COM6018 Data Science with Python

Overview

• Review of Lab Class 3
• Common traps when using NumPy
• Introducing Pandas
• Preview of Lab Class 4
• Some words about the 1st assignment

Review of Lab Class 3

• We continued our analysis of the atmospheric gas concentration data.
• We want to try and better understand the oscillations in the data.
• We are interested in extracting information such as:
  • The period of the oscillations.
  • The amplitude of the oscillations.
  • Dates at which the peaks and dips occur.

The Problem

Finding the peaks and dips in the oscillations is not so straightforward.

Problem:

• The data is very noisy.
• The daily measurements can bounce up and down in a seemingly random way.
• There are many local peaks and dips that are not part of the oscillations we are interested in.

We need to smooth the data to remove the noise.

Tasks

• Reading datasets from their files.
• Smoothing the data
  • Using a simple moving average
  • By applying a window weighting function
• Finding the peaks and dips in the smoothed data.
• Visualising the peaks and dips in the data.
• Answering questions about the period and size of the oscillations, etc.

We will do all of this using NumPy.

The Solution Notebook

Open the Solution Notebook

Common Traps when using NumPy

Open Notebook

Introducing Pandas

What is Pandas?

• Data Tool: Pandas is an open-source Python library for data manipulation and analysis.

• Tabular Structures: It provides DataFrames for easy handling of structured data.

• Analysis Functions: Offers diverse functions for cleaning, transforming, and analyzing data.

• Integration Capability: Seamlessly integrates with popular Python libraries, e.g., NumPy, Matplotlib, etc.

• Time Series Support: Suitable for time series analysis and handling panel data.

Pandas Tutorial

Link to the Pandas tutorial

Lab Class 4 Preview

• We will use Pandas to extend our analysis of greenhouse gas data.
• We will see how using Pandas makes it very easy to read and manipulate datasets.

Lab Class 4

Link to lab class

Assignment 1

• Will be released next Monday.
• Worth 40% of the module mark.
• Will be due on Friday Week 6, November 8th (i.e., you will have 2 weeks to complete it).

Assignment 1

• You will be given a dataset and a template Jupyter notebook.
• The template will ask a series of 4 questions about the dataset.
• You will need to use Pandas and Matplotlib to answer the question.
• Each question will be answered by producing a plot and a short paragraph of text.
• Your mark will be based on:
  • the quality of the code,
  • the clarity of the plots, and
  • the answer to the question, i.e., the text.

Next Steps

• Review the Week 3 materials:

  • Review the Lab 3 solution notebook if you have not already done so.
  • Read the Week 3 tutorial on NumPy

• Prepare for the Week 4 lab class:

  • Read through the Pandas tutorial.

• Checkout the reading list for Week 4 on Blackboard.