Python for data analysts: the skills hiring managers actually look for
Data analyst job postings increasingly list Python alongside SQL and Excel. This guide covers the exact libraries and workflows you need — pandas, NumPy, and visualisation — mapped to a structured learning path you can start today for free.
From 1,000+ Python learners
What Python skills data analysts use day-to-day
This is not a wishlist of advanced techniques. These are the tasks analysts run every week — in finance, marketing, operations, and product — where Python saves hours that Excel wastes.
Loading and cleaning data
pandas read_csv, read_excel, and read_sql pull data from anywhere. dropna, fillna, astype, and str methods turn messy raw files into something you can actually work with. This is where most analyst time goes.
Aggregating and summarising
groupby().agg() replaces the most complex Excel SUMIF setups in a single line. Pivot tables in pandas are faster and more reproducible than their spreadsheet equivalents. Once you know this, going back feels painful.
Visualising trends
Matplotlib handles the fundamentals: line charts, bar charts, scatter plots, histograms. Seaborn adds statistical charts with less code. Neither is as point-and-click as Tableau, but both produce publication-quality output that is fully reproducible.
Automating reports
Scheduled Python scripts replace the Monday morning ritual of refreshing the same spreadsheet. A script that reads a database, computes KPIs, and emails a formatted summary is within reach after a month of pandas practice.
Querying databases
pandas read_sql runs SQL queries directly and returns a DataFrame. SQLAlchemy connects to Postgres, MySQL, or Snowflake. Analysts who can go directly to the source database without waiting for a data engineer are genuinely more effective.
Statistical analysis
scipy.stats and pandas describe() handle descriptive stats, correlation, and hypothesis testing. Not data science-level modelling — but enough to answer 'is this difference meaningful?' without sending it to a data scientist.
The data analyst Python learning path
Structured for analysts who want practical skills fast. The Fundamentals path is free. The Data Science path (Steps 2 through 8) is included in Pro at $12/month.
Python Fundamentals (free)
Weeks 1 to 6Variables, data types, lists, dictionaries, loops, functions, file I/O
NumPy
Weeks 7 to 8Arrays, vectorised operations, broadcasting, array indexing
pandas
Weeks 9 to 12DataFrames, reading and writing data, filtering, sorting, groupby, merging
Data Cleaning
Weeks 13 to 14Missing values, duplicates, outlier handling, data type coercion
Visualisation
Weeks 15 to 16Matplotlib charts, Seaborn plots, labelling, exporting
Descriptive Statistics
Weeks 17 to 18Mean, median, variance, correlation, hypothesis testing basics
ML Introduction
Weeks 19 to 20scikit-learn basics, linear regression, train/test split
Capstone Project
Weeks 21 to 22End-to-end analysis on a real dataset: ingest, clean, analyse, visualise, present
Python vs Excel for data analysts
Python does not replace Excel. Most data analysts will use both for the foreseeable future, and that is a reasonable outcome. Excel is excellent for quick ad-hoc exploration, sharing files with non-technical stakeholders, and building formatted reports that need a certain look. Python is better at everything that happens before and after that: pulling data from sources, cleaning it reliably, processing datasets too large for a spreadsheet, and producing the same analysis again next week with a single command.
The practical difference is reproducibility and scale. An Excel model is a series of manual steps embedded invisibly in cells. Run it again in three months and there is a good chance someone has edited a formula without noting it. A Python script is version-controllable, reviewable, and produces the same output every time. For operations teams running recurring reports, this matters enormously. For datasets above a few hundred thousand rows, Python is not optional — Excel simply cannot handle the volume reliably.
There is also a salary argument. LinkedIn and Glassdoor data consistently show that data analysts who list Python on their profiles earn higher than those who do not — the difference sits at roughly 15 to 25% depending on the market. The market is pricing the skill. Adding Python to an Excel-heavy background does not require abandoning Excel; it expands what you can offer and which roles you can compete for.
From data and BI analysts who made the switch
“I spent three years building dashboards in Power BI without ever touching Python. Once I learned pandas, I could clean the data before it even reached Power BI. The reports that used to take half a day to prep now take ten minutes.”
“Coming from an Excel-heavy BI background, I was sceptical Python was necessary. After six weeks on MyPyMentor's Data Science path I was automating weekly reports that previously required two hours of manual pivot table work every Monday. I won't go back.”
“I'd tried to learn Python twice before from video courses and never got past week three. The difference with MyPyMentor was that Py kept giving me data problems that looked like my actual work. By month two I was using pandas at my job every day.”
Frequently asked questions
Do data analysts need to know Python?
Increasingly, yes. Python has become a core skill in data analyst job postings — not just data scientist roles. It handles datasets Excel cannot, it automates repetitive work, and it lets you connect to APIs and databases directly. You don't need to be a programmer, but knowing pandas and basic Python puts you in a stronger position than pure Excel analysts for most roles in 2025 and beyond.
What Python libraries should data analysts learn first?
pandas first, without question. pandas is the workhorse of data analysis in Python — reading data, cleaning it, filtering, grouping, and summarising. After pandas, add NumPy for numerical operations, then Matplotlib or Seaborn for visualisation. Those three libraries cover the vast majority of daily data analyst work in Python.
Can I get a data analyst job with just Python and Excel?
Yes — that combination is exactly what most hiring managers want for analyst roles. SQL is also worth adding to the mix, but Python and Excel together cover cleaning, analysis, visualisation, and reporting. Companies want analysts who can handle the full workflow, and Python plus Excel does that well.
How long does it take to learn Python for data analysis?
With 30 minutes a day, most learners are comfortable with pandas and basic data analysis in 8 to 12 weeks. That means completing Python Fundamentals (4 to 6 weeks) then the Data Science path (4 to 6 weeks). You won't be a data scientist in that time, but you'll be able to do real analyst work — clean data, run summaries, produce charts, and automate reports.
Is pandas enough for a data analyst role?
pandas plus basic Python is enough to land and perform well in most data analyst roles. You'll want to add SQL and some visualisation knowledge (Matplotlib, Seaborn, or a BI tool), but pandas is the foundation. pandas handles data loading, cleaning, aggregation, and transformation — which is 80% of what analysts actually do day to day.