Python for data analysis: your first steps
How to start analysing data with Python, the libraries to learn first and a simple first project.
EvoAstra team14 Nov 2026 · 3 min read- Start with pandas for tables and matplotlib or seaborn for charts
- Use Jupyter or Google Colab to experiment
- Practise on real, messy datasets
Python has become the most popular language for data analysis because it is readable, free and has powerful libraries. You can start analysing real data within days.
import pandas as pd
df = pd.read_csv("sales.csv")
df.head()
df.groupby("city")["revenue"].sum().sort_values()
df["revenue"].plot(kind="hist")Set up quickly
Google Colab runs Python in your browser with no installation. Jupyter notebooks on your own computer work well too. Both let you run code step by step and see results immediately.
The libraries to learn first
- pandas for loading, cleaning and summarising tables
- NumPy for numerical work
- matplotlib and seaborn for charts
- scikit-learn later, for machine learning
- 1Download a dataset
- 2Load with pandas
- 3Clean columns
- 4Summarise and chart
- 5Write findings
Practise on real data
Kaggle, government open-data portals and public datasets give you realistic, messy data. Cleaning it is half the learning.
Build habits
Comment your code, name variables clearly, save notebooks to GitHub and write a short summary of what you found. These habits impress interviewers.
- Load and inspect data
- Filter and group
- Handle missing values
- Make three chart types
- Share one notebook
Tell us what you’re working on. Free estimate within 24 hours, NDA on request.