Image©2025 ux-qa.com

Using Python for Data Analysis

Python Libraries for Math

pandas = Data janitor and reshaper

NumPy = Math engine

matplotlib = Visual detective

sklearn = Model builder and evaluator

Why Use Python?

Python is a useful language for data work, with a large ecosystem of libraries that perform everything from cleaning datasets to building scalable models for machine learning.

matplotlib: Visual Debugging

Matplotlib lets you see data distribution, trends, outliers, plot histograms, scatter plots, and spot issues like skew, missing data, or clustering.

pandas: Data Cleaning

Pandas is for loading, cleaning, reshaping, and slicing datasets, and for addressing missing values, outliers, formatting issues.

numpy: Math

It powers the math behind pandas and most ML libraries. Use it when you need vectorized operations, matrix manipulation.

sklearn (scikit-learn): Data Modeling & Evaluation

Sklearn is used for machine learning and statistical modeling, feature engineering, train-test splits, pipelines, model fitting, and evaluation.

How Python Sanitizes Data

• Detecting and handling missing values
• Standardizing formats
• Outlier removal
• Encoding categorical variables
• Scaling/normalizing features