Summarise a table
Use pandas to read a CSV, filter rows and group values. Turn an expense list into totals by category.
Data and Python on Mac
From raw data
to something you can see.
Read a CSV, compare measurements or plot a result. Work on the code and inspect its output from your Mac workspace. The image-classification example additionally needs the free Helper and a PyTorch download of several hundred megabytes.
Libraries for everyday Python work
Ask a question of your data.
Calculate across an array
Use NumPy for measurements, averages and formulas applied to a whole collection of numbers.
Draw the result
Use Matplotlib to compare categories or follow changes over time. Save a chart to share with your report.
pandasNumPyMatplotlibPillowRequestsBeautiful Soup
Try a small example
A few rows.
A useful answer.
Group expenses by category and add up the amounts. Then replace the sample with a CSV you want to understand.
This example uses pandas, included in the Mac app.
Create your first project →expenses.py
import pandas as pd
expenses = pd.DataFrame({
"category": ["Food", "Travel", "Food"],
"amount": [12, 8, 18],
})
totals = expenses.groupby("category")["amount"].sum()
print(totals.to_string())category
Food 30
Travel 8
Food 30
Travel 8
Explore a larger example
Follow an image classifier
from data to results.
AI Vision Lab creates a small dataset of patterns, trains a model and generates a report. Inspect the training code, then compare the predictions with the expected labels.
Git, the Terminal, the Python Console, debugging, packages and the profiler need the free Helper, a separate download. Examples also need project dependencies. Follow the setup guide.
Bring more into your scripts
Work with images and web data.
Prepare images
Use Pillow to resize an image, inspect its dimensions or create a processed copy.
Read an API response
Use Requests to fetch JSON and select the fields your analysis needs.
Extract information from HTML
Use Beautiful Soup to find text and links in pages you have permission to access.
Open the data. Start asking questions.
Download the Mac app with Python and useful libraries included.