🐍 Python for Analytics

Python for Data Analyst Interviews

Master Pandas, NumPy, Matplotlib and more. Learn what interviewers actually test and how to answer with confidence.

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Learning Path
Python roadmap for data analysts

Follow this order to go from zero to interview-ready in Python for analytics.

1

Python Fundamentals

Variables, data types, loops, functions, list comprehensions — the basics every analyst needs before touching data libraries.

VariablesLoopsFunctionsList comprehensions
2

Pandas for Data Manipulation

DataFrames, filtering, groupby, merge, pivot tables — the most important library for every data analyst interview.

DataFramegroupbymergepivot_table
3

NumPy for Numerical Analysis

Arrays, mathematical operations, statistical functions — essential for analytics and ML interview questions.

Arraysnp.meannp.percentileBroadcasting
4

Data Visualisation

Matplotlib and Seaborn — create charts interviewers love and explain insights visually in case study rounds.

MatplotlibSeabornBar chartsHeatmaps
5

Real Interview Problems

End-to-end data problems using real datasets — clean, analyse, and present insights like a professional analyst.

EDAData cleaningCohort analysisA/B testing
Python Libraries
Libraries covered in interviews

These are the Python libraries every data analyst is tested on. Click any to explore in depth.

Data Manipulation

Pandas

The most important library for data analysts. DataFrames, filtering, groupby, merging — all tested in interviews.

Learn Pandas →
Numerical

NumPy

Fast numerical operations, array manipulation, and statistical functions used in analytics and ML pipelines.

Learn NumPy →
Visualisation

Matplotlib

The foundation of Python data visualisation. Create line, bar, scatter charts for presentations and reports.

Learn Matplotlib →
Visualisation

Seaborn

Beautiful statistical charts built on Matplotlib. Heatmaps, pairplots, violin charts — loved by data analysts.

Learn Seaborn →
Built-in

Core Python Libraries

datetime, collections, itertools, os — the built-in libraries that come up in real data engineering questions.

Learn Built-ins →
All Libraries

Complete Libraries Guide

A full overview of every Python library used in the UK and Indian data analytics industry in 2026.

See Full Guide →
Code Example
Pandas — most common interview question
import pandas as pd
 
# Load data
df = pd.read_csv(‘orders.csv’)
 
# Top 5 customers by revenue
top_customers = (
  df.groupby(‘customer_id’)
    .agg(orders=(‘order_id’, ‘count’),
         revenue=(‘amount’, ‘sum’))
    .sort_values(‘revenue’, ascending=False)
    .head(5)
)
 
print(top_customers)

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