Python for data analyst
jobs in 2027

For analyst jobs, Python mostly means pandas — the library for working with tables of data in Python — plus the habits that make analysis trustworthy: reproducible notebooks, sensible cleaning, correct joins and clear charts.³ It does not usually mean web development, algorithm puzzles or deep object-oriented design.

Python is the most-tagged skill on Indian data analyst job listings: AmbitionBox's skill-based listings in October 2026 show about 40,300 Python-tagged data analyst jobs, ahead of SQL at about 32,700.¹ Among professional developers, Stack Overflow's 2025 survey found Python used by more than half of respondents and rising.² For people aiming beyond analysis, data scientists in the US earned a median $120,230 in May 2025.⁷

Python-tagged data analyst jobs on AmbitionBox (Oct 2026)

~40,300

SQL-tagged data analyst jobs, for comparison

~32,700

Professional developers using Python (Stack Overflow 2025)

>50%

That runs top to bottom — the strongest take-home signal

1 notebook

Table: Python depth by data role (editorial summary based on common job descriptions)

Table: How Python is tested for analyst roles (editorial summary)

"loc vs iloc?"

loc selects by labels (and boolean masks); iloc by integer positions.

"Average order value by city and month."

df.assign(month=df.order_date.dt.to_period("M")).groupby(["city","month"]).order_value.mean() — mention parsing dates first.

"Why did my merge create more rows than expected?"

Duplicate keys on one or both sides (many-to-many). Check key uniqueness with duplicated() or validate="one_to_many".

"How do you handle missing values?"

Find out why they're missing, then drop, fill (with a justified value), or flag — and report how many were affected.

"apply vs vectorised operations?"

Vectorised operations work on whole columns at C speed; apply runs Python per row and is slower. Prefer vectorised.

"Turn wide monthly columns into rows."

melt with id_vars for identifiers and value_vars for month columns.

"Weekly active users from an events table."

Parse timestamps, set as index or use dt.to_period("W"), then groupby week with nunique on user_id.

"How would you make your analysis reproducible?"

A notebook that runs top to bottom, pinned library versions, raw data untouched, cleaning steps in code and a README.

Table: How Python for analysts has changed (editorial summary)

Newer tools change the edges, not the core: Polars offers much faster DataFrames,⁴ DuckDB lets you run SQL directly on files and DataFrames from Python,⁵ and Jupyter remains the standard notebook environment.⁶

• One public notebook answering a real business question, with the answer first.
• Cleaning decisions explained in markdown cells.
• Row counts checked after merges and filters.
• Two clear charts, labelled for a non-technical reader.
• A verified Python-for-analysis score to link.

"Python (pandas, NumPy, Matplotlib/Seaborn, Jupyter) — cleaning, cohort analysis, automation." With outcomes: "Automated a weekly churn report in Python, replacing 5 hours of manual Excel work and catching a billing-data duplication issue."

AI assistants generate pandas code instantly, so interviews have shifted towards reading code: "What does this do, and what's wrong with it?" The analysts who stand out check row counts, inspect intermediate results and can explain every step. Treat AI as a fast drafter whose work you must verify.

1. Week 1: Python basics for data — Types, lists, dicts, functions, reading files.
2. Week 2: pandas core — Selecting, filtering, groupby, merge.
3. Week 3: Cleaning — Missing values, types, duplicates, text and dates.
4. Week 4: Time series and charts — resample, rolling, plotting.
5. Week 5: Take-home practice — Two full analyses on public data; take the Python for Data Analysis Certification.
6. Week 6: Polish — Publish the best notebook; practise explaining it in ten minutes.

Related tests: Data Cleaning and Preparation, Statistics for Data Analysts and SQL Fundamentals.

import pandas as pd

subs = pd.read_csv("subscriptions.csv", parse_dates=["start_date", "end_date"], dayfirst=True)
print(len(subs), subs["customer_id"].nunique())  # spot duplicate customers early

subs = subs.drop_duplicates(subset=["customer_id", "start_date"])
months = pd.period_range("2026-01", "2026-09", freq="M")

rows = []
for m in months:
    start, end = m.start_time, m.end_time
    active_at_start = subs[(subs.start_date < start) & (subs.end_date.isna() | (subs.end_date >= start))]
    churned = active_at_start[active_at_start.end_date.between(start, end)]
    rows.append({"month": str(m), "active": len(active_at_start), "churned": len(churned)})

churn = pd.DataFrame(rows).assign(churn_rate=lambda d: d.churned / d.active)

Three choices to explain: dayfirst=True because Indian exports often write dates as DD/MM; the duplicate check before analysis; and a churn definition stated in code (customers active at the start of the month who ended during it). The loop is readable and fast enough for monthly data — clarity beats cleverness in a take-home.

1. One dataset, ten questions — Pick a public dataset (data.gov.in, RBI's database or a Kaggle e-commerce set) and answer ten business questions in a notebook.
2. Merge audit — Merge two tables with duplicate keys, catch the row explosion with a row-count check and fix it.
3. Time series — Resample daily data to weekly and monthly, add a rolling average and a chart.
4. Cleaning log — Clean a messy file and record every decision in markdown cells.
5. Restart test — Restart the kernel and run your notebook from top to bottom; fix anything that breaks.

Python opens the higher-paid half of data careers. Typical pay on AmbitionBox, October 2026:

Table: Roles that use Python for data and their typical pay in India (AmbitionBox, October 2026; indicative)

• Over-engineering instead of answering.
• No row-count checks.
• Unexplained cleaning choices.
• Charts without labels.
• Notebooks that don't run from a fresh start.

Job-listing figures come from AmbitionBox's data analyst page (read October 2026); language usage from Stack Overflow's 2025 survey; pay context from the US Bureau of Labor Statistics. Library references are the official pandas, Polars, DuckDB and Jupyter sites. Test formats and the history table are editorial summaries. Science of Apps publishes the free certification linked here. Last reviewed October 2026.

• pandas — the main Python library for working with tables of data.
• DataFrame — a table of rows and columns in pandas.
• groupby — splitting data into groups, applying a calculation and combining results.
• merge — joining two DataFrames on key columns.
• NaN — "not a number", pandas' marker for missing values.
• Notebook — an interactive document mixing code, results and text, usually Jupyter.