Complete Python Libraries Guide for Analytics | Data Analyst Interview
🗂️ Complete Guide

Complete Python Libraries Guide for Data Analytics 2026

A full overview of every Python library used in the UK and Indian data analytics industry — from EDA to production pipelines. Know what each tool is for before your next interview.

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Complete Overview
Every Python library used in data analytics

A full map of the Python ecosystem for data analysts in the UK and India in 2026 — from EDA to production.

📊Data Manipulation

The core tools for loading, cleaning, transforming, and summarising structured data.

  • Pandas — DataFrames and SQL-like ops
  • Polars — faster Pandas alternative
  • Dask — parallel Pandas for big data
  • SQLAlchemy — SQL from Python

🔢Numerical Computing

Array maths, linear algebra, and statistical operations that power every analytics pipeline.

  • NumPy — fast array operations
  • SciPy — statistics and optimisation
  • SymPy — symbolic mathematics

📈Visualisation

From quick EDA charts to interactive dashboards — the visualisation stack every analyst should know.

  • Matplotlib — low-level foundation
  • Seaborn — statistical charts
  • Plotly — interactive charts
  • Altair — declarative grammar

🤖Machine Learning

The libraries interviewers reference when asking “have you built any models?”

  • Scikit-learn — classical ML
  • XGBoost / LightGBM — gradient boosting
  • Statsmodels — regression and tests

🗄️Data Engineering

Move, transform, and schedule data — the libraries that turn analysis into production pipelines.

  • requests / httpx — API calls
  • boto3 — AWS S3 and cloud storage
  • Apache Airflow — workflow scheduling
  • Great Expectations — data quality

🔧Productivity & Profiling

Tools that make you faster in the interview and on the job.

  • tqdm — progress bars in loops
  • loguru — clean logging
  • memory_profiler — RAM usage
  • line_profiler — line-by-line timing
End-to-End Example
Full analytics pipeline — data to chart

This is what a complete take-home interview task looks like in Python — load, clean, analyse, visualise.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
 
# 1. Load
df = pd.read_csv(‘orders.csv’, parse_dates=[‘order_date’])
 
# 2. Clean
df = df.dropna(subset=[‘customer_id’, ‘amount’])
df = df[df[‘amount’] > 0]
 
# 3. Analyse
df[‘month’] = df[‘order_date’].dt.to_period(‘M’)
monthly = df.groupby(‘month’)[‘amount’].agg([‘sum’,‘count’])
monthly.columns = [‘revenue’, ‘orders’]
monthly[‘aov’] = monthly[‘revenue’] / monthly[‘orders’]
 
# 4. Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
monthly[‘revenue’].plot(ax=axes[0], marker=‘o’, title=‘Monthly Revenue’)
monthly[‘aov’].plot(ax=axes[1], marker=‘s’, color=‘#16a34a’, title=‘Avg Order Value’)
for ax in axes: ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig(‘analysis.png’, dpi=300, bbox_inches=‘tight’)
Library Comparisons
Which library — when?

Interviewers ask “have you used X?” — understand the trade-offs and you will always have a sharp answer.

Pandas vs Polars — when would you switch?
Pandas is the default for most analytics work and universally understood in interviews. Polars is faster (Rust backend, lazy evaluation) for datasets above a few GB or multi-core workloads. Mention Polars if you’re asked about “scaling” — it signals you’re aware of modern tooling. Don’t switch just for speed; Pandas is fine up to ~1M rows.
Matplotlib vs Plotly — when do you choose each?
Matplotlib/Seaborn for static figures in reports, PDFs, and presentations. Plotly for interactive charts in web dashboards, Jupyter notebooks, or Streamlit apps where users need to zoom, hover, or filter. In a take-home task, static Matplotlib is safer unless they specifically ask for interactivity.
When would you use Statsmodels instead of Scikit-learn?
Statsmodels when you need inference — p-values, confidence intervals, hypothesis tests, and diagnostic plots. Scikit-learn when you need prediction — cross-validation, pipelines, and generalisation metrics. Data analysts typically need Statsmodels for business questions like “is this trend significant?”
What is Great Expectations and why does it matter?
Great Expectations is a data quality framework that lets you define and test expectations about your data — row counts, null rates, value ranges — as code. It’s become standard in data engineering roles at larger companies. Mentioning it in interviews signals you think about data reliability, not just analysis.
How do you handle datasets that don’t fit in memory?
Several options: chunk large CSVs with pd.read_csv(chunksize=n); switch to Polars with lazy evaluation; use Dask for distributed Pandas-like operations; push computation to the database with SQLAlchemy and retrieve aggregated results only. Always ask the interviewer about data size before assuming you can load everything into a DataFrame.
Quick Reference
The complete Python data analytics stack

Every library a UK or Indian data analyst needs to know in 2026, mapped to its purpose.

LibraryCategoryUse case
pandasData manipulationDataFrames, groupby, merging, EDA
numpyNumericalArray maths, statistics, broadcasting
matplotlibVisualisationStatic charts, reports, exports
seabornVisualisationStatistical charts, heatmaps, pairplots
plotlyVisualisationInteractive dashboards and web charts
scikit-learnMachine learningClassification, regression, clustering
statsmodelsStatisticsOLS, logistic regression, hypothesis tests
scipyScientificStatistical tests, optimisation, signal
polarsData manipulationFast Pandas alternative for large data
sqlalchemyData engineeringDatabase connections and ORM
requestsData engineeringREST API calls and web scraping
boto3CloudAWS S3, Redshift, and cloud services

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