Data exploration: theory

This page includes both the theoretical and the first pracice part of the data exploration section. It goes hand in hand with the 2 second practice sessions (data-munging with pandas).

Before class

Theory

• read Gabor & Békés 1.1, 1.5, 1.7 <!–
• prepare for Class discussions:
  • what are the disadvantages & avantages of relying on the different sources (survey, administrative, data collection) of data for the analysis?
  • What type of variable do you know? –>

Practice

• Video introduction to pandas (8 minutes)
• Video on pandas dataframe (10 minutes)
• Pandas Basics [15 minutes]

In class

Theory

Link to the slides: html, pdf

Practice:

The lecture introduces pandas, a Python library used for working with data sets.

• Using the intro to pandas notebook: present the basic concepts used in pandas

After class

• Exercises 1-12 from the intro to pandas notebook

Learning objectives

Theory

• Data should not be thought out of its context
• Important questions to ask when starting to collect / work with data
• Importance of the sampling
• Understand types of variables and observations

Practice

• create pandas Series
• create pandas DataFrames from Series, dictionaries, lists
• access data in a DataFrame with loc and iloc
• reset index
• rename columns
• access metadata of DataFrames

Links:

1. pandas basics
2. Data-munging with pandas <!–
3. Case study on XXX (cf. lino) –>

Reference

• 10 minutes to pandas