🖥️ Week 01, Day 04 - Lecture

Why We Like Our Data in Tables

🥅 Learning Objectives

By the end of the lecture, you should be able to: i) Explain why tabular data beats hand-written loops for inspection and filtering, ii) Load a CSV into a pandas DataFrame and inspect its shape, columns, and types, iii) Summarise a column with .describe(), filter rows with a Boolean condition, and group by a key, iv) Explain why the documentation page matters when you use AI-generated code, v) State what the midterm asks you to do and how the OpenWeather API differs from Open-Meteo.

⏰ Thursday, 16 July 2026 | 10:00am - 1:00pm 📍 CBG.2.01 (see LSE’s 🗺️ campus map)

Yesterday you fetched weather data with requests and wrote a CSV by hand. This morning we ask what changes when the same data is in a table, and we redo yesterday’s analysis with pandas.

🗣️ Lecture Overview

• From loops to tables: what you built this week, and why pandas replaces the bookkeeping
• pd.read_csv() and the inspection toolkit: shape, columns, dtypes, .head()
• Filter, summarise, group: Boolean filters, .describe(), adding a column, .groupby()
• Documentation as ground truth: styling a table with AI, then checking against the pandas docs
• Midterm project: the question, the OpenWeather API, and how the afternoon lab starts your setup

🎬 Lecture Slides

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After the Lecture

You have seen the pandas operations on yesterday’s London rainfall data. The afternoon lab is where you start the midterm: OpenWeather signup, project folder setup, and your first authenticated API request.

💻 Today’s Lab

Authenticating to APIs: API key, project folder, first request.

➡️ Go to Lab Instructions

Questions?

Ask your class teacher during the lab.