Clustering – automatically grouping similar objects

Dimensionality Reduction – reducing the number of random variables

Model Selection – comparing, validating, and choosing models and parameters

Preprocessing – feature extraction and normalization

Spark

Spark consists of a driver program that runs the user’s main function and executes parallel operations across a cluster. Its most attractive feature is the Resilient Distributed Dataset (RDD), a partitioned collection of elements that can be processed in parallel.

RDDs can be created from files in Hadoop or other supported file systems, or from existing in‑memory collections. Spark also supports shared variables: broadcast variables for caching data on all nodes, and accumulators for aggregating values such as counters.