Data Analysis with Python and Pandas

Learn how to accelerate your data analyses using Pandas, a Python library specifically designed for working with medium-sized data sets. Together with JupyterLab it enables a convenient environment for interactive data analysis.

Pandas is part of the so-called PyData ecosystem, and in this workshop we’ll start by providing an overview of PyData and explain where Pandas stands and how it interacts with other libraries such as NumPy and Seaborn. Pandas introduces a few new data structures, most importantly the DataFrame, which are essential to understand how to work with tabular data efficiently.

Pandas offers many features, and in one day, through a good balance of presentation and interactive exercises, we’re going to cover the most important ones, including: importing, filtering, grouping, joining, exploring, and visualising data. By the end of this workshop, you’ll understand the fundamentals of Pandas, be aware of common pitfalls, and be ready to perform your own analyses.

What you’ll learn

• Load data from text files, spreadsheets, databases, and APIs
• Use the Split-Apply-Combine paradigm to summarise data
• Performing advanced joins and merges
• Generating insightful pivot tables
• Transforming data between wide and long formats
• Working with time series data
• Explore data using a variety of visualisation types
• Avoid common pitfalls in NumPy and Pandas by understanding general concepts and principles

This workshop is for you because

• You have experience in Excel or R and want to learn about Pandas and the PyData ecosystem
• You have programming experience in Python and want to start analysing data using pandas
• You want to improve your understanding of Pandas

Schedule

• Overview of the PyData ecosystem
  • NumPy, SciPy, Pandas
  • Matplotlib, Seaborn
  • SciKit-Learn
• Essential data structures
  • Numpy data types
  • Numpy arrays
  • Pandas Series
  • Pandas DataFrame
  • Pandas Index, MultiIndex
• Importing data
  • From CSV
  • From Excel
  • From Databases
  • From APIs
• Manipulating data
  • Selecting rows and columns
  • Filtering rows
  • Joining and concatenating
  • Missing values, duplicates
  • Converting data types
  • Dates and times
  • Working with categorical data
  • String manipulation
• Exploring data
  • Computing aggregate statistics
  • Pivot tables
  • Correlations
• Visualising data
  • Histogram
  • Densityplot
  • Boxplot
  • Bar chart

Prerequisites

You’re expected to have some experience with programming in Python. Our workshop Introduction to Programming in Python is one option that can help you with that. Roughly speaking, if you’re familiar with the following Python syntax and concepts, then you’ll be fine:

• assignment, arithmetic, boolean expression, tuple unpacking
• bool, int, float, list, tuple, dict, str, type casting
• in operator, indexing, slicing
• if, elif, else, for, while
• range(), len(), zip()
• def, (keyword) arguments, default values
• import, import as, from import ...
• lambda functions, list comprehension
• JupyterLab or Jupyter Notebook

Recommended preparation

We’re going to use Python together with JupyterLab and the following packages:

• numpy
• pandas
• seaborn

The recommended way to get everything set up is to download and install the Anaconda Distribution.

Alternatively, if you don’t want to use Anaconda, then you can install everything using pip. In any case, if running import pandas, seaborn doesn’t produce any errors then you know you’ve set up everything correctly.